1944results about "Testing food" patented technology

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 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.

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 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 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 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.

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 provides a device for detecting pesticide residues in food, and relates to the technical field of food detection equipment, the device comprises a loading box, a sample crushing cylinder, an inner cylinder, a crushing mechanism, a freezing box, a first sampling bottle, a second sampling bottle and a rotating mechanism; the top of the loading box is covered with a cover body, and the sample crushing cylinder is fixedly connected to the inner side wall of the loading box; the inner cylinder body is arranged in the sample crushing cylinder, crushing mechanisms are arranged inside and outside the inner cylinder body, the sample crushing cylinder is used for sampling food skin, and the inner cylinder body is used for sampling the middle part in food; the freezing box is arranged in the loading box, is positioned on the lower side of the sample crushing cylinder, and is used for freezing a sample to be rechecked; the rotating mechanism drives the sample and the extraction solvent to be fully fused, so that the pesticide residue extraction is more sufficient, the accuracy of the detection result is improved, and the accurate separation and extraction mode is beneficial to more comprehensive and accurate evaluation of the pesticide residue condition of the food.

The invention relates to the technical field of nourishment detection, and discloses a system and a method for detecting enzymolysis reaction components of brewing nourishment. The method comprises the following steps: collecting dynamic data such as an enzyme activity value, a substrate concentration value and a product generation rate value in an enzymolysis reaction process in real time through a multi-source sensor; performing multi-modal feature fusion processing on the dynamic data to generate an enzymolysis reaction feature vector; classifying enzymolysis reaction state categories based on the feature vectors, and dynamically adjusting detection parameter thresholds according to different state categories; and according to the deviation value between the real-time reaction state and the parameter threshold value, correcting an enzymolysis reaction process control instruction by adopting a nonlinear optimization algorithm. According to the method, the dynamic state of the enzymolysis reaction can be captured in real time, accurate detection of enzymolysis reaction components and reasonable regulation and control of the reaction process are achieved through multi-dimensional data fusion and dynamic adjustment, and the method is suitable for efficient detection of the enzymolysis reaction of the brewing nourishment.

The invention discloses a cloud-side-end collaborative meat intelligent detection system and a cloud-side-end collaborative meat intelligent detection method. The system realizes high precision, low delay and low resource consumption of meat detection through three-level cooperation of the mobile terminal, the edge device and the cloud server and a dynamic task scheduling strategy based on confidence, and is suitable for meat safety supervision in a large-scale and complex environment.

The invention discloses a method for multispectral nondestructive detection of fruit and vegetable sugar degree, acidity and maturity, and relates to the technical field of fruit and vegetable detection, historical sample data is collected, candidate wave band combination is established, according to an analysis result, collection configuration is set, real-time collection data is formed, dark current deduction, gain unification, polarization mirror inhibition and visual angle normalization are executed, and the fruit and vegetable sugar degree, acidity and maturity are detected. According to the method, the sugar degree, acidity and maturity of fruits and vegetables can be efficiently and nondestructively detected, the sample error and sampling time in traditional detection are reduced, the data quality is improved, accurate fruit surface information extraction is ensured, and the accuracy of fruit surface detection is improved. The accuracy of sugar degree, acidity and maturity prediction is remarkably improved through multi-dimensional feature extraction and multi-task modeling, the method is more stable especially in a complex environment, in addition, efficient decision support is provided for agricultural production, an accurate harvesting time window and a sorting strategy are generated, resource waste is reduced, and the production efficiency is improved.

A computing platform is configured to (1) extract first, second, third, fourth, and fifth source datasets from a first database containing data about a set of product-level ingredients, a second database containing data about food products, a third database containing data about manufacturing plants, a fourth database containing data about ingredient source locations, and a fifth database containing data about ingredient transportation modes, respectively, (2) merge the first, second, third, fourth, and fifth source datasets into a first merged dataset, (3) update the first merged dataset by inserting a column representing distance measurements between source locations and plant locations for product-level ingredients, (4) extract a sixth source dataset from a sixth database containing environmental-impact values for ingredients, (5) merge the updated first merged dataset and the sixth source dataset into a second merged dataset, and (6) determine a group of environmental-impact indicators for each product-level ingredient in the set.

