Quality detection method and system for sinonovacula constricta samples in different producing areas under control variables

Abstract

The invention provides a quality detection method and system for sinonovacula constricta samples in different producing areas under control variables, and relates to the technical field of data processing.The method comprises the steps that on the basis of traceability information of different producing areas, representative sinonovacula constricta living samples are collected respectively, fishing time window synchronization control is carried out on the samples in all the producing areas, and the samples in all the producing areas are obtained; carrying out purification treatment in an environment with standardized temperature, salinity and temporary rearing duration, and executing unified cleaning, shelling and tissue separation pretreatment operation to obtain a variable-controlled standardized sample; and performing morphological and textural analysis on the standardized sample to obtain physical index data including individual specifications, shell surface color characteristics, shell meat mass ratio and muscle elasticity modulus. Accurate quality detection and production area traceability of sinonovacula constricta in different production areas under control variables can be realized.

Description

Technical Field

[0001] This invention relates to the fields of aquaculture and aquatic product quality and safety, and in particular to a method and system for quality testing of razor clam samples from different production areas under controlled variables. Background Technology

[0002] Razor clams, an important economic shellfish species along the southeastern coast of my country, occupy a significant position in the aquaculture industry due to their tender texture and rich nutritional components such as protein and unsaturated fatty acids. Because of significant differences in the natural environment of different production areas, the growth rate, nutrient accumulation, and sensory quality of razor clams exhibit distinct regional characteristics.

[0003] However, existing technologies for quality testing and origin identification of razor clams have various shortcomings: First, key variable control is lacking, and the reliability of data traceability is insufficient. For example, the spatiotemporal conditions of sample collection, temporary holding environment, and pretreatment process are not standardized and controlled, leading to confusion between origin differences and environmental interference factors, making it impossible to determine the origin attribution of test data. Second, the test indicators are fragmented, and the quality characterization is incomplete. For example, they only focus on a single or a few indicators and do not integrate key dimensions such as morphology, texture, and multi-component chemistry, which cannot fully reflect the "origin quality characteristic spectrum" of razor clams and make it difficult to explain the intrinsic relationship of "high nutritional content but poor sensory characteristics." They also face problems such as non-standardized sensory evaluation, strong subjectivity of results, rudimentary discrimination methods, and lack of multi-source data fusion capabilities. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for quality detection of razor clam samples from different production areas under controlled variables, which can realize accurate quality detection and traceability of razor clams from different production areas under controlled variables.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, a method for quality testing of razor clam samples from different production areas under controlled variables, the method comprising: Based on traceability information from different production areas, representative live razor clam samples were collected. The harvesting time window of samples from all production areas was synchronized and controlled. The samples were purified in a standardized environment with standardized temperature, salinity and holding time. Uniform cleaning, shelling and tissue separation pretreatment operations were performed to obtain standardized samples with controlled variables. Morphological and textural analyses were performed on standardized samples to obtain physical index data including individual size, shell color characteristics, shell-to-meat mass ratio, and muscle elastic modulus. Based on the standardized samples of the same batch corresponding to the physical index data, qualitative and quantitative analysis of biochemical components was carried out, and chemical index data including crude protein, total sugar, amino acid composition and content, fatty acid composition and content, and mineral element content were determined. Trained sensory evaluators were organized to perform standardized cooking treatment on the same standardized sample that had completed physical and chemical testing, and quantitative scores were made for odor, taste, texture and overall acceptability to obtain sensory evaluation data. By integrating physical, chemical, and sensory evaluation data, and employing multivariate statistical analysis, a quality characteristic map of razor clams from different production areas is established to obtain real-time and effective identification and traceability of production area quality based on controlled variables.

[0006] Furthermore, based on traceability information from different production areas, representative live razor clams were collected. The harvesting time window for samples from all production areas was synchronized, and the samples underwent purification treatment in a standardized environment with standardized temperature, salinity, and holding time. Uniform pretreatment operations, including washing, shelling, and tissue separation, were performed to obtain standardized samples with controlled variables, including: Based on the predetermined geographical coordinates of each production area, live razor clam samples with representative production areas are collected synchronously within the same tidal cycle. Live razor clam samples with representative origins were placed in a temporary holding tank with controlled temperature and salinity, and standardized purification treatment was carried out according to uniform temporary holding time and light conditions to eliminate environmental residues and metabolic interference factors, resulting in purified samples. After purification, the samples were mechanically cleaned, shelled to a uniform size, and separated into muscle tissues to obtain razor clam muscle samples with consistent morphology and free from external contamination. Samples of razor clam muscle with consistent morphology and free from external contamination were aliquoted, labeled, and stored in a low-temperature environment to obtain a standardized sample set for multidimensional detection.

[0007] Furthermore, morphological and textural analyses were performed on standardized samples to obtain physical index data, including individual size, shell color characteristics, shell-to-meat mass ratio, and muscle elastic modulus, including: The morphological measurements were performed on samples taken from the standardized sample set. Two-dimensional images of the dorsal, ventral and lateral views of the samples were acquired using an image acquisition device. The body length, body width and projected area of ​​the individual were quantitatively calculated based on the image analysis algorithm to obtain the specification parameter set. Based on the same sample to be tested corresponding to the specification parameter set, the color is measured. The L, a, b color space values ​​of a specific area on the shell surface are measured using a colorimeter, and the color characteristics are quantified according to the standard colorimetric card to obtain the shell surface color data. The samples that have completed the morphology and color determination were dissected and separated. The shell weight and meat weight were weighed separately using a precision balance. The shell-meat mass ratio was calculated based on the weighed weights. Based on the shell-to-meat mass ratio, the elastic modulus and resilience parameters of the razor clam muscle tissue were determined to obtain textural property data. By integrating and fusing textural property data, shell-to-meat mass ratio, shell surface color data, and specification parameter sets, a physical index dataset is obtained for chemical composition analysis and multi-source data fusion.

[0008] Furthermore, based on the standardized samples from the same batch corresponding to the physical index data, qualitative and quantitative analysis of biochemical components was performed, determining chemical index data including crude protein, total sugar, amino acid composition and content, fatty acid composition and content, and mineral element content, including: Based on the standardized samples of the same batch corresponding to the physical index dataset, samples were taken and subjected to homogenization and freeze-drying pretreatment to prepare samples for component analysis. Based on the sample used for component analysis, its crude protein content and total sugar content were determined to obtain basic nutritional data. For the same sample after the basic nutritional composition data were determined, an amino acid analyzer was used to separate and determine the content of amino acids from protein hydrolysis, and the amino acid composition and content data were obtained. Lipids were extracted from the same sample after the basic nutritional components were determined. The fatty acids were separated, identified and quantitatively analyzed by gas chromatography and mass spectrometry to obtain fatty acid composition and content data. The sample solution, after lipid extraction and separation and digestion of amino acids through protein hydrolysis, was subjected to quantitative analysis of multiple mineral elements to obtain mineral element content data. Data on mineral element content, basic nutrient composition, amino acid composition and content, and fatty acid composition and content are integrated to obtain a chemical index dataset for integration with sensory evaluation data.

