Lighting Roasting

Abstract

Disclosed herein are embodiments of a system, method, and computer program product for illuminated roasting. Illumination attributes may be determined based on an indication that a food item is in a chamber and the type of food item. Based on illumination of the food item according to the illumination attributes (e.g., wavelength value, lumen value, wattage value, etc.), changes to the temperature profile of the food item may occur. The changes in the temperature profile operate to roast the food item.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of U.S. Provisional Application No. 63 / 373,283, filed August 23, 2022. [Background technology]

[0002] Over 2 billion cups of coffee are consumed daily worldwide. Coffee is a beverage brewed from coffee beans, the seeds of the Coffea arabica (Coffee) plant. To make coffee suitable for consumption, green coffee beans are roasted and ground before brewing. Coffee roasting is a complex chemical process in which raw green coffee beans are heated to cause caramelization of the beans through the non-enzymatic browning process of the Maillard reaction. The Maillard reaction results in the formation of a diverse range of aroma and color compounds, as well as compounds broadly classified as Advanced Glycation End Products (or AGEs), which all contribute to the color, odor, taste, and nutritional content of the final food. Coffee roasts are classified into three broad categories (e.g., light roast, medium roast, and dark roast), each reflecting a different stopping point along the Maillard reaction chain and therefore resulting in different and widely distinct flavor profiles.

[0003] The length of time associated with roasting coffee beans varies routinely according to factors including, but not limited to, the type of roaster used, the temperature of the roast, the moisture content of the coffee beans used, the amount of air flowing through the roaster, and humidity. Due to these factors, as well as the variability in the types of coffee beans available, the roasting process requires skilled labor to monitor every roasting step. Thus, the roasting process is highly labor-intensive and does not effectively control the variability in flavor and quality of any resulting product.

[0004] Coffee roasters come in a wide variety of sizes and styles, but two roasting methods are commonly used: drum and fluidized bed. In the drum method, green coffee beans are placed in a heated rotating drum (e.g., via gas, wood, or electricity), and heat is transferred to the beans via direct conduction and / or convection. In the fluidized bed method, green coffee beans are placed in a chamber into which hot air is blown. The fluidized bed method uses forced air to agitate the coffee beans, and heat is transferred primarily via convection. Fluidized bed roasters tend to roast faster than drum-type roasters. Both the drum and fluidized bed methods routinely result in over- and / or under-roasted coffee beans due to the inability to effectively monitor and control the Maillard reaction.

[0005] Furthermore, coffee beans roasted using conventional roasting processes internally produce carbon dioxide gas, which adversely affects the flavor of the resulting coffee product. For better taste, coffee beans roasted using conventional roasting processes must be degassed before being ground and consumed. Degassing carbon dioxide typically takes at least one day (e.g., 24 hours) and can vary depending on the type of coffee and roast. The roasting process fails to provide a "roast-to-cup" experience in which green coffee beans are roasted and immediately brewed with optimal flavor and ready to be consumed, affecting the taste and / or enjoyment of coffee roasted by existing processes for end users. [Brief explanation of the drawings]

[0006] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate the present disclosure and, together with the description, serve to explain the principles of the disclosure and further enable one skilled in the art to make and use the disclosure. [Figure 1A] 1 illustrates an exemplary system for illuminated roasting, according to some embodiments. [Figure 1B] 1 illustrates an exemplary system for illuminated roasting, according to some embodiments. [Figure 2]1 illustrates an exemplary system for training an imaging module that may be used for illuminated roasting, according to some aspects. [Figure 3] FIG. 1 illustrates a flow diagram of an exemplary training method for generating a machine learning classifier for classifying data used for illuminated roasting, according to some aspects. [Figure 4] FIG. 1 illustrates a flow diagram of an exemplary method for illuminated roasting, according to some embodiments. [Figure 5] FIG. 1 illustrates a flow diagram of an exemplary method for illuminated roasting, according to some embodiments. [Figure 6] 1 shows a schematic block diagram of an exemplary computer system in which described aspects may be implemented. [Figure 7A] 1 shows exemplary results of lighting roasting of food and penetration depth of food when illuminated with light of different wavelengths. [Figure 7B] 1 shows exemplary results of lighting roasting of food and penetration depth of food when illuminated with light of different wavelengths. [Figure 8] 1 shows an exemplary overview of lighting for roasting food and depth penetration testing at different wavelengths. [Figure 9] 10 shows an exemplary output of a sensing device used during an illuminated roasting process. DETAILED DESCRIPTION OF THE INVENTION

[0007] Exemplary system, apparatus, device, method, computer program product embodiments, and / or combinations and subcombinations thereof for illuminated roasting are provided herein. The system, apparatus, device, method, computer program product embodiments, and / or combinations and subcombinations thereof for illuminated roasting provide a novel approach to food roasting that utilizes controlled illumination via a light source, controlled pressure around the food, and optical / image data to roast the food. For example, according to some aspects of the present disclosure, green coffee beans (e.g., unroasted coffee beans) can be controllably heated in a vacuum chamber via illumination from a high-irradiance light source (e.g., laser, light-emitting diode (LED), lamp, etc.). As described herein, roasting food in a vacuum allows for control over the food roasting process and final flavor outcome while using optical heating to overcome the limitations of convection heating (e.g., the inability to use convection heating in a vacuum).

[0008] According to some aspects of the present disclosure, embodiments of systems, apparatuses, devices, methods, computer program products, and / or combinations and subcombinations thereof for illuminated roasting utilize sensor (e.g., optical sensors, pressure transducers, etc.) data, machine learning, computer vision, and / or automated indicators for detecting food product stages and / or conditions (e.g., first and / or second crack stages of coffee beans indicating roasting progress, etc.). For example, according to some aspects of the present disclosure, embodiments of systems, apparatuses, devices, methods, computer program products, and / or combinations and subcombinations thereof for illuminated roasting use optical imaging to monitor food product attributes (e.g., coffee bean color, etc.) at different wavelengths and / or intensities, and predictive models specifically trained to characterize and optimize food roasting. For example, according to some aspects of the present disclosure, optical pyrometry may be used to measure, identify, and / or characterize the temperature profile (e.g., temperatures indicated at different depth levels, etc.) of a food product during the roasting process. According to some aspects of the present disclosure, the moisture content of a food product may be measured, identified, and / or characterized based on information received from a pressure sensor for an enclosed chamber in which the food product is roasted. For example, a measure of moisture in originally unroasted beans may be determined based on pressure measurements compared to temperature readings during the roasting process (e.g., a higher determined pressure compared to a determined temperature may indicate a higher moisture content of the food product, etc.).

[0009] According to some aspects of the present disclosure, embodiments of systems, apparatuses, devices, methods, computer program products, and / or combinations and subcombinations thereof for illuminated roasting utilize high irradiance light sources (e.g., light emitting diodes (LEDs) and the like) to enable roasting of food products under vacuum (e.g., approximately 10 Torr and the like) in a molecular flow regime.

[0010] According to some aspects of the present disclosure, embodiments of systems, apparatuses, devices, methods, computer program products, and / or combinations and subcombinations thereof for illuminated roasting output roasted foods, including, but not limited to, coffee beans, with significantly lower acrylamide content than existing roasting processes. Acrylamide is a substance formed in plant-based foods (e.g., coffee beans, potatoes, grain-based foods, etc.) through a natural chemical reaction between sugars and the amino acid asparagine. Acrylamide is formed during high-temperature cooking, such as frying, roasting, and baking. Acrylamide is considered unhealthy for human consumption. By controlling pressure in a sealed roasting chamber, embodiments of systems, apparatuses, devices, methods, computer program products, and / or combinations and subcombinations thereof for illuminated roasting limit the accumulation of acrylamide in the roasted foods.

[0011] According to some aspects of the present disclosure, embodiments of systems, apparatuses, devices, methods, computer program products, and / or combinations and sub-combinations thereof for illuminated roasting enable faster roasting of food products, such as, but not limited to, green beans, over a reduced cycle time (e.g., the period from when the food product goes from a green state to when it is suitable for consumption, the roast-to-cup timeframe, etc.). Reducing cycle times for food products, such as, but not limited to, green beans, facilitates reduced production costs of the food products. For example, embodiments of systems, apparatuses, devices, methods, computer program products, and / or combinations and sub-combinations thereof for illuminated roasting enable high-quality roasting of food products, such as, but not limited to, green beans, in less than 10 minutes, whereas conventional systems have long cycle times (e.g., 10 minutes or longer).

[0012] According to some aspects of the present disclosure, embodiments of systems, apparatuses, devices, methods, computer program products, and / or combinations and sub-combinations thereof for illuminated roasting reduce the degree of human intervention associated with traditional roasting systems. For example, in traditional roasting systems, a user must monitor the roasting operation throughout the process to ensure that the roasted food item does not burn and / or is roasted according to the intended state / condition (e.g., first crack, second crack, light roast, dark roast, etc.). Embodiments of systems, apparatuses, devices, methods, computer program products for illuminated roasting, and / or combinations and sub-combinations thereof utilize dedicated sensing devices and machine learning to automate the roasting process to ensure that the food item does not burn and / or is roasted according to the intended state / condition (e.g., first crack, second crack, light roast, dark roast, etc.).

[0013] According to some aspects of the present disclosure, embodiments of systems, apparatuses, devices, methods, computer program products for illuminated roasting, and / or combinations and sub-combinations thereof, are scalable in size and / or may be implemented to roast any quantity of food (e.g., large / industrial quantities of coffee beans, small / personal quantities of coffee beans for consumption in a home or coffee shop setting, etc.). For example, according to some aspects of the present disclosure, embodiments of systems, apparatuses, devices, methods, computer program products for illuminated roasting, and / or combinations and sub-combinations thereof, may enable a "roast-to-cup" experience in which green coffee beans are roasted and immediately brewed for optimal flavor and ready for consumption, and / or a "just-in-time" experience in which a user can receive high-quality roasted coffee beans when desired, rather than purchasing large quantities of roasted beans that must be consumed within a predetermined period of time (e.g., several weeks) before optimal flavor is lost.

[0014] According to some aspects of the present disclosure, embodiments of systems, apparatuses, devices, methods, computer program products for illuminated roasting, and / or combinations and sub-combinations thereof may selectively affect the flavor of foods, including, but not limited to, coffee beans, through control of the moisture content, temperature profile, etc., of the food during the roasting process. According to some aspects of the present disclosure, embodiments of systems, apparatuses, devices, methods, computer program products for illuminated roasting, and / or combinations and sub-combinations thereof may be coupled with fine grinding of foods to enable the food to be consumed with optimal flavor (e.g., reduced bitterness, etc.) and / or in controlled portion sizes. According to some aspects of the present disclosure, optimal flavoring, indicated by the absence of bitterness in finely ground coffee beans, reduces any need to discard roasted coffee beans that are “outside of the acceptable range” for optimal consumption. Thus, users (e.g., baristas, etc.) of embodiments of systems, apparatuses, devices, methods, computer program products for illuminated roasting, and / or combinations and sub-combinations thereof require less training to produce high-quality coffee-based products. Furthermore, embodiments of systems, apparatuses, devices, methods, computer program products for illuminated roasting, and / or combinations and sub-combinations thereof, can make unattended production of high-quality coffee-based products easier and more cost-effective. Coffee beans roasted according to embodiments of systems, apparatuses, devices, methods, computer program products for illuminated roasting, and / or combinations and sub-combinations thereof, have a less bitter taste when brewed into espresso or coffee. This less bitter taste is due to roasting under vacuum and promoting enhanced outgassing of chemicals. Coffee beans can be brewed over a much wider range for acceptable bitterness. Embodiments of systems, apparatuses, devices, methods, computer program products for illuminated roasting, and / or combinations and sub-combinations thereof can roast foods such as coffee beans faster (e.g., in less than five minutes) than conventional roasters or other devices, while maintaining the taste and / or quality level of the food.These and other technical advantages are described herein.

[0015] FIG. 1A shows a block diagram of an exemplary system 100 for illuminated roasting, according to some embodiments. System 100 is merely one example of a suitable system environment and is not intended to suggest any limitation on the scope of use or functionality of the embodiments described herein. Furthermore, system 100 should not be interpreted as having any dependency or requirement relating to any single device, module, component, and / or combination thereof described herein. According to some embodiments of the present disclosure, system 100 may include a control module 102, an enclosed chamber 104, an illuminator 106, and a stirring element 108. While multiple components are shown in system 100 to facilitate the roasting process of food products, it will be understood that system 100 may operate to facilitate the roasting process of food products through the use of more or fewer components of system 100.

