498 results about "Food quality" patented technology

The invention discloses a dietary pattern evaluation method and system based on digital intelligent analysis, and relates to the technical field of dietary pattern evaluation.The method comprises the steps that a mobile terminal is used for synchronously collecting multi-modal data, and the multi-modal data are preprocessed; food pictures are retrieved based on keywords to form a data set, an image recognition model is constructed, the volume and area of food materials are calculated in combination with a 3D reconstruction technology, a food material density database is matched, and automatic estimation of the quality of the food materials is achieved; building a voice interaction model fusing characters and regional voice features, and recognizing food material types and food material quality by using voice data; a dynamic allocation weight is calculated through multi-modal confidence, a graph attention mechanism is utilized to detect and correct data conflicts, and a space-time constraint rule base is combined to align dispersed intake records; and constructing a data visualization chart library. According to the method, the rise of dietary structure analysis from extensive statistics to refined intelligent evaluation is realized, and the problem of error accumulation caused by dependence on a single data source in a traditional method is effectively solved.

The invention discloses a food detection system and method based on computer vision, and relates to the technical field of food visual detection and intelligent identification, and the system comprises an image collection module which obtains initial food image data from a production line through a preset image collection device, processes the data through an edge detection algorithm to extract contour features, and sends the contour features to an image processing module; meanwhile, a boundary point coordinate extraction process is fused to obtain appearance boundary description of the food; the boundary clustering module is used for grouping boundary points by adopting a clustering algorithm according to the extracted appearance boundary description, carrying out processing under an iterative convergence condition in combination with a distance metric function and cluster center initialization, and judging that the shape is irregular if the grouping number exceeds a preset threshold value, so as to obtain a shape classification label; according to the food detection system and method based on computer vision, the accuracy and adaptability of food quality detection are remarkably improved, and the quality control requirement of a multi-scene production line is met.

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 technical field of machine vision and food quality detection, in particular to a food defect real-time detection method and system based on image processing. The method specifically comprises the following steps: integrating multi-modal data, and adaptively adjusting a weight coefficient to improve data quality; multi-stage noise reduction is carried out, key frames are extracted, and a key frame verification mechanism is enhanced through a 2FA control variable; constructing a YOLO-BioNet model, and optimizing the detection efficiency and precision in a complex scene through dynamic convolution kernel allocation and a spiking neural network; integrating Kalman filtering, LSTM (Long Short Term Memory) time sequence modeling and a deep reinforcement learning model, analyzing a food surface change track and predicting a defect state; real-time data and historical information are fused, and a dynamic threshold value is calculated through a Bayesian network; performing linkage response between an automatic sorting instruction and a production line; a distributed storage and graph neural network optimization model is adopted, and defect tracing is supported. The method has remarkable advantages in the aspects of real-time performance, detection accuracy and self-adaptive capability.

There is a challenge in creating a seamless food supply chain that aligns with unique characteristics of each food item while considering business values. The present disclosure address the challenges associated with the food supply chain by providing a system and method for evaluating sustainability of a food supply chain network using digital twin. The present discourse describes developing a set of modeling abstractions and a digital twin-based approach to explore ideal supply chains for food, aiming to reduce food wastage, ensure food quality and improve profit margins. In the present disclosure, unique characteristics of fruit supply chains are described which are further applied to food supply chains. Using the characteristics of climacteric fruits, unique requirements that contemporary perishable food supply chains need to consider are described. Furthermore, a digital twin-based approach specifically designed for climacteric fruits is provided to address modeling requirements and enable evidence-based and justification-backed holistic decision-making.

The invention relates to the technical field of food quality detection, in particular to a tea oil authenticity identification method based on an in-situ mass spectrum-machine learning coupling technology. Comprising the following steps: S1, preparing a sample; s2, acquiring mass spectrum data; s3, feature extraction: performing dynamic binning processing on the mass spectrum data, extracting feature peak information, and performing time dimension feature aggregation to form an initial feature matrix; s4, data preprocessing; s5, training a machine learning model; and S6, intelligent identification and visual output: inputting the mass spectrum original data of the tea oil sample to be detected into the trained multi-model integrated system, outputting an identification result through weighted voting or an optimal model strategy, and giving a confidence interval to quantify the reliability of the result.