The utility model discloses a fermented meat detection electronic tongue instrument which comprises a bottom plate, the outer side of the bottom plate is movably connected with a base, the inner side of the base is movably connected with a driving mechanism, according to the specification of a test tube loaded with a solution to be detected in the fermented meat detection process, a knob is pinched and held to rotate anticlockwise, and pressing rings and screws on the two sides are loosened, so that the solution to be detected is detected. The strength of the gasket on one side of the pressing ring for extruding the side piece is released, then the U-shaped limiting hole is matched to move the side piece in the vertical direction to the position, at the required height, of the auxiliary plate, the screw is rotated clockwise, the strength of the gasket on one side of the pressing ring for extruding the limiting hole is increased, and therefore the height position of the side piece is fixed, and test tubes are inserted into the inner sides of the inserting grooves. At the moment, the buffer pad can additionally limit the bottom end of the test tube, and fermented meat detection is performed through the analysis end and the detection end, so that the shaking amplitude of the test tube, which is influenced by vibration, in the equipment in the detection process is effectively reduced, and the detection stability is guaranteed.

The invention relates to the technical field of livestock and poultry food detection, in particular to a livestock and poultry food residue detector and method.The livestock and poultry food residue detector comprises a horizontal rack, a grinding frame is arranged on the horizontal rack, a grinding block matched with the grinding frame is arranged at the top of the grinding frame, and a feeding mechanism is arranged on the grinding frame; the lifting plate can slowly move upwards in the height direction of the grinding frame in the driving process of the lifting assembly, and then the meat tissue is driven to move upwards under the cooperation of the fixing assembly, so that the meat tissue which slowly moves upwards can be ground layer by layer in the rotating process of the grinding block; the condition that the internal structure of the meat tissue is fully damaged due to insufficient grinding of the meat tissue caused by relative sliding of the meat tissue at the bottom of the grinding block is avoided, so that the extracting solution is fully permeated into the meat tissue.

The invention relates to a vegetable pesticide content detection process, which belongs to the technical field of vegetable pesticide detection and comprises a multi-source data acquisition module, a risk pre-judgment module, a sample preprocessing module, a rapid screening module, a high-precision detection and multi-pesticide identification module and a report generation module. The multi-source data acquisition module comprises a planting data acquisition unit, an Internet of Things environment data acquisition unit, a meteorological data acquisition unit and an unstructured data acquisition unit. According to the method, the risk probability is pre-judged through multi-source data modeling, so that accurate detection can be triggered only for high-risk samples, resource waste caused by high-cost analysis of all the samples is avoided, the detection period can be effectively shortened, and the detection accuracy is improved by combining the enzyme inhibition rate method for rapid preliminary screening and the near infrared spectrum-chromatography-mass spectrum for accurate confirmation. The timeliness of rapid screening is reserved, and the defect that a traditional single spectrum technology cannot identify specific pesticide types is overcome, so that the detection accuracy can be effectively improved.

The invention relates to the technical field of meat quality detection, and discloses a fresh beef quality analysis system suitable for a beef jerky processing factory, which comprises a data acquisition module, a quality analysis module, a central control module, an early warning feedback module and a tracing linkage module, a sensor unit, a sample processing unit and a data preprocessing unit are integrated through a data acquisition module, and multiple physical, chemical and biological index data such as color, texture and pH value of fresh beef are acquired in real time in a non-intrusive manner by adopting a multi-source sensing technology, so that the comprehensiveness and reliability of quality analysis input are ensured; meanwhile, sample preparation and positioning are standardized through the sample processing unit, actual storage conditions of a processing plant are simulated, and the accuracy of data acquisition is improved; and a threshold value alarm unit of the early warning feedback module automatically triggers multi-level alarm according to a quality analysis result, so that real-time early warning and graded response of quality abnormal conditions are realized.

The invention discloses a preparation method and application of chalcogenide doped graphdiyne, chalcogenide is implanted into a chalcogenide cavity in a simple thermal driving mode by using a chalcogenide cavity formed in para-nitrogen substituted graphdiyne as an anchoring site, so that the chalcogenide is distributed in a lattice shape, and a high-load monatomic graphdiyne material is obtained. The nano-enzyme sensor is constructed by utilizing the activity of peroxidase-like enhanced by the chalcogenide doped graphdiyne to detect bisphenol and thiosulfate ions, and powerful support is provided for monitoring of food safety and environmental safety.