[0016] According to some aspects of the present disclosure, the control module 102 (e.g., a controller, processor, control device, etc.) may include any hardware, software, and / or combination thereof for communicating with and / or controlling the operation of components of the system 100, including, but not limited to, the illuminator 106, the stirring element 108, the vacuum pump 110, the sensing devices 112-114, the throttle valve 118, and / or the heating element 120.

[0017] According to some aspects of the present disclosure, the control module 102 may include and / or communicate with one or more input devices and / or components, such as, for example, a keyboard, a pointing device (e.g., a computer mouse, a remote control), a microphone, a joystick, a tactile input device (e.g., a touchscreen, a glove, etc.), etc. According to some aspects of the present disclosure, interaction with the input devices and / or components may enable a user to set parameters for the roasting process (e.g., lighting / illumination attributes, roast time, food identifier, temperature settings, chamber pressure settings, etc.), view / obtain status of the roasting process, and / or interact with and control devices / components of the system 100.

[0018] According to some aspects of the present disclosure, the control module 102 may include one or more processors, machine learning models, etc. for managing the operation of the system 100, including, but not limited to, a lighting-based roasting process. According to some aspects of the present disclosure, the control module 102 may manage and / or control the food product roasting process according to a process profile. The control module 102 may include and / or communicate with a storage element that stores any number of process profiles. A process profile may indicate parameters for roasting a particular food product, such as, for example, green coffee beans. For example, a food product's process profile may define the temperature to which the food product should be heated for optimal roasting, wavelength and / or intensity information of light applied to the food product to achieve a specified result, expected chemical reactions and / or gases that occur on the food product during the roasting process, visual / audio indicators to identify the stage of the food product, roasting intervals and other time-related data, etc. According to some aspects of the present disclosure, the process profile may be pre-configured and / or stored by the control module 102, or customized and / or generated according to data / information received from a user (e.g., via a user interface, etc.).

[0019] According to some embodiments of the present disclosure, the sealed chamber 104 may be constructed of a sturdy material, including, but not limited to, stainless steel, aluminum, ceramic, etc. According to some embodiments of the present disclosure, the sealed chamber 104 may be a vacuum chamber. According to some embodiments of the present disclosure, the sealed chamber 104 may be, for example, a vacuum chamber. -3 The sealed chamber 104 may be airtight and / or leak-resistant, with a temperature of less than 100°F (200°C). According to some embodiments of the present disclosure, the food product 101 may be placed within the sealed chamber 104 for roasting. According to some embodiments of the present disclosure, the food product may include green (unroasted) coffee beans, fish, vegetables, legumes, meat, beans, grains, etc. The food product 101 may be any food product, product, item, etc. According to some embodiments of the present disclosure, the sealed chamber 104 and / or the food product 101 may be heated via illumination from the illuminator 106. For example, the control module may control the operation of the illuminator 106 to penetrate light of various wavelengths and / or intensities into the sealed chamber 104 to heat / roast the food product 101.

[0020] According to some aspects of the present disclosure, the illuminator 106 may include, but is not limited to, an array of light-emitting diodes (LEDs), lasers, laser diodes, incandescent lamps, metal halide lamps, arc lamps, infrared emitters, etc. According to some aspects of the present disclosure, the illuminator 106 may operate according to one or more illumination attributes of the food product indicated in the processing profile. The illumination attributes may include, but are not limited to, wavelength values, lumen values, wattage values, intensity levels, illumination duration / length of time, etc. According to some aspects of the present disclosure, the illuminator 106 may output an array of wavelengths selected based on the amount of heat transfer the illuminator 106 provides according to the light absorption characteristics of the food product 101 and the penetration depth of the wavelengths.

[0021] According to some aspects of the present disclosure, to optimize any roasting process, the wavelength of the light 103 output by the illuminator 106 may be selected by the control module 102. For example, the control module 102 may include a machine learning model trained to select illumination attributes and / or wavelengths output by the illuminator 106 during the roasting process to affect the depth of roasting / heating of the food product. According to some aspects of the present disclosure, the control module 102 may select illumination attributes and / or wavelengths output by the illuminator 106 according to attributes of the food product within or disposed within the sealed chamber 104. For example, to optimize a coffee roasting process, the illuminator 106 may output a wavelength having high surface absorptivity (e.g., at least 450 nm) to the coffee beans. The illuminator 106 may output a wavelength having significant optical transparency of the coffee beans (e.g., 940 nm, etc.).

[0022] As described in more detail later in this specification, according to some aspects of the present disclosure, the control module 102 may select illumination attributes and / or wavelengths output by the illuminator 106 according to data / information received from various components of the system 100, including, but not limited to, the sensing devices 112-114.

[0023] According to some embodiments of the present disclosure, the top of the sealed chamber 104 may include and / or be sealed with an optical window 116 that allows optical access and / or light 103 to penetrate the entire interior of the sealed chamber 104. According to some embodiments of the present disclosure, the optical window 116 may include a heating element (not shown) that may be controlled by the control module 102 to heat the optical window 116. According to some embodiments of the present disclosure, heating the optical window 116 may prevent and / or mitigate condensation of water and / or other outgassing products from the roasting process (e.g., emitted from the food product 101, etc.) from accumulating on the sealed chamber 104, ensuring consistent optical access and / or illumination of the food product 101. At the bottom of the chamber is a mixing blade that is fixed to a vacuum-compatible rotary feedthrough. This blade rotates the bean bed during the roasting process.

[0024] According to some embodiments of the present disclosure, the sealed chamber 104 may include a stirring element 108. According to some embodiments of the present disclosure, the stirring element 108 may include a mixing blade attached to a shaft connected to a vacuum-compatible rotary feedthrough. According to some embodiments of the present disclosure, the speed and direction of rotation of the stirring element 108 may be varied by the control module 102 (e.g., via instructions received via the control module 102, based on one or more instructions / outputs of a machine learning model of the control module 102, etc.). For example, during roasting of coffee beans within the sealed chamber 104, the stirring element 108 may operate at a rotational speed of approximately 120 RPM, but the speed may be varied to optimize the roasting process and / or prevent burning of the food product. According to some embodiments of the present disclosure, the operating speed of the stirring element 108 may be based on the size of the sealed chamber 104 and / or the volume of the food product 101. According to some embodiments of the present disclosure, the stirring element 108 may operate to continuously turn the food product 101 over to ensure that each portion of the food product 101 (e.g., each coffee bean, etc.) is roasted evenly and that each portion of the food product 101 is exposed to illumination from the illuminator 106 for the same length of time during the roasting process.

[0025] According to some embodiments of the present disclosure, the stirring element 108 may include different mixing elements and / or operations. For example, the stirring element 108 may include blades, paddles, wires, and / or any movable object that is controllably operated via the control module 102 and can withstand the high roasting temperatures and / or various pressure conditions within the enclosed chamber 104. According to some embodiments of the present disclosure, the stirring element 108 may be a vibrating element that orients the food product 101 such that portions of the food product 101 are exposed to the light 103 through the optical window 116.

[0026] According to some embodiments of the present disclosure, the food product 101 within the sealed chamber may be (optionally) further heated via one or more heating elements 120. According to some embodiments of the present disclosure, the heating elements 120 may be resistive heaters disposed along the base plate and bottom wall of the sealed chamber 104. The one or more heating elements 120 may operate in a temperature range of 20-350°C.

[0027] As described, according to some embodiments of the present disclosure, the sealed chamber 104 may be a vacuum chamber. According to some embodiments of the present disclosure, the system 100 may include a vacuum pump 110. The vacuum pump 110 may be controlled by the control module 102 and operated to pump air from the interior of the sealed chamber 104 to create and / or maintain a vacuum pressure condition within the sealed chamber 104 during the roasting process. According to some embodiments of the present disclosure, when food (e.g., food 101) is heated within the sealed chamber 104 by illumination from the illuminator 106, heated water, oil, and other materials may be released from the food (e.g., outgassing, etc.), increasing the pressure level within the sealed chamber. The vacuum pump 110 may be used to regulate the pressure within the sealed chamber 104 during the roasting process.

[0028] According to some embodiments of the present disclosure, vacuum pump 110 may be operated without oil (e.g., oil-free) to prevent contamination of the food product during the roasting process. According to some embodiments of the present disclosure, vacuum pump 110 may be operated to achieve a base pressure of at least less than 1 Torr within sealed chamber 104 whenever the food product is within the sealed chamber during the roasting process. According to some embodiments of the present disclosure, system 100 may include a throttle valve 118 connected to vacuum pump 110 to adjust the rate at which pressure increases / decreases within sealed chamber 104.

[0029] According to some embodiments of the present disclosure, the system 100 may include a sensing device 112. The sensing device may be, for example, one or more vacuum-pressure transducers, pressure sensors (e.g., strain gauges, variable capacitance, solid-state, micromachined silicon (MMS), etc.), capacitance manometers, etc. The sensing device 112 may be used to detect and / or monitor the pressure within the sealed chamber 104 during different phases of the roasting process. According to some embodiments of the present disclosure, the sensing device 112 detects and / or monitors the pressure within the sealed chamber 104 independently of any gas composition within the sealed chamber 104. One example of this type of transducer is a capacitance manometer.

[0030] According to some aspects of the present disclosure, the sensing device 112 may send pressure measurements about the sealed chamber 104 to the control module 102, and the control module may use the pressure measurements and / or related information to control the operation of the vacuum pump 110 and / or the throttle valve 118 to regulate the pressure level within the sealed chamber 104. For example, the sensing device 112 may send pressure-related information to the control module 102, which may indicate the moisture content of the food product within the sealed chamber 104, the onset and duration of a "first crack" scenario (indicative of the roast level of the coffee beans) for the coffee bean food product, the onset of a "second crack" scenario (indicative of the dark roast level of the coffee beans) for the coffee bean food product, etc. For example, at approximately 200-220°C (392-428°F), originally unroasted coffee beans emit a crackling sound commonly referred to as a "first crack," which marks the onset of a light roast. When the coffee beans are at approximately 224-245°C (435-473°F), the coffee beans emit a "second crack." During the first and second "cracks," the pressure inside the beans increases to the point where the bean structure breaks down, rapidly releasing gas and thus an audible sound. By detecting and identifying these pressure changes in the coffee beans within the sealed chamber 100, the roast state of the coffee beans can be identified.

[0031] For example, according to some aspects of the present disclosure, air may be removed from the sealed chamber 104 via the vacuum pump 110 to achieve a base and / or set pressure level (e.g., a user-defined base pressure level, an artificial intelligence determined base pressure level, etc.). According to some aspects of the present disclosure, the base pressure level may correspond to the outgassing rate of the food product (e.g., coffee beans, food product 101, etc.) during initial heating. As the pressure level in the sealed chamber increases, the rate of change may be used to optimize the roast by indicating the need for more or less light output and / or illumination from the illuminator 106 to compensate for higher or lower moisture content of the food product (e.g., coffee beans, food product 101, etc.).

[0032] According to some aspects of the present disclosure, the system 100 may (optionally) include a sensing device 113. According to some aspects of the present disclosure, the sensing device 113 may be and / or include a microphone, etc. The sensing device 113 may detect the sound level (e.g., amplitude level, frequency, etc.) and / or amount of acoustic energy emitted from the sealed chamber 104 during the roasting process. The sensing device 113 may send data / information indicative of the sound level and / or amount of acoustic energy emitted from the sealed chamber 104 to the control module 102. According to some aspects of the present disclosure, the control module 102 may determine whether the measured acoustic energy is within an acceptable range of a processing profile of the food product. The processing profile of the food product may include reference acoustic characteristics of the food product roasting process. For example, the acoustic characteristics may indicate the sound level and / or amount of acoustic energy emitted during a cracking stage (e.g., first crack, second crack, etc.) of coffee beans, etc. The control module 102 may use the acoustic indications of the roasting process to control the lighting attributes of the illuminators 106, the stirring speed of the stirring elements 108, the pressure level exerted by the vacuum pump 110, etc.

[0033] According to some aspects of the present disclosure, system 100 may include sensing device 114. According to some aspects of the present disclosure, sensing device 114 may be and / or include an optical pyrometer, an infrared imaging device, a high-resolution imaging device, a colorimeter, a hyperspectral imaging device, etc. According to some aspects of the present disclosure, sensing device 114 may measure the temperature of the food product (e.g., coffee beans, etc.) during the roasting process. For example, according to some aspects of the present disclosure, sensing device 114 may include an optical pyrometer. The optical pyrometer may measure, detect, identify, and / or determine the emissivity of the food product within sealed chamber 104 during the roasting process.