[0009] Furthermore, trained sensory evaluators conducted standardized cooking treatments on the same standardized samples that had undergone physical and chemical testing, and quantitatively scored the odor, taste, texture, and overall acceptability to obtain sensory evaluation data, including: Based on the same batch of standardized samples corresponding to the chemical index dataset, samples were taken and heat-treated according to a preset standardized cooking procedure to prepare samples for sensory evaluation. Based on the samples used for sensory evaluation, a trained sensory evaluation team was organized to conduct independent evaluations in a sensory evaluation room with controlled environmental conditions. The evaluators in the sensory evaluation team first standardized the intensity of the odor attributes of the samples used for sensory evaluation to obtain odor characteristic score data; Based on the same sample corresponding to the odor characteristic score data, the evaluator is required to assess the intensity of the taste attributes, including umami, sweetness, saltiness and bitterness, to obtain the taste characteristic score data. Based on the same evaluation process corresponding to the taste feature scoring data, the evaluator is required to continue to perceive and score the sample texture characteristics, including hardness, elasticity, and chewiness, to obtain texture perception scoring data. Based on the texture perception score data, taste characteristic score data, and odor characteristic score data, the evaluators are required to give a comprehensive score for the overall acceptability of the sample in order to obtain the comprehensive score data. Based on the comprehensive scoring data, after intragroup consistency testing and data standardization, a sensory evaluation dataset for multi-source data fusion is obtained.

[0010] Furthermore, physical, chemical, and sensory evaluation data are integrated, and multivariate statistical analysis is used to establish quality characteristic maps of razor clams from different production areas. This allows for real-time and effective identification and traceability of production area quality based on controlled variables, including: The system receives and preprocesses physical index datasets, chemical index datasets, and sensory evaluation datasets, performs checks and imputations on missing and outlier values, and standardizes the data in each dimension to obtain a multi-source heterogeneous fusion dataset. Based on a multi-source heterogeneous fusion dataset, principal component analysis is used to perform feature dimensionality reduction and visualization, extracting principal component features that characterize the quality differences of different production areas, and obtaining the principal component feature space. Based on the principal component feature space, the discriminant analysis method is used to calculate the classification boundary and discrimination coefficient of samples from different production areas in the feature space, and the production area discrimination rule is obtained. The multi-source detection data of samples from unknown production areas are calculated using production area discrimination rules. By comparing the distance and discrimination value of the samples with the feature space center of each production area, the probability and quality feature similarity of the production area to which the sample belongs are obtained. Based on the principal component feature space, production area discrimination rules, and the probability and quality feature similarity of the production areas, a visual fingerprint map reflecting the inherent quality characteristics of razor clams from different production areas is constructed. By integrating quality characteristic fingerprinting with production area discrimination rules, the quality of razor clam samples from different production areas can be effectively judged in real time and traced to their place of origin under controlled variable conditions.

[0011] Furthermore, based on a multi-source heterogeneous fusion dataset, principal component analysis is used for feature dimensionality reduction and visualization to extract principal component features representing quality differences across different production areas, resulting in a principal component feature space, including: Receive a multi-source heterogeneous fusion dataset and calculate the covariance matrix between the dimensions of each variable in the dataset. The covariance matrix is ​​decomposed into eigenvalues ​​to calculate each eigenvalue and its corresponding eigenvector. The feature vectors are sorted in descending order of their feature values, and the top k feature vectors whose cumulative contribution rate exceeds a preset threshold are selected as the principal component directions. The multi-source heterogeneous fusion dataset is projected onto the principal component direction, and the score of each sample on the principal component is calculated to obtain the principal component score matrix. Based on the principal component score matrix, a principal component feature space is constructed using the first and second principal components as coordinate axes to visualize the distribution characteristics of samples from different production areas.

[0012] Secondly, a quality testing system for razor clam samples from different production areas under controlled variables includes: The acquisition module is used to collect representative live razor clam samples based on traceability information from different production areas, and to implement synchronized control of the harvesting time window for samples from all production areas. The samples are purified in a standardized environment with standardized temperature, salinity and temporary holding time, and uniform cleaning, shelling and tissue separation pretreatment operations are performed to obtain standardized samples with controlled variables. The reasoning module performs morphological and textural analysis on standardized samples to obtain physical index data including individual size, shell color characteristics, shell-to-meat mass ratio and muscle elastic modulus. The calculation module performs qualitative and quantitative analysis of biochemical components based on standardized samples from the same batch corresponding to physical index data, and measures chemical index data including crude protein, total sugar, amino acid composition and content, fatty acid composition and content, and mineral element content. The processing module organizes trained sensory evaluators to perform standardized cooking treatment on the same standardized sample that has completed physical and chemical testing, and quantitatively scores the odor, taste, texture and overall acceptability to obtain sensory evaluation data. The summary module integrates physical index data, chemical index data, and sensory evaluation data, and uses multivariate statistical analysis to establish quality characteristic maps of razor clams from different production areas, so as to obtain real-time and effective identification and traceability of production area quality based on controlled variable conditions.

[0013] The above-described solution of the present invention has at least the following beneficial effects: Because it employs variable-controlled sample preparation technology that synchronizes the harvesting time window, standardizes the temporary holding environment, and unifies pretreatment, combined with morphological and textural physical index detection, precise analysis of multi-component chemical indicators, and standardized sensory evaluation, and further integrates multi-source data through multivariate statistical analysis, along with a supporting system module including sample collection, temporary holding, pretreatment, detection, data processing, and traceability management, it effectively overcomes the shortcomings of existing technologies where uncontrolled sample variables lead to a lack of origin attribution in the data, and fragmented detection indicators fail to fully characterize the morphology, nutrition, and sensory aspects of razor clams. The technical challenges of subjective and error-prone sensory evaluation, as well as the low accuracy of production area identification due to weak multi-source data fusion capabilities, have been addressed. This has resulted in significantly improved reliability and comparability of razor clam testing data from different production areas under controlled variables; comprehensive characterization of quality features that can explain the intrinsic relationship between nutritional components and sensory experience; objective and controllable sensory evaluation results; and precise and efficient production area identification and traceability. Furthermore, it provides practical technical support for the protection of razor clam geographical indication product rights, quality monitoring during the breeding process, and the governance of the "inferior goods sold as superior goods" phenomenon in the market, thus contributing to the standardization and branding development of the razor clam industry. Attached Figure Description

[0014] Figure 1 This is a schematic flowchart of a method for quality testing of razor clam samples from different production areas under controlled variables, provided by an embodiment of the present invention.

