1311results about "Testing food" patented technology

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 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.

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 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 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 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.

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 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.

The invention relates to sensing compositional information about material by measuring thermal properties of the material. In one arrangement there is provided a needle probe for sensing compositional information. The probe comprises a needle having a tip region. A resistive element is attached to the needle at the tip region. A measurement system is configured to: 1) drive an electrical current through the resistive element to apply heating to the resistive element, and 2) measure an electrical response of the resistive element to the heating. A processing unit analyses the measured electrical response of the resistive element to the heating to determine compositional information about material in contact with the tip region.

The present disclosure is related to a bimetallic monatomic nano-enzyme electrochemical sensor, and a preparation method therefor and an application thereof. The bimetallic monatomic nano-enzyme electrochemical sensor comprises a working electrode, a counter electrode, a reference electrode and an electric signal control device; the working electrode is formed by coating a gallium-copper bimetallic monatomic nano-enzyme and a conductive carbon paste on a flexible carrier; and according to the present disclosure, the electrochemical sensor is constructed by loading a catalytic material of the gallium-copper bimetallic monatomic nano-enzyme on an electrode substrate material, which is used for detecting a volatile amine content in fresh meat, so as to detect a freshness of fresh meat.

The invention belongs to the technical field of tea processing, and particularly relates to a high-precision grading and directional processing system for Qihong tea. The system comprises an intelligent tea leaf grading module based on hyperspectral imaging and machine vision, wherein the intelligent tea leaf grading module is used for realizing accurate classification of raw materials; an AI-controlled dynamic withering module is used for optimizing enzyme activity and water evaporation; a pressure-rotating speed self-adaptive intelligent rolling module ensures that cells are uniformly broken; an electronic nose is combined with a multispectral real-time fermentation monitoring module to accurately control the fermentation degree; a far infrared-hot air coupled precise drying module is used for realizing gradient dehydration; the near infrared spectrum and deep learning quality detection module is used for completing finished product quality evaluation; the data traceability module of the block chain technology is used for recording whole-process processing parameters; and a big data-driven process optimization module continuously improves the product stability. According to the invention, the intellectualization, precision and standardization of the Qihong Huang tea processing process are realized, and the consistency and the superior product rate of the product quality are obviously improved.

One or more implementations of the present disclosure relate to a wireless, multi-sensor food thermometer that includes a temperature probe having a linear array of temperature sensors that are operative to measure: temperature profiles within a food product during a cooking process, a temperature at a core of the food product, a temperature at the surface of the food product, and a temperature of the ambient cooking environment in which the food is being cooked. The temperature probe includes a wireless interface that transmits temperature information to one or more external devices for use thereby. Rather than transmitting raw temperature data, the temperature probe may transmit coefficients that correspond to a function that describes the temperature profile within the food or parameters necessary to solve a governing heat equation, which reduces data transmission requirements and reduces power consumption. The food thermometer may include a charger case that houses the temperature probe and charges a power source of the temperature probe using a replaceable battery.

The invention relates to the field of detection, and provides a food safety rapid detection device based on artificial intelligence analysis, the food safety rapid detection device comprises a casing, a hollow heat conduction drill bit and a heat insulation pipe, the casing is provided with a machine cavity, the heat insulation pipe is rotatably connected to the inner wall of the machine cavity, the lower end of the heat insulation pipe extends below the casing, and the hollow heat conduction drill bit is fixedly connected to the lower end of the heat insulation pipe; a sampling opening is formed in the hollow heat conduction drill bit, a heat conduction copper piece is fixedly connected to the heat insulation pipe, a heat conduction strip is fixedly connected into the heat insulation pipe, the heat conduction copper piece and the hollow heat conduction drill bit abut against the heat conduction strip, two or more permanent magnets are fixedly connected to the inner wall of the machine cavity, the permanent magnets are arranged around the heat insulation pipe, and the polarities of the upper ends of every two adjacent permanent magnets are different. A driving assembly, a liquid pumping assembly and more than two sample boxes are connected in the machine cavity, probes are arranged in the sample boxes, and an artificial intelligence analysis module is arranged on the machine shell; the driving assembly is connected with the heat-conducting copper piece, and the liquid pumping assembly is movably connected with the heat-insulating pipe. Rapid multi-layer sampling of frozen food is achieved, and the detection efficiency is improved.