[0034] According to some aspects of the present disclosure, the sensing device 114 may include an imaging device, such as, but not limited to, a high-resolution camera. The sensing device 114 may be positioned to allow a portion or all of the sealed chamber 104 to be imaged through the optical window 116. According to some aspects of the present disclosure, the sensing device 114 may include one or more optical filters (e.g., high-pass filters, low-pass filters, band-pass filters, etc.) that filter and / or eliminate the perception of light from the illuminator 106. Image data of the food item within the sealed chamber 104 may be analyzed by the control module 102 to identify the state of the food item during the roasting process. For example, according to some aspects of the present disclosure, the amount of light output reflected by the food item at different wavelengths may be evaluated, determined, and / or identified by a brightness, luminance, and / or intensity scale depicted by the image data. Images and / or image data showing the food item when light of various wavelengths is applied to the food item may be analyzed by the control module 102 to determine the roast level and / or state of the food item and to cause variations in intensity illumination by the illuminator 106 to optimize the roasting process.

[0035] According to some aspects of the present disclosure, the illuminator 106 may include a coherent light source (e.g., a laser, a laser diode, etc.). Data / information indicative of optical scattering of light from the food product may be detected, determined, and / or identified by the sensing device 114 and provided to the control module 102 for optical speckle analysis. When a food product, such as coffee beans, is heated by light from the illuminator within the vacuum-pressurized sealed chamber 104, the food product begins to produce, output, and / or release oils that coat the surface of the coffee beans, causing changes in the surface roughness / texture of the coffee beans. The changes in the surface roughness / texture of the coffee beans may be identified via optical speckle analysis and used to classify the state of the coffee beans during the roasting process. For example, for coffee beans in a dark roast state, a greater amount of produced oil, output oil, and / or released oil may be detected, determined, and / or identified on the surface of the coffee beans.

[0036] According to some embodiments of the present disclosure, at the start of the roasting process, the sealed chamber 104 may be empty, open to the atmosphere, and preheated to a preset temperature (e.g., via the heating element 120). The preset temperature may vary according to the type of food product to be roasted in the sealed chamber 104. Before food product, such as unroasted coffee beans, may be placed in the sealed chamber 104, the stirring speed of the stirring element 108 may be set and initiated, for example, via the control module 102. For example, the stirring element 108 may be a mixing blade, and the rotational speed of the mixing blade may be set to a low speed (e.g., approximately 1 RPM). According to some embodiments of the present disclosure, food product, such as unroasted coffee beans, may be placed inside the sealed chamber 104. The stirring of the coffee beans by the stirring element 108 may distribute the coffee beans throughout the sealed chamber 104 and prevent the coffee beans from scorching on the heated walls of the sealed chamber 104 while at atmospheric pressure.

[0037] According to some aspects of the present disclosure, placing food products, such as unroasted coffee beans, into the sealed chamber 104 may be performed manually by a user. For example, a user may place coffee beans into the sealed chamber 104 by opening an optical window or through a side port (not shown) in the sealed chamber 104. According to some aspects of the present disclosure, placing food products, such as unroasted coffee beans, into the sealed chamber 104 may be performed through an automatic hopper (not shown), which may be attached to the sealed chamber 104 through a side port (not shown). According to some aspects of the present disclosure, the automatic hopper may be multiplexed with other hoppers, each having a different type of food product (e.g., coffee bean type / variety, etc.), allowing for automatic loading and selection of various food products.

[0038] According to some embodiments of the present disclosure, once a food product, such as unroasted coffee beans, has been placed within the sealed chamber 104, the vacuum pump 110 may be operated to remove air from the sealed chamber 104. The sealed chamber 104 may be pumped down to a user-desired vacuum level, and the agitation of the stirring element 108 may be set to a high speed (e.g., >2 RPM, etc.) to ensure rapid and continuous mixing of the food product within the sealed chamber 104.

[0039] According to some aspects of the present disclosure, the stirring element 108 may (optionally) include a heating element 150. The heating element 150 may generate radiant heat that is transferred to the food product 101 via the stirring element 108. According to some aspects of the present disclosure, the temperature of the heating element 150 may be controlled and / or varied via instructions from the control module 102. The heating element 150 may generate heat at any temperature, such as in the temperature range of 20-350°C.

[0040] According to some aspects of the present disclosure, the system 100 may (optionally) include a sensing device 115. According to some aspects of the present disclosure, the sensing device 115 may measure the temperature of the food product (e.g., coffee beans, etc.) and / or the enclosed chamber 104 during the roasting process. For example, the sensing device 115 may be and / or include a temperature probe, a thermocouple probe, etc. For example, a measurement junction of the sensing device 115 may be near and / or in contact with the food product 101 to accurately measure the temperature of the food product 101. According to some aspects of the present disclosure, the sensing device 115 facilitates determining the actual (real-time) temperature of the feed product 101 at any time during the roasting process, rather than relying on a proxy guess of the temperature of the food product 101. In particular, the sensing probe 115 enables the system 100 to provide more accurate food temperature determinations than conventional food temperature altering devices.

[0041] According to some embodiments of the present disclosure, data / information collected by the sensing devices 112-115 during the roasting process may be used to modify, enhance, and / or optimize the roasting process. For example, prior to lighting the food in the sealed chamber 104, the food will outgas due to being in a vacuum, and the pressure level in the sealed chamber indicated by the sensing device 112 may be used to adjust the time, temperature, and / or pressure of the roasting process. According to some embodiments of the present disclosure, the components of the food, such as green coffee bean outgassing products, may be further analyzed by the control module 102. According to some embodiments of the present disclosure, the sensing device 112 may include a residual gas analyzer (RGA) to monitor the type and amount of molecules in the sealed chamber 104 during the roasting process. The type and amount of molecules in the sealed chamber 104 may be provided to the control module 102, which may adjust the operation of one or more components / devices of the system 100 to optimize the roasting process (e.g., increasing / decreasing illumination by the illuminator 106, changing the stirring speed of the stirring element 108, changing the pressure level by operating the vacuum pump 110, etc.).

[0042] As described, according to some aspects of the present disclosure, the illuminator 106 may be operated to illuminate a food item (e.g., food item 101) within the sealed chamber 104. The illuminator 106 may be operated according to illumination attributes (e.g., wavelength value, lumen value, wattage value, etc.) identified for the food item (e.g., based on a processing profile, etc.) to optimize the roasting process. According to some aspects of the present disclosure, the control module 102 may identify a pressure level within the sealed chamber 104 based on information received from the sensing device 112. The control module 102 may send a signal to the illuminator 106 to cause the illuminator 106 to modify the illumination attributes of the light applied to the food item based on the pressure level. The modified illumination attributes may cause a change in one or more temperatures in the temperature profile of the food item (e.g., the temperature of the item at various depths from the surface to the core, etc.).

[0043] According to some aspects of the present disclosure, the control module 102 may receive imaging data from the sensing device 114 indicative of, for example, the food product (e.g., high-definition imaging indicative of the condition of the food product, optical pyrometer information, hyperspectral imaging information, speckle field-related information, etc.). From the imaging data, the control module 102 may determine and / or identify that one or more temperatures in the temperature profile of the food product (e.g., the temperature of the item at various depths of the item from the surface to the core, etc.) meet a temperature threshold that indicates the food product is in a particular state (e.g., a first or second crack state for coffee beans, etc.). Additionally, according to some aspects of the present disclosure, a voltage generated by the sensing device 115 due to the temperature of the food product 101 and / or the sealed chamber may be converted by the control module 102 into a temperature reading. As described, the control module 102 may interface with the heating mechanisms of the sealed chamber 104 (e.g., the illuminator 106, the heating element 120, the heating element 150, etc.) to enable real-time adjustment of the heating based on the readings.

[0044] Additional data / information (e.g., pressure-related information, acoustic / audio information, etc.) from sensing devices 112-115 may also be used by control module 102 (e.g., predictive models of control module 102, etc.) to identify and / or verify / confirm the state of the food product within sealed chamber 104 during the roasting process. According to some aspects of the present disclosure, stirring elements 108 may be controllably operated at different speeds to agitate the food product and ensure each and / or various portions of the food product are identified as being in a user-desired state.

[0045] According to some aspects of the present disclosure, after a set amount of time (e.g., as indicated by a processing profile, etc.) and / or based on identifying and / or determining the condition of the food product within the sealed chamber 104, illumination of the food product by the illuminator 106 may be terminated and the stirring speed of the stirring element 108 may be set to a low speed (e.g., 1 RPM, etc.). The throttle valve 118 and / or the vacuum pump 110 may be operated to vent the sealed chamber 104 to atmospheric pressure levels.

[0046] According to some aspects of the present disclosure, food products, such as coffee beans, roasted within the sealed chamber 104 based on illumination from the illuminator 106 being identified as being in a user-desired state may be removed from the sealed chamber 104. For example, the roasted coffee beans may be quickly removed from the sealed chamber 104 manually (through a release opening (not shown) in the sealed chamber 104) and / or through a suction trap configured with the system 100. For example, the computing module 102 may send a signal and / or command to the suction device based on an indication that the food products in the sealed chamber 104 are in a user-desired state. The signal and / or command sent to the suction device to cause the suction device to output may be a suction force that causes the food products to be removed / extracted from the sealed chamber 104.

[0047] FIG. 1B illustrates an exemplary suction trap of system 100. According to some embodiments of the present disclosure, system 100 may include a food trap 130 (e.g., a reservoir, container, bagging unit, etc.) and a suction device 132 (e.g., a vacuum device, air suction pump, etc.). According to some embodiments of the present disclosure, based on an indication that the food in sealed chamber 104 is in a user-desired state, the pressure level in the sealed chamber may be equalized to atmospheric pressure based on air inlet to the sealed chamber via air inlet valve 134. A food extraction valve may be opened, and control module 102 may cause suction device 132 to generate suction that draws the food in sealed chamber 104 through an opening controlled by food discharge valve 136. The food falls into food trap 130, which may include an inlet for the suction generated by suction device 132. A wired mesh or the like may cover the inlet to prevent the food from being drawn out of food trap 130. The food may cool from the roasting process while in food trap 130.

[0048] FIG. 2 is an exemplary system 200 for training the control module 102 to manage illuminated roasting via control of the illuminators 106, vacuum pump 110, stirring elements 106, etc., and / or optimize any roasting process based on data / information received from components of the system 100, including, but not limited to, sensing devices 112-115. FIG. 2 is described with reference to FIG. 1A. According to some aspects of the present disclosure, the control module 102 may be trained to determine the roasting condition, temperature profile, etc., of a food item being heated within the sealed chamber 104. The system 200 may use machine learning techniques to train at least one machine learning-based classifier 230 (e.g., a software model, a neural network classification layer, etc.) configured to classify features extracted from data / information received from components / devices of the system 100 of FIGS. 1A-1B based on analysis of one or more training datasets 210A-210N by the control module 102 of FIG. 1A. The machine learning based classifier 230 may classify features extracted from the data received from the sensing devices 112-115 to identify the food product and determine settings for the system 100 to optimize the food product roasting process.

[0049] One or more training datasets 210A-210N may include categorized baseline data such as categorized food types (e.g., green (unroasted) coffee beans, legumes, meats, grains, etc.), categorized roasting scenarios (e.g., coffee beans in a "first crack" state, coffee beans in a "second crack" state, lighting attributes, pressure conditions in a sealed chamber, results of roasting different foods under various conditions, etc.), categorized lighting / lighting attribute effects on food products, categorized pressure measurement indicators of food condition and / or pressure-related indicators of food condition, categorized temperature profiles (e.g., temperature of an item at various depths of the item from the surface to the core) and related attributes, categorized acoustic indicators of food condition, etc.

[0050] The categorized baseline data may be stored in one or more databases. Data from components / devices of system 100 indicative of and / or associated with the food roasting process may be randomly assigned to a training data set or a test data set. According to some aspects of the present disclosure, the assignment of data to a training data set or a test data set may not be completely random. In this case, one or more criteria may be used during the assignment, such as to ensure that similar roasting scenarios, similar lighting / lighting attribute effects on the food, similar pressure-related indicators of food condition, similar temperature profiles and related attributes, similar acoustic indicators of food condition, dissimilar roasting scenarios, dissimilar lighting / lighting attribute effects on the food, dissimilar pressure-related indicators of food condition, dissimilar temperature profiles and related attributes, dissimilar acoustic indicators of food condition, etc., may be used in each of the training and test data sets. In general, any suitable method may be used to assign data to a training or test data set.