The invention belongs to the field of food quality evaluation, and provides a delicate flavor synergistic effect evaluation method based on an electroencephalogram monitoring technology. Psychological or physiological factors such as subjective judgment, forced selection or scoring, individual umami cognition and umami sensitivity difference of sensory officers may affect the accuracy of sensory experiment results; the invention discloses methods such as umami sample selection and strength evaluation, electroencephalogram signal acquisition experiment normal form, quantitative evaluation and statistical analysis, feature extraction and response difference analysis, and the like, and the response mechanism of typical umami compounds in the human brain is explored by taking the response of people to umami signals as a research object and taking electroencephalogram signal detection analysis as a main method. A new theoretical basis is provided for quantitative evaluation based on the human palatable taste intensity, and the method is worthy of wide popularization.

The invention relates to the technical field of electrochemical detection and food quality safety, and particularly discloses an edible oil rancidity degree rapid detection system based on an electrochemical sensor, comprising: a data acquisition module for receiving an original electrochemical response signal and performing format standardization, abnormal data rejection and integrity verification; the data preprocessing module adopts a statistical optimization algorithm to reduce noise, correct baseline drift, generate a unified dimension data set and complete stability verification; the feature analysis module is used for extracting rancidity related feature parameters through statistical correlation analysis, and carrying out fusion calculation after significance verification to obtain feature values representing rancidity; and the evaluation and judgment module inputs the characteristic value into a pre-training model, outputs a quantitative index through multivariate statistical calculation, dynamically sets a safety threshold in combination with the edible oil type, and outputs a rancidity judgment conclusion after comparison. The method depends on statistical data processing and analysis, the detection reliability and accuracy are improved, and the method is suitable for edible oil quality safety rapid screening and quality monitoring.

The invention discloses an intelligent food transportation monitoring method and system based on a multi-dimensional dynamic coupling algorithm, and the method comprises the steps: collecting food transportation data in real time through wireless communication; constructing a digital twinborn model of the food transportation process, and fusing the digital twinborn model and the risk prediction model; simulating a transportation environment change trend to obtain a current multi-dimensional dynamic coupling environment influence factor; obtaining a food heat accumulation amount, and predicting a food quality deterioration probability factor; judging whether the food quality deterioration probability factor exceeds a threshold value or not, and predicting a transportation risk multi-dimensional quantitative index by combining the multi-dimensional dynamic coupling environment influence factor and the food quality deterioration probability factor; generating an optimization strategy, and outputting an intervention scheme; according to the method, the defect of the traditional technology in the aspect of risk prediction of food transportation monitoring is overcome, and through accurate risk prediction and timely intervention measures, the deterioration loss of food in the transportation process is remarkably reduced, the transportation resource allocation is optimized, and the transportation efficiency is improved.

The invention discloses an information processing method and system for improving a food detection effect, and relates to the technical field of food detection.The information processing method comprises the steps that food data are collected, the food data comprise the pH value, the moisture content, the appearance image, spectral data and VOCs concentration data, and the food data are preprocessed; feature extraction is carried out on the food data, alignment and splicing are carried out according to timestamps, an input matrix is obtained and mapped to a high-dimensional space through linear transformation, an embedded vector is generated, position codes are added to the embedded vector, and a time-enhanced embedded vector is obtained. According to the method, position codes are generated from odd and even dimensions of an embedded vector by adopting sine and cosine functions respectively, and the position codes and the embedded vector are added element by element, so that anti-noise embedding of time sequence dynamic characteristics is realized, the error of time sequence alignment is reduced, and the robustness of a food quality degradation path is enhanced.

The invention discloses a taste receptor derived peptide capable of effectively detecting caffeine and a biosensor, and belongs to the technical field of biosensors. Currently, traditional spectrum and chromatography on which caffeine detection depends have the problems of expensive instruments, time-consuming detection, complex procedures and the like, and a sensor based on a taste receptor is also faced with the limitations of high cost, complex preparation process and easy inactivation of the receptor. In order to solve the problem, the taste receptor derived peptide provided by the invention has an amino acid sequence as shown in SEQ ID NO: 4, the biosensor comprises a substrate and a disc interdigital electrode, and a carbon nanotube is firmly attached to the electrode and is connected with the taste receptor derived peptide. The biosensor can specifically detect caffeine in a beverage, has no response to other substances, and has application potential in the fields of food quality control, clinical drug monitoring, public health safety assessment and the like.

