System for the detection of microbial contamination in food processing systems

Abstract

A system for detecting microbial contamination in food processing environments, comprising: a sensor array with a variety of biosensors for detecting microbial markers that indicate a specific microbial contamination; a microfluidic analysis unit operatively coupled to the sensor array, the microfluidic unit comprising microchannels that direct environmental or food sample fluids through a series of analyzable stages, including sample concentration, enrichment, and pathogen identification; and a central processing unit (CPU) communicatively connected to the sensor array and the microfluidic device, the CPU being configured to receive and process real-time data from the sensor array, apply machine learning techniques to detect microbial contamination, and trigger an automated response based on predetermined contamination severity thresholds, the system being designed to continuously monitor microbial contamination at various stages of food processing, providing operators with real-time feedback and enabling immediate corrective action.

Description

Field of the invention:

[0001] The present invention relates to the field of food safety and quality control and, more particularly, to a system for real-time detection and monitoring of microbial contamination in food processing systems. Background of the invention:

[0002] Microbial contamination in food processing poses a significant threat to food safety and public health. Current methods for detecting such contamination typically involve regular sampling and laboratory testing, which are time-consuming and may not provide real-time results. These traditional methods can lead to delays in detecting contamination, allowing it to spread further throughout the food production process. Furthermore, manual inspection methods are often limited and only detect microbial threats after they have already contaminated the final product. There is a need for a continuous, real-time system for monitoring microbial contamination in food processing that detects a broad spectrum of pathogens with high sensitivity and specificity, enabling timely interventions to prevent contamination.

[0003] Microbial contamination in food processing systems poses a significant risk to food safety and public health. Microorganisms such as bacteria, viruses, fungi, and parasites can thrive in food processing environments, potentially leading to food contamination and the spread of foodborne illness. These microorganisms can originate from various sources, including raw materials, water, air, surfaces, and even equipment used in the processing chain. The presence of pathogenic microbes in food poses a major threat to consumers, as it can lead to foodborne illness, outbreaks, and even death. Therefore, there is an urgent need for effective methods for detecting microbial contamination in food processing systems to prevent contamination from reaching the final consumer.

[0004] Existing solutions for detecting microbial contamination in food processing systems are primarily based on traditional methods such as visual inspection, culture-based microbiological testing, and polymerase chain reaction (PCR). While these methods are essential for monitoring microbial safety, they have several limitations that limit their effectiveness, particularly in modern, high-volume food processing environments.

[0005] Visual inspection, in which human personnel check for signs of contamination, is one of the oldest and simplest methods in food safety management. It involves detecting visible signs of microbial growth or contamination, such as mold, unusual discoloration, or slime formation, with the naked eye. However, this method is inherently limited in its scope, as many contaminants are microscopic and only show visible symptoms once contamination has reached a significant level. Furthermore, visual inspection is highly subjective and depends on the experience and skill of the operator, and subtle or early signs of contamination can easily be missed.Furthermore, this method cannot detect the presence of microbial contamination at the molecular or cellular level and therefore does not provide any insight into the type or concentration of microorganisms present in the food processing environment.

[0006] Culture-based microbiological testing is another widely used method for detecting microbial contamination. This procedure involves taking a sample from the environment or a food product and incubating it under controlled conditions to promote the growth of any microorganisms present. After incubation, colony growth is observed and identified based on characteristics such as shape, size, and color. While culture-based methods are effective in identifying a wide range of microorganisms, they are time- and labor-intensive. Results can take between 24 and 72 hours, depending on the microorganism being cultured, which can lead to significant delays in contamination identification. Furthermore, culture-based methods are limited in their sensitivity because some microbes do not grow well in culture medium or require specific environmental conditions that cannot always be reproduced in the laboratory.In addition, culture-based methods typically only detect live microorganisms, so the presence of dead microorganisms in the sample carries the risk of false negative results.