[0051] The control module 102 may train the machine learning-based classifier 230 by extracting feature sets from the classified baseline data according to one or more feature selection techniques. According to some aspects of the present disclosure, the control module 102 may further define the feature sets obtained from the classified baseline data by applying one or more feature selection techniques to the classified baseline data in one or more training datasets 210A-210N. The control module 102 may extract feature sets from the training datasets 210A-210N in various manners. The control module 102 may perform feature extraction multiple times, each time using a different feature extraction technique. In some cases, feature sets generated using different techniques may each be used to generate a different machine learning-based classification model 240. According to some aspects of the present disclosure, the feature set with the highest quality metric may be selected for use in training. The control module 102 may use the feature set to build one or more machine learning based classification models 240A-240N configured to determine and / or predict roasting scenarios, lighting / lighting attribute effects on food products, pressure-related indicators of food product condition, temperature profiles and related attributes, acoustic indicators of food product condition, etc.

[0052] According to some aspects of the present disclosure, the training data sets 210A-210N and / or the categorized baseline data may be analyzed to determine any dependencies, associations, and / or correlations between roasting scenarios, illumination / lighting attribute effects on food products, pressure-related indicators of food product condition, temperature profiles and related attributes, acoustic indicators of food product condition, etc. in the training data sets 210A-210N and / or the categorized baseline data. As used herein, the term "feature" may refer to any characteristic of an item of data that may be used to determine whether the item of data falls within one or more particular categories. For example, features described herein may include any data / information that may be used to identify the moisture content of a food product, the state of moisture content of a food product during a roasting scenario, the onset of a food product condition (e.g., indicators of the onset of a first or second crack state in coffee beans, etc.), etc.

[0053] According to some aspects of the present disclosure, feature selection techniques may include one or more feature selection rules. The one or more feature selection rules may include determining which features in the classified baseline data appear a threshold number of times in the classified baseline data and identifying those features that meet the threshold as candidate features. For example, any feature that appears more than twice in the classified baseline data may be considered a candidate feature. Any feature that appears less than twice may be excluded from consideration as a feature. According to some aspects of the present disclosure, a single feature selection rule may be applied to select a feature, or multiple feature selection rules may be applied to select a feature. According to some aspects of the present disclosure, feature selection rules may be applied in a cascading manner, where the feature selection rules are applied in a particular order and apply to the results of previous rules. For example, feature selection rules may be applied to the classified baseline data to generate information that can be used to illuminate roasting operations for the system 100 (e.g., an indication of the condition of the food, instructions to change the condition of the food according to the heat source and / or enclosure conditions, etc.). The final list of candidate features may be analyzed according to additional characteristics.

[0054] According to some aspects of the present disclosure, the control module 102 may generate information (e.g., an indication of the food's condition, instructions for changing the food's condition according to heat source and / or enclosure conditions, etc.) that can be used for illuminated roasting operations for the system 100 based on a wrapper method. The wrapper method may be configured to train a machine learning model using a subset of features. Features may be added to and / or removed from the subset based on inferences drawn from previous models. Wrapper methods include, for example, forward feature selection, backward feature elimination, recursive feature elimination, combinations thereof, etc. According to some aspects of the present disclosure, forward feature selection may be used to identify one or more candidate roasting scenarios, lighting / lighting attribute effects on the food, pressure-related indicators of food condition, temperature profiles and related attributes, audio indicators of food condition, etc. Forward feature selection is an iterative method that starts with no features in the machine learning model. In each iteration, the feature that best improves the model is added until adding new variables no longer improves the machine learning model's performance. According to some aspects of the present disclosure, recursive elimination may be used to identify one or more candidate roasting scenarios, lighting / lighting attribute effects on food products, pressure-related indicators of food condition, temperature profiles and related attributes, acoustic indicators of food condition, etc. Recursive elimination is an iterative method that starts with all features in a machine learning model. At each iteration, the lowest-ranking features are removed until no improvement in feature removal is observed. According to some aspects of the present disclosure, recursive feature elimination may be used to identify one or more candidate roasting scenarios, lighting / lighting attribute effects on food products, pressure-related indicators of food condition, temperature profiles and related attributes, acoustic indicators of food condition, etc. Recursive feature elimination is a greedy optimization algorithm that aims to find the best-performing feature subset. Recursive feature elimination iteratively builds models and eliminates the best or worst-performing features at each iteration. Recursive feature elimination builds a next model with the remaining features until all features are exhausted. Recursive feature elimination then ranks the features based on their order of removal.

[0055] According to some aspects of the present disclosure, one or more candidate roasting scenarios, lighting / lighting attribute effects on the food product, pressure-related indicators of food condition, temperature profiles and related attributes, acoustic indicators of food condition, etc. may be determined according to an embedding method. The embedding method combines the qualities of filter and wrapper methods. Embedding methods include, for example, Least Absolute Shrinkage and Selection Operator (LASSO) and Ridge Regression, which implement a penalty function to reduce overfitting. For example, LASSO regression performs L1 regularization, which applies a penalty equivalent to the absolute value of the magnitude of the coefficients, while Ridge Regression performs L2 regularization, which applies a penalty equivalent to the square of the magnitude of the coefficients.

[0056] After the control module 102 generates the feature set, the control module 102 may generate a machine learning-based predictive model 240 based on the feature set. A machine learning-based predictive model may refer to a complex mathematical model for data classification generated using machine learning techniques. For example, this machine learning-based classifier may include a map of support vectors representing boundary features. By way of example, the boundary features may be selected from the feature set and / or may represent the highest-ranking features within the feature set.

[0057] According to some aspects of the present disclosure, the control module 102 may use feature sets extracted from the training datasets 210A-210N and / or the classified baseline data to construct machine learning-based classification models 240A-240N to determine and / or predict roasting scenarios, lighting / lighting attribute effects on food products, pressure-related indicators of food product condition, temperature profiles and related attributes, acoustic indicators of food product condition, etc. According to some aspects of the present disclosure, the machine learning-based classification models 240A-240N may be combined into a single machine learning-based classification model 240. Similarly, the machine learning-based classifier 230 may represent a single classifier including single or multiple machine learning-based classification models 240 and / or multiple classifiers including single or multiple machine learning-based classification models 240. According to some aspects of the present disclosure, the machine learning-based classifier 230 may also include each of the training datasets 210A-210N and / or each feature set extracted from the training datasets 210A-210N and / or the classified baseline data. Although shown separately, the control module 102 may include a machine learning-based classifier 230 .

[0058] Features extracted from data from components / devices of system 100 may be combined into classification models trained using machine learning techniques, such as discriminant analysis, decision trees, nearest neighbor (NN) algorithms (e.g., k-NN models, replicator NN models, etc.), statistical algorithms (e.g., Bayesian networks, etc.), clustering algorithms (e.g., k-means, mean shift, etc.), neural networks (e.g., reservoir networks, artificial neural networks, etc.), support vector machines (SVMs), logistic regression algorithms, linear regression algorithms, Markov models or chains, principal component analysis (PCA) (e.g., for linear models), multi-layer perceptron (MLP) ANNs (e.g., for nonlinear models), reservoir-type recurrent networks (e.g., for nonlinear models, typically for time series), random forest classification, combinations thereof, etc. The resulting machine learning based classifier 230 may include decision rules or mappings that use the imaging data to determine and / or predict roasting scenarios, lighting / lighting attribute effects on food products, pressure-related indicators of food condition, temperature profiles and related attributes, acoustic indicators of food condition, etc.

[0059] The imaging data and the machine learning-based classifier 230 may be used to determine and / or predict the roasting scenario, the lighting / lighting attribute effect on the food product, the pressure-related indicators of the food product's condition, the temperature profile and related attributes, the acoustic indicators of the food product's condition, etc., for the test samples in the test dataset. For example, the results for each test sample may include a confidence level corresponding to the likelihood or probability that the corresponding test sample will accurately determine and / or predict the roasting scenario, the lighting / lighting attribute effect on the food product, the pressure-related indicators of the food product's condition, the temperature profile and related attributes, the acoustic indicators of the food product's condition, etc. The confidence level may be a value between 0 and 1, representing the likelihood that the determined / predicted roasting scenario, the lighting / lighting attribute effect on the food product, the pressure-related indicators of the food product's condition, the temperature profile and related attributes, the acoustic indicators of the food product's condition, etc., matches the calculated value. Multiple confidence levels may be provided for each test sample and each candidate (approximate) roasting scenario, the lighting / lighting attribute effect on the food product, the pressure-related indicators of the food product's condition, the temperature profile and related attributes, the acoustic indicators of the food product, etc. The best-performing candidate roast scenario, lighting / lighting attribute effects on food products, pressure-related indicators of food state, temperature profile and related attributes, acoustic indicators of food state, etc. may be determined by comparing the results obtained for each test sample with the calculated roast scenario, lighting / lighting attribute effects on food products, pressure-related indicators of food state, temperature profile and related attributes, acoustic indicators of food state, etc. for each test sample. Generally, the best-performing candidate roast scenario, lighting / lighting attribute effects on food products, pressure-related indicators of food state, temperature profile and related attributes, acoustic indicators of food state, etc. has results that closely match the calculated roast scenario, lighting / lighting attribute effects on food products, pressure-related indicators of food state, temperature profile and related attributes, acoustic indicators of food state, etc. The best-performing candidate roast scenario, lighting / lighting attribute effects on food products, pressure-related indicators of food state, temperature profile and related attributes, acoustic indicators of food state, etc. may be used for illuminated roasting operations.

[0060] FIG. 3 is a flow diagram illustrating an example training method 300 for generating a machine learning classifier 230 using the control module 102, according to some embodiments. The control module 102 may implement supervised, unsupervised, and / or semi-supervised (e.g., reinforcement-based) machine learning-based classification models 240. The method 300 shown in FIG. 3 is an example of a supervised learning method. Variations on this example training method are discussed below, but other training methods may similarly be implemented to train unsupervised and / or semi-supervised machine learning (predictive) models. The method 300 may be performed by processing logic, which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executing on a processing device), or a combination thereof. It should be understood that not all steps are required to practice the disclosure provided herein. Furthermore, some of the steps may be performed simultaneously or in a different order than that shown in FIG. 3, as will be understood by those skilled in the art.

[0061] The method 300 is described with reference to Figures 1 and 2. However, the method 300 is not limited to the aspects of these figures.

[0062] At 310, the control module 102 determines (e.g., accesses, receives, retrieves, etc.) data / information indicative of and / or related to the food product roasting process from components / devices of the system 100. The data / information from components / devices of the system 100 indicative of and / or related to the food product roasting process may include one or more data sets, each data set associated with a roasting scenario, lighting / lighting attribute effects on the food product, pressure-related indicators of food product condition, temperature profiles and related attributes, audio indicators of food product condition, etc.

[0063] At 320, the control module 102 generates a training data set and a test data set. According to some aspects of the present disclosure, the training data set and the test data set may be generated by illustrating a roasting scenario, lighting / lighting attribute effects on the food, pressure-related indicators of food condition, temperature profiles and related attributes, audio indicators of food condition, etc. According to some aspects of the present disclosure, the training data set and the test data set may be generated by randomly assigning a roasting scenario, lighting / lighting attribute effects on the food, pressure-related indicators of food condition, temperature profiles and related attributes, audio indicators of food condition, etc. to either the training data set or the test data set. According to some aspects of the present disclosure, the assignment of imaging data as training samples or test samples may not be completely random. According to some aspects of the present disclosure, only classified baseline data for a particular feature extracted from particular sensing device data may be used to generate the training data set and the test data set. According to some aspects of the present disclosure, a majority of the classified baseline data extracted from imaging data may be used to generate the training data set. For example, 75% of the categorized baseline data extracted from imaging data for determining roasting scenarios, illumination / lighting attribute effects on food products, pressure-related indicators of food condition, temperature profiles and related attributes, acoustic indicators of food condition, etc. may be used to generate a training data set and 25% may be used to generate a test data set. Any method or technique may be used to create the training and test data sets.

[0064] At 330, the control module 102 determines (e.g., extracts, selects, etc.) one or more features that may be used, for example, by a classifier (e.g., a software model, a classification layer of a neural network, etc.) to classify features extracted from various data / information from components / devices of the system 100 indicative of and / or related to the food product roasting process. The one or more features may include an indication of the roasting scenario, lighting / lighting attribute effects on the food product, pressure-related indicators of food product condition, temperature profile and related attributes, audio indicators of food product condition, etc. According to some aspects of the present disclosure, the control module 102 may determine a set of training baseline features from a training dataset. Features of the imaging data may be determined by any method.

[0065] At 340, the control module 102 trains one or more machine learning models, for example, using the one or more features. According to some aspects of the present disclosure, the machine learning models may be trained using supervised learning. According to some aspects of the present disclosure, other machine learning techniques, including unsupervised learning and semi-supervised learning, may be employed. The machine learning models trained at 340 may be selected based on different criteria (e.g., how close the predicted roasting scenario, lighting / lighting attribute effects on the food product, pressure-related indicators of food condition, temperature profile and related attributes, acoustic indicators of food condition, etc., are to the actual roasting scenario, lighting / lighting attribute effects on the food product, pressure-related indicators of food condition, temperature profile and related attributes, acoustic indicators of food condition, etc.) and / or data available in the training dataset. For example, machine learning classifiers may be subject to different degrees of bias. According to some aspects of the present disclosure, two or more machine learning models may be trained.