The invention relates to the technical field of fruit and vegetable production equipment, and discloses a full-automatic fruit and vegetable food processing and packaging assembly line which comprises a bubble cleaning pool. The first mechanical arm is arranged on the front side of the bubble cleaning pool, a mechanical claw is installed at the output end of the first mechanical arm, and the fruits and vegetables stored in the frame shape are put into the bubble cleaning pool through the mechanical claw; the first elevator is arranged on one side of the bubble cleaning pool, and the input end of the first elevator is located in the bubble cleaning pool. According to the full-automatic fruit and vegetable food processing and packaging assembly line, automation and intellectualization of the fruit and vegetable food processing and packaging assembly line are achieved through innovative design, the efficiency of the links such as cleaning, selecting and packaging is greatly improved, and the production efficiency is improved. According to the method, quality fluctuation and resource waste caused by manual operation are reduced, the food quality stability and safety are ensured, meanwhile, the labor cost is remarkably reduced, and the market competitiveness and sustainable development capacity of enterprises are enhanced.

The invention provides a food quality risk early warning method and system, and belongs to the technical field of food detection, and the method comprises the steps: obtaining a visible light image and a near-infrared image of food; based on the visible light image of the food, determining the category and the segmentation mask of the food by adopting an improved multi-scale feature fusion network; performing binary mask filtering on the visible light image and the near-infrared image based on the segmentation mask to obtain image data, calling different food quality detection models according to food categories, and inputting the image data into the food quality detection models to determine the quality risk of the food; wherein the food quality detection model is an improved Transform network, and the improved Transform network comprises an encoder, a feature mapping module and a multi-layer perceptron. The visible light image and the near-infrared imaging are fused, and the detection accuracy is improved by detecting the appearance quality and the internal quality at the same time.

The invention discloses a multi-modal fusion-based roast chicken eating quality grading intelligent judgment method, which comprises the following steps of: performing matrix setting on a roasting process, preparing roast chickens with different quality grades, evaluating sensory attributes and overall scores of the roast chickens, and quantifying different modal data characteristics by adopting multiple quality detection methods. By constructing a process-sensory-multi-modal feature full-link database and combining a random forest for determining optimization and an XGBoost interpretable machine learning model, accurate grading and intelligent prediction of the roast chicken eating quality are realized, and the core problems of high subjectivity, poor single-modal generalization ability, data islands and the like of a traditional method are solved. Quality accurate grading prediction and digital traceability from processing to finished products are realized, and data support is provided for process optimization and market positioning prediction. The method has remarkable practical application value in the aspects of product comprehensive grade prediction and data management, and has prediction potential suitable for multiple scenes.

The invention provides a chicken deterioration marker screening and AICAR prediction method and system based on multi-task learning and transfer learning. The method comprises the following steps: selecting chicken samples with different storage conditions and shelf lives, collecting metabolite data of multiple batches, and carrying out quantitative analysis on the metabolite by utilizing high performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and other technologies. Through the multi-task learning model, multiple differential metabolites are deeply mined, and the prediction precision and the calculation efficiency are improved. And providing chicken storage period optimization suggestions according to the AICAR predicted value. The innovative method and system can significantly improve scientificity and accuracy of chicken quality evaluation, have wide application prospects, and are especially suitable for the fields of food quality monitoring, supply chain management and intelligent storage and transportation.

The invention discloses a food quality safety tracking management system based on big data, and relates to the technical field of food, and the system comprises the steps: collecting the label identification data and storage stacking parameters of to-be-tracked food; extracting a label risk coefficient between a label inconsistency degree and a quality compliance risk based on the historical supervision data, and processing the label risk coefficient and the label identification data to obtain a label risk value; according to the storage stacking parameters of the to-be-tracked food, the stacking abnormal degree value of the to-be-tracked food in the storage process is judged; processing the stacking abnormal degree value and the label identification data to obtain a storage standard interference value; according to the storage specification interference value, the storage specification interference degree of the to-be-tracked food is judged, and first-grade storage specification interference and second-grade storage specification interference are obtained; the method has the effect of improving the efficiency and effect of food quality safety management.