[0015] Figure 2 This is a schematic diagram of a quality detection system for razor clam samples from different production areas under controlled variables, provided by an embodiment of the present invention. Detailed Implementation

[0016] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0017] like Figure 1 As shown, embodiments of the present invention propose a method for quality testing of razor clam samples from different production areas under controlled variables. The method includes the following steps: Step 1: Based on the traceability information of different production areas, representative live razor clams were collected. The harvesting time window of all samples from all production areas was synchronized and controlled. The samples were purified in a standardized environment with standardized temperature, salinity and temporary holding time. Uniform cleaning, shelling and tissue separation pretreatment operations were performed to obtain standardized samples with controlled variables. Step 2: Perform morphological and textural analysis on the standardized samples to obtain physical index data including individual size, shell color characteristics, shell-to-meat mass ratio, and muscle elastic modulus. Step 3: Based on the standardized samples of the same batch corresponding to the physical index data, perform qualitative and quantitative analysis of biochemical components, and determine chemical index data including crude protein, total sugar, amino acid composition and content, fatty acid composition and content, and mineral element content. Step 4: Organize trained sensory evaluators to perform standardized cooking treatment on the same standardized sample that has completed physical and chemical testing, and quantitatively score the odor, taste, texture and overall acceptability to obtain sensory evaluation data. Step 5: Integrate physical index data, chemical index data, and sensory evaluation data, and use multivariate statistical analysis to establish quality characteristic maps of razor clams from different production areas, so as to obtain real-time and effective identification and traceability of production area quality based on controlled variable conditions.

[0018] In this embodiment of the invention, a sample preparation method is adopted that combines synchronized control of the fishing time window, standardized purification of the temporary holding environment, and unified pretreatment. This is combined with physical index detection that integrates morphology and texture, and qualitative and quantitative chemical index analysis of multiple biochemical components. Furthermore, sensory evaluation data is made objective through training evaluators, standardized cooking treatment, and controlled environment scoring. Finally, the three types of data are integrated and a quality characteristic map is constructed using multivariate statistical analysis. This technical approach effectively overcomes the technical problems in existing razor clam testing, such as chaotic sample variables leading to a lack of origin correlation, single indicators failing to comprehensively characterize quality, large subjective errors in sensory evaluation, and inefficient origin identification due to difficulty in integrating multi-source data. This approach achieves the beneficial effects of strictly controlled sample variables, comprehensive coverage of quality characteristics, reliable and comparable sensory data, and real-time and accurate origin identification and traceability.

[0019] In a preferred embodiment of the present invention, step 1 above may include: Step 1.1: Based on the pre-determined geographical coordinates of each production area, live razor clams with representative characteristics of the production area are collected synchronously within the same tidal cycle. Specifically, this includes: first, determining the precise latitude and longitude coordinates of the core aquaculture area of ​​each razor clam production area through a geographic information system; then, querying the tidal forecast data of the sea area where each production area is located to determine a unified tidal cycle; and during this time period, going to the aquaculture area of ​​each production area to select live razor clams with a body length of 5 to 8 cm, intact shells, no damage, and good activity as samples with representative characteristics of the production area. 40 samples are collected from each production area to ensure the representativeness of the sample size.

[0020] Step 1.2: Live razor clam samples representative of their production areas are placed in temperature- and salinity-controlled holding tanks. Standardized purification treatment is performed according to uniform holding time and lighting conditions to eliminate environmental residues and metabolic interference factors, resulting in purified samples. Specifically, live razor clam samples collected from various production areas are placed in holding tanks equipped with temperature control devices, salinity adjustment devices, and lighting control components. The tank temperature is stably controlled at 25±1℃, the salinity is maintained at 28±1‰ using an automatic salinity regulator, and the lighting equipment is turned on to maintain the light intensity in the tank at 500±50 lux (lumens). The holding time is set to 8 hours. During the holding period, the dissolved oxygen content and pH value in the tank are monitored every 2 hours using a water quality monitor to eliminate any residual mud, microorganisms, and other environmental residues in the samples, as well as metabolic interference substances caused by harvesting stress, ultimately yielding purified samples.

[0021] Step 1.3 involves mechanically cleaning the purified samples, uniformly removing the shells, and separating the muscle tissue to obtain razor clam muscle samples with consistent morphology and free from external contamination. Specifically, the purified razor clam samples are placed in a fully automated shellfish cleaning device. The water flow intensity is set to medium, the cleaning speed to 800 rpm, and the cleaning time to 3 minutes to remove impurities and mucus from the surface of the shell. After cleaning, the samples are removed and shelling is performed using a shelling tool with a fixed-size limiting groove to ensure complete separation of the shell and muscle tissue each time. Then, a sterile scalpel is used to remove the viscera, digestive tract, and other non-detection parts from the muscle sample, leaving only the muscle tissue. This results in razor clam muscle samples with consistent morphology and size, free from external impurities and non-detection tissue contamination.

[0022] Step 1.4 involves aliquoting and labeling razor clam muscle samples with consistent morphology and free from external contamination, and storing them in a low-temperature environment to obtain a standardized sample set for multidimensional detection. Specifically, this includes: preparing 50 ml sterile centrifuge tubes as aliquot containers, placing 10 g razor clam muscle samples from each production area into the centrifuge tubes, labeling each centrifuge tube with the production area name, collection date, sample number, and other information. After labeling, the centrifuge tubes are placed in a freezer box with a sealing gasket to prevent external contamination during transfer. The freezer box is then placed in an ultra-low temperature freezer set to -80°C for storage, ultimately forming a standardized sample set that can be used for physical, chemical, and sensory evaluation detection.

[0023] In this embodiment of the invention, a standardized sample preparation technique is adopted. This technique involves synchronously collecting live razor clams within the same tidal cycle based on the geographical coordinates of the production area, purifying the samples in a water tank with controlled temperature and salinity and uniform light and holding time, and performing mechanical cleaning, standardized shell removal, muscle tissue separation, and low-temperature packaging and labeling on the purified samples. This effectively overcomes the technical problems in the prior art, such as asynchronous sample collection, chaotic holding environment, inconsistent pretreatment methods, and improper storage affecting sample validity. Consequently, subsequent detection data are affected by non-production area factors, lacking origin attribution and comparability. Ultimately, this technique achieves strictly controlled sample variables, consistent morphology, and no exogenous contamination, while ensuring stable sample quality. It provides a standardized sample basis with clear origin correlation for the accurate detection of physical indicators, chemical indicators, and sensory evaluation.

[0024] In a preferred embodiment of the present invention, step 2 above may include: Step 2.1: Take the test samples from the standardized sample set for morphological measurement. Use an image acquisition device to acquire two-dimensional images of the dorsal, ventral, and lateral views of the samples. Quantitatively calculate the individual body length, width, and projected area based on image analysis algorithms to obtain a set of specification parameters. Specifically, this includes: randomly selecting a sufficient number of test samples from the standardized sample set; placing each sample flat on a transparent stage with a scale, ensuring that the dorsal and ventral sides of the sample are completely in contact with the stage without obstruction; starting the image acquisition device and adjusting the lens height and focal length to make the sample image clear and the scale markings accurately identifiable; taking two-dimensional images of the sample from the dorsal, ventral, and lateral views respectively; importing the captured images into the image analysis system; where the image analysis system automatically identifies the sample outline through edge detection and, combined with the scale information of the stage scale, quantitatively calculates the body length, width, and projected area of ​​each sample; and classifying and organizing the calculation results of all samples according to production area to obtain a set of specification parameters.