[0066] At 350, the control module 102 optimizes, improves, and / or cross-validates the trained machine learning model. For example, the data for the training data set and / or test data set may be updated and / or modified to include more categorized data indicative of different roasting scenarios, lighting / lighting attribute effects on food products, pressure-related indicators of food condition, temperature profiles and related attributes, acoustic indicators of food condition, etc.

[0067] At 360, the control module 102 selects one or more machine learning models to build a predictive model (e.g., a machine learning classifier, a prediction engine, etc.). The predictive model may be evaluated using a test dataset.

[0068] At 370, the control module 102 analyzes the test data set and executes the predictive model to generate classification values ​​and / or prediction values.

[0069] At 380, the control module 102 evaluates the classification values ​​and / or prediction values ​​output by the predictive model to determine whether such values ​​achieved a desired level of accuracy. The performance of a predictive model may be evaluated in several ways based on the true positive, false positive, true negative, and / or false negative classifications of some of the data points represented by the predictive model. For example, a false positive of a predictive model may refer to the number of times the predictive model incorrectly predicted and / or determined a roasting scenario, illumination / lighting attribute effects on the food product, pressure-related indicators of food condition, temperature profile and related attributes, acoustic indicators of food condition, etc. Conversely, a false negative of a predictive model may refer to the number of times the machine learning model incorrectly predicted and / or determined a roasting scenario, lighting / lighting attribute effects on food products, pressure-related indicators of food state, temperature profile and related attributes, acoustic indicators of food state, etc., when the roasting scenario, lighting / lighting attribute effects on food products, pressure-related indicators of food state, temperature profile and related attributes, acoustic indicators of food state, etc., matched the actual predicted and / or determined roasting scenario, lighting / lighting attribute effects on food products, pressure-related indicators of food state, temperature profile and related attributes, acoustic indicators of food state, etc. True negatives and true positives may refer to the number of times the predictive model correctly predicted and / or determined a roasting scenario, lighting / lighting attribute effects on food products, pressure-related indicators of food state, temperature profile and / or related attributes, acoustic indicators of food state, etc. Related to these measurements are the concepts of recall and precision. Generally, recall refers to the ratio of true positives to the sum of true positives and false negatives, which quantifies the sensitivity of a predictive model. Similarly, precision refers to the ratio of true positives as the sum of true positives and false positives.

[0070] At 390, the control module 102 outputs the predictive model (and / or the output of the predictive model). For example, the control module 102 may output the predictive model when such a desired level of accuracy is reached. The output of the predictive model may terminate the training phase.

[0071] According to some aspects of the present disclosure, if the desired level of accuracy is not reached, then at 390 the control module 102 may perform subsequent iterations of the training method 300 beginning at 310, with variations such as considering a larger collection of data from components / devices of the system 100 indicative of and / or related to the roasting process.

[0072] 4 illustrates a flow diagram of an exemplary method 400 for lighting roasting, according to some embodiments. Method 400 may be performed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executing on a processing device), or a combination thereof. It should be understood that not all steps are required to practice the disclosure provided herein. Furthermore, some of the steps may be performed simultaneously or in a different order than that shown in FIG. 4, as will be understood by one of ordinary skill in the art.

[0073] The method 400 will be described with reference to Figures 1-3. However, the method 400 is not limited to the aspects of these figures.

[0074] The food product, such as coffee, that has been roasted under the lighting device while in the sealed chamber is then immediately ground and prepared for consumption according to embodiments of the systems, apparatus, devices, methods, computer program products for illuminated roasting, and / or combinations and sub-combinations thereof described herein.

[0075] At 410, the control module 102 determines lighting attributes. According to some aspects of the present disclosure, the control module 102 determines the lighting attributes based on an indication that food is in the sealed chamber and the type of food. According to some aspects of the present disclosure, the food may be unroasted coffee beans. The lighting attributes may be determined, for example, from a lookup table that maps food types to lighting attributes. The lighting attributes may include wavelength values, lumen values, wattage values, etc. The lighting attributes may be used as operational settings for lighting devices (e.g., arrays of laser diodes, arrays of LEDs, incandescent lamps, metal halide lamps, arc lamps, etc.) associated with the sealed chamber. According to some aspects of the present disclosure, the sealed chamber may be a vacuum chamber.

[0076] At 420, the control module 102 causes a change in the temperature profile of the food item. According to some aspects of the present disclosure, the control module 102 causes a change in the temperature profile of the food item based on illuminating the food item according to the lighting attributes. According to some aspects of the present disclosure, the control module 102 causes a change in the temperature profile of the food item by sending instructions to a lighting device to illuminate the food item according to the lighting attributes.

[0077] According to some aspects of the present disclosure, the method 400 may further include the control module 102 identifying a pressure level within the sealed chamber. According to some aspects of the present disclosure, the control module 102 identifies the pressure level within the sealed chamber based on information received from a pressure-sensing device (e.g., a pressure transducer, etc.) associated with the sealed chamber. According to some aspects of the present disclosure, the control module 102 alters the lighting attributes based on the pressure level. According to some aspects of the present disclosure, the control module 102 causes a change to another temperature value in the altered temperature profile based on illumination of the food item according to the altered lighting attributes.

[0078] According to some aspects of the present disclosure, the method 400 may further include the control module 102 causing a stirring element in the sealed chamber to stir the food product. According to some aspects of the present disclosure, the control module 102 causes a stirring element in the sealed chamber to stir the food product based on the indication that food product is in the sealed chamber. According to some aspects of the present disclosure, the stirring speed of the stirring element may be based on a pressure level in the sealed chamber. According to some aspects of the present disclosure, the stirring speed of the stirring element may be based on a temperature of the food product and / or a temperature in the sealed chamber.

[0079] According to some aspects of the present disclosure, the method 400 may further include the control module 102 identifying that the food item is in a first state (e.g., via computer vision, object recognition, image color analysis, etc.). According to some aspects of the present disclosure, the control module 102 determines, based on the image data representing the food item, that a temperature value of the altered temperature profile meets a temperature threshold indicating that the food item is in a second state. According to some aspects of the present disclosure, the control module 102 causes the food item to be removed from the sealed chamber based on determining that the temperature value meets the temperature threshold. According to some aspects of the present disclosure, the control module 102 sends a command to the suction device to cause the food item to be removed from the sealed chamber via a suction force generated by the suction device.

[0080] 5 illustrates a flow diagram of an exemplary method 500 for lighting roasting, according to some embodiments. Method 500 may be performed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executing on a processing device), or a combination thereof. It should be understood that not all steps are required to practice the disclosure provided herein. Furthermore, some of the steps may be performed simultaneously or in a different order than that shown in FIG. 5, as will be understood by one of ordinary skill in the art.

[0081] The method 500 will be described with reference to Figures 1-3. However, the method 500 is not limited to the aspects of these figures.

[0082] The food product, such as coffee, that has been roasted under the lighting device while in the sealed chamber is then immediately ground and prepared for consumption according to embodiments of the systems, apparatus, devices, methods, computer program products for illuminated roasting, and / or combinations and sub-combinations thereof described herein.

[0083] At 510, the control module 102 determines lighting attributes. According to some aspects of the present disclosure, the control module 102 determines the lighting attributes based on an indication that a food item is in the vacuum chamber and the type of food item. According to some aspects of the present disclosure, the food item may be unroasted coffee beans. The lighting attributes may be determined, for example, from a lookup table that maps food items to lighting attributes. The lighting attributes may include wavelength values, lumen values, wattage values, etc. The lighting attributes may be used as operational settings for lighting devices associated with the vacuum chamber (e.g., an array of laser diodes, an array of LEDs, an incandescent lamp, a metal halide lamp, an arc lamp, etc.).

[0084] At 520, the control module 102 causes a change in the temperature profile of the food item. According to some aspects of the present disclosure, the control module 102 causes a change in the temperature profile of the food item based on illuminating the food item according to the lighting attributes. According to some aspects of the present disclosure, the control module 102 causes a change in the temperature profile of the food item by sending instructions to a lighting device to illuminate the food item according to the lighting attributes.

[0085] At 530, the control module 102 determines that the temperature values ​​of the altered temperature profile meet the temperature threshold that indicates the food item is in the second state. According to some aspects of the present disclosure, the control module 102 determines that the temperature values ​​of the altered temperature profile meet the temperature threshold that indicates the food item is in the second state based on the image data that shows the food item.

[0086] At 540, the control module 102 causes the food item to be removed from the vacuum chamber. According to some aspects of the present disclosure, the control module 102 causes the food item to be removed from the vacuum chamber based on determining that the temperature value meets the temperature threshold. According to some aspects of the present disclosure, the control module 102 causes the food item to be removed from the sealed chamber based on determining that the temperature value meets the temperature threshold. According to some aspects of the present disclosure, the control module 102 sends a command to the suction device to cause the food item to be removed from the sealed chamber via the suction force generated by the suction device.

[0087] According to some aspects of the present disclosure, the method 500 may further include the control module 102 identifying a pressure level within the vacuum chamber. According to some aspects of the present disclosure, the control module 102 identifies the pressure level within the vacuum chamber based on information received from a pressure-sensing device (e.g., a pressure transducer, etc.) associated with the vacuum chamber. According to some aspects of the present disclosure, the control module 102 alters lighting attributes based on the pressure level. According to some aspects of the present disclosure, the control module 102 causes a change in the altered temperature profile to another temperature value based on illumination of the food item according to the altered lighting attributes.

[0088] According to some aspects of the present disclosure, the method 500 may further include the control module 102 causing a stirring element in the vacuum chamber to stir the food product. According to some aspects of the present disclosure, the control module 102 causes the stirring element in the vacuum chamber to stir the food product based on the indication that the food product is in the vacuum chamber. According to some aspects of the present disclosure, the stirring speed of the stirring element may be based on the pressure level in the vacuum chamber. According to some aspects of the present disclosure, the stirring speed of the stirring element may be based on the temperature of the food product and / or the temperature in the vacuum chamber.

[0089] According to some aspects of the present disclosure, the method 500 may further include inputting the image data representing the food product into a predictive model trained to identify the state of the product based on visual attributes of the product. According to some aspects of the present disclosure, the control module 102 receives an indication from the predictive model that the food product is in the second state based on a color attribute of the food product.

[0090] According to some aspects of the present disclosure, the method 500 may further include initiating a brewing process for the food product. According to some aspects of the present disclosure, the control module 102 may send one or more signals to the food brewing device to initiate the brewing process for the food product based on the food product being removed from the vacuum chamber.

[0091] FIG. 6 is an exemplary computer system useful for implementing various embodiments. Various embodiments may be implemented using one or more well-known computer systems, such as, for example, computer system 600 shown in FIG. 6. For example, one or more computer systems 600 may be used to implement any of the embodiments described herein, as well as combinations and subcombinations thereof. According to some aspects of the present disclosure, control module 102 of FIGS. 1A-1B (and / or any other devices / components described herein) may be implemented using computer system 600. According to some aspects of the present disclosure, computer system 600 may be used to implement methods 400 and 500.

[0092] Computer system 600 may include one or more processors (also referred to as central processing units or CPUs), such as processor 604. Processor 604 may be connected to a communication infrastructure or bus 606.

[0093] The computer system 600 may also include user input / output devices 602 , such as a monitor, keyboard, pointing device, etc., which may communicate with a communications infrastructure or bus 606 via the user input / output devices 602 .

[0094] One or more of the processors 604 may be a graphics processing unit (GPU). In an embodiment, a GPU may be a processor that is a dedicated electronic circuit designed to process mathematically intensive applications. A GPU may have a parallel structure that is efficient for parallel processing of large blocks of data, such as mathematically intensive data common in computer graphics applications, images, video, etc.

[0095] Computer system 600 may also include a main or primary memory 608, such as random access memory (RAM). Main memory 608 may include one or more levels of cache. Main memory 608 may store control logic (i.e., computer software) and / or data internally.

[0096] Computer system 600 may also include one or more secondary storage devices or memories 610. Secondary memory 610 may include, for example, a hard disk drive 612 and / or a removable storage device or drive 614. Removable storage drive 614 may be a floppy disk drive, a magnetic tape drive, a compact disk drive, an optical storage device, a tape backup device, and / or any other storage device / drive.