The invention provides a cooking equipment control method, equipment, a storage medium and a program product, and the method comprises the steps: obtaining a first characteristic frequency point of each food in a cavity of the cooking equipment, the characteristic frequency point being a microwave scanning frequency suitable for each food during microwave heating; respectively determining the position information of each food in the cavity according to the first characteristic frequency points; according to the first characteristic frequency point and the position information, the cooking equipment is controlled to conduct microwave heating on food at the corresponding positions in the cavity. According to the microwave oven, personalized heating can be carried out according to the characteristics of different foods, the situation of uneven heating or excessive heating possibly occurring in traditional microwave heating is reduced, and therefore the cooking efficiency and the food quality are improved.

The invention discloses a rapid food preservation control method based on a flash freezing technology, and relates to the technical field of food preservation, and the method comprises the following steps: obtaining first data of food, and spraying a nano coating containing antifreeze proteins with different concentrations on the surface of the food; inputting the first data into a pre-trained neural network model to obtain second data; according to the second data, a refrigerant injection device and a pulse electric field excitation device are synchronously started, food dynamic data are obtained, feature extraction and trend prediction are conducted on the food dynamic data, and the food dynamic data comprise the real-time core temperature and the ice crystal formation dynamic state; according to the prediction result, the refrigerant flow rate and the electric field frequency are dynamically adjusted, and the release rate of antifreeze protein in the nano-coating is synchronously adjusted; according to the method, the refrigerant flow rate, the electric field frequency and the antifreeze protein release rate are accurately and synchronously adjusted, so that the problems of non-uniform freezing, overlarge ice crystals and food quality deterioration in a traditional freezing method are effectively solved, and the food freezing fineness and the fresh-keeping effect are improved.

The utility model discloses a dissolving device for food quality inspection in the technical field of food detection, which comprises a dissolving tank, the top of the dissolving tank is detachably provided with a tank cover, the center of the bottom of the tank cover is rotatably provided with a screen drum, the bottom end of the screen drum is detachably provided with a bottom cover, the tank cover is provided with a crushing assembly positioned in the screen drum, and the crushing assembly is provided with a crushing assembly. The crushing assembly is connected with a rotating assembly through a transmission assembly, the rotating assembly drives the screen drum to reversely rotate relative to the crushing assembly, a plurality of stirring assemblies are fixed to the outer ring of the screen drum, a feeding port is formed in the position, above the screen drum, of the tank cover, and the crushing assembly comprises a servo motor fixedly installed at the top of the tank cover through a support; the dissolving device for food quality inspection solves the problems that when an existing dissolving device for food quality inspection is used, substances which cannot be fully dissolved are inconvenient to screen, overall cleaning of a follow-up device is not facilitated, the actual dissolving efficiency of the dissolving device is poor, and the dissolving device is not convenient for people to use.

The invention belongs to the technical field of food detection, and particularly relates to a food quality detection device which comprises a rectangular frame, a rotating plate is rotatably arranged in the middle of the rectangular frame, a pressure detection assembly is arranged at the upper end of the rectangular frame, and the pressure detection assembly comprises an extrusion plate slidably arranged at the upper end of the rectangular frame. First L-shaped supporting plates are fixedly connected to the two sides of the rectangular frame correspondingly, a bottom plate is fixedly connected to the lower ends of the first L-shaped supporting plates, a collecting box is arranged on one side of the lower end of the rectangular frame, a first conveying belt is arranged on the other side of the lower end of the rectangular frame, and the first conveying belt is installed at the upper end of the bottom plate. A detection camera is mounted at the upper end of the first L-shaped supporting plate on one side through a support, food packaging bags are extruded by driving an extrusion plate, the extruded food packaging bags are detected by the detection camera, and the detected food packaging bags are sorted and detected in advance by driving a rotating and fixing plate to rotate in different directions; and the risk of damage in subsequent transportation is reduced.

