490results about "Testing beverages" patented technology

Integrated intelligent system for beverage production, including distilled spirits and various brewed or fermented products, combining traditional methods with advanced technologies. System incorporates controlled fermentation unit, advanced distillation apparatus, and accelerated aging unit using novel methods like ultrasonic waves and thermal cycling. Real-time analysis system employs multiple sensors for continuous chemical composition monitoring throughout production process. Intelligent control mechanism optimizes processes and adjusts parameters dynamically. Chemical fingerprinting system enables precise quality control and authenticity verification. Flexible interface allows for customization of beverage profiles based on desired flavor characteristics and market demands. Specialized unit creates complex non- alcoholic alternatives. System adapts to different production scales and beverage types. Represents significant advancement in beverage production technology, offering unprecedented control, consistency, efficiency, and rapid product development across various beverage categories.

The invention relates to the technical field of beer brewing monitoring, and discloses an intelligent monitoring system and method for a beer brewing process. The method comprises the following steps: acquiring real-time production data in a beer brewing process, wherein the real-time production data comprises raw material parameters, fermentation environment parameters and equipment operation parameters; then, based on the raw material parameters and the fermentation environment parameters, key process indexes of the current brewing stage are identified, and monitoring areas of the beer brewing process are divided according to the key process indexes; and finally, according to the equipment operation parameters and the monitoring areas, constructing a multi-dimensional monitoring network of the beer brewing process, and collecting dynamic process data of each monitoring area through the multi-dimensional monitoring network. According to the method, the whole brewing process can be comprehensively covered, monitoring key point dynamic adjustment is achieved, a multi-dimensional monitoring system is constructed, the problems of traditional monitoring data fragmentation, monitoring blind areas and the like are solved, the brewing process is accurately controlled in an assisted mode, and the modern beer brewing production requirement is met.

The invention relates to the technical field of intelligent monitoring, in particular to a Baijiu brewing microbial fermentation intelligent monitoring system and method.The Baijiu brewing microbial fermentation intelligent monitoring system comprises a multi-source sensing module, an edge computing unit, a cloud decision-making model, a dynamic execution unit, an AR visual terminal and a safety early warning module, the multi-source sensing module is used for acquiring temperature gradient, material components and microorganism density data in the fermentation tank; the edge calculation unit is used for implementing space-time fusion processing on the original data; the cloud decision model adopts a hybrid neural network, fuses real-time monitoring data and a historical fermentation atlas database, and outputs an ethyl acetate generation efficiency predicted value and a regulation and control instruction; the dynamic execution unit is used for receiving the regulation and control instruction; the AR visual terminal is used for dynamically displaying the fermentation process three-dimensional thermodynamic diagram and labeling the concentration extreme point of the target metabolite; the safety early warning module is used for starting an emergency program. Therefore, the problems of non-real-time monitoring, non-intelligent data processing, insufficient control precision, low safety and the like in related technologies are solved.

The invention provides a Baijiu flavor preference degree analysis method. The Baijiu flavor preference degree analysis method comprises the following steps: (1) collecting preference degree sensory evaluation data and electroencephalogram data after a testee stimulates different Baijiu flavors; (2) preprocessing the electroencephalogram data obtained in the step (1) to obtain different electroencephalogram segment data; (3) taking the different brain wave segment data obtained in the step (2) as input features, combining the preference degree sensory evaluation data collected in the step (1), performing XGBoost model training to obtain a trained XGBoost prediction model, and predicting the preference degree of the testee based on the XGBoost prediction model to obtain a predicted value X; and (4) calculating a preference degree result Y of the testee based on the predicted value X obtained in the step (3) in combination with ratio data between different band powers. According to the method, the XGBoost model is used for carrying out feature learning on the electroencephalogram data, so that the preference degree of common consumers to different white spirit flavors can be explored, and data support is provided for development of new white spirit products.