[0007] Polymerase chain reaction (PCR) testing has emerged as a more advanced method for detecting microbial contamination. PCR enables the rapid detection of specific DNA sequences associated with pathogens, providing a significantly faster and more sensitive alternative to traditional culture-based methods. PCR-based testing can identify contamination within hours, making it an attractive option for food processors looking to reduce turnaround times. Furthermore, PCR can detect a wide range of pathogens, including bacteria, viruses, and fungi, with high sensitivity and specificity. However, PCR also has limitations. A major drawback is its complex and expensive setup, which requires specialized equipment and trained personnel.Additionally, PCR is not typically used for continuous or real-time monitoring because it also requires sampling and laboratory processing. Furthermore, while PCR is highly sensitive, it can lead to false positive results if the test is not properly validated or if contamination from previous tests is present.

[0008] In response to these limitations, there is growing interest in the development of real-time monitoring systems for the continuous detection of microbial contamination. These systems are designed to provide food processors with immediate feedback on the microbial status of their processing environment, enabling more proactive contamination prevention measures. These systems are typically based on sensors, such as biosensors, that can detect microbial markers such as nucleic acids, proteins, or metabolites. The sensors are often integrated into the food processing equipment, enabling real-time detection and analysis. One promising approach is the use of optical biosensors, which detect changes in light properties when microbial particles interact with the sensor surface.These sensors can be highly sensitive and specific, providing a rapid and non-invasive method for detecting microbial contamination. However, optical biosensors can be affected by environmental factors such as temperature and humidity, which can compromise the accuracy of their readings. Furthermore, these sensors require frequent calibration to ensure their reliability.

[0009] The development of continuous and highly sensitive real-time detection systems has the potential to fundamentally transform the monitoring and control of microbial contamination in food processors, thus ensuring safer food products and reducing the risk of foodborne illness. Summary of the invention:

[0010] The present invention provides a system for continuous, real-time detection of microbial contamination in food processing systems. It utilizes an integrated combination of sensors, microfluidic technologies, and data analysis methods. The system comprises a series of sensor arrays placed at strategic points in the food processing system, for example, in areas such as raw material handling, food preparation, packaging, and storage. These sensor arrays detect microbial signatures, including bacterial, fungal, and viral pathogens, by analyzing environmental conditions and food samples, including air, water, and surface swabs.

[0011] The system also includes a machine / structure fixture with a series of detection chambers housing the sensor arrays. These detection chambers capture air, water, or surface samples from the processing environment and direct them into microfluidic channels, where the sample is analyzed in real time. The sensors in the device are selected for their ability to detect specific microbial markers such as nucleic acids, proteins, and metabolites, which are commonly associated with microbial contamination.

[0012] The data collected by the sensor arrays is transmitted to a central processing unit, which analyzes the results in real time using machine learning and artificial intelligence. The system can distinguish between harmless microorganisms and potentially harmful pathogens and triggers automatic alerts when contamination levels exceed specified limits. This enables rapid corrective actions, such as adjusting processing parameters or quarantining affected batches, to minimize the risk of contamination. SHORT DESCRIPTION OF THE FIGURE

[0013] These and other features, aspects, and advantages of the present invention will become more readily understood when the following detailed description is read in conjunction with the accompanying drawings, in which like characters represent like parts throughout. Fig. Figure 1 shows a block diagram of a system for detecting microbial contamination in food processing systems

[0014] Those skilled in the art will also appreciate that the elements in the drawings are shown for convenience and are not necessarily to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to device construction, one or more components of the device may be represented in the drawings by conventional symbols. The drawing may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawing with details that would be readily apparent to those skilled in the art from the present description. Detailed description of the invention

[0015] To facilitate an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and a clear description thereof. However, the scope of the invention is not limited thereby. Changes and further modifications to the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to one skilled in the art to which the invention pertains.

[0016] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.

[0017] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, the language "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not refer to the same embodiment.