[0025] Step 2.2: Based on the same test sample corresponding to the specification parameter set, perform color measurement. Use a colorimeter to measure the L, a, b chromaticity space values ​​of a specific area on the shell surface, and quantify the color characteristics according to the standard colorimetric card to obtain shell surface color data. Specifically, this includes: taking the same test sample that has completed specification parameter measurement, gently wiping the sample shell surface with a lint-free cloth to remove surface dust and water stains, determining the specific area on the shell surface as one-third of the distance from the top of the shell and avoiding any damage or stains on the shell surface, calibrating the colorimeter in advance to ensure measurement accuracy, vertically attaching the measuring probe of the colorimeter to the determined specific area on the shell surface, waiting for the data to stabilize after each measurement before recording the L, a, b chromaticity space values, repeating the measurement three times for each sample's specific area and taking the average value, and comparing the measured chromaticity values ​​with the color levels on the standard colorimetric card. Further quantify the color characteristics of the sample according to the quantification standard corresponding to the color levels, and organize the quantification results of all samples according to the production area and sample number to obtain shell surface color data.

[0026] Step 2.3 involves dissecting and separating the samples that have undergone morphological and color determination. A precision balance is used to weigh the shell and flesh separately, and the shell-to-flesh weight ratio is calculated based on the weighed values. Specifically, this includes: taking a sample that has undergone color determination, gently cutting along the natural seams of the sample's shell with a sterile scalpel, carefully separating the shell from the flesh to avoid damaging the flesh or leaving flesh residue on the shell. The separated shell and flesh are placed in clean weighing dishes. The precision balance is preheated and calibrated beforehand and set to a suitable weighing accuracy. First, the weighing dish containing the shell is placed on the balance and its weight is recorded. Then, the weighing dish containing the flesh is placed on the balance and its weight is recorded. Based on the recorded shell and flesh weight values, the shell-to-flesh weight ratio for each sample is obtained by dividing the shell weight by the flesh weight. The shell-to-flesh weight ratios of all samples are recorded according to the production area and sample number, forming shell-to-flesh weight ratio data.

[0027] Step 2.4: Based on the shell-to-meat mass ratio, the elastic modulus and resilience parameters of the razor clam muscle tissue are determined to obtain textural property data. Specifically, this includes: taking the muscle tissue corresponding to the sample with the calculated shell-to-meat mass ratio; cutting the muscle tissue into uniformly sized pieces to ensure test consistency; pre-calibrating the texture analyzer and selecting a suitable probe; setting the texture analyzer's test parameters, including test speed, compression ratio, and trigger force; placing the cut muscle tissue sample on the texture analyzer's test platform; starting the texture analyzer so that the probe compresses the muscle tissue according to the set parameters; during the test, the texture analyzer automatically records the changes in pressure and displacement; calculating the elastic modulus and resilience parameters of the muscle tissue based on the recorded data; and organizing the elastic modulus and resilience parameters of each sample according to the production area and sample number to obtain textural property data.

[0028] Step 2.5 involves integrating and fusing textural property data, shell-to-meat mass ratio, shell color data, and specification parameter set to obtain a physical index dataset for chemical composition analysis and multi-source data fusion. Specifically, this includes importing the textural property data, shell-to-meat mass ratio, shell color data, and specification parameter set into the data processing system. The system first performs validity checks on various types of data to remove outliers. Then, it associates and matches various physical data of the same sample according to the sample number, ensuring that the specification parameter color data, shell-to-meat mass ratio, and textural property data of each sample correspond one-to-one. After that, the associated data are classified and integrated, and data sub-databases are established by production area to form structured data tables. Finally, a physical index dataset that can be used for chemical composition analysis and multi-source data fusion is obtained.

[0029] In this embodiment of the invention, through the coordinated implementation of each sub-step, the physical quality characteristics of razor clam samples can be obtained comprehensively and accurately. Specifically, using an image acquisition device combined with image analysis to determine the sample's length, width, and projected area avoids subjective errors from manual measurement, ensuring the objectivity and accuracy of the specification parameter set and accurately quantifying the morphological differences between samples from different production areas. Using a colorimeter to measure the L, a, and b chromaticity space values ​​of specific areas on the shell surface and combining this with a standard colorimetric card to quantify color overcomes the subjectivity of traditional color evaluation, providing a unified comparison standard for the shell color characteristics of razor clams from different production areas, achieving standardization and comparability of color data. Through precise astronomical observation... Calculating the shell-to-meat mass ratio by weighing the shell and meat accurately reflects the shell-to-meat distribution in the sample, supplementing key physical characteristic dimensions beyond morphology and color, and enriching the characterization range of physical indicators. Measuring the elastic modulus and resilience parameters of the razor clam's muscle tissue can precisely capture the sample's textural properties, filling the gap in the insufficient quantification of razor clam texture characteristics in traditional testing, and further improving the characterization dimensions of physical quality. Integrating textural characteristic data, shell-to-meat mass ratio, shell surface color data, and specification parameters into a complete physical indicator dataset effectively solves the problem of fragmented physical testing indicators and the inability to comprehensively characterize the physical quality of razor clams in existing technologies.

[0030] In a preferred embodiment of the present invention, step 3 above may include: Step 3.1: Based on the standardized samples of the same batch corresponding to the physical index dataset, samples are taken and subjected to homogenization and freeze-drying pretreatment to prepare samples for component analysis. Specifically, this includes: selecting the same number of body samples from each production area from the standardized samples of the same batch corresponding to the physical index dataset; weighing an equal amount of tissue from each sample as the test sample; placing the weighed samples into sterile homogenization tubes; adding an appropriate amount of ultrapure water to the homogenization tubes; placing the homogenization tubes in a tissue homogenizer; setting an appropriate homogenization speed and time to fully break down the samples and form a uniform homogenized mixture; transferring the homogenized mixture to a freeze-drying tray; placing it in a vacuum freeze dryer; setting the cold trap temperature and vacuum degree of the freeze dryer; and after the homogenized mixture is completely freeze-dried, grinding the freeze-dried material into powder using a mortar and pestle; sieving and collecting the powder, which is the sample for component analysis.