The invention relates to the technical field of white spirit aroma analysis, discloses a method and a device for evaluating aroma contribution of flavor substances in white spirit, and aims to solve the problems of poor consistency and accuracy caused by incapability of realizing quantitative evaluation in an existing aroma contribution evaluation method. According to the scheme, the method mainly comprises the following steps: pre-constructing a function relationship between compound concentration and aroma intensity score; the method comprises the following steps: acquiring the actual concentration of a compound corresponding to a flavor substance in Baijiu, calculating to obtain an aroma intensity score corresponding to the compound according to the actual concentration and based on a pre-constructed function relationship, and judging the aroma contribution degree of the corresponding flavor substance in the Baijiu according to the aroma intensity score corresponding to the compound. The method realizes quantitative analysis of aroma contribution, improves the consistency and accuracy of analysis, and is suitable for various white spirits.

Methods of predicting perceptual characteristics may include: (a) receiving an optical signal from a sample of a grape product or a wine; (b) extracting features from the optical signal by transforming information in the optical signal to a latent space; and (c) providing the features to a machine learning model that outputs (i) chemical composition information in the grape product or the wine, and / or (ii) the one or more perceptual characteristics of a finished wine produced from the grape product or of the wine. The chemical composition information and / or the perceptual characteristics may pertain to smoke taint.

A method for rapid determination of a concentration of free sulfur dioxide (SO2) in a liquid is disclosed. The method involves receiving a liquid sample in an enclosed volume and, while a temperature of the liquid sample remains below 35° C., conditioning the liquid sample to cause gasified SO2 to accumulate in a headspace of the enclosed volume above a surface of the liquid sample. The method further involves directing UV light having a wavelength between 250 nm and 320 nm through a gaseous sample taken from the headspace, and measuring an attenuation of the UV light due to absorption within the gaseous sample The concentration of gaseous SO2 within the gaseous sample is determined from the measured attenuation.

The invention discloses a fruit wine quality prediction and improvement method based on machine learning, and relates to the field of fruit wine and machine learning. According to the method, a multi-fractal detrending fluctuation cross correlation analysis (MF-DCCA) and a transfer entropy (TE) method are combined, a nonlinear causal relationship between physicochemical properties and quality scores of the fruit wine is analyzed, and key features are extracted; then, key characteristics of the fruit wine are adjusted within a reasonable range, and the quality of the adjusted fruit wine is predicted through a gradient lifting regression model. By analyzing the prediction result, data support can be provided for optimization and improvement of new fruit wine products. The controllability of the quality of the fruit wine can be improved, the research and development period is shortened, and material waste is reduced.

The invention discloses a sampling device for detecting the quality of white spirit, and aims to solve the problem that the content and the distribution condition of suspended particles in the white spirit cannot be comprehensively known due to the fact that only one position is used for sampling when the existing sampling device is used for sampling the white spirit. A tooth block and a push block on the two sides of a movable rod are used for driving different assemblies respectively, the push block drives a connecting valve to rotate by shifting an elastic shifting piece, a water passing hole in the push block is aligned with a liquid inlet pipe to communicate the liquid inlet pipe, it is guaranteed that white spirit enters a liquid pumping pipe through the liquid inlet pipe and the water passing hole, and the tooth block is matched to drive a connecting gear to rotate; the reciprocating frame reciprocates to drive the piston in the liquid extraction pipe to reciprocate so as to extract white spirit and inject the white spirit into the sample storage box, so that the sampling of the white spirit can be completed, multi-point sampling can be carried out at different positions in the white spirit, and the uniformity and the quality stability of the white spirit can be evaluated more accurately.

The utility model discloses a tool for detecting the alcoholic strength of a wine product. The alcoholic strength detection tool for the wine product comprises an alcoholic strength detection mechanism, the alcoholic strength detection mechanism comprises a base, the upper end of the base is fixedly connected with a fixing frame through a screw, the upper end of the interior of the fixing frame is fixedly connected with a servo motor through a bolt, and a threaded lead screw is connected with a moving block in a penetrating mode; by means of the technical scheme, it can be avoided that wine liquid is poured into the positions above the alcohol meter and the thermometer which are covered with residues, inaccurate data of the alcohol meter and the thermometer are prevented, a single pipeline is used for replacing multiple pipelines, the installation space can be saved, and the interior of the container, the alcohol meter and the thermometer can be conveniently washed and cleaned.