[0018] The terms "comprises," "comprising," or variations thereof are intended to be non-exclusive inclusion. A process or method that includes a list of steps includes not only those steps, but may also include additional steps not expressly listed or inherent in the process or method. Likewise, the statement "comprises" for one or more devices, subsystems, elements, structures, or components does not exclude, without further limitation, the existence of other devices, subsystems, elements, structures, or components, or of additional devices, subsystems, elements, structures, or components.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. The systems, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0020] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0021] Fig.Figure 1c shows a block diagram of a system for detecting microbial contamination in food processing systems. The system 100 includes: a sensor array (102) with multiple biosensors for detecting microbial markers indicative of specific microbial contamination; a microfluidic analysis unit (104) operatively coupled to the sensor array. The microfluidic unit includes microchannels that direct environmental or food samples through various analysis stages, including sample concentration, enrichment, and pathogen identification; and a central processing unit (CPU) (106) communicatively coupled to the sensor array and the microfluidic unit (108).The CPU is configured to receive and process real-time data from the sensor array, apply machine learning techniques to detect microbial contamination, and trigger an automated response based on predefined contamination thresholds. The system is designed to continuously monitor microbial contamination at various stages of food processing, providing operators with real-time feedback and enabling immediate corrective action.

[0022] In one embodiment, the sensor array (102) comprises at least one of the following biosensors: conductive polymers, carbon nanotubes, gold nanoparticles, and metal oxide semiconductors, configured to detect interactions between microbial markers and the sensor surface that result in a measurable change in electrical conductivity, impedance, or optical properties.

[0023] In one embodiment, the microfluidic analysis unit (104) comprises a plurality of microchannels of different dimensions and configurations, each channel having integrated micropumps and valves controlled by the CPU, the micropumps enabling precise sample flow rates and the valves enabling switching between different fluid operations such as dilution, enrichment and filtration to ensure effective detection of microbial markers.

[0024] In one embodiment, the central processing unit (CPU) (106) further comprises a memory unit containing a database of microbial markers and corresponding pathogen profiles, wherein the CPU compares incoming sensor data with the database to determine the presence of a particular microbial contamination and assigns contamination severity levels based on the detected marker concentrations.

[0025] In one embodiment, the sensor array (102) comprises both optical and electrochemical sensors, wherein the optical sensors are configured to measure changes in light absorption, scattering, or fluorescence when microbial particles interact with the sensor surface, and the electrochemical sensors are configured to detect changes in current or voltage caused by microbial activity.

[0026] In one embodiment, the microfluidic analysis unit (104) is designed to process both liquid and solid samples. The microchannels include a sample preparation area that includes filtration or centrifugation steps to isolate microbial contaminants from the sample matrix before proceeding with pathogen identification.

[0027] In one embodiment, the sensor array (102) comprises at least one surface plasmon resonance (SPR) sensor configured to detect microbial contamination by monitoring changes in the refractive index at the sensor surface upon binding of microbial particles, wherein the SPR sensor is integrated into the microfluidic analysis unit to enable simultaneous measurement during sample processing.

[0028] In one embodiment, the automated response system comprises an actuator module communicatively connected to the CPU, wherein the actuator module is configured to initiate corrective actions upon detection of microbial contamination, such as adjusting environmental parameters (temperature, humidity), activating disinfection processes, or isolating affected production areas.

[0029] In one embodiment, the central processing unit (106) is further configured to use a machine learning-based decision technique to improve the detection process, wherein the technique is trained using historical microbial contamination data and continuously refines the detection accuracy and sensitivity using newly incoming sensor data.

[0030] In one embodiment, the system is modular, with each sensor array and microfluidic analysis unit housed in a standalone module that can be easily swapped, maintained, or replaced to accommodate changes in the food processing environment or to scale the system for larger operations.

[0031] The system for detecting microbial contamination in food processing environments enables continuous, real-time monitoring through an integrated combination of advanced sensor technology, microfluidic analysis, and machine learning. The sensor array comprises multiple biosensors that detect microbial markers such as specific proteins, nucleic acids, or metabolites that indicate microbial pathogens. These biosensors are made of various materials such as conductive polymers, carbon nanotubes, and metal nanoparticles, and each can detect specific molecular changes associated with microbial activity. The biosensors measure changes in electrical conductivity, impedance, optical properties, or fluorescence when microbial markers interact with the sensor surface, thus providing the real-time data needed for contamination detection.