[0097] The removable storage drive 614 may communicate with a removable storage unit 618. The removable storage unit 618 may include a computer-usable or readable storage device having computer software (control logic) and / or data stored therein. The removable storage unit 618 may be a floppy disk, magnetic tape, compact disk, DVD, optical storage disk, and / or any other computer data storage device. The removable storage drive 614 may read from and / or write to the removable storage unit 618.

[0098] Secondary memory 610 may include other means, devices, components, equipment, and / or other techniques for allowing computer programs and / or other instructions and / or data to be accessed by computer system 600. Such means, devices, components, equipment, and / or other techniques may include, for example, removable storage unit 622 and interface 620. Examples of removable storage unit 622 and interface 620 may include a program cartridge and cartridge interface (such as those found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface.

[0099] Computer system 600 may further include a communications or network interface 624. Communications interface 624 may enable computer system 600 to communicate and interact with any combination of external devices, external networks, external entities, etc. (individually and collectively referred to by reference numeral 628). For example, communications interface 624 may enable computer system 600 to communicate with external or remote devices 628 via communications path 626, which may be wired and / or wireless (or a combination thereof) and may include any combination of a LAN, a WAN, the Internet, etc. Control logic and / or data may be transmitted to and from computer system 600 via communications path 626.

[0100] Computer system 600 may also be any of a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet, a smartphone, a smartwatch or other wearable, an appliance, part of the Internet of Things, and / or an embedded system, or any combination thereof, to name a few non-limiting examples.

[0101] Computer system 600 may be a client or server accessing or hosting any application and / or data via any delivery paradigm, including remote or distributed cloud computing solutions, local or on-premise software ("on-premise" cloud-based solutions), "as a service" models (e.g., content as a service (CaaS), digital content as a service (DCaaS), software as a service (SaaS), managed software as a service (MSaaS), platform as a service (PaaS), desktop as a service (DaaS), framework as a service (FaaS), backend as a service (BaaS), mobile backend as a service (MBaaS), infrastructure as a service (FaaS), etc.). service, Infrastructure as a Service (IaaS), etc.), and / or hybrid models including any combination of the foregoing examples or other service or delivery paradigms.

[0102] Any applicable data structures, file formats, and schemas in computer system 600 may be derived from standards, including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), Yet Another Markup Language (YAML), Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or any other functionally similar representations, alone or in combination. Alternatively, proprietary data structures, formats, and / or schemas may be used exclusively or in combination with known or open standards.

[0103] In some embodiments, a tangible, non-transitory apparatus or article of manufacture comprising a tangible, non-transitory computer-usable or readable medium having control logic (software) stored therein may be referred to herein as a computer program product or program storage device, including, but not limited to, computer system 600, main memory 608, secondary memory 610, removable storage units 618 and 622, and tangible articles of manufacture embodying any combination of the above. Such control logic, when executed by one or more data processing devices (such as computer system 600), may cause such data processing devices to operate as described herein.

[0104] Based on the teachings contained herein, it will be apparent to one skilled in the art how to make and use embodiments of the present disclosure using data processing devices, computer systems, and / or computer architectures other than those shown in Figure 6. In particular, embodiments may operate with software, hardware, and / or operating system implementations other than those described herein.

[0105] It is understood that the Detailed Description section, and not any other section, is intended to be used to interpret the claims, which other sections may describe one or more, but not all, example embodiments contemplated by the inventors, and are therefore not intended to limit the scope of the disclosure or the appended claims in any way.

[0106] Additionally and / or alternatively, this disclosure describes exemplary embodiments for exemplary fields and applications, but it should be understood that this disclosure is not limited thereto. Other embodiments and modifications thereto are possible and are within the scope and spirit of this disclosure. For example, without limiting the generality of this paragraph, the embodiments are not limited to the software, hardware, firmware, and / or entities illustrated in the figures and / or described herein. Moreover, the embodiments (whether or not explicitly described herein) have significant utility for fields and applications other than the examples described herein.

[0107] One or more portions of the above implementations may include software. Software is a general term connoting specific functions and relationships thereof. The boundaries of these functional building blocks are arbitrarily defined herein for convenience of description. Alternative boundaries may be defined so long as the specified functions and relationships (or their equivalents) are appropriately performed. Also, alternative embodiments may execute functional blocks, steps, operations, methods, etc. using an order different from that described herein.

[0108] The use of phrases such as "one embodiment," "embodiment," "exemplary embodiment," or similar expressions indicates that the described embodiment may include a particular feature, structure, or characteristic, but not all embodiments necessarily include the particular feature, structure, or characteristic. Moreover, such phrases do not necessarily refer to the same embodiment. Furthermore, if a particular feature, structure, or characteristic is described in connection with an embodiment, it is believed to be within the knowledge of one of ordinary skill in the relevant art to incorporate such feature, structure, or characteristic into other embodiments, whether or not explicitly described herein. Additionally, some embodiments may be described using the terms "coupled" and "connected," along with their derivatives. These terms are not necessarily intended as synonyms for each other. For example, some embodiments may be described using the terms "connected" and / or "coupled" to indicate that two or more elements are in direct physical or electrical contact with each other. However, the term "coupled" can also mean that two or more elements are not in direct contact with each other, but still cooperate or interact with each other.

[0109] Exemplary lighting roasting results Embodiments of the systems, apparatuses, devices, methods, computer program products, and / or combinations and sub-combinations thereof for illuminated roasting described herein utilize the wavelength-dependent light absorption characteristics of food products, including but not limited to coffee beans, to optimize the roasting process. Embodiments of the systems, apparatuses, devices, methods, and computer program products for illuminated roasting described herein, and / or combinations and sub-combinations thereof, improve upon conventional roasting devices and techniques by roasting food products, including but not limited to coffee beans, from the outside in, enhancing the roast level of the surface of the beans relative to the interior of the beans. For example, embodiments of the systems, apparatuses, devices, methods, and computer program products for illuminated roasting described herein, and / or combinations and sub-combinations thereof, adjust the wavelength of their light source (e.g., illuminator 106, etc.) to control the heating of the food product with greater precision than any conventional roasting process. For example, coffee beans are roasted with a very high degree of precision in terms of roast level (e.g., dark roast vs. medium roast). The degree of roast of coffee beans can be quantified by shrinkage, which is the percentage loss in weight of the beans after roasting compared to before roasting. While typical roasters control shrinkage to + / - 1% (e.g., 14%-16%), the process here demonstrated control to + / - 0.2%.

[0110] 7A-7B show exemplary results (e.g., results from the test system 100 depicted in FIG. 1A , the test system 100 depicted in FIG. 1B , an example system 100 with fewer components than depicted in any of FIGS. 1A-1B , and an example system 100 with more components than depicted in any of FIGS. 1A-1B ) of the test system 100 for illumination roasting of coffee bean particles (e.g., ground coffee beans) with an initial temperature of 26° C. in the sealed chamber 104 and exposure of the coffee beans to illumination for a minimum of 15 minutes. Sensing devices (e.g., thermocouples) labeled TC1-TC2 were used to test the temperatures of coffee beans roasted by the system 100 at atmospheric pressure. Sensing device TC4 was positioned closest to the surface of the coffee, sensing device TC1 was positioned furthest from the surface of the coffee beans, and sensing devices TC2 and TC3 were positioned at different depths between the positions of TC1 and TC2.

[0111] Figure 7A shows the results of illumination and / or light heating and the penetration depth of the coffee beans when the illuminator 106 is set to illuminate the coffee beans with a wavelength of 450 nm, and Figure 7B shows the results of illumination and / or light heating and the penetration depth of the coffee beans when the illuminator 106 is set to illuminate the coffee beans with a wavelength of 850 nm.

[0112] As shown in Figures 7A-7B, light having a wavelength of 450 nm penetrates green coffee beans less than light having a wavelength of 850 nm. However, light having a wavelength of 450 nm causes the surface of the green coffee beans to become much hotter (approximately 36°C vs. approximately 32°C after 15 minutes). To summarize the results of the test, the following characteristics and / or metrics were used in the analysis: 1. Surface temperature: the final temperature of the sensing device TC4 at 15 minutes, which is an indicator of the surface absorption of the green coffee beans. As shown, the higher the temperature, the greater the surface absorption of light. 2. Difference between the top two sensing devices (e.g., sensing devices TC4 and TC3): The difference between the top two sensing devices at 15 minutes of exposure indicates the optical penetration depth into the green coffee bean grain. As shown, the greater this difference, the greater the surface absorption of light and the less light penetration into the green coffee bean grain. 3. Average Rise: The average value of all sensing devices (e.g., sensing devices TC1-TC4) at 15 minutes minus the average value of all sensing devices at the beginning of exposure, indicates the overall heating efficiency of the wavelength of light applied to the coffee bean grain. As shown, the higher this value, the more efficient the light coupling into the green coffee bean grain.

[0113] FIG. 8 shows an overview of light heating and depth penetration tests on green coffee beans at various wavelengths. As shown in FIG. 7, the solid data corresponds to the left y-axis, and the dashed data corresponds to the right y-axis. As shown in FIG. 7, light emitted at a wavelength of 450 nm had the highest surface absorption on green coffee beans, while light emitted at a wavelength of 950 nm had lower surface absorption than light emitted at 450 nm, but was slightly more efficient at heating the entire sample. As shown in FIG. 7, light emitted at an 850 nm wavelength had the highest amount of light penetration, but also the lowest heating efficiency and lowest surface temperature of the wavelengths tested.

[0114] The results in Figure 8 illustrate the effect of temperature on a sample food product and can be used by the control module 102 to assist in the selection of wavelengths to optimize the roasting process. Other wavelengths can also be used, and their light coupling and transmission characteristics can also be used to optimize the roasting process. Generally, one or more wavelengths are desirable, with at least one wavelength having very high surface absorption (e.g., above 450 nm) and another wavelength having greater light transmission (e.g., 940 nm). These wavelengths can then be adjusted during the roasting process to affect the depth of roast of the food product (e.g., coffee beans).

[0115] Embodiments of the illuminated roasting systems, apparatus, devices, methods, computer program products, and / or combinations and sub-combinations thereof may be applied to any food product, including, but not limited to, any coffee bean variety, which may have unique optical properties. Embodiments of the illuminated roasting systems, apparatus, devices, methods, computer program products, and / or combinations and sub-combinations thereof may optimize the roasting process of any food product, including, but not limited to, any coffee bean variety, by adapting wavelengths to different food products.

[0116] Lighting roasting at different pressures According to some aspects of the present disclosure, embodiments of systems, apparatuses, devices, methods, computer program products for illuminated roasting, and / or combinations and subcombinations thereof, incorporate one or more sensing devices, including but not limited to, pressure transducers, etc., to monitor the roasting process and optimize the roasting process using data obtained from monitoring the roasting process. According to some aspects of the present disclosure, the pressure transducers, etc., when used in conjunction with a throttle valve (e.g., to control the pumping rate of a sealed chamber), generate information that can be used to indicate: the moisture content of a food product, such as coffee beans, before roasting; the current state of moisture of the food product during roasting; the onset and / or duration of states of the food product, including but not limited to, a "first crack" state indicating the level of roast, and a "second crack" state indicating the degree of dark roast.

[0117] FIG. 9 illustrates an example output of a sensing device (e.g., sensing device 112, etc.) of system 100 during the roasting process. As shown in FIG. 9, significant portions of the sensing device output can be seen to correspond to various portions of the roasting process. According to some embodiments of the present disclosure, after the sealed chamber 104 is pumped down (e.g., via vacuum pump 110, etc.), a base pressure is reached within the sealed chamber 104. For a constant chamber pumping speed, this base pressure is related to the outgassing rate of an exemplary food product, such as coffee beans, during initial heating, which in turn is directly related to the moisture level and temperature in the coffee beans. During the initial pressure rise within the sealed chamber 104 (indicated by oval region 800), a pressure increase occurs as the coffee beans heat and outgassing begins to increase. The rate of change of this pressure (i.e., the slope of the line) is also related to the moisture content of the coffee beans. According to some aspects of the present disclosure, the control module 102 uses these two initial portions of the signal to optimize the roast by identifying the need for more or less light output from the illuminator 106 to compensate for higher or lower moisture content in the coffee beans.

[0118] As shown in FIG. 9, the first crack state of the coffee beans begins just before the 500 second mark, indicating that the coffee beans have reached a temperature sufficient for water to evaporate in the cold atmosphere. According to some embodiments of the present disclosure, this directly correlates to "first crack" in a standard roasting process; coffee beans removed at the beginning of this time frame are light roast. Coffee beans extracted toward the end of the second crack state (approximately 580 seconds) were medium roast. The second crack state is shown in FIG. 9 and begins at approximately 680 seconds; coffee beans extracted around this time were dark roast. After approximately 800 seconds, the pressure within the sealed chamber 104 begins to drop rapidly, and by this time and / or before this time, all of the coffee beans are extracted.