The invention discloses a grain quality safety data storage method and system based on optomagnetic fusion, and the method comprises the steps: collecting grain quality data from multiple sources, collecting the collected multi-source heterogeneous data in a unified data access mode, and carrying out the standardization and modularization processing of the multi-source heterogeneous data; classifying the processed multi-source heterogeneous data, determining data from key business nodes and non-tampered data in supervision abnormal events as high-value data, and determining data needing frequent access as hot data; the high-value data is stored in an optical disk, and the hot data is stored in a hard disk; integrating the high-value data stored in the optical disk and the hot data stored in the hard disk, and establishing a grain quality safety database; statistical analysis is carried out on the grain quality data through a calculation layer of the grain quality safety database, key indexes for identifying the grain quality data are determined, and key factors influencing the grain quality safety are determined.

To provide a food quality preservative enabling further reduction of acetaldehyde concentration in a food packaging body.SOLUTION: A food quality preservative is used, the preservative comprising at least an ethanol vapor-releasing body, an oxygen absorber, and an aminooxyalkyl group-containing polysiloxane having a structural unit represented by the following general formula (1). [In the formula, R independently represents an alkyl group having 1 to 4 carbon atoms, an alkoxy group having 1 to 4 carbon atoms, or a hydroxyl group. The alkyl group may have an amino group (-NH2) at any position. Q independently represents a divalent organic group having 1 to 20 carbon atoms. n represents a real number of 0 to 100].SELECTED DRAWING: None

The invention discloses a high-performance shellac nano emulsion paint protein film and a preparation method thereof. The preparation method comprises the following steps: S1, preparing a protein film-forming solution; s2, pouring the protein film-forming liquid prepared in the step S1 on a plane template, casting to form a film, drying and taking down to form the film; s3, the shellac ethanol solution and polyethylene glycol are subjected to high-pressure homogenization, and shellac nano emulsion paint liquid is prepared; and S4, uniformly brushing the shell-lac nano emulsion paint solution prepared in the step S3 on two surfaces of the protein film in the step S2, and drying to obtain the shell-lac nano emulsion paint protein film. The preservative has the beneficial effects that the preservative is easy to biodegrade, free of environmental pollution, good in mechanical property, high in heat stability, high in water, light and oxygen blocking capacity and good in antibacterial and antioxidant effect, food spoilage bacteria can be effectively inhibited, oxygenolysis of nutritional ingredients in food can be slowed down, the preservative has good preservation performance, the food quality can be guaranteed, and the shelf life of the food can be prolonged.

The invention relates to an intelligent gel label integrating freshness monitoring and preservation as well as a preparation method and application of the intelligent gel label. The intelligent gel label is composed of a porous gel substrate, a plant source preservative eugenol and an aggregation-induced emission (AIE) copper nanocluster carried on the gel substrate. The cerium ions serve as a bridge between the gel and the copper nanoclusters, the copper nanoclusters are anchored to the gel, and indicator leakage is prevented. Besides, the porous gel structure realizes an embedding effect on eugenol, and cerium ions induce the copper nanoclusters to gather on the surface of the gel, so that eugenol is further blocked in a gel substrate. When the freshness of the aquatic product is reduced, the copper nano aggregate responds to the TVB-N, the structure disintegrates, the fluorescence intensity of the label is reduced, and the visual detection of the freshness is realized. In the aspect of preservation, a multi-factor trigger eugenol release mechanism is designed, so that the gel label can quickly respond to food quality change, and integration of food freshness monitoring and intelligent preservation is realized.

The invention relates to the technical field of food quality detection, in particular to a diester oil quality detection method and system based on multi-source data, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out feature extraction and dimension reduction, and generating a quality feature vector; multi-source data fusion is realized through a dynamic weight distribution model, and time sequence quality evaluation data is generated; performing error compensation on the low-confidence data by adopting a self-adaptive correction algorithm, and performing chain verification on the high-confidence data; and through bidirectional consistency verification, optimizing path planning, generating optimization feedback data and finishing final quality confirmation. Through multi-source data fusion, real-time analysis and automatic verification, the accuracy and efficiency of diester oil quality detection are remarkably improved, and the modern industrial intelligent requirement is met.