Described embodiments relate to methods of determining quality assessment measures of carcases. The method comprises determining video data comprising a sequence of frames, wherein at least some of the frames depict a part of a carcase to be quality assessed. For a first frame of the sequence of frames, the method comprises a) determining a frame suitability score for assessing a first trait; b) responsive to the frame suitability score being greater than a threshold frame suitability score for the first trait, determining the frame as a suitable frame for assessing the first trait; c) determining, by a segmentation model, a region of interest in the frame for assessing the first trait; d) determining, by a first trait prediction model, a prediction score for the first trait based on the determined region of interest; and e) determining an assessment confidence rating for the first trait. The method further comprises responsive to determining that the assessment confidence rating for the first trait has not exceeded the assessment confidence threshold for the first trait, performing steps a) to e) for a subsequent frame in the sequence; and responsive to determining that the assessment confidence rating for the first trait has exceeded the assessment confidence threshold for the first trait, determining a quality assessment measure of the first trait based on the prediction score for the first trait of the determined suitable frames.

The invention relates to the technical field of sensory evaluation, in particular to a sensory evaluation method of Kokumi taste. The Kokumi taste sense organ evaluation method comprises the following steps: establishing a sense organ group by screening and training product evaluators, and determining optimal sense organ evaluation conditions in the aspects of matrix selection, Kokumi substance addition amount, sample amount, temperature, swallowing mode and whether a nasal splint is used or not. Then selecting a consumer to carry out CATA sensory inspection on the blank matrix and the matrix added with the Kokumi substance under the optimal sensory condition; and carrying out statistics on descriptors with significance in the CATA questionnaire, carrying out time-intensity (TI) analysis, determining sensory attributes as descriptors of Kokumi taste according to a TI result, establishing a reference system, and finally, carrying out scoring quantification on each attribute of the sample by an evaluation group. According to the sensory evaluation method disclosed by the invention, the taste attribute of the Kokumi can be accurately described, the sensory difference between different substances with the Kokumi attribute can be identified, and finally, the Kokumi taste sensory evaluation method is successfully established.

The invention relates to the technical field of chewing testing, in particular to a transmission matching type alternating bionic chewing device and a simulation testing method thereof.The transmission matching type alternating bionic chewing device comprises a supporting frame, a processing tank and a fixing plate, the processing tank and the fixing plate are arranged on the supporting frame, and a feeding port is formed in the processing tank; the pushing assemblies are symmetrically arranged on the fixing plate, and pushing plates are connected to the pushing assemblies; the elastic supporting mechanism is arranged on the push plate, a supporting rod is connected to the elastic supporting mechanism, a chewing mechanism is arranged on the supporting rod, and a first meshing plate and a second meshing plate which are matched with each other are connected to the chewing mechanism; the translation assembly is arranged on the chewing mechanism, a deflection mechanism is further arranged on the chewing mechanism, and the translation assembly can control the first meshing plate and the second meshing plate to execute reciprocating deflection action through the deflection mechanism when the chewing mechanism moves, so that the uniformity of material chewing is guaranteed.

The invention provides a method for determining a current homogeneity parameter of a fodder mixture for farm animals while mixing the fodder mixture in a mixing container of a fodder mixing apparatus, wherein the fodder mixture comprises at least two different fodder components, comprising recording, in sequence, optical spectra of the fodder mixture with a spectral sensor while mixing, wherein the method further comprises the steps of: a) receiving, by an electronic control unit, at least three optical spectra recorded by the spectral sensor, b) calculating, by the electronic control unit, a correlation between the at least three optical spectra, and c) determining, by the electronic control unit, the current homogeneity parameter based on the correlation between the at least three optical spectra.