[0119] 9, using the base pressure and initial pressure rise rate can be used to adjust the light output of the illuminator 106 to maintain an optimal roast rate regardless of the moisture content of the coffee beans. The timing and duration of the first crack state was used to indicate when to terminate the roasting process for either a light or medium roast. The timing and duration of the second crack state was used to indicate when to terminate the roasting process for a dark roast.

[0120] For one or more embodiments, at least one of the components depicted in one or more of the previous figures may be configured to perform one or more operations, techniques, processes, and / or methods as described in the example sections below. For example, system 100 described above in connection with one or more of the previous figures may be configured to operate according to one or more of the examples described below. [Example]

[0121] Example 1: A non-transitory computer-readable medium having instructions stored therein that, when executed by at least one computing device, cause the at least one computing device to perform operations including: determining lighting attributes based on an indication that a food item is in a sealed chamber and the type of food item; and causing a change in a temperature profile of the food item based on illumination of the food item according to the lighting attributes.

[0122] Example 2: The non-transitory computer-readable medium of Example 1, wherein the food is in a first state, and the operations further include: determining, based on image data representing the food, that a temperature value of the changed temperature profile satisfies a temperature threshold indicating that the food is in a second state; and causing the food to be removed from the sealed chamber based on determining that the temperature value satisfies the temperature threshold.

[0123] Example 3: The non-transitory computer-readable medium of any one of the preceding examples, wherein the sealed chamber comprises a vacuum chamber.

[0124] Example 4: A non-transitory computer-readable medium as described in any one of the preceding examples, wherein the operations further include causing the food to be removed from the sealed chamber based on the length of time for illuminating the food according to the lighting attributes satisfying a timing threshold.

[0125] Example 5: The non-transitory computer-readable medium of any one of the preceding examples, wherein the food product comprises at least one of coffee beans, legumes, meat, or grains.

[0126] Example 6: The non-transitory computer-readable medium of any one of the preceding examples, wherein causing a change in the temperature profile of the food product includes sending instructions to a lighting device to illuminate the food product according to lighting attributes.

[0127] Example 7: The non-transitory computer-readable medium of any one of the preceding examples, wherein the lighting device includes at least one of a laser diode, a light-emitting diode (LED), an incandescent lamp, a metal halide lamp, or an arc lamp.

[0128] Example 8: The non-transitory computer-readable medium of any one of the preceding examples, wherein the lighting attribute includes at least one of a wavelength value, a lumen value, or a wattage value.

[0129] Example 9: The non-transitory computer-readable medium of any one of the preceding examples, wherein the operations further include identifying a pressure level within the sealed chamber based on information received from the pressure sensing device, modifying lighting attributes based on the pressure level, and causing a change in the altered temperature profile to another temperature value based on illumination of the food item in accordance with the modified lighting attributes.

[0130] Example 10: The non-transitory computer-readable medium of any one of the preceding examples, wherein the operations further include causing a stirring element in the sealed chamber to stir the food product based on an indication that the food product is in the sealed chamber, the sealed chamber being a vacuum chamber, and the stirring speed of the stirring element being based on a pressure level in the vacuum chamber.

[0131] Example 11: The non-transitory computer-readable medium of any one of the preceding examples, wherein the operations further include, based on an indication that food is in the sealed chamber, causing a stirring element in the sealed chamber to stir the food, wherein the stirring speed of the stirring element is based on the temperature of the food.

[0132] Example 12: The non-transitory computer-readable medium of any one of the preceding examples, wherein the operations further include sending instructions to the suction device to cause the food item to be removed from the sealed chamber via suction force generated by the suction device.

[0133] Example 13: A non-transitory computer-readable medium of any one of the preceding examples, wherein the operations further include inputting image data showing a food product into a predictive model trained to identify a state of the product based on visual attributes of the product, and receiving an indication from the predictive model that the food product is in a second state based on color attributes of the food product.

[0134] Example 14: The non-transitory computer-readable medium of any one of the preceding examples, wherein the food product is in a first state, and the method further includes inputting pressure information indicating the amount of pressure surrounding the food product in the sealed chamber and an indication of the food product into a predictive model trained to identify the state of the product based on the type of product and the pressure information indicating the amount of pressure surrounding the product, and receiving an indication from the predictive model that the food product is in a second state.

[0135] Example 15: A method of roasting coffee beans, the method comprising: based on an indication that unroasted coffee beans of a certain type are in a sealed chamber, determining an illumination attribute of the coffee beans of that type; and converting the unroasted coffee beans to roasted coffee beans based on illumination of the unroasted coffee beans in accordance with the illumination attribute.

[0136] Example 16: A method for roasting coffee beans, the method comprising: illuminating coffee beans within an enclosed chamber; receiving audio information from a first sensing device associated with the enclosed chamber; determining an indication of a state of the coffee beans based on the audio information; determining that a temperature of the coffee beans corresponds to a state of the coffee beans based on temperature information received from a second sensing device associated with the enclosed chamber; and terminating illumination of the coffee beans based on the temperature of the coffee beans corresponding to the state of the coffee beans.

[0137] Example 17: An apparatus comprising: a sealed chamber configured with an optical window; an illumination element configured to illuminate food within the sealed chamber according to an illumination attribute via light passing through the optical window; a pressure sensing device configured to detect a pressure level within the sealed chamber; a stirring element within the sealed chamber configured to stir the food at a rate determined based on the pressure level within the sealed chamber; and an optical device configured to capture image data indicative of a temperature profile of the food within the sealed chamber.

[0138] Example 18: The device of Example 17, wherein the food product comprises at least one of coffee beans, legumes, meat, or grains.

[0139] Example 19: The device described in Examples 17-18, further comprising at least one heating element external to the sealed chamber configured to heat the sealed chamber according to heating parameters received via the user interface.

[0140] Example 20: The apparatus of Examples 17 to 19, further comprising a pressure control element configured to change the pressure level within the sealed chamber, the pressure control element including at least one of a vacuum valve, a throttle valve, or a vacuum pump.

[0141] Example 21: The apparatus of Examples 17-20, wherein the optical device includes at least one of an optical pyrometer, a hyperspectral imaging device, or a speckle field detection device.

[0142] Example 22: An apparatus as described in Examples 17 to 21, further comprising an interactive user interface configured to receive information describing at least one of the type of food in the sealed chamber, lighting attributes, or instructions to cause the stirring element to stir.

[0143] Example 23: An apparatus as described in Examples 17 to 22, wherein the lighting element is further configured to illuminate food in the sealed chamber according to another lighting attribute, the another lighting attribute being determined based on information received from a pressure sensing device indicating a pressure level in the sealed chamber and information mapping the pressure level to a lighting attribute based on the food type.

[0144] Example 24: A method comprising: determining lighting attributes based on an indication that food is in an open chamber and the type of food; and causing a change in the temperature profile of the food based on illumination of the food in accordance with the lighting attributes.

[0145] Example 25: The method described in Example 24, wherein the food is in a first state, and the method further includes determining, based on image data representing the food, that a temperature value of the changed temperature profile satisfies a temperature threshold indicating that the food is in a second state, and causing the food to be removed from the open chamber based on determining that the temperature value satisfies the temperature threshold.

[0146] Example 26: The method of Examples 24-25, wherein the open chamber comprises a vacuum chamber.

[0147] Example 27: The method of Examples 24-26, further comprising causing the food to be removed from the open chamber based on the length of time for illuminating the food according to the lighting attribute satisfying a timing threshold.

[0148] Example 28: The method of Examples 24-27, wherein the food product comprises at least one of coffee beans, legumes, meat, or grains.

[0149] Example 29: The method of any one of Examples 24 to 28, wherein causing a change in the temperature profile of the food product includes sending instructions to a lighting device to illuminate the food product according to lighting attributes.

[0150] Example 30: The method of Example 29, wherein the lighting device includes at least one of a laser diode, a light-emitting diode (LED), an incandescent lamp, a metal halide lamp, or an arc lamp.

[0151] Example 31: The method of Examples 24 to 30, wherein the lighting attribute includes at least one of a wavelength value, a lumen value, or a watt value.

[0152] Example 32: The method described in Examples 24 to 31, further comprising identifying a pressure level in the open chamber based on information received from the pressure sensing device, modifying lighting attributes based on the pressure level, and causing a change in the altered temperature profile to another temperature value based on illumination of the food item according to the modified lighting attributes.

[0153] Example 33: The method of any one of Examples 24 to 32, further comprising, based on an indication that the food product is in the open chamber, causing a stirring element in the open chamber to stir the food product, wherein the open chamber is a vacuum chamber and the stirring rate of the stirring element is based on the pressure level in the vacuum chamber.

[0154] Example 34: The method of Examples 24-33, further comprising, based on an indication that food is in the open chamber, causing a stirring element in the open chamber to stir the food, wherein the stirring rate of the stirring element is based on the temperature of the food.

[0155] Example 35: The method of any one of Examples 24-34, further comprising sending a command to the suction device to cause the food product to be removed from the open chamber via suction force generated by the suction device.

[0156] Example 36: The method of any one of Examples 24 to 35, further comprising inputting image data showing a food product into a predictive model trained to identify a state of the product based on visual attributes of the product, and receiving an indication from the predictive model that the food product is in a second state based on color attributes of the food product.

[0157] Example 37: The method of any of Examples 24 to 36, wherein the food product is in a first state, and the method further includes inputting pressure information indicating the amount of pressure surrounding the food product in the open chamber and an indication of the food product into a predictive model trained to identify the state of the product based on the type of product and the pressure information indicating the amount of pressure surrounding the product, and receiving an indication from the predictive model that the food product is in a second state.

[0158] Example 38: An apparatus comprising: a sealed chamber configured with an optical window; an illumination element configured to illuminate food within the sealed chamber according to an illumination attribute via light passing through the optical window; a pressure sensing device configured to detect a pressure level within the sealed chamber; a stirring element within the sealed chamber configured to stir the food at a rate determined based on the pressure level within the sealed chamber; and an optical device configured to capture image data indicative of a temperature profile of the food within the sealed chamber.

[0159] Example 39: The device of Example 38, wherein the food product comprises at least one of coffee beans, legumes, meat, or grains.

[0160] Example 40: The device described in Examples 38 to 39, further comprising at least one heating element external to the sealed chamber configured to heat the sealed chamber according to heating parameters received via the user interface.

[0161] Example 41: The device described in Examples 38 to 40, further comprising a pressure control element configured to change the pressure level within the sealed chamber, the pressure control element including at least one of a vacuum valve, a throttle valve, or a vacuum pump.

[0162] Example 42: The apparatus described in Examples 38 to 41, wherein the optical device includes at least one of an optical pyrometer, a hyperspectral imaging device, or a speckle field detection device.

[0163] Example 43: An apparatus as described in Examples 38 to 42, further comprising an interactive user interface configured to receive information describing at least one of the type of food in the sealed chamber, lighting attributes, or instructions to cause the stirring element to stir.

[0164] Example 44: An apparatus as described in Examples 38 to 43, wherein the lighting element is further configured to illuminate food in the sealed chamber according to another lighting attribute, the another lighting attribute being determined based on information received from a pressure sensing device indicating a pressure level in the sealed chamber and information mapping the pressure level to a lighting attribute based on the food type.

[0165] Example 45: The device described in Examples 38 to 44, wherein the lighting element includes at least one of a laser diode, a light-emitting diode (LED), an incandescent lamp, a metal halide lamp, or an arc lamp.

[0166] Example 46: An apparatus as described in Examples 38 to 45, further comprising a controller, which determines, based on image data representing the food received from the optical device, that a temperature value of the temperature profile satisfies a temperature threshold indicating that the food has changed from a first state to a second state based on illumination from the lighting element, and causes the food to be removed from the sealed chamber based on a determination that the temperature value satisfies the temperature threshold.

[0167] Example 47: The device of Example 46, wherein the controller is further configured with a predictive model trained to identify the state of the product based on pressure information indicating the type of product and the amount of pressure surrounding the product, and the predictive model is configured to receive an indication of the food being in a first state and output an indication that the food is in a second state based on an indication of the pressure level within the sealed chamber.

[0168] Example 48: The device described in Examples 38 to 47, wherein the controller is further configured with a predictive model trained to identify the state of the product based on visual attributes of the product, and the predictive model is configured to receive image data showing the food in a first state and output an indication that the food is in a second state based on color attributes of the food.