Systems and methods for monitoring conductivity of a fluid comprising generating a first measurement indicative of the conductivity of the fluid, determining whether a value of the first measurement is within the first calibration range, responsive to determining that the value of the first measurement is not within the first calibration range, calibrating the conductivity sensor with a second calibration range that includes the value of the first measurement, generating a second measurement indicative of the conductivity of the fluid.

The invention discloses an intelligent rapid detection device for selenium content in selenium-rich tea and a use method thereof, and relates to the related field of selenium content detection.The intelligent rapid detection device comprises a pretreatment box, a detection box is fixed to the outer wall of the right side of the pretreatment box, and a treatment bottle is placed in the pretreatment box; a grinding mechanism is arranged in the sealing cover, a sample in the sealing cover can be fully crushed through the arrangement of the grinding mechanism, a pressurizing mechanism is further arranged in the treatment bottle, the air pressure in the sealed treatment bottle can be increased through the arrangement of the pressurizing mechanism, and the reaction rate between mixed acid and the sample can be increased. According to the intelligent rapid detection device for the selenium content in the selenium-rich tea and the use method thereof, the grinding roller rotates to drive the rotating rod to rotate, and then the selenium is discharged into the treatment bottle from the air outlet, so that the air pressure in the bottle is increased, the high-pressure environment can improve the temperature and the acid oxidation capacity, the selenium digestion time is shortened, the recovery rate is remarkably higher than that of a normal-pressure wet method, the detection time is greatly shortened, and the detection efficiency is improved. And the detection efficiency is improved.

The invention belongs to the technical field of tea leaf component detection methods, and particularly relates to a method for simultaneously detecting 4 alkaloids and 10 catechin components in tea leaves, which utilizes high performance liquid chromatography to simultaneously detect 4 alkaloids and 10 catechin components in tea leaf samples. And obtaining the contents of the 14 characteristic components in the tea sample according to the respective standard curves of the four alkaloids and the 10 catechin components. According to the detection method provided by the invention, 14 compounds in the tea leaves can be simultaneously detected and separated, and the problem that the separation effect is poor when multiple compounds in the tea leaves are simultaneously analyzed in the prior art is solved, so that the high-standard requirement of tea leaf component analysis is met; a more efficient, accurate and reliable analysis tool is provided for tea manufacturers, detection mechanisms and scientific researchers, and powerful support is provided for the fields of tea quality control, scientific research, deep processing and the like.

The invention discloses a quantitative evaluation method for the dry mouth degree of a wine product after drinking, which comprises the following steps: step 1, tested mice are divided into a control group and a test group, the mice in the control group do not ingest alcohol, the test group performs single gavage on wine products with different dry mouth degrees, and the total amount of alcohol in the single gavage on the wine products of the mice in the test group is the same; and step 2, at different time points, detecting the change conditions of the mouth dryness biochemical index contents of the mice in the control group and the test group, scoring the mouth dryness degree, and evaluating the mouth dryness degree of the corresponding wine body drunk by the mice according to the score. The method has the advantages of being easy and convenient to operate, high in sensitivity, high in practicability and the like, subjective evaluation can be conducted on the dry mouth effect of the wine sample instead of a crowd test, damage to the human body is reduced, the evaluation result is more objective, an evaluation system for the comfort degree of the wine after drinking is widened, and a new evaluation method is provided for further researching and improving the comfort degree of the wine product after drinking.

A computer-implemented method for managing / monitoring production of agricultural raw materials useful for the production of alcoholic beverages, as well as the production, storage, ageing, consumption, quality, authenticity, traceability and / or selling price of alcoholic beverages comprises: (a) collecting two samples of the alcoholic beverage in an inert container sealed with an inert stopper; (b) assigning data on the alcoholic beverage to each sample; (c) storing at least some of the samples collected in step (a), under specified conditions; (d) analysing each sample to determine at least one mineral profile, preferably metallic; (e) forming a database relating to the samples and resulting from step (b) and step (d); (h) processing these data by statistical analysis, preferably using AI; and (i) using the processed data to manage / monitor the entire supply chain of the alcoholic beverage until its consumption. A device for implementing steps (a) and (c) of the method is also provided.