[0031] Step 3.2: Based on the samples used for component analysis, determine their crude protein content and total sugar content to obtain basic nutritional data. Specifically, this includes: determining the crude protein content of the samples used for component analysis by mixing the sample with catalyst and concentrated sulfuric acid in a digestion tube, heating and digesting it in a digestion furnace until the solution turns transparent blue-green, cooling it, transferring the digestion liquid to a Kjeldahl nitrogen analyzer, adding alkaline solution for distillation, absorbing the distilled ammonia with boric acid solution, and then titrating it with a standard acid solution. Calculate the crude protein content of the sample based on the volume of standard acid consumed in the titration. Simultaneously, take samples from the same batch used for component analysis to determine the total sugar content. Dissolve the sample in ultrapure water and dilute to a certain volume. Take an appropriate amount of the solution, add phenol solution and concentrated sulfuric acid, mix thoroughly, and place it in a constant temperature water bath for heating and reaction. After the reaction, cool to room temperature and measure the absorbance of the solution at a specific wavelength using a spectrophotometer. Calculate the total sugar content of the sample based on the standard curve. Record the crude protein content and total sugar content of all samples according to the production area and sample number to obtain basic nutritional data.

[0032] Step 3.3: For the same sample after the basic nutrient composition data has been determined, the amino acids from protein hydrolysis are separated and their content is determined using an amino acid analyzer to obtain amino acid composition and content data. Specifically, this includes: taking the same sample after the basic nutrient composition data has been determined, weighing an appropriate amount of the sample and placing it in a hydrolysis tube, adding a certain concentration of hydrochloric acid solution to the hydrolysis tube, sealing the hydrolysis tube and placing it in a constant temperature drying oven for hydrolysis at a specific temperature. After hydrolysis is complete, the hydrolysis tube is cooled to room temperature, the sealing cap is opened, the hydrolysate is filtered into a volumetric flask, and the hydrolysis tube is washed multiple times with ultrapure water. Combine the samples into a volumetric flask, bring the volume to the mark, and shake well. Take an appropriate amount of the diluted hydrolysate and filter it through a 0.22-micron filter membrane. Transfer the filtered hydrolysate to the sample vial of the amino acid analyzer. Set the parameters of the amino acid analyzer, such as the injection volume, separation column temperature, and mobile phase flow rate. Start the instrument for analysis. The instrument automatically separates the protein hydrolysates and amino acids in the hydrolysate and detects the peak area of ​​each amino acid. Calculate the content of each amino acid based on the peak area and concentration relationship of the standard amino acid solution. Record the composition and corresponding content of each amino acid in each sample to obtain amino acid composition and content data.

[0033] Step 3.4: Lipid extraction is performed on the same sample after the basic nutrient composition data were determined. Gas chromatography-mass spectrometry (GC-MS) is used for the separation, identification, and quantitative analysis of fatty acids to obtain fatty acid composition and content data. Specifically, this includes: taking the same sample after the basic nutrient composition data were determined, weighing an appropriate amount of the sample and placing it in the extraction flask of a Soxhlet extractor, adding an appropriate amount of anhydrous diethyl ether as the extraction solvent, assembling the Soxhlet extractor, placing it in a constant temperature water bath, heating to reflux the solvent, and continuing extraction for a certain time to fully extract the lipid components from the sample. After extraction, the extract is transferred to a rotary evaporator, and the solvent is evaporated under a certain temperature and vacuum to obtain the lipid extract of the sample; taking an appropriate amount of the lipid extract and adding... Add the methyl esterification reagent, mix thoroughly, and place in a constant temperature water bath for the methyl esterification reaction. After the reaction is complete, add an appropriate amount of physiological saline and shake. After standing and separating the layers, take the upper organic phase and filter it through a 0.22-micron filter membrane. Transfer the filtered organic phase to the injection vial of the gas chromatography-mass spectrometry (GC-MS) instrument. Set the instrument's injection port temperature, column temperature program, ion source temperature, mass scan range, and other parameters, and start the instrument for analysis. The instrument first separates the fatty acid methyl esters in the organic phase through the chromatographic column, and then identifies the separated components through a mass spectrometer detector. Calculate the content of each fatty acid based on the chromatographic peak area and the standard calibration curve. Record the composition and corresponding content of each fatty acid in each sample to obtain fatty acid composition and content data.

[0034] Step 3.5 involves quantitative analysis of multiple mineral elements in the sample solution after lipid extraction and protein hydrolysis to obtain mineral element content data. Specifically, this includes: taking the remaining sample after lipid extraction and protein hydrolysis, transferring the sample to a polytetrafluoroethylene digestion tube, adding an appropriate amount of a mixed solution of nitric acid and hydrogen peroxide, placing the digestion tube in a microwave digester, setting the temperature program and holding time, and performing microwave digestion to fully dissolve the mineral elements in the sample and convert them into detectable forms; after digestion, removing the digestion tube and cooling it to room temperature, transferring the digestion solution to a volumetric flask, washing the digestion tube multiple times with ultrapure water, combining the washings with the volumetric flask, and diluting to the mark before mixing. Inductively coupled plasma (ICP-C) Inductively coupled plasma mass spectrometry (ICP-MS) is a technique that converts a sample into plasma and uses a mass spectrometer to analyze the mass of ions, thereby enabling the simultaneous quantitative detection of multiple elements. It is suitable for the accurate determination of multiple mineral elements in razor clam samples. An appropriate amount of digested solution, after being brought to a constant volume, is added to an internal standard solution, thoroughly mixed, and then transferred to the sample vial of the ICP-MS. The instrument is tuned and calibrated to ensure detection accuracy. Parameters such as the instrument's radio frequency power, nebulizer flow rate, and sampling depth are set. A collision reaction cell is used to reduce interference. The instrument automatically measures the signal intensity of multiple mineral elements in the digested solution. The content of each mineral element is calculated based on the relationship between the signal intensity and concentration of the standard solution. The content of each type of mineral element in each sample is recorded to obtain mineral element content data.

[0035] Step 3.6 involves integrating the data on mineral element content, basic nutritional components, amino acid composition and content, and fatty acid composition and content to obtain a chemical indicator dataset for integration with sensory evaluation data. Specifically, this includes importing the data into a data management system. The system first matches the various chemical indicator data of the same sample according to the sample number, ensuring a one-to-one correspondence between different chemical indicator data for each sample. Then, the validity of the linked data is tested, and outlier data is identified and removed using statistical methods. Next, the qualified data is categorized and organized according to production area. The various chemical indicator data of all samples from each production area are summarized, and the average value, standard deviation, and other statistical parameters of each indicator in that production area are calculated. Finally, the categorized data and statistical parameters of all production areas are integrated into a structured data table. The table clearly presents the values ​​of each chemical indicator for each sample and its production area information, thus obtaining the chemical indicator dataset for integration with sensory evaluation data. In this embodiment of the invention, by uniformly homogenizing and freeze-drying standardized samples from the same batch corresponding to the physical index dataset, the consistency and representativeness of the samples used for component analysis are ensured, and detection errors caused by differences in pretreatment are avoided, providing a stable analytical basis for the determination of chemical indicators. Measuring crude protein and total sugar content to obtain basic nutritional data can intuitively reflect the basic nutritional value of razor clams, filling the gap of focusing only on a single nutritional indicator. Using an amino acid analyzer to determine the composition and content of hydrolyzed amino acids in proteins can accurately capture the differences in characteristic amino acids among razor clams from different production areas, providing data support for explaining the relationship between nutrition and flavor. After lipid extraction from the same sample, the composition and content of fatty acids are analyzed using gas chromatography-mass spectrometry (GC-MS), which can comprehensively characterize the nutritional characteristics of lipids and overcome the limited dimensions of traditional fatty acid detection. Inductively coupled plasma mass spectrometry (ICP-MS) is used to quantitatively analyze multiple mineral elements in the processed sample solution, allowing for a systematic understanding of mineral distribution characteristics and enriching the characterization dimensions of chemical indicators. Integrating various chemical data to form a chemical indicator dataset effectively solves the problem of scattered chemical detection indicators and their inability to synergistically reflect quality characteristics in existing technologies, laying a comprehensive and systematic chemical data foundation for integration with physical indicator data and sensory evaluation data.

