2677results about "Testing food" patented technology

The invention relates to the technical field of tea leaf detection, and discloses a tea leaf quality grade detection method and system.The method comprises the steps that a tea leaf sample is collected and pretreated; extracting chemical component indexes and physical properties of the pretreated tea leaves, and performing fusion and analysis; and establishing a tea quality grade classification model, inputting characteristics of a tea sample to be detected, outputting a detection result, and generating a tea quality grade report. Through combination of the support vector machine, the gradient boosting decision tree and the improved logistic regression algorithm, the precision of the tea quality classification model is optimized. The dynamic threshold function and the segmented excitation function are introduced to further solve the boundary fuzzy problem of the high-grade tea leaves and the low-grade tea leaves, and accurate judgment of tea leaf grade classification is achieved. And data restoration is carried out through weighted compensation correction and a recheck process, so that the output tea grade report is ensured to have high accuracy and reliability.

The invention relates to a blueberry sugar degree hyperspectral inversion method based on improved Laplacian mapping, and belongs to the technical field of blueberry hyperspectral acquisition and analysis. The method comprises the following steps: firstly, collecting blueberry samples of different varieties and different maturity degrees in a complex scene, measuring hyperspectral data of the blueberry samples and sugar content of corresponding blueberries, then dividing the blueberry sample data into a training set and a test set according to a proportion by using a statistical algorithm, and performing data preprocessing by using an MSC + FOD combination to obtain a training set and a test set; extracting characteristic wave bands from the preprocessed spectral data by using ILE provided by the invention, establishing a blueberry sugar degree prediction model by using CNN, training the model by using a training set, and finally comprehensively evaluating the model by using a test set in combination with regression indexes R2, RMSE and RPD. The method can quickly and accurately predict the sugar degrees of blueberries of different varieties and different maturity degrees in a complex scene, and provides powerful technical support for prediction of the sugar degrees of the blueberries.

The invention discloses an unconventional feed quality evaluation grading method and system, and particularly relates to the technical field of feed quality evaluation. Conventional nutrients, functional nutrients and harmful substance components are comprehensively analyzed through a high-precision detection technology; constructing a multi-factor synergistic toxicity evaluation model, quantifying the synergistic effect of the various harmful substances based on the toxicity equivalent factors and a matrix interaction analysis method, and outputting a synergistic toxicity index; the overall quality of feed samples is evaluated by combining detection results and toxicity indexes, the feed samples are classified into high-quality, qualified and unqualified grades, targeted application suggestions and risk control measures are provided, a cloud database is constructed to record evaluation data, and an Internet of Things sensor is used for monitoring supply chain quality fluctuation in real time, automatically identifying abnormities and triggering early warning. Dynamic management of feed quality is achieved, scientificity and accuracy of quality evaluation of unconventional feed are effectively improved, potential safety hazards are reduced, and safe popularization and efficient application of the system in the breeding industry are promoted.

The invention relates to the technical field of agricultural and sideline product quality detection, in particular to an agricultural and sideline product component content detection method and system.According to the method, the influence degree of different production links on the qualified rate of finished products is analyzed, and then the traceability production track information of all the finished products of the same production batch is obtained based on the final production link; analyzing the similarity degree of the traceability production track information of the to-be-detected finished product and the qualified finished product under all traceability production tracks in the production batch so as to obtain the qualification degree of the to-be-detected finished product under all traceability production tracks, and determining the to-be-detected finished product; and then the detection priorities of different components of the finished product needing to be detected are obtained by analyzing the difference production links of the finished product needing to be detected and the qualified finished product and combining the influence degrees of the different production links on the qualified rate of the finished product. Through the method and the system, the problem that part of problems cannot be found in time due to randomness of sampling inspection is avoided, and the comprehensiveness and the reliability of quality detection are improved.