The invention relates to the technical field of food quality safety, and discloses a food quality safety traceability method based on big data, which comprises the following steps: step 1, binding a unique batch identification code for each food batch, and deploying Internet of Things sensing equipment to collect environmental parameters and geographical location information, after the environment parameters and the geographical location information are associated with the batch identification codes, a data packet with an identity label is formed; and 2, the cloud big data platform receives and gathers the data packets with the identity labels, integrates preset static basic data and node event data, and constructs a dynamic digital file for recording a complete life cycle process by taking batch identification codes as main keys. According to the technical scheme of binding unique identification codes for food batches and deploying Internet of Things sensing equipment to automatically collect and associate uploaded data to a cloud platform for convergence and fusion, automatic and integrated management of whole-course data of a supply chain is realized.

The invention discloses a quality mining and evaluation method of a wolfberry characteristic flavor nourishment, and relates to the technical field of food quality analysis and sensory evaluation, and the method comprises the following steps: S1, carrying out non-targeted analysis on the wolfberry characteristic flavor nourishment by adopting headspace solid phase microextraction in combination with comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry, and obtaining original abundance data of the volatile compound. According to the quality mining and evaluation method for the Chinese wolfberry characteristic flavor nourishment, accurate identification and quantitative evaluation of key flavor substances in the Chinese wolfberry characteristic flavor nourishment are realized by establishing a flavor contribution potential index model and combining a dual verification mechanism. According to the method, the limitation that only a single chemical component is concerned or actual sensory experience is ignored is solved, and the accuracy and reliability of flavor component screening are improved by integrating multi-dimensional parameters such as instrument response intensity, human olfaction threshold and matrix release characteristics and through cross validation of a gas chromatography-olfaction technology and a standard substance addition experiment.

A food quality indicator including an ink layer configured to detect and indicate the presence of analytes, such as volatile organic compounds (VOC), an adhesive layer, wherein the adhesive layer is a double-sided adhesive layer, and a semi-permeable layer that is permeably to gases and impermeable to liquids. The ink layer including of a reactive ink absorbed on a porous substrate and is enclosed between the adhesive layer and the semi-permeable layer. The semi-permeable may further include a coated protective layer configured to prevent the migration of ink from the ink layer. A method for preparing the ink and the protective layer of the food quality indicator as well as the food quality indicator are provided.

The invention discloses a low-temperature preservation method of a prepared meat product, and belongs to the technical field of food processing. In order to solve the technical problems that in the low-temperature storage process of the conditioned meat product, microorganism control is not thorough, secondary pollution and fat oxidation aggravation are caused by accumulation of high-oxygen conditioned packaging condensate water, and texture damage is caused by ultrahigh-pressure treatment, metal ion catalysis is blocked through phytic acid pretreatment, oxidation is inhibited through composite antioxidant vacuum rolling and kneading, and the quality of the conditioned meat product is improved. Gradient ultrahigh pressure destroys microbial structures in stages, citric acid and a D-ribose buffer solution block damaged thalli from repairing, a composite packaging bag is lined with a hygroscopic film and filled with high-oxygen mixed gas to maintain meat color and adsorb condensate water, and finally, the composite packaging bag is deeply frozen to form a microcrystalline structure and then transferred to an ice-temperature environment for storage. The method realizes microbial safety control under the condition of no chemical preservative, effectively maintains the cell integrity of meat products, reduces juice loss and fat oxidation, prolongs the shelf life and maintains the eating quality.

The invention provides a deuterium-labeled quetiapine fumarate isotope compound, a preparation method thereof and a food detection method, and relates to the technical field of food detection. The deuterium-labeled quetiapine fumarate isotope compound is obtained by introducing deuterated Boc piperazine to react with 11-chlorodibenzo [b, f] [1, 4] thiazepine, then removing a protecting group and grafting 2-chloroethoxyethanol. According to the preparation method provided by the invention, by screening proper reaction reagents and optimizing the reaction temperature and the reaction duration, the product conversion rate is greatly improved, and stable mass production in the industry is facilitated. The chemical purity of the deuterium-labeled quetiapine fumarate obtained by the preparation method provided by the invention can reach 98.3% or above, the isotope abundance exceeds 97%, and the deuterium-labeled quetiapine fumarate can be used as an internal standard substance in the field of food quality inspection, and has important practical application value.