The invention discloses a method for constructing a model for evaluating the softness of Maotai-flavor liquor, which comprises the following steps: carrying out sensory evaluation on a Maotai-flavor liquor sample to obtain a softness score, and measuring the content of volatile aroma compounds in the liquor sample; correlation analysis is carried out through the softness score and the content change rate of the volatile aroma compounds, and a first group of key flavor substances influencing the softness are determined from the multiple volatile aroma compounds; selecting a matrix sample, adding one or more of the first group of key flavor substances to the matrix sample, and comparing with the matrix sample for sensory analysis so as to determine a plurality of second key flavor substances from the first group of key flavor substances; and constructing a soft degree evaluation model of the Maotai-flavor liquor through the second group of key flavor substances. According to the method for evaluating the softness of the Maotai-flavor liquor based on the key flavor substance basic quality control model, the softness of the liquor body is scientifically regulated and controlled, and the quality control level of the Maotai-flavor liquor is improved.

The invention discloses moisture detection equipment and method based on tea processing, and belongs to the technical field of tea moisture detection.The moisture detection equipment comprises a main frame, a double-area storage box used for storing dried tea materials is installed at the bottom of the main frame, and conveying frames are installed on the two sides of the bottom end of the double-area storage box; and a double-cavity material feeding channel is installed on one side of the top end of the double-area material storage box, and a chain plate elevator used for enabling tea materials to move is installed in the conveying frame. Dried tea materials are fed into the double-area material storage box through the double-cavity material feeding channel, power is transmitted to the chain plate elevator and the first-stage double-side-belt synchronous transmission structure through the rotary driving unit, then the tea materials are rapidly cooled, water vapor is emitted, the dried tea materials are fed into the double-area material storage box, and the tea drying efficiency is improved. And the driven air inlet module is used for cooling the tea materials and discharging water vapor, so that moisture detection can be carried out more uniformly after cooling, and the interference of water vapor on a detection result is reduced.

A household fruit wine fermentation device relates to the technical field of fermentation and comprises a tank body, a sealing cover is arranged at the top of the tank body, a stirring mechanism is mounted in the middle of the sealing cover, the sealing cover comprises an upper sealing plate, a center hole is formed in the middle of the upper sealing plate, the stirring mechanism comprises a center shaft, the center shaft is rotatably connected in the center hole, and the center shaft is connected with the bottom of the tank body. A rotating disc is arranged at the upper end part of the central shaft, a stirring shaft is in threaded connection with the lower end part of the central shaft, a plurality of stirring teeth are arranged on the stirring shaft, and a filtering cover is arranged in the tank body. According to the utility model, the detachable stirring shaft provides a flexible stirring mode, and the one-way exhaust valve ensures the safety in the fermentation process; the thermometer and the alcoholic strength sensor provide real-time fermentation data monitoring for a user; the transparent material of the tank body is convenient for a user to observe and manage; the effective filtration of the filter cover ensures the purity of the fruit wine, and the stability design of the filter cover further improves the user experience.

The invention provides an EEG-based method for distinguishing Baijiu with different mellow feeling degrees. The EEG-based method comprises the following steps: (1) acquiring EEG signal data induced by a tested object under stimulation of Baijiu samples with different mellow feeling degrees; (2) preprocessing the EEG signal data obtained in the step (1); (3) calculating a power spectrum density value of each frequency band according to the preprocessed EEG signal data; and (4) distinguishing the white spirit samples according to the frequency band power spectrum density value. The method comprises the following steps: preprocessing collected EEG signal data, calculating the PSD, carrying out difference analysis on the calculated PSD value, screening out a specific frequency band suitable for distinguishing Baijiu with different mellow feeling degrees, and distinguishing the mellow feeling degrees on the basis of the size of the PSD value of the screened frequency band, so that the Baijiu with different mellow feeling degrees is distinguished on the basis of an EEG technology, and the distinguishing accuracy of the Baijiu with different mellow feeling degrees is improved. The distinguishing cost is reduced, and the distinguishing efficiency is improved.