[0036] In a preferred embodiment of the present invention, step 4 above may include: Step 4.1: Based on the standardized samples of the same batch corresponding to the chemical index dataset, samples are taken and heat-treated according to a preset standardized cooking procedure to prepare samples for sensory evaluation. Specifically, this includes: selecting an equal amount of tissue samples with consistent condition from each production area from the standardized samples of the same batch corresponding to the chemical index dataset; weighing a fixed weight of tissue from each sample and placing it into a clean ceramic container; setting the standardized cooking procedure in advance, adjusting the temperature of the constant temperature water bath to a specific value and keeping it stable; placing the ceramic container containing the sample into the water bath, ensuring that the bottom of the container is completely submerged in the water; and performing heat treatment according to the preset heating time; after heating, removing the ceramic container from the water bath and allowing it to cool naturally at room temperature to the same temperature as the room temperature; and obtaining the samples for sensory evaluation after cooling.

[0037] Step 4.2: Based on the sensory evaluation samples, organize a trained sensory evaluation team to conduct independent evaluations in a controlled sensory evaluation room. This includes: conducting advance training for sensory evaluators, covering the definition of sensory evaluation indicators, scoring standards, evaluation methods, and precautions for razor clams. The training duration should be no less than 40 hours. After training, qualified evaluators will be selected through theoretical and practical assessments to form the sensory evaluation team. The sensory evaluation room will be environmentally controlled in advance, adjusting the indoor temperature to a suitable range, maintaining a fixed relative humidity, turning off ventilation equipment, and removing odor sources to ensure no external odor interference. Soft and uniform lighting will also be used. The prepared sensory evaluation samples will be placed into numbered disposable evaluation cups, with only one sample per cup to avoid cross-contamination. Evaluators will receive evaluation cups in random order upon entering the evaluation room and conduct evaluations at their designated stations. Communication is prohibited during the evaluation process.

[0038] Step 4.3: The evaluators in the sensory evaluation team first standardize the scoring of the odor attribute intensity of the sensory evaluation samples to obtain odor characteristic scoring data. Specifically, after receiving the sensory evaluation samples, the evaluators open the lid of the tasting cup, bring the tasting cup to about 5 cm from their nose, and slowly smell the odor emitted by the sample for 3 to 5 seconds to fully perceive the odor characteristics. According to the preset odor attribute intensity scoring standards, they judge from aspects such as the intensity of the odor and the obviousness of the characteristic odor, and fill in the corresponding scores on the scoring sheet. The score range covers different levels from no obvious odor to extremely strong odor. After all evaluators have completed the odor scoring of a single sample, they submit the scoring sheet to the data collector. The data collector organizes the scores according to the sample number and evaluator number to obtain the odor characteristic scoring data.

[0039] Step 4.4: Based on the same sample corresponding to the odor characteristic scoring data, the evaluator is required to assess the intensity of taste attributes including umami, sweetness, saltiness, and bitterness to obtain taste characteristic scoring data. Specifically, this includes: after completing the odor scoring, the evaluator takes an appropriate amount of the sensory evaluation sample, places it in their mouth, and gently chews it to release the taste substances in the sample, fully experiencing the taste in the mouth for about 10 to 15 seconds, avoiding swallowing the sample during this period; judging the intensity of each taste attribute in the order of umami, sweetness, saltiness, and bitterness, referring to the score range consistent with the odor scoring standard, and filling in the corresponding score for each taste on the scoring sheet in sequence; after evaluating one taste, rinsing the mouth with purified water 2 to 3 times, and only evaluating the next taste after there is no residual taste in the mouth; after all taste attributes have been evaluated, the scoring sheet is submitted, and the data collector organizes the scores to obtain the taste characteristic scoring data.

[0040] Step 4.5: Based on the same evaluation process corresponding to the taste characteristic scoring data, the evaluator is required to continue to perceive and score the sample texture characteristics, including hardness, elasticity, and chewiness, to obtain texture perception score data. Specifically, after the taste evaluation, the evaluator takes an appropriate amount of the same sample again and puts it in their mouth, perceiving the texture of the sample through chewing and tongue squeezing, focusing on the hardness of the sample during chewing and the elasticity recovery after chewing; according to the preset texture characteristic scoring standards, the hardness, elasticity, and chewiness are judged separately, and the corresponding scores are filled in on the scoring sheet. The scoring of each texture indicator is carried out independently to avoid mutual interference; if there is a lot of sample remaining in the mouth during the evaluation process, it can be spat out and rinsed with purified water to ensure the accuracy of texture perception. After all texture indicators are evaluated, the scoring sheet is submitted, and the data collector sorts the scores to obtain texture perception score data.

[0041] Step 4.6: Based on the texture perception score data, taste characteristic score data, and odor characteristic score data, the evaluator is required to conduct a comprehensive score for the overall acceptability of the sample to obtain comprehensive score data. Specifically, this includes: the data collector summarizing the odor characteristic score data, taste characteristic score data, and texture perception score data of the same evaluator for the same sample and providing feedback to the evaluator; the evaluator, combining their previous perceptual experience of the sample's odor, taste, and texture, comprehensively judges the overall quality performance of the sample, considering whether they would accept the sample as a consumer product, and fills in the corresponding comprehensive score on the scoring sheet according to the preset overall acceptability scoring criteria. The score range covers different levels from completely unacceptable to very acceptable; after the evaluator completes the comprehensive score, they submit the final scoring sheet, and the data collector organizes all comprehensive scores according to the sample number and evaluator number to obtain comprehensive score data.