The utility model relates to the field of automatic detection, in particular to a detection arm for an automatic detector, which comprises a movable sliding table which at least performs reciprocating rectilinear motion along a horizontal single degree of freedom and is provided with a movable execution end; the upper end part of the lifting mechanism is fixedly connected with the movable execution end of the sliding table, and the movable end can vertically lift relative to the upper end part; the fixed end of the telescopic device is fixedly connected with the movable end of the lifting mechanism, and the telescopic execution end of the telescopic device is fixedly connected with the hollow supporting seat; the middle sleeve is rotatably supported in an inner cavity of the hollow supporting seat through the bearing, the hollow shaft motor is circumferentially fixed with the periphery of the middle sleeve to drive the middle sleeve to rotate, and the middle sleeve is provided with an inner hole. The efficiency and reliability of an automatic detection process are effectively improved, the requirements of high throughput, high precision and low pollution of grain detection are met, and higher detection efficiency and accuracy are provided for grain safety detection.

The invention relates to the technical field of conditioning meat products, and provides a method for controlling and researching frozen storage quality deterioration of conditioned chicken wings by steady-state EGCG (epigallocatechin gallate), which comprises the following steps: step 1, sample treatment: conditioning unfrozen chicken wings; step 2, measuring the color, the unfreezing loss, the ferrimyoglobin, the sulfydryl, the Shore band, the thiobarbituric acid value TBARS and the volatile basic nitrogen TVB-N of the conditioned chicken wings; 3, analyzing a result; changes of color, unfreezing loss, ferrimyoglobin content, sulfydryl, Shore band, thiobarbituric acid value TBARS and volatile basic nitrogen TVB-N in the chicken wing freezing and thawing process are analyzed and conditioned; and 4, performing sensory evaluation according to the sensory evaluation table. According to the method, the control of the steady-state EGCG on the frozen storage quality deterioration of the conditioned chicken wings can be better studied.

A spectrometer for detecting a trace element in an agricultural product and use thereof is provided. The spectrometer includes: a light source device configured to generate light required by spectroscopic detection; a convergence device configured to converge the light generated by the light source device; a chopping device configured to modulate a frequency of light converged by a convergence device; a filter device configured to shield light of a wavelength other than a preset wavelength; a detection device configured to receive light passing through a to-be-detected agricultural product and the filter device, and convert an optical signal into an electrical signal; and a processing device configured to generate spectroscopic analysis data according to the electrical signal outputted by the detection device and output a substance content analysis result according to the spectroscopic analysis data, where the preset wavelength of the filter device ranges from 400 nm to 5,000 nm.

The invention discloses a peanut nitrogen nutrition index diagnosis method based on critical nitrogen concentration, and belongs to the technical field of plant nutrition diagnosis. According to the method, a peanut critical nitrogen dilution model is constructed by utilizing field actually measured agronomic parameters, a universal critical nitrogen dilution model suitable for peanuts is established, the model is Nc = 4.193 * W-0. 189, peanut plant nitrogen nutrition diagnosis is carried out through a peanut plant nitrogen nutrition index PNNI, the relationship between the PNNI and a yield index is further analyzed, and the yield index of the peanut plants is determined according to the relationship between the PNNI and the yield index. The nitrogen state of the peanut plant can be accurately reflected; a peanut ANNI regression prediction model is constructed and screened by adopting a machine learning algorithm, and the nitrogen nutrition of the overground part of the peanut is further diagnosed. The nitrogen nutrition condition of the peanut plant and the overground part is integrally analyzed by combining the two constructed models, the peanut fertilization strategy is adjusted, the growth yield condition of the peanut is predicted, and a more convenient and efficient method is provided for early nitrogen nutrition diagnosis of the peanut.

The present invention provides a Shatian pomelo white skin layer debitterizing technology, and belongs to the field of pomelo peel processing technologies, and the technology comprises the following steps: peeling and dicing a Shatian pomelo to obtain a Shatian pomelo white skin layer, debitterizing the Shatian pomelo white skin layer, soaking and rinsing the debittered Shatian pomelo white skin layer, and then drying to obtain a finished product. The Shatian pomelo white skin layer is used as a research object, the debitterizing rate and the sensory score are used as index values, the influence of a single debitterizing method (blanching, saline water and enzymolysis) and a composite debitterizing method (blanching saline water compounding and blanching enzymolysis compounding) on the white skin layer debitterizing effect is explored, and the result shows that the blanching saline water compounding debitterizing effect (hot water for 2 minutes, 4% salt soaking for 4 hours and clear water soaking for 1 hour) is optimal; at the moment, the debitterizing rate is as high as 80.1%, the sensory score is 90, and the treatment time is shortened by 29% compared with that of a single debitterizing method.