The invention discloses a method and a system for detecting yeast metabolic activity in a beer fermentation process, relates to the technical field of yeast metabolic activity detection, and aims to solve the problem of inaccurate acquisition of yeast metabolic activity change in the beer fermentation process. And the metabolite data is mapped into the classification rule, so that the accuracy and scientificity of classification are improved. The total relative abundance of each classification type and the relative abundance of different fermentation stages are calculated through statistical analysis, a reliable data basis is provided for dynamic change speculation, static metabolite data are concerned, relative abundance change data are obtained through dynamic change speculation, and the relative abundance of the fermentation stages is obtained. Therefore, metabolic activity changes of the yeast in the beer fermentation process can be reflected in real time, quantitative analysis and qualitative analysis are combined, obtained data are more comprehensive and accurate, and the metabolic activity of the yeast in the beer fermentation process can be known more deeply.

The invention discloses a screening method of mango pulp processing mode markers and an identification method of mango pulp processing modes, and relates to the technical field of analytical chemistry. According to the method, non-targeted metabonomics is combined with quasi-targeted metabonomics and targeted quantitative analysis, so that the characteristic markers capable of accurately distinguishing mango pulp in different processing modes are systematically screened and verified for the first time. The feature marker screened by the method can be used for constructing a discrimination model of the mango pulp processing mode, high-precision and high-reliability processing mode discrimination is realized based on the established multi-model fusion discrimination method, the discrimination efficiency and accuracy of the mango pulp processing mode are greatly improved, and the method is suitable for popularization and application. And a new technical means and theoretical basis are provided for product quality control and market supervision.

The invention provides a plant beverage flavor component detection system and method based on artificial intelligence, and the method comprises the steps: analyzing the sensory contribution spectrum of a target plant beverage sample through a multi-dimensional original data matrix of the flavor substance content in the target plant beverage sample; extracting principal component vectors of flavor substances in the target plant beverage sample from the multi-dimensional original data matrix, and determining key flavor markers of flavor components in the target plant beverage sample in combination with a sensory contribution spectrum; extracting key spectrum fingerprint characteristics of flavor components in the target plant beverage sample from a spectrum detection result of the target beverage sample based on the key flavor marker; and constructing a flavor component prediction model of the target plant beverage sample, and further determining a flavor score of the flavor component in the target plant beverage sample in combination with the key spectrum fingerprint characteristics. By adopting the scheme of the invention, the specific contribution of each substance to the overall sensory attribute can be analyzed so as to identify the key flavor substance in the flavor evaluation of the traditional plant beverage.

A method of training a machine learning model for evaluating flavor profiles of an alcoholic beverage includes a spectral sensor collecting optical properties of the beverage or flavoring agent suspended in the beverage within a container. Temperature and pressure sensors collect environment data of the container during a flavoring session. A control apparatus displays a user interface requesting a taste tester to select labels for the session. A control apparatus memory stores selected labels responsive to the taste tester's selections. A feature extraction module extracts features from the optical properties and environment data based on predetermined criteria. A machine learning engine trains a machine learning model to continuously learn flavor characteristics under a plurality of flavoring sessions using the extracted features and selected labels. The trained machine learning model is deployed for quality control and for determining the difference between a good batch and poor batch of the alcoholic beverage.

To provide a device with which it is possible to quantitatively derive the freshness of roasted coffee beans.SOLUTION: A freshness management device comprises: an acquisition unit for acquiring a signal outputted from a sensor in a detection period during which a sensor having a sensitive part that reacts to at least one substance generated from roasted coffee beans and physically changes, outputting a signal corresponding to the physical change switches from a first state where it is not exposed to the at least one substance to a second state where it is exposed to the at least one substance and then switches from the second state to the first state again; and an estimation unit for estimating the freshness of the coffee beans on the basis of relation information indicating the relationship between the feature amount of a signal waveform and the freshness of coffee beans, and the signal.SELECTED DRAWING: Figure 6

Provided is a water examination device including: a main body; a fluid accommodation unit formed in the main body; a wave source for irradiating waves toward the fluid accommodation unit; a detector for detecting a laser speckle at every set time period that is set in advance, the laser speckle being generated due to multiple scattering of the waves in the fluid; a controller for estimating existence of impurities in the fluid in real-time by using the detected laser speckle; and a calibration unit for controlling the wave source or the detector such that an intensity of light irradiated from the wave source and measured by the detector is within a certain range set in advance.