[0042] Step 4.7: Based on the comprehensive scoring data, after intragroup consistency testing and data standardization, a sensory evaluation dataset for multi-source data fusion is obtained. Specifically, this includes: processing the comprehensive scoring data of all evaluators, calculating the consistency coefficient of all evaluators' scores for the same sample within the evaluation group, determining whether the intragroup scores meet the preset consistency requirements, and identifying and removing evaluators' scores as outliers if the deviation from the group average score exceeds the allowable range; standardizing the remaining scoring data after removing outliers, converting the scores of different evaluators to the same numerical range to eliminate the impact of differences in scoring scales; and finally, organizing the processed scoring data by sample number and production area to form a structured table containing the aroma, taste, texture, and overall acceptability score of each sample, which is the sensory evaluation dataset for multi-source data fusion.

[0043] In this embodiment of the invention, the problems of inconsistent sensory evaluation processes, subjective results, and incomplete dimensions in existing razor clam sensory evaluation methods are effectively solved through standardized procedures and multi-dimensional evaluation, providing reliable support for multi-source data fusion. Evaluation samples are prepared using the same batch of samples and standardized cooking procedures to ensure consistent pretreatment and eliminate interference from non-originating factors. Training evaluators and using a controlled evaluation environment reduces subjective errors and environmental interference. Scoring is applied to odor, multiple flavors, and multiple textures to comprehensively cover key sensory characteristics and avoid the shortcomings of single-dimensional evaluation. Overall acceptability is evaluated by combining multi-dimensional scores, aligning with actual consumer needs. Consistency testing and data standardization further enhance data reliability.

[0044] In a preferred embodiment of the present invention, step 5 above may include: Step 5.1: Receive and preprocess the physical indicator dataset, chemical indicator dataset, and sensory evaluation dataset. Perform checks and imputation on missing and outlier values, and standardize the data for each dimension to obtain a multi-source heterogeneous fusion dataset. Specifically, this includes: receiving the generated physical indicator dataset, chemical indicator dataset, and sensory evaluation dataset from the data storage area; initially associating the three datasets by sample number to ensure a one-to-one correspondence between data of the same sample number; performing row-by-row and column-by-column checks on the associative datasets to identify missing and outlier values; imputing missing values ​​using the average value of the same type of indicator from the same production area; removing outlier values ​​that exceed a reasonable fluctuation range by comparing the distribution range of the indicator in the overall dataset; after handling missing and outlier values, standardizing the data for each dimension to convert indicator data of different magnitudes into the same numerical range, ensuring equal weight for each indicator in subsequent analysis; and finally integrating the processed data to form a multi-source heterogeneous fusion dataset.

[0045] Step 5.2: Based on the multi-source heterogeneous fusion dataset, principal component analysis (PCA) is used for feature dimensionality reduction and visualization to extract principal component features representing the quality differences between different production areas, thus obtaining the principal component feature space. Specifically, this includes: importing the multi-source heterogeneous fusion dataset into data analysis software, starting the PCA function, first calculating the covariance matrix between the dimensions of each variable in the dataset, using the covariance matrix to reflect the degree of correlation between different indicators; performing eigenvalue decomposition on the covariance matrix to obtain multiple eigenvalues ​​and corresponding eigenvectors, arranging the eigenvectors in descending order according to the eigenvalue magnitude, and selecting the top few eigenvectors whose cumulative contribution rate reaches a preset standard as principal component directions; projecting the multi-source heterogeneous fusion dataset onto the selected principal component directions, calculating the score of each sample on each principal component, and forming a principal component score matrix; constructing a two-dimensional coordinate system with the first principal component as the horizontal axis and the second principal component as the vertical axis, and labeling the principal component scores of samples from each production area in the coordinate system to form a visual distribution map. This coordinate system and sample distribution together constitute the principal component feature space.

[0046] Step 5.3: Based on the principal component feature space, discriminant analysis is used to calculate the classification boundaries and discrimination coefficients of samples from different production areas in the feature space, thus obtaining the production area discrimination rules. Specifically, this includes: based on the constructed principal component feature space, selecting an appropriate discriminant analysis method to analyze the principal component scores of samples from each production area; calculating the central position of each production area sample in the principal component feature space, and determining the classification boundaries used to distinguish different production areas based on the distribution differences of the central positions of different production areas; obtaining the discrimination coefficients through statistical calculation of the principal component scores of samples from each production area, which reflect the contribution of each principal component in the production area discrimination; and integrating the parameters of the classification boundaries with the discrimination coefficients to form a complete set of judgment criteria, clarifying the conditions under which the principal component scores of a sample belong to a certain production area, thus obtaining the production area discrimination rules.

[0047] Step 5.4: Calculate the multi-source detection data of unknown production area samples using the production area discrimination rules. By comparing the distance and discriminant value between the unknown sample and the feature space center of each production area, obtain the probability of its belonging to that production area and the similarity of its quality characteristics. Specifically, this includes: acquiring multi-source detection data of unknown production area samples; performing missing and outlier handling and standardization on these data according to the preprocessing method to ensure the data format is consistent with the multi-source heterogeneous fusion dataset; inputting the preprocessed unknown sample data into the production area discrimination rules, calculating the score of the sample in the principal component feature space, calculating the distance between the unknown sample and the feature space center of each production area based on the score, and simultaneously calculating the discriminant value by combining the discrimination method coefficient; determining the probability of the unknown sample belonging to each production area through comprehensive analysis of distance and discriminant value, with a higher probability value indicating a higher degree of fit between the sample and the quality characteristics of that production area; and simultaneously calculating the similarity of the quality characteristics between the unknown sample and samples from each production area, which is obtained by comparing the degree of difference between the two in key indicators.

[0048] Step 5.5: Based on the principal component feature space, production area discrimination rules, and the probability and quality characteristic similarity of the production areas, a visual fingerprint map reflecting the inherent quality characteristics of razor clams from different production areas is constructed. Specifically, this includes: using the principal component feature space as the basic framework, marking the distribution areas of samples from each production area in a two-dimensional coordinate system, with different colors used to distinguish the distribution areas of different production areas; within each production area's distribution area, labeling the core quality characteristic parameters of that production area, including the typical numerical ranges of major physical, chemical, and sensory evaluation indicators; labeling the production area discrimination rules of each production area next to the corresponding distribution area with concise text descriptions; and adding a module to the coordinate system to display the probability and quality characteristic similarity of unknown samples to their respective production areas. When unknown sample data is input, its position in the coordinate system and related probability similarity information can be displayed in real time. Through the integration of the above elements, a visual fingerprint map that intuitively reflects the inherent quality characteristics of razor clams from different production areas is constructed.

[0049] Step 5.6 integrates the quality characteristic fingerprint spectrum with the production area discrimination rules to achieve real-time and effective quality discrimination and origin traceability of razor clam samples from different production areas under controlled variable conditions. Specifically, this includes: integrating the quality characteristic fingerprint spectrum and production area discrimination rules into a single operating platform with data input analysis and result output functions; when quality discrimination and origin traceability of razor clam samples under controlled variable conditions are required, the multi-source detection data of the sample to be tested is input into the platform. The platform first automatically completes data preprocessing and calculates its score in the principal component feature space. Then, the platform marks the position of the sample to be tested in the visualized fingerprint spectrum, and simultaneously calls the production area discrimination rules to calculate the probability and quality characteristic similarity of each production area; finally, the platform outputs the quality discrimination results of the sample to be tested in the form of a combination of spectral annotation and textual description, clarifying whether it conforms to the inherent quality characteristics of a certain production area, and providing the corresponding origin traceability conclusion, thus achieving real-time and effective discrimination of the quality and origin of razor clam samples.