[0032] Once the sample—whether liquid, air, or a surface swab—enters the detection chamber, it is processed by the microfluidic analysis unit. The microfluidic unit is equipped with a series of microchannels through which the sample flows. Within these channels, the sample undergoes various preparation steps such as concentration, enrichment, and filtration to ensure that microbial markers are sufficiently isolated for precise detection. The microfluidic unit features integrated pumps, valves, and mixers that control the sample flow, allowing precise adjustments during sample flow through the system. This configuration enables complex fluidic operations such as mixing or dilution of the sample, which may be necessary to improve the sensitivity of the biosensors or to create optimal detection conditions.Additionally, the microfluidic system can utilize surface plasmon resonance (SPR) sensors to detect microbial contamination by monitoring changes in the refractive index at the sensor surface upon binding of microbial particles.

[0033] The central processing unit (CPU) is the heart of the system and is responsible for processing the data generated by the sensor array. The CPU is equipped with machine learning techniques that analyze the data in real time and continuously update its understanding of microbial contamination profiles. The techniques used by the CPU are based on predictive models trained on large datasets of microbial contamination. These datasets contain known microbial behavior patterns and markers associated with specific pathogens. As new data is acquired, the machine learning model continuously adjusts and refines its predictions, improving the accuracy of contamination detection over time.

[0034] The machine learning method used in this system is primarily supervised and involves classifying sensor data into predefined categories of microbial contamination severity. These categories can include levels such as low, medium, and high contamination, with each level corresponding to a specific set of intervention protocols. The method is trained using labeled examples of sensor data from known contamination events, allowing it to learn the typical sensor responses for different contamination types. Additionally, the system can incorporate unsupervised learning techniques to detect new or emerging microbial threats that were not part of the original training set. In this case, the system can flag unknown contamination patterns and notify operators for further investigation.

[0035] Once contamination is detected, the system deploys an automated response mechanism. The CPU communicates with the actuator module, which initiates corrective actions such as adjusting environmental parameters (temperature, humidity) or activating cleaning protocols. The automated system ensures that these actions are taken immediately after contamination is detected, thus preventing the spread of microbial pathogens throughout the food processing system. For example, if a specific bacterial species is detected, the system can trigger a local disinfection process. This increases the likelihood of eliminating the contamination before it affects the final product.

[0036] Machine learning technology further enhances the system's functionality by continuously analyzing historical contamination data and refining its predictive models to adapt to new contamination patterns or changing environmental factors. The system can identify trends and predict when contamination is likely based on variables such as temperature fluctuations, humidity, or changes in raw material quality. This predictive capability enables the system to proactively minimize contamination risks before they become a problem, ensuring greater safety and efficiency in food processing.

[0037] The CPU is also connected to a user interface that allows operators to set contamination thresholds, view real-time data visualizations, and receive microbial contamination alerts. The interface provides users with access to detailed contamination reports, including information on detected pathogens, contamination levels, and corrective actions taken. This data is stored in system memory for audit and compliance purposes, allowing food manufacturers to maintain detailed logs for regulatory reporting and traceability.

[0038] The system for detecting microbial contamination in food processing systems consists of several key components: a sensor array, a detection chamber, a microfluidic analysis unit, and a central processing unit.

[0039] The sensor array is used to detect microbial contamination by sensing environmental factors such as pH, temperature, humidity, and the presence of specific microbial markers. The sensor array contains biosensors that can detect changes in the molecular structure of the environment, such as the binding of pathogens to specific ligands on the sensor surface. These sensors can be made of various materials such as conductive polymers, carbon nanotubes, or metal nanoparticles, which change their properties upon contact with microbial contamination.

[0040] The detection chamber is a specially designed housing for collecting environmental samples. The chamber is equipped with a highly efficient filter system that ensures the sample is representative of the processing environment. Samples of air, water, or surface swabs are drawn into the chamber and introduced into a microfluidic channel. The microfluidic unit directs the sample through a series of microscopic channels, where it undergoes pretreatment (e.g., dilution or enrichment) and is then analyzed for microbial activity by the sensor array.