[0169] Example 49: An apparatus comprising: a sealed chamber configured with an optical window; an illumination element configured to illuminate food in the sealed chamber according to illumination attributes via light passing through the optical window; a pressure sensing device configured to detect a pressure level in the sealed chamber; a pressure control element configured to alter the pressure level in the sealed chamber, comprising at least one of a vacuum valve, a throttle valve, or a vacuum pump; a stirring element in the sealed chamber configured to stir the food at a speed determined based on the pressure level in the sealed chamber; and a temperature sensor configured to detect a temperature profile of the food in the sealed chamber.

[0170] Example 50: The device of Example 49, wherein the food product comprises at least one of coffee beans, legumes, meat, fish, or grains.

[0171] Example 51: An apparatus described in Examples 49 to 50, further comprising at least one heating element within the stirring element configured to heat at least one of the food product or the sealed chamber according to heating parameters received via the user interface.

[0172] Example 52: An apparatus as described in Examples 49 to 51, further comprising an interactive user interface configured to receive information describing at least one of the type of food in the sealed chamber, lighting attributes, or instructions to cause the stirring element to stir.

[0173] Example 53: An apparatus as described in Examples 49 to 52, wherein the lighting element is further configured to illuminate food in the sealed chamber according to another lighting attribute, the another lighting attribute being determined based on information received from a pressure sensing device indicating a pressure level in the sealed chamber and information mapping the pressure level to a lighting attribute based on the food type.

[0174] Example 54: An apparatus described in Examples 49 to 53, further comprising an optical device configured to capture image data indicative of the temperature profile of the food product within the sealed chamber.

[0175] Example 55: An apparatus as described in Examples 49 to 54, further comprising a controller, which determines, based on image data representing the food received from the optical device, that a temperature value of the temperature profile satisfies a temperature threshold indicating that the food has changed from a first state to a second state based on illumination from the lighting element, and causes the food to be removed from the sealed chamber based on a determination that the temperature value satisfies the temperature threshold.

[0176] Example 56: The device described in Examples 49 to 55, wherein the controller is further configured with a predictive model trained to identify the state of the product based on pressure information indicating the type of product and the amount of pressure surrounding the product, and the predictive model is configured to receive an indication of the food being in a first state and output an indication that the food is in a second state based on an indication of the pressure level within the sealed chamber.

[0177] Example 57: The device described in Examples 49 to 56, wherein the controller is further configured with a predictive model trained to identify the state of the product based on visual attributes of the product, and the predictive model is configured to receive image data showing the food in a first state and output an indication that the food is in a second state based on color attributes of the food.

[0178] Example 58: An apparatus comprising a chamber, an illumination element configured to illuminate food within the chamber according to an illumination attribute, and a stirring element within the chamber configured to stir the food.

[0179] Example 59: The apparatus of Example 58, further comprising an optical device configured to capture image data indicative of the temperature profile of food within the chamber, the food comprising at least one of coffee beans, nuts, legumes, meat, or grains.

[0180] Example 60: An apparatus described in Examples 58 to 59, wherein the optical device includes at least one of an optical pyrometer, a hyperspectral imaging device, or a speckle field detection device.

[0181] Example 61: An apparatus described in Examples 58 to 60, further comprising at least one heating element external to the chamber configured to heat the chamber according to heating parameters received via the user interface.

[0182] Example 62: The apparatus described in Examples 58 to 61, further comprising: a pressure sensing device configured to detect the pressure level in the chamber, wherein the stirring element is configured to stir the food at a speed determined based on the pressure level in the chamber; and a pressure control element configured to change the pressure level in the chamber, wherein the pressure control element includes at least one of a vacuum valve, a throttle valve, or a vacuum pump.

[0183] Example 63: An apparatus as described in Examples 58 to 62, further comprising an interactive user interface configured to receive information describing at least one of the type of food in the chamber, lighting attributes, or instructions to cause the stirring element to stir.

[0184] Example 64: An apparatus described in Examples 58 to 63, wherein the lighting element is further configured to illuminate the food in the chamber according to another lighting attribute, the other lighting attribute being determined based on information received from a pressure sensing device indicating the pressure level in the chamber and information mapping the pressure level to the lighting attribute based on the food type.

[0185] Example 65: A device described in Examples 58 to 64, wherein the lighting element includes at least one of a laser diode, a light-emitting diode (LED), an incandescent lamp, a metal halide lamp, or an arc lamp.

[0186] Example 66: An apparatus as described in Examples 58 to 65, further comprising a controller, which determines, based on image data representing the food received from the optical device, that a temperature value of the temperature profile satisfies a temperature threshold indicating that the food has changed from a first state to a second state based on illumination from the lighting element, and causes the food to be removed from the chamber based on a determination that the temperature value satisfies the temperature threshold.

[0187] Example 67: The device described in Example 66, wherein the controller is further configured using a predictive model trained to identify the state of the product based on pressure information indicating the type of product and the amount of pressure surrounding the product, and the predictive model is configured to receive an indication of the food being in a first state and output an indication that the food is in a second state based on an indication of the pressure level in the chamber.

[0188] Example 68: The device described in Examples 58 to 67, wherein the controller is further configured with a predictive model trained to identify the state of the product based on visual attributes of the product, and the predictive model is configured to receive image data showing the food in a first state and output an indication that the food is in a second state based on color attributes of the food.

[0189] Example 69: An apparatus comprising a chamber, an illumination element configured to illuminate food in the chamber according to an illumination attribute, a pressure control element configured to change the pressure level in the chamber, the pressure control element including at least one of a vacuum valve, a throttle valve, or a vacuum pump, a stirring element in the chamber configured to stir the food, and a temperature sensor configured to detect the temperature profile of the food in the sealed chamber.

[0190] Example 70: The device described in Example 69, further comprising a temperature sensor configured to detect a temperature profile of food within the chamber, the food comprising at least one of coffee beans, nuts, legumes, meat, or grains.

[0191] Example 71: An apparatus described in Examples 69 to 70, further comprising at least one heating element within the stirring element configured to heat at least one of the food or the chamber according to heating parameters received via the user interface.

[0192] Example 72: An apparatus as described in Examples 69 to 71, further comprising an interactive user interface configured to receive information describing at least one of the type of food in the chamber, lighting attributes, or instructions to cause the stirring element to stir.

[0193] Example 73: An apparatus as described in Examples 69 to 72, wherein the lighting element is further configured to illuminate the food in the chamber according to another lighting attribute, the other lighting attribute being determined based on information received from a pressure sensing device indicating the pressure level in the chamber and information mapping the pressure level to the lighting attribute based on the food type.

[0194] Example 74: An apparatus described in Examples 69 to 73, further comprising an optical device configured to capture image data indicative of the temperature profile of the food product within the chamber.

[0195] Example 75: An apparatus as described in Examples 69 to 74, further comprising a controller, which determines, based on image data representing the food received from the optical device, that a temperature value of the temperature profile satisfies a temperature threshold indicating that the food has changed from a first state to a second state based on illumination from the lighting element, and causes the food to be removed from the chamber based on a determination that the temperature value satisfies the temperature threshold.

[0196] Example 76: The device described in Examples 69 to 75, wherein the controller is further configured with a predictive model trained to identify the state of the product based on pressure information indicating the type of product and the amount of pressure surrounding the product, and the predictive model is configured to receive an indication of the food being in a first state and output an indication that the food is in a second state based on an indication of the pressure level in the chamber.

[0197] Example 77: The device described in Examples 69 to 76, wherein the controller is further configured with a predictive model trained to identify the state of the product based on visual attributes of the product, and the predictive model is configured to receive image data showing the food in a first state and output an indication that the food is in a second state based on color attributes of the food.

[0198] Example 77: The controller determines the status of the product based on the type of product and temperature information indicating the temperature of the food product. receiving an indication that the food product is in a first state; and outputting an indication that the food is in a second state based on an indication of the temperature profile of the food.

[0199] The breadth and scope of the present disclosure should not be limited by any of the above-described aspects, examples, and / or exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the described aspects, examples, and / or exemplary embodiments described herein. Such equivalents are intended to be encompassed by the following claims.

Claims

1. 1. An apparatus comprising: a chamber; an illumination element configured to illuminate food within the chamber according to an illumination attribute; a stirring element within the chamber configured to stir the food product.

2. 10. The apparatus of claim 1, further comprising an optical device configured to capture image data indicative of a temperature profile of the food product within the chamber, the food product comprising at least one of coffee beans, nuts, legumes, meat, or grains.

3. The apparatus of claim 2 , wherein the optical device comprises at least one of an optical pyrometer, a hyperspectral imaging device, or a speckle field detection device.

4. The apparatus of claim 1 , further comprising at least one heating element external to the chamber configured to heat the chamber according to heating parameters received via a user interface.

5. a pressure sensing device configured to detect a pressure level within the chamber, wherein the stirring element is configured to stir the food product at a rate determined based on the pressure level within the chamber; and 10. The apparatus of claim 1, further comprising: a pressure control element configured to vary the pressure level in the chamber, the pressure control element comprising at least one of a vacuum valve, a throttle valve, or a vacuum pump.

6. 10. The device of claim 1, further comprising an interactive user interface configured to receive information describing at least one of the type of food in the chamber, the lighting attributes, or instructions to cause the stirring element to stir.

7. 10. The apparatus of claim 1, wherein the lighting element is further configured to illuminate the food item in the chamber according to another lighting attribute, the another lighting attribute being determined based on information received from a pressure sensing device indicating a pressure level in the chamber and information mapping pressure levels to lighting attributes based on food type.

8. The apparatus of claim 1 , wherein the lighting element comprises at least one of a laser diode, a light emitting diode (LED), an incandescent lamp, a metal halide lamp, or an arc lamp.

9. The method further includes a controller, wherein the controller determines, based on image data representing the food product received from the optical device, determining that a temperature value of the temperature profile meets a temperature threshold that indicates the food item has changed from a first state to a second state based on illumination from the lighting element; The apparatus of claim 1 , further comprising: causing the food product to be removed from the chamber based on the determination that the temperature value meets the temperature threshold.

10. The controller is further configured with a predictive model trained to identify a state of a product based on a type of the product and pressure information indicative of an amount of pressure surrounding the product, the predictive model comprising: receiving an indication that the food product is in a first state; 10. The apparatus of claim 9, configured to output an indication that the food product is in a second state based on the indication of the pressure level within the chamber.

11. The controller is further configured with a predictive model trained to identify a state of the product based on visual attributes of the product, the predictive model comprising: receiving image data indicating the food product is in a first state; 10. The device of claim 9, configured to output an indication that the food is in a second location based on a color attribute of the food.

12. 1. An apparatus comprising: a chamber; an illumination element configured to illuminate food within the chamber according to an illumination attribute; a pressure control element configured to vary the pressure level within the chamber, the pressure control element comprising at least one of a vacuum valve, a throttle valve, or a vacuum pump; a stirring element within the chamber configured to stir the food product; a temperature sensor configured to detect a temperature profile of the food product within the chamber.

13. 13. The apparatus of claim 12, further comprising a temperature sensor configured to detect a temperature profile of the food product within the chamber, the food product comprising at least one of coffee beans, nuts, legumes, meat, or grains.

14. a controller configured to determine a product status based on the product type and temperature information indicating the temperature of the food product; receiving an indication that the food product is in a first state; and outputting an indication that the food product is in a second state based on an indication of the temperature profile of the food product.

15. 13. The device of claim 12, further comprising an interactive user interface configured to receive information describing at least one of the type of food in the chamber, the lighting attributes, or instructions to cause the stirring element to stir.

16. 13. The apparatus of claim 12, wherein the lighting element is further configured to illuminate the food item in the chamber according to another lighting attribute, the another lighting attribute being determined based on information received from a pressure sensing device indicating a pressure level in the chamber and information mapping pressure levels to lighting attributes based on food type.

17. 13. The apparatus of claim 12, further comprising an optical device configured to capture image data indicative of a temperature profile of the food product within the chamber.

18. The method further includes a controller, wherein the controller determines, based on image data representing the food product received from the optical device, determining that a temperature value of the temperature profile meets a temperature threshold that indicates the food item has changed from a first state to a second state based on illumination from the lighting element; The apparatus of claim 12 , further comprising causing the food product to be removed from the chamber based on the determination that the temperature value meets the temperature threshold.

19. The controller is further configured with a predictive model trained to identify a state of a product based on a type of the product and pressure information indicative of an amount of pressure surrounding the product, the predictive model comprising: receiving an indication that the food product is in a first state; 20. The device of claim 18, configured to output an indication that the food product is in a second state based on the indication of the pressure level in the chamber.

20. The controller is further configured with a predictive model trained to identify a state of the product based on visual attributes of the product, the predictive model comprising: receiving image data indicating the food product is in a first state; 20. The device of claim 18, configured to output an indication that the food is in a second location based on a color attribute of the food.

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