The invention relates to the technical field of food detection, in particular to a food freshness detection system and method based on a gas sensor array, and the system comprises the following steps: the gas sensor array is used for detecting gas information of at least one target volatile organic compound in a target sample and converting the gas information into a target voltage signal; target processing is conducted on the target voltage signal through the signal control module to obtain a first processing signal, and finally the food freshness of the target sample is analyzed through the intelligent mode recognition display system on the basis of the first processing signal and a target principal component analysis and multi-layer sensor fusion model. And obtaining a food freshness detection result of the target sample. Therefore, the problems that the food freshness detection result is inaccurate due to the cross sensitivity of sensors in related technologies, the detection range and sensitivity of a single sensor cannot meet the detection requirements in a complex environment, and the accuracy and reliability of food freshness detection are reduced are solved.

The invention discloses a shrimp meat refrigeration storage quality attenuation prediction method, and relates to the technical field of refrigeration storage, and the method comprises the steps: connecting a production line product input end, carrying out sampling and continuous bimodal detection on a shrimp meat product, and obtaining input detection data; and fitting analysis data, generating an equivalent input detection data column, and constructing a storage state sequence. And performing initial quality calibration based on the warehousing state sequence, and establishing an initial quality state. And initializing a quality attenuation mapping model, and determining a quality attenuation factor in combination with the refrigerated storage parameter correction model. And finally, predicting the refrigerated storage quality change of the shrimp meat product according to the attenuation factor. Therefore, the technical effects of realizing more flexible and finer refrigeration storage quality attenuation prediction of the shrimp meat and improving the prediction accuracy and reliability are achieved.

There is provided a system, installed on a vehicle or trailer, for performing an agricultural task. The system comprises a platform connecting to the vehicle or trailer and comprising a socket for an accessory. A plurality of accessories is provided, wherein one accessory is selected among them for installation in the accessory socket. A vision system is operatively connected to a computer and collects data of the environment about the accessory, the computer using the collected data to determine an instruction for performing the agricultural task and to send the instruction to the accessory installed in the accessory socket to perform the agricultural task. The accessory is interchangeable in the accessory socket depending on the agricultural task. The box handling stage and pallet mounting stage can be robotized for efficient automation.

The invention provides a method for carrying out corn quality seed selection by using a hyperspectral imaging technology, and belongs to the technical field of corn quality seed selection. Corn kernels are subjected to spectrum scanning in a wavelength range of 400nm to 2500nm, spectrum data are preprocessed through a continuous wavelet transform algorithm, baseline drift and noise interference are removed, and a high-quality seed selection result is obtained. And accurately extracting spectral characteristic peaks of protein, starch, grease and moisture. And establishing a characteristic peak distribution matrix to record peak intensity distribution, and calculating a characteristic peak intensity weight coefficient and a position offset. And constructing a characteristic peak quality incidence matrix, establishing a numerical mapping relationship between the spectral characteristics and the quality parameters, and obtaining a peak width parameter and a spectral noise level. The spectral quality fusion function is adopted to process the multi-dimensional characteristic parameters, and the comprehensive quality evaluation index and the single quality evaluation index are calculated, so that the technical problem that the multi-dimensional quality characteristics of the corn kernels cannot be accurately identified and accurately graded is solved.

The invention discloses a meat product foreign matter on-line detection method and system, and belongs to the field of visual detection.The detection method comprises the steps that a fixed detection area is set, a multi-mode sensor is installed above the fixed detection area, and the multi-mode sensor collects data and transmits the data to a data processing center through a wireless network; the data processing center decompresses and preprocesses the received data and fuses the image data twice to obtain a final fused image; according to the feature information in the final fusion, extracting fusion feature data; analyzing the feature data by using a support vector machine, and judging whether foreign matters exist or not and classifying the types of the foreign matters; a detection result is fed back to a production line control system in real time, and meat products containing foreign matters are positioned and removed. According to the method, the multi-mode sensor and an efficient data analysis technology are combined, and the accuracy and the real-time performance of meat product foreign matter detection are effectively improved.