[0041] Once the sample is processed, the data generated by the sensors is wirelessly transmitted to a central processing unit. This unit is responsible for analyzing the sensor data, comparing it with a database of known microbial profiles, and detecting microbial contamination. The system uses machine learning to continuously improve its detection capabilities, learn from new data, and adapt to emerging contamination threats. If contamination is detected, the system can trigger automated responses, such as adjusting environmental conditions (e.g., temperature or humidity), activating a cleaning protocol, or flagging the batch for further testing.

[0042] The system is modular and can be easily integrated into existing food processing infrastructures. It can be adapted to various stages of food production, from raw ingredient processing to final packaging, and is scalable for operations of various sizes, from small processing plants to large industrial facilities. Furthermore, the system can be integrated into existing quality management systems, providing real-time feedback and improving traceability throughout the entire food production process.

[0043] The drawings and the foregoing description show examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the order shown; nor do all actions necessarily have to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and use of materials, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0044] Advantages, further benefits, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, advantages, solutions to problems, and any components that may result in or enhance an advantage, benefit, or solution are not to be construed as critical, required, or essential features or components of any or all of the claims. REFERENCES 100 A system for detecting microbial contamination in food processing systems. 102 sensor array 104 Microfluidic analysis unit 106 Central processing unit (CPU) 108 microfluidic unit

Claims

[1] A system for detecting microbial contamination in food processing environments, comprising: a sensor array with a variety of biosensors for detecting microbial markers that indicate a specific microbial contamination; a microfluidic analysis unit operatively coupled to the sensor array, the microfluidic unit comprising microchannels that direct environmental or food sample fluids through a series of analyzable stages, including sample concentration, enrichment, and pathogen identification; and a central processing unit (CPU) communicatively connected to the sensor array and the microfluidic device, the CPU being configured to receive and process real-time data from the sensor array, apply machine learning techniques to detect microbial contamination, and trigger an automated response based on predetermined contamination severity thresholds, the system being designed to continuously monitor microbial contamination at various stages of food processing, providing operators with real-time feedback and enabling immediate corrective action. [2] The system of claim 1, wherein the sensor array comprises at least one of the following biosensors: conductive polymers, carbon nanotubes, gold nanoparticles, and metal oxide semiconductors configured to detect interactions between microbial markers and the sensor surface that result in a measurable change in electrical conductivity, impedance, or optical properties. [3] The system of claim 1, wherein the microfluidic analysis unit comprises a plurality of microchannels of different dimensions and configurations, each channel having integrated micropumps and valves controlled by the CPU, the micropumps enabling precise sample flow rates and the valves enabling switching between different fluid operations such as dilution, enrichment and filtration to ensure effective detection of microbial markers. [4] The system of claim 1, wherein the central processing unit (CPU) further comprises a memory unit containing a database of microbial markers and corresponding pathogen profiles, the CPU comparing incoming sensor data with the database to determine the presence of a particular microbial contamination and assigning contamination severity levels based on the detected marker concentrations. [5] The system of claim 1, wherein the sensor array comprises both optical and electrochemical sensors, the optical sensors configured to measure changes in light absorption, scattering, or fluorescence when microbial particles interact with the sensor surface, and the electrochemical sensors configured to detect changes in current or voltage caused by microbial activity. [6] The system of claim 1, wherein the microfluidic analysis unit is designed to process both liquid and solid samples, and wherein the microchannels comprise a sample preparation area comprising filtration or centrifugation steps to isolate microbial contaminants from the sample matrix before proceeding with the identification of the pathogen. [7] The system of claim 1, wherein the sensor array comprises at least one surface plasmon resonance (SPR) sensor configured to detect microbial contamination by monitoring changes in the refractive index at the sensor surface upon binding of microbial particles, the SPR sensor being integrated into the microfluidic analysis unit to enable simultaneous measurement during sample processing. [8] The system of claim 1, wherein the automated response system comprises an actuator module communicatively connected to the CPU, the actuator module being configured to initiate corrective actions upon detection of microbial contamination, such as adjusting environmental parameters (temperature, humidity), activating disinfection processes, or isolating affected production areas.

Images