The invention discloses a meat product fresh-keeping state monitoring and dynamic evaluation system, and belongs to the field of food safety and quality monitoring. Comprising an acquisition module integrating multi-source sensing of gas, temperature, humidity, optics and the like, and multi-dimensional data related to meat product preservation is acquired in real time. The system is provided with a data preprocessing and fusion unit which is used for performing synchronous acquisition, de-noising and feature conversion on original signals and outputting high-quality feature data; and the intelligent monitoring and dynamic evaluation unit realizes high-precision discrimination and deterioration risk prediction of the fresh-keeping state based on a fusion algorithm. Monitoring and evaluation results are uploaded to a cloud analysis platform in a wired or wireless mode, archiving, trend analysis and decision optimization of historical and real-time data are achieved, and quality tracing of the whole process is supported. And the central control and display terminal manages each node in a centralized manner to realize intelligent early warning and visual display.

The invention relates to the technical field of food evaluation, and particularly discloses a multi-element evaluation method and system for potato sirloin prefabricated dishes, and the method comprises the steps: obtaining a target sample; collecting the near infrared spectrum, the total bacterial count, the shearing force and the response value of the electronic nose sensor of the target sample; preprocessing the near infrared spectrum to obtain a near infrared optimized spectrum; carrying out dimension reduction processing on the near-infrared optimized spectrum to obtain a near-infrared light model dimension reduction spectrum; taking the near-infrared light model dimensionality reduction spectrum as input, taking a corresponding target evaluation index as output, combining a target training algorithm corresponding to the target evaluation index, and utilizing the training set to train to obtain a multivariate evaluation model; and receiving a near-infrared light target dimension reduction spectrum, calling the multivariate evaluation model, and synchronously outputting an evaluation result corresponding to each target evaluation index by using the mapping relationship. According to the scheme, training of a multivariate evaluation model is realized by means of a near infrared spectrum technology, multi-dimensional index evaluation can be performed on the sirloin prefabricated dish, and the evaluation accuracy and efficiency are improved.

A method for detecting the extent of microplastic accumulation in bivalve organisms in extreme deep-sea environments is provided. This method is performed through sampling of biological communities to represent the community structure of that biological bed layer, then morphological characterization statistics are conducted to classify the age stage data of individual bivalves, multivariate factor analysis is used to obtain the individual bivalves with the greatest degree of contribution in each classified age stage, subsequently tissue-specific microplastic extraction is performed, the morphology of seafloor microplastics is restored and streamlined identification of full-size microplastics is considered, carbon-14 dating is used to trace the duration of microplastic adsorption by each individual bivalve, dynamic accumulation curves for seafloor bivalves are constructed by connecting each bivalve's survival duration, and finally this is scaled up to the entire bivalve bed to derive the microplastic accumulation rate and historical accumulation of the entire extreme ecosystem.

The invention relates to the technical field of food quality risk assessment, and particularly discloses a food quality risk assessment system based on big data analysis, and the system comprises a food logistics tracking module and a risk management center. The food logistics tracking module is integrated with a temperature sensor, a humidity sensor and a vibration sensor and is used for collecting temperature and humidity data and vibration data of each batch of food logistics in real time; and the risk management center is used for evaluating the food quality risk according to the food category, the temperature and humidity data acquired in real time and the vibration data. According to the invention, the accuracy and controllability of data acquired by the risk management center can be ensured, on the basis of acquiring the temperature and humidity conditions in the food transportation process, the vibration data in the food transportation process is acquired, and the quality risk of food is comprehensively evaluated and judged according to the temperature and humidity data and the vibration data, so that the risk management accuracy is improved. Therefore, the food state can be determined more accurately, and the accuracy of the determination result is improved.