The invention discloses an improved food sensory quality evaluation method and system based on a fuzzy mathematical method, and relates to the technical field of food inspection and detection. Performing a single-factor test on the improved food sample, and performing a response surface test on the basis of the single-factor test; performing comprehensive sensory evaluation on each sample in the response surface test by adopting a fuzzy mathematical analysis method; and physical and chemical indexes of the improved food under the optimal process are determined. According to the invention, the technology of the improved food is optimized, and the physicochemical indexes of the improved food with the optimized response surface are measured; a single-factor test and a response surface test are carried out in sequence, sensory scores are taken as evaluation standards, and factors influencing the quality of the improved food are determined; the optimal formula of the improved food is finally determined through a verification experiment; a fuzzy mathematics comprehensive sensory evaluation model is established for the improved food, and the obtained sensory evaluation result is used for response surface optimization evaluation.

For analysis of the state of a plant (2) or of another eukaryote, an evaluation device or an evaluation system (30) has an evaluation circuit (34) which is designed to carry out an evaluation (36) of spectral data. The spectral data or data generated on the basis thereof by preprocessing are divided into multiple parts and analyzed using an analysis logic. The analysis logic comprises at least one attention mechanism.

The present invention relates to the technical field of detection equipment, and specifically discloses a real-time detection device for vinegar fermentation, comprising a fermentation tank, and further comprising: a sampling component, the sampling component is arranged in the fermentation tank, and the sampling component is used to sample the vinegar in the fermentation tank; an adjustment component is arranged on the outside of the fermentation tank, and the adjustment component is used to detect and adjust the vinegar after sampling. The present invention can sample the vinegar in the upper layer, middle layer and lower layer of the fermentation tank at set intervals through the sampling component, and can automatically adjust the sampling position of the vinegar as the liquid level of the vinegar in the fermentation tank changes, thereby increasing the accuracy of sampling, and further improving the representativeness of the detection result, so that it is convenient to sample and detect vinegar at different depths, and through the adjustment component, according to the detection after the vinegar sampling, the interval time of subsequent vinegar sampling can be adjusted to ensure that the fermentation of the vinegar meets the requirements.

The invention discloses an automatic quality detection system for a food production line, and relates to the technical field of food detection.The system collects food surface images in real time through an industrial camera, and an appearance feature data set is generated after preprocessing; using a deep learning model constructed by a convolutional neural network to extract appearance features, forming a food defect data set, calculating an appearance score through an image processing algorithm, and evaluating the appearance score and a surface flaw score threshold value; component content and microbial pollution data are acquired and processed in real time through near infrared spectrum analysis and multi-sensor detection, and component content scores and pollution scores are generated; performing quality judgment by calculating a comprehensive quality score and evaluating the comprehensive quality score with a set comprehensive quality score threshold value; in an abnormal condition, the system triggers an alarm and informs a worker through wireless communication; the whole system realizes automatic, precise and multi-dimensional detection of food quality, and the production detection efficiency and the food safety control capability are remarkably improved.

The invention relates to a method for on-line monitoring of gel strength change in a surimi hot processing process based on a hyperspectral imaging technology, and the method comprises the following steps: collecting original hyperspectral data of a surimi sample in a heating process, and determining the gel strength of the surimi sample; carrying out preprocessing on the original hyperspectral data; constructing a data set based on the preprocessed original hyperspectral data and gel strength; training and verifying a PLS (Partial Least Squares) model by utilizing the data set to obtain a gel strength prediction model; and monitoring the gel strength change in the surimi hot working process by using the gel strength prediction model. According to the invention, online, rapid and nondestructive monitoring of the gel strength in the two-stage heating process of the minced fillet in industry can be realized.

