370results 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 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.

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.

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 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.

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 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.

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.

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.

The invention relates to the technical field of alcoholic strength measurement, and discloses a white spirit alcoholic strength measuring device which comprises a supporting seat, a sealing mechanism is arranged on the supporting seat, a first rotating rod is rotationally connected into the supporting seat, and a first twisting block is fixedly connected to the top of the first rotating rod. A first twisting block drives a first gear to rotate so as to enable the first gear to rotate in a supporting seat, a gear ring drives a second gear and a third gear to rotate, the third gear drives a first rack plate and a fixing plate to move, and along with continuous movement of the fixing plate, the fixing plate drives a rubber pad and a connecting plate to make contact with a protruding position of the edge of a wine jar opening. When the wine jar is used, the supporting seat is fixed on the wine jar opening, and the wine jar is in a sealed state, so that the supporting seat can be fixed on the wine jars with different sizes, the supporting seat can seal the wine jars with different sizes, white wine in the wine jars is prevented from being in contact with oxygen in the air for a long time in the measurement process, and it is ensured that oxidation and deterioration of alcohol cannot be caused.

The invention discloses a pure grain solid-state white spirit strict selection method based on Longshi multi-dimensional evaluation, and relates to the technical field of white spirit quality screening. According to the method, a standardized evaluation system is constructed through six dimensions of production area, process, safety, sense organ, traceability and detection. The method specifically comprises the following steps: checking geographical indication authentication, verifying a raw material ratio and a fermentation period, detecting aflatoxin and methanol content, evaluating hops, hanging cup and empty cup fragrance retention characteristics, analyzing taste coordination, and detecting key flavor substances through block chain traceability and GC-MS. Different parameter thresholds are set according to different flavors, for example, strong-flavor total ester is larger than or equal to 1.5 g / L, and soy sauce flavor type empty cup fragrance is longer than or equal to 30 minutes. According to the method, HACCP authentication and third-party detection data are combined, so that the problems of high subjectivity and fragmentation of instrument detection in traditional sensory product identification are solved, accurate screening of pure grain solid-state white spirit is realized, and the product credibility and market transparency are improved.

A system for managing health, comprising: a wearable diagnostic device having means for measuring, in use, at least one biological feature of a user, and means for communicating biological feature measurements; a drinking container having means for measuring, in use, at least one component of a liquid contained in the drinking container, and means for conveying the component measurements; and a computer-implemented application that, in use, receives the biometric measurements and the composition measurements as inputs and communicates to the user interface an output to be presented to the user, the output including information relating to user health management based on the biometric measurements and the composition measurements.

The invention relates to a new use of an oligomeric Ru molecular catalyst for the determination of ethanol in alcoholic beverages, such as wine. It also relates to a method of ethanol determination. Said Ru complex allows to decrease the limit of detections to 0.01% v / v and to discriminate between methanol and ethanol.

Devices, systems, and methods for measuring properties of liquids or surrounding gasses within a sealed container. Some embodiments of the devices, systems, and methods disclosed herein are useful for determining and monitoring alcohol (ethanol) content of a liquid within a sealed container. Some embodiments are also useful for determining and monitoring additional properties of the liquid within the sealed container, such as liquid depth (volume) and / or presence of trace chemicals and flavor components.

Embodiments of the present application provide a method, device, medium, equipment and product for classifying wine samples, relating to the technical field of artificial intelligence. The method comprises: contacting odor molecules of a wine sample with at least one recombinant cell expressing an olfactory receptor and a reporter protein, the reporter protein generating a detectable signal after the receptor binds to the odor molecules; obtaining time series data of the signal, calculating the maximum value and baseline value thereof, and determining a response characteristic value of each olfactory receptor according to the same; and inputting the characteristic value into a trained machine learning model to obtain a wine sample classification result. The present method accurately extracts a stable characteristic value of the response of an olfactory receptor to a wine sample, thereby eliminating interference caused by initial value fluctuations and improving the accuracy of model classification.

This application provides a method, apparatus, device and computer-readable storage medium for analyzing coffee particles. The method includes: controlling a vibration source to drive coffee particles to vibrate at least twice, collecting images of the coffee particles after at least two vibrations to obtain a set of test images of coffee particles having different distributions; obtaining initial identification information of coffee particles in each test image in the set of test images; and determining final identification information of the coffee particles based on the initial identification information of coffee particles in at least some of the frames of the test images in the set of test images.

The invention discloses a Baijiu production detection device, and relates to the technical field of Baijiu production detection. A mounting plate is fixedly connected to the middle of the bottom of the machine box through bolts, top plates are connected to the two sides of the top of the machine box through bolts, an inner cavity is formed in the machine box, an intermittent adjusting mechanism is arranged on the top of the mounting plate, and a sealing cover is arranged on one side of the bottom of each top plate. The bottom of the sealing cover is attached to one side of the adjusting end of the intermittent adjusting mechanism, one side of the top of the top plate is connected with an ethanol detector through bolts, the outer wall of the top of the top plate is connected with a displayer through bolts, a detection rod is inserted into the sealing cover, and the top end of the detection rod is connected with the input end of the ethanol detector through a signal line. Compared with the prior art, the detection and comparison work of alcohol of various baijiu is realized, errors possibly existing in single measurement are effectively eliminated through the detection, the reliability of a detection result is ensured, and meanwhile, a production enterprise can better position a product by comparing the alcohol content of different baijiu.

The utility model discloses a screw cap tinplate canned beer CO2 content detection fixing tool which comprises a U-shaped support, a positioning sleeve barrel is fixedly connected to the inner wall of the U-shaped support, a fixing sleeve is fixedly connected to the bottom end of the inner wall of the positioning sleeve barrel, a wine tank is inserted into the positioning sleeve barrel, a tank cover is arranged at the bottom end of the wine tank, and a screw cap is arranged at the bottom end of the tank cover. According to the fixing tool for detecting the CO2 content of the tinplate canned beer with the screw cover, a tested tinplate canned beer sample can be fixed by the fixing tool, so that a carbon dioxide tester can be conveniently punctured from the bottom of the tinplate canned beer by a laboratory technician, and the CO2 content of the tinplate canned beer can be accurately detected. Not only is the operation of a laboratory technician standardized, but also the accuracy and the consistency of detection data are guaranteed.

The present application relates to the technical field of production control, and discloses a production control method and system for cassia tea and a production process thereof, which comprises cassia raw material pretreatment, cassia tea production and cassia tea detection. The quality data of the first pretreated cassia raw material and the processing data in the pretreatment process are analyzed to obtain a cassia pretreatment quality index, which can timely find possible problems in the pretreatment process and ensure the stability of the cassia raw material pretreatment. The production data and environmental data of the cassia tea in the production process are analyzed to evaluate the production process of the cassia tea, timely find possible problems in the production process and ensure the quality stability of the cassia tea. The environmental data and packaging quality data in the packaging process are analyzed to monitor and evaluate the packaging process of the cassia tea, timely find and solve possible problems in the packaging process and ensure the packaging quality stability of the cassia tea.

To easily and rapidly analyze trigonelline and pyrocatechol as coffee components.SOLUTION: A method for analyzing coffee components includes: a preparation step of preparing a sample liquid containing coffee components extracted from coffee beans; a dilution step of diluting the sample liquid; and a detection step of detecting the coffee components by performing liquid chromatography on the diluted sample liquid. In the detection step, the sample liquid is caused to pass through a column filled with a filler having a pentafluorophenyl propyl group, and then, the trigonelline and the pyrocatechol in the sample liquid having passed through are detected by a photo diode array ultraviolet visible absorbance detector.SELECTED DRAWING: Figure 1