The invention provides a Chinese liquor AI evaluation system and method based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting multi-modal data of a liquor sample; the data preprocessing module is used for preprocessing the collected multi-modal data; the feature fusion module is used for fusing the multi-modal data and constructing feature vectors comprehensively reflecting the quality features of the white spirit; the evaluation model is used for learning a mapping relation between the multi-modal characteristics of the white spirit and the quality grade and evaluating the quality of the white spirit; and the output module is used for outputting the liquor quality identification result. Through multi-modal data fusion and a deep learning algorithm, the subjectivity of artificial evaluation is effectively reduced, the accuracy of an identification result is improved, automatic identification of the quality of the white spirit is realized, the identification efficiency is greatly improved, the identification cost is reduced, the quality of the white spirit can be comprehensively and systematically evaluated, and the method has an important application value.

The invention discloses a baijiu storage year prediction method, and relates to the technical field of baijiu year analysis, the method comprises the following steps: sample data acquisition and pretreatment: extracting flavor substance information in a baijiu sample through GC-O-MS and GC-FID technologies; key flavor substances are screened, AEDA and OAV are adopted for analysis, and flavor substances with significant differences in baijiu of different years are recognized and screened out; feature data extraction and optimization: key flavor substance features related to storage years are determined through K-MEANS clustering and OPLS-DA analysis; prediction model construction and training: based on the screened key flavor substance data, adopting a machine learning algorithm to establish and optimize a prediction model of the storage years of the Baijiu; and year prediction and output: inputting the flavor substance data of the white spirit sample to be predicted into the trained prediction model, and outputting and storing a year prediction value. Therefore, the problems that in the prior art, storage year labeling lacks objective basis, the prediction technology is limited greatly, and it is difficult to accurately predict the storage year of Baijiu are solved.

A preparation and use of a green alternative template molecularly imprinted-carbon quantum dot nanogel fluorescent sensor. The morphology, chemical structure, and optical properties of the fluorescent sensor were investigated using techniques such as Transmission Electron Microscopy (TEM), X-ray Photoelectron Spectroscopy (XPS), Fourier Transform Infrared Spectroscopy (FTIR), Fluorescence Spectroscopy. Using Bisphenol A as a detection model, the selectivity and sensitivity of the sensor were evaluated based on changes in fluorescence intensity. The green alternative template molecularly imprinted-carbon quantum dot nanogel sensor provided by the present disclosure exhibits excellent selectivity, high sensitivity, and a low detection limit. It can achieve highly selective recognition of Bisphenol A in plastic barrel-packed Baijiu samples and is suitable for the enrichment and purification of Bisphenol A in organic phases. The prepared green alternative template molecularly imprinted-carbon quantum dot nanogel fluorescent sensor can be used for highly selective fluorescence detection of Bisphenol A substances in complex matrices.

The invention discloses a method for evaluating the quality grade of soft-taste white spirit base liquor, and particularly relates to the technical field of white spirit base liquor quality detection. Volatile and non-volatile components in baijiu base liquor are accurately separated through a multi-dimensional chromatography technology, flavor substances are extracted, target flavor components are quantitatively analyzed in combination with a multivariable correction model, the ratio of the flavor components is calculated, and the flavor compounds of the baijiu base liquor are detected through a high-sensitivity sensor. Analyzing the detection data through an artificial intelligence model, training flavor feature distribution of different quality grades, calculating similarity scores of new samples and standard samples, and automatically determining the quality grade of the base liquor in combination with clustering analysis; according to the method, the problem that a traditional detection means is greatly influenced by the matrix effect is solved, precision, intelligence and standardization of quality evaluation of the baijiu base liquor are achieved, and a scientific basis is provided for quality control, product grading and stability monitoring in the baijiu production process.

Provided in the present application are an analysis method and device for coffee particles. The device comprises: a vibration source configured to drive the coffee particles to vibrate at least twice; a camera configured to respectively capture images of the coffee particles after the at least two vibrations, obtaining a to-be-detected image set containing images of coffee particles with different distributions; one or more processors communicatively coupled to the camera; and a memory storing instructions executable by the one or more processors, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: acquire initial recognition information of the coffee particles in each to-be-detected image of the to-be-detected image set; determine final recognition information of the coffee particles based on the initial recognition information of the coffee particles in at least some frames of to-be-detected images in the to-be-detected image set.