The invention relates to the field of optical nondestructive testing, and discloses a visualization method for detecting mango quality based on hyperspectrum, which comprises the following steps of: firstly, selecting mango samples, and simulating different mango ripening stages by dividing the mango samples into a plurality of groups of samples; acquiring a high-dimensional spectral image by using a hyperspectral imaging system, and measuring the hardness, the soluble solid content and the titratable acid content; correcting and extracting the high-dimensional spectral image to obtain and optimize the average reflection spectrum of the sample, and establishing a mango maturity classification model and a quality regression prediction model; performing characteristic wavelength contribution degree analysis and screening, and optimizing a quality regression prediction model by adopting a key wave band; the method is applied to each pixel point of a high-dimensional spectral image, a two-dimensional distribution diagram is generated, and mango quality visualization is achieved. The method solves the problems that in the prior art, rapid and lossless judgment of the mango ripening stage cannot be achieved, accurate prediction and visual distribution of key internal quality indexes cannot be achieved, and intelligent grading and quality control of picked fruits are not ideal.

The invention discloses a dish maturity qualitative and quantitative detection method and system based on image deep learning, and the method comprises the steps: S1, carrying out the physicochemical property index data measurement of dishes with different maturity, carrying out the analysis of the data, and determining the processing technology of the dishes with different maturity; s2, constructing an image acquisition system and a dish maturity database based on cooking environment conditions and image construction requirements; s3, training an image deep learning model by utilizing the dish maturity database, realizing target detection and instance segmentation of dishes with different maturity, and respectively constructing maturity judgment systems; and S4, based on the probability density function and a dish maturity prediction result output by the maturity judgment system, performing quantitative detection on physicochemical properties of dishes with different maturity. Based on the self-learning and self-thinking ability of the deep learning model, the deep learning model is applied to intelligent determination of the maturity of the dishes, and the problems that no relevant standard exists at present and maturity determination depends on experience are effectively solved.

The invention provides a quality evaluation method of fresh corn. The quality evaluation method comprises the following steps: step 1, treating the fresh corn by adopting different blanching modes; step 2, selecting a plurality of evaluation indexes to respectively measure the fresh corns treated by different blanching modes; step 3, screening a variation coefficient gt; 15% of evaluation indexes are used as key quality indexes; step 4, carrying out data standardization processing on the screened key quality indexes; 5, performing principal component analysis on the key quality indexes by adopting SPSS data analysis software, and calculating the weighted variance contribution rate of each principal component; 6, calculating the coefficient of each key quality index; step 7, obtaining a characteristic equation of each principal component; and step 8, accumulating the product of the feature vector of each principal component in the blanching mode and the contribution rate of the weighted variance of the corresponding principal component to obtain the comprehensive quality score of the fresh corn in the blanching mode.

The invention relates to the technical field of grease detection, in particular to a grease detection data analysis system and method based on multi-source data. The method comprises the following steps: acquiring grease multi-source detection data, and performing near infrared spectrum detection feature extraction according to the grease multi-source detection data so as to obtain infrared spectrum detection data; performing signal enhancement processing on the infrared spectrum detection data to generate enhanced infrared spectrum data; carrying out physicochemical index feature extraction based on the grease multi-source detection data so as to obtain physicochemical index data; performing detection anomaly analysis according to the physicochemical index data and the enhanced infrared spectrum data so as to obtain grease anomaly detection data; and performing type division on the grease anomaly detection data so as to obtain grease chemical component anomaly data and grease microorganism anomaly data. Based on the grease detection technology, the abnormal recognition rate and the quality evaluation accuracy of grease detection are improved.

The invention belongs to the technical field of edible oil production quality detection, and particularly relates to a rapid detection method and system for sesame oil quality evaluation. A sampling data preprocessing mode in combination with dielectric constant data of sesame oil samples is provided in a spectral data sampling stage, so that sampling data errors are reduced, and a data foundation is laid for subsequent accurate evaluation of sesame oil quality; meanwhile, when the standard relational expression is established, firstly, eight standard points for establishing the standard relational expression are obtained through solution preparation, then one standard point is abandoned for the eight standard points to establish an initial standard relational expression, and different numbers of standard points are generated by considering the concentration difference value obtained through calculation of the abandoned points and the standard relational expression; in this way, the influence of inaccurate standard points is weakened, and then the standard relational expression is established according to all the standard points, so that the standard relational expression is established more accurately.