An X-ray inspection device includes a conveying unit, an X-ray irradiation unit, an X-ray detection unit, and a control unit configured to inspect whether foreign matter is included in an article based on an X-ray transmission image. The control unit performs a first inspection of using a predetermined threshold value to inspect whether the foreign matter having a lower attenuation rate of X-rays than the article is included in the article, performs a second inspection of using a machine learning algorithm to inspect whether the foreign matter is included inside the article, and determines that the foreign matter is included in the article in a case where the foreign matter common to inspection results of the first inspection and the second inspection is included in the article.

The invention provides a sparus latus meat quality detection method and system based on a hyperspectral imaging technology, and relates to the technical field of sparus latus meat quality detection.According to the sparus latus meat quality detection method and system, rich spectrum detail information is extracted through multi-scale wavelet transformation, a dynamic weight adjustment mechanism is supplemented, the key wave band recognition capability and the accuracy of spectral feature expression are enhanced, and the detection accuracy of sparus latus meat quality is improved. Irrelevant information interference is reduced, and high-precision mapping of hyperspectral data and meat quality indexes is realized; fat features serve as intermediary variables, a nonlinear mapping model is constructed in combination with multi-dimensional physical meat quality indexes, the internal relation between the fat content and the meat quality is scientifically revealed, and the biological rationality of meat quality evaluation is improved; meanwhile, a weighted scoring system of multi-dimensional meat quality indexes is integrated, a standardized meat quality grading standard is established, the industrial application requirement is met, and rapid, non-destructive and intelligent detection and grading of the meat quality of the sparus latus are achieved.

The invention relates to the technical field of food safety detection, in particular to a food safety detection system based on normal-temperature sausages and a detection method.The system comprises a machine body, a detection cover is arranged on the machine body, a driving motor is arranged on the machine body, and two transmission rollers are arranged in the machine body through first bearings; one transmission roller is fixed to the output end of the driving motor, two transmission belts are movably installed between the two transmission rollers jointly, and a plurality of conveying rollers are rotationally installed on the two transmission belts jointly; a gear is meshed with a bent rack, so that a conveying roller is driven to rotate, meanwhile, a driving motor drives a sponge barrel to rotate, the sponge barrel cleans the passing conveying roller, an external water conveying device is controlled to convey water to the inner side of a water collecting shell, the water is sprayed to the sponge barrel through a spray head, and the sponge barrel is cleaned.

This invention is a smart ring with spectroscopic sensors. Changes in the spectrum of light which are caused by interaction with (e.g. reflection by or transmission through) matter are analyzed to get information about the composition of that matter. In an example, light emitters can direct light outward toward an environmental object to get information concerning the molecular composition of the object. In an example, light emitters can direct light inward toward the surface of a person's finger to measure one or more biometric parameters.

A system configured to track, trace and monitor product. In some aspects, the system is configured to track product and capture product related information and includes a network interface configured to receive product related information over a network from a plurality of tracking devices located at a plurality of facilities. The system also includes a database configured to store the product related information from the plurality of tracking devices located at the plurality of facilities. The system also includes a processor configured to be responsive to and control at least the database and the network interface. The system also includes the processor and the database being further configured to implement blockchain technology with respect to the product related information to generate a blockchain ledger of the product related information.

The invention discloses a cold-chain pork quality dynamic evaluation method and system based on multi-source data fusion, and relates to the technical field of data processing and intelligent evaluation, and the method comprises the following steps: collecting multi-source signal information of a whole cold-chain transportation process, sorting according to a unified time sequence, continuously arranging all signals on a time axis, and carrying out data processing on all the signals; a complete dynamic record is formed; and extracting each time interval and signal amplitude change track from the formed dynamic record, analyzing a signal section with repeated wave crests in a short time, and generating a preliminary data manuscript for reflection interference judgment. According to the invention, through time sequence arrangement and dynamic association of multi-source signals, a wave crest track analysis mechanism is constructed, real-time identification and correction of reflection interference are realized, and continuity and accuracy of a gas concentration curve are ensured; and by generating a reflection source position list and adjusting numerical value distribution of repeated fragments, false fluctuation is eliminated, and the stability and reliability of cold-chain pork quality evaluation are improved.