The present disclosure relates generally to methods and test kits for identifying SNPs associated with desirable meat eating quality traits in ovine animals. In particular, the present disclosure relates to a SNP-based diagnostic test and method for identifying ovine animals with desirable fat melting point (FMP), intramuscular fat (IMF) and omega-3 long chain polyunsaturated fatty acids (n-3 LC PUFAs) which are characteristic of Australian White sheep or Lamb (e.g., Tattykeel Australian White Lamb), and the use of those tests and methods in animal breeding programs.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a food raw material quality grade automatic evaluation method based on machine learning, and the method comprises the steps: collecting component, process, environment and historical trend multi-source data, and firstly carrying out the dynamic grouping and condition standardization processing of the component data based on process relevance; carrying out tensor product-based interactive modeling and quality prior attention weighted fusion on the multi-modal features; and finally, combining a process perception attention mechanism and comprehensive boundary regularization loss to carry out quality grade prediction. The problems that in the prior art, static analysis neglects process dynamic influences, conventional standardization damages process-component association, multi-modal information fusion is insufficient, and component boundaries under process constraints cannot be perceived, so that evaluation is inaccurate are effectively solved. Accurate, stable and automatic grade evaluation of food raw material quality in a complex production environment is realized.

The invention relates to the technical field of food quality detection, and discloses a meat product comprehensive scoring method based on principal component analysis, and the method comprises the steps: firstly obtaining physicochemical index (such as protein and fat content) and sensory attribute (such as color saturation and elasticity index) data of multiple batches of samples, and carrying out the structural arrangement of the two types of data according to batches and index dimensions; respectively carrying out Z-score standardization, range normalization and other processing, and extracting a physicochemical index feature vector and a sensory attribute feature vector; constructing a dynamic weight strategy through a variance contribution rate and a feature root ratio, and performing weighted fusion on the two types of feature vectors to obtain a fused feature vector; and finally, inputting the data into a scoring model, and after centesimal system conversion and distribution calibration, dividing quality grades in combination with an industry standard threshold to generate a comprehensive scoring result containing alphanumeric identifiers. The method realizes multi-dimensional data fusion and dynamic empowerment, improves the scoring accuracy, and is suitable for meat product quality evaluation.

The invention relates to the field of edible oil quality monitoring, in particular to an edible oil quality on-line monitoring system and method based on a multi-modal sensor, and the method comprises the steps: extracting the squeezing state characteristics of corresponding outflow oil liquid in the squeezing process; calculating a pressing quality characterization value of the flowing-out oil liquid so as to mark the flowing-out oil liquid; further, the quality of the flowing-out oil liquid is evaluated and analyzed; the method comprises the following steps: extracting flow out-of-order characteristics of oil flowing out in the whole squeezing process, judging whether a flow deviation fault-tolerant requirement is met or not, evaluating a quality deviation characterization coefficient of a corresponding crude oil product according to the splashing height of the oil flowing out into an oil receiving container and in combination with the retention time of foam in the oil receiving container and the foam accumulation area, and determining a quality deviation grade. Corresponding color marking is carried out, and early warning prompt information is sent out. Through real-time online monitoring, edible oil production abnormity can be found in time, multi-dimensional and comprehensive edible oil quality monitoring is realized, and the monitoring accuracy is improved.

The invention provides an optimization method for reproduction of meat flavor of ham-flavored potato chips, and belongs to the technical field of food processing.According to the optimization method, reaction parameter optimization is guided by establishing a Maillard reaction precursor matching database, accurate control is achieved through a sectional temperature control Maillard reaction system, and the quality of ham-flavored potato chips is improved. The method comprises the following steps: monitoring the concentration of key flavor molecules in real time by using a gas chromatography-mass spectrometry technology, carrying out self-adaptive adjustment, and constructing a pre-adjustment flavor release rate matrix, a middle adjustment flavor release rate matrix, a post-adjustment flavor release rate matrix and a concentration distribution matrix to realize quantitative modeling of a flavor release process; sodium alginate-chitosan controlled release microcapsules are prepared by adopting a fluidized bed coating technology to adjust a flavor release time sequence, a double-layer optimization model based on a game theory is established to balance flavor intensity and production cost, and a flavor intensity evaluation model is constructed by combining an electronic nose technology to provide real-time quality feedback. The technical problem that the meat flavor release of ham-flavored potato chips is unstable is solved.