The present invention relates to the field of food processing equipment, and specifically to a circulating and guiding conveying device for food detection, which includes a conveying device for supporting and conveying. Symmetrically and fixedly arranged on the upper end surface of the conveying device are detection devices, and the two groups of detection devices are fixedly connected through a transmission guide shaft. A driven pulley is fixedly installed near the end of the upper end surface of the conveying device, and symmetrically rotatably clamped on the inner end surface of the detection device located on one side are toothed pulleys. By setting the detection device, when detecting food, the guiding device can cooperate with the supporting device and the limiting device, and further cooperate with the intermittent conveying of the conveying device, so that the detection adaptation at the lower part of the installation support can quickly and stably detect the processed food quickly and accurately, effectively improving the efficiency of the equipment for detecting food.

The invention belongs to the technical field of food detection, and particularly relates to an oyster freshness distinguishing method based on an electronic nose and prior information. According to the method, oyster smell data collected by an electronic nose system is combined with traditional oyster physicochemical indexes, and the prior weight of an oyster freshness prediction model is set by calculating the correlation coefficient of the oyster smell data collected by the electronic nose system and oyster physicochemical index information closely related to freshness change; and subsequently, the weight is adjusted through a compression and incentive mechanism to construct a reliable classification model, so that the screening capability, reliability and accuracy of the freshness key characteristics at the accurate distinguishing moment are improved, and high-accuracy oyster freshness distinguishing is realized for the oyster freshness.

The invention discloses an intelligent detection system for impurities and defects of raw coffee based on deep learning. The intelligent detection system comprises a visual detection module, a smell detection module, an optical detection module, a data management module, a data output system and a display device, the visual inspection is used for observing the surface of the raw coffee; the smell detection is used for detecting the taste of the raw coffee; and optical detection, which is used for analyzing and measuring the material composition and structure of the raw coffee and detecting whether foreign matters are contained in the coffee beans. According to the intelligent raw coffee impurity and defect detection system based on deep learning, the image processing module is arranged, images can be detected in all directions, the images are segmented through an image separation algorithm, the processing efficiency can be improved, meanwhile, the data management module can store and classify data, an independent database can be established, and the detection accuracy is improved. The coffee beans can be detected more accurately through internal data, and the detection effect of the device can be improved.

A method of detecting one or more PFAS in a sample of animal tissue, comprising: spraying electrically charged solvent droplets onto the sample of animal tissue so as to generate analyte ions from analyte in the animal tissue; passing the analyte ions into a mass spectrometer; and mass analysing the analyte ions so as to determine if the one or more PFAS is in the animal tissue.

A pathogen surrogate, formed by a DNA tag or bar code and a carrier, is described for use in the validation and verification of sanitation, such as in food processing operations and for wash water systems for fresh produce. The carrier material is selected so that the pathogen surrogate mimics the behavior of a pathogen when subjected to a sanitation operation. One or more surrogates can be introduced in to an environment, which is then subjected to sanitation process, followed by a detection process using the DNA tag of the surrogate.

The present invention initially relates to a method for the technical design of an analytical device for the spectral analysis of a sample (02). The analytical device comprises a spectrometer having a plurality of measurement channels for selected frequencies according to a trained model. The analytical device is configured to apply a trained reconstruction model to reconstruct a spectrum based on individual measured values ​​(06) recorded with the spectrometer for the selected frequencies. According to the method, a first machine learning system formed jointly by the model of the spectrometer and the reconstruction model is trained (13, 14), whereby the trained model of the spectrometer and the trained reconstruction model are obtained. The invention further relates to a computer program product, a trained machine learning system, and an analytical device.

The invention relates to the technical field of biological sample detection, in particular to a biological sample quantitative detection device which comprises a containing part, an air inlet mechanism, a disinfecting and killing mechanism, a drying mechanism and a temperature control mechanism. The containing part comprises a bearing plate, a machine body shell and a partition plate, and the bearing plate and the partition plate are both arranged in the machine body shell; a pH value detection table and an analysis computer are arranged at the upper end of the machine body shell; the air inlet mechanism is fixedly installed in the machine body shell and used for controlling air interaction between the outside and an inner cavity of the machine body shell. The sterilizing mechanism is fixedly mounted on the inner wall of the machine body shell and sterilizes air entering the machine body shell through a disinfectant; the drying mechanism comprises a moisture removal box, an absorption box and a drying plate, through cooperation of the structures, the interaction of air inside and outside a storage part can be kept while infectious microbes in external air can be prevented from polluting a sample, in addition, the temperature of a sample storage environment is controlled, and temperature quantification treatment of sample detection is achieved.

The invention discloses a Jinhua ham taste optimization method based on metabonomics and an enzyme activity regulation and control technology. The method belongs to the field of meat quality control, and comprises the following optimization steps: carrying out sensory scoring on samples in different processing stages, and carrying out non-targeted analysis by using hydrophilic interaction liquid chromatography and MS / MS; analyzing and mining the metabonomics data; screening out differential metabolites in the processing process by applying a significance analysis p value, a difference multiple and a variable weight value; combining activity change results of cathepsin B and cathepsin L in the processing process; the activity of cathepsin B and cathepsin L is regulated and controlled by using a PF-DBO optimization algorithm, samples are collected and analyzed, and the influence of enzyme activity change and the change of the enzyme activity change on the content of the characteristic taste marker are evaluated. By precisely regulating and controlling the activity of cathepsin B and cathepsin L, the problem of complex relationship between taste substance change and cathepsin activity in the Jinhua ham processing process is solved; the control strategy enables the overall quality of the Jinhua ham to be significantly improved.

The invention relates to an inspection system for quality analysis of a food product, comprising a conveyor for moving it to an inspection area, a lighting device consisting of LEDs that emit a light sequence directed towards the inspection area to light up the product by transmission, a linear camera with at least one line of pixels for collecting a plurality of images in each light sequence of the lighting device, and a focusing member for focusing the beam emitted by the lighting device. Advantageously, the lighting device is activated in a pulsed manner, generating a light sequence with at least two different illuminations, at least one of which is a transmission-pulsed illumination. The lighting device is aligned on the axis formed by the product to be inspected and the linear camera.

The invention relates to the technical field of quality detection, in particular to a meat dish quality detection system which comprises a data acquisition module, a first determination module, a second determination module, a judgment module, a correction module and an alarm sending module. According to the method, the quality control level of the meat ball production line is effectively improved through accurate multi-parameter analysis and a step-by-step judgment mechanism, potential quality problems such as cracks, over-wet or over-dry defects can be accurately recognized by monitoring the moisture content, the sphericity and the deformation quantity of the meat balls in real time, the clamping pressure is dynamically adjusted, and the quality of the meat balls is improved. The system can adapt to different production conditions in real time, the influence of too large or too small clamping force on the shape and taste of meatballs is avoided, a real-time feedback mechanism of the correction module ensures that the production process can be rapidly adjusted during fluctuation, the defective rate in production is effectively reduced, and the production efficiency is improved. The problems of low detection precision and slow response speed caused by dependence on static data are effectively solved.

A flexible pH sensor and a measurement system, belonging to the technical field of sensors. Using a material that does not react with an electrolyte, mixed with a metal, an anode substrate is printed, and by means of an electrochemical method, one-step synthesis of a metal-doped polyaniline composite material is implemented. By means of controlling the metal content, the doping amount of metal ions in the polyaniline can be changed. Adding metal increases the conductivity of the anode substrate, which facilitates deposition of polyaniline. The one-step method for preparing a polyaniline composite material is simple, low-cost, and pollution-free; compared with pure polyaniline, the composite material has improved sensitivity. The metal-doped polyaniline composite material is used in a pH sensor for the first time, thus implementing a wide pH measurement range (2-12) and a high bending stability. The flexible pH sensor can be used in saliva pH measurement, and to measure the freshness of fresh food.