[0050] In this embodiment of the invention, multi-stage collaborative processing effectively solves the problems of heterogeneous multi-source data, difficulty in identifying production areas, and inefficient traceability, providing precise technical support for the quality identification and traceability of razor clams from different production areas. Missing / outlier processing and standardization are performed on physical, chemical, and sensory datasets to eliminate data discrepancies and lay the foundation for multi-source fusion. Principal component analysis is used to reduce dimensionality and extract regional difference features, solving the problem of difficult analysis of high-dimensional data and clearly presenting quality differences. Production area identification rules are constructed to provide a clear basis for sample classification. The rules are used to calculate the production area probability and similarity of unknown samples, achieving preliminary accurate identification. A visual fingerprint map is constructed to make the quality characteristics of production areas more intuitive and identifiable. Integrating the map and the identification rules, real-time and effective quality identification and origin traceability of razor clams from different production areas are ultimately achieved under controlled variables.

[0051] In a preferred embodiment of the present invention, step 5 above may include: Step 5.21: Receive the multi-source heterogeneous fusion dataset and calculate the covariance matrix between each variable dimension in the dataset. Specifically, this includes: receiving the organized multi-source heterogeneous fusion dataset, which contains physical, chemical, and sensory evaluation index data associated by sample number; first confirming the name and corresponding numerical range of each variable dimension in the dataset; then using the covariance calculation function in the data analysis tool to calculate the covariance between each pair of all variable dimensions in the dataset; arranging the calculation results in the order of variable dimensions to form a matrix; each element in the matrix represents the covariance value between the corresponding two variable dimensions, which is the covariance matrix between each variable dimension in the dataset.

[0052] Step 5.22 involves performing eigenvalue decomposition on the covariance matrix to calculate each eigenvalue and its corresponding eigenvector. Specifically, this includes: importing the covariance matrix into a matrix decomposition tool, starting the eigenvalue decomposition program, and having the tool automatically perform mathematical operations on the covariance matrix to decompose it into multiple eigenvalues ​​and their corresponding eigenvectors. The eigenvalues ​​represent the information intensity carried by the corresponding eigenvectors, while the eigenvectors reflect the combination of this information along the original variable dimensions. All eigenvalues ​​and their corresponding eigenvectors are organized and recorded in the order of calculation to ensure a one-to-one correspondence between each eigenvalue and its corresponding eigenvector.

[0053] Step 5.23: Sort the feature vectors in descending order of feature value, and select the top k feature vectors whose cumulative contribution rate exceeds a preset threshold as principal component directions. Specifically, this includes: arranging the feature values ​​in descending order, and simultaneously sorting their corresponding feature vectors; calculating the proportion of each feature value to the sum of all feature values, i.e., the contribution rate; then accumulating the contribution rates of the preceding feature values ​​to obtain the cumulative contribution rate; stopping the accumulation when the cumulative contribution rate exceeds a preset threshold; selecting the feature vectors corresponding to the top k feature values ​​participating in the accumulation at this time as principal component directions; and recording the value of k and the corresponding feature vector.

[0054] Step 5.24: Project the multi-source heterogeneous fusion dataset onto the principal component directions, calculate the score of each sample on the principal component, and obtain the principal component score matrix. Specifically, this includes: using the first k feature vectors as the projection matrix, using each sample data in the multi-source heterogeneous fusion dataset as a vector, performing matrix multiplication with the projection matrix to obtain the projection value of each sample on the k principal component directions, i.e., the score of each sample on each principal component, arranging the principal component scores of all samples according to the sample number and the order of principal components, to obtain a two-dimensional table in which rows represent samples and columns represent principal components, which is the principal component score matrix.

[0055] Step 5.25: Based on the principal component score matrix, using the first and second principal components as coordinate axes, construct a principal component feature space for visualizing the distribution characteristics of samples from different production areas. Specifically, this includes: extracting all sample scores corresponding to the first and second principal components in the principal component score matrix; using the score of the first principal component as the horizontal axis coordinate value and the score of the second principal component as the vertical axis coordinate value; marking the coordinate points of each sample in a two-dimensional plane coordinate system; distinguishing sample points from different production areas using different shapes or colors; and labeling the principal component names and explained variance proportions corresponding to the horizontal and vertical axes next to the coordinate system, thus obtaining a principal component feature space that can intuitively display the distribution and aggregation characteristics of samples from different production areas.

[0056] In this embodiment of the invention, ordered principal component analysis effectively solves the problems of high dimensionality, complex variable correlations, and difficulty in intuitively identifying regional differences in multi-source heterogeneous fusion data. Calculating the covariance matrix between the dimensions of each variable accurately captures the degree of correlation between different indicators, laying the foundation for extracting key features and avoiding interference from irrelevant or redundant variables. Eigenvalue decomposition of the covariance matrix separates eigenvalues ​​and eigenvectors reflecting the core information of the data, clarifying the contribution of each variable to the data differences. Selecting the top k principal component directions with cumulative contribution rates exceeding a preset threshold in descending order of eigenvalues ​​significantly reduces data dimensionality while maximizing the retention of key information characterizing regional quality differences, avoiding the analytical complexity brought by high-dimensional data. Projecting the fusion dataset onto the principal component directions yields the principal component score matrix, transforming complex multi-dimensional data into concise principal component scores for easier quantitative analysis. Constructing a principal component feature space using the first and second principal components visualizes the distribution of samples from different production areas, making the quality differences between samples from each production area readily discernible.

[0057] like Figure 2 As shown, embodiments of the present invention also provide a quality testing system for razor clam samples from different production areas under controlled variables, comprising: The acquisition module is used to collect representative live razor clam samples based on traceability information from different production areas, and to implement synchronized control of the harvesting time window for samples from all production areas. The samples are purified in a standardized environment with standardized temperature, salinity and temporary holding time, and uniform cleaning, shelling and tissue separation pretreatment operations are performed to obtain standardized samples with controlled variables. The reasoning module is used to perform morphological and textural analysis on standardized samples to obtain physical index data including individual size, shell color characteristics, shell-to-meat mass ratio and muscle elastic modulus. The calculation module is used to perform qualitative and quantitative analysis of biochemical components based on the standardized samples of the same batch corresponding to the physical index data, and to determine chemical index data including crude protein, total sugar, amino acid composition and content, fatty acid composition and content, and mineral element content. The processing module is used to organize trained sensory evaluators to perform standardized cooking treatment on the same standardized sample that has completed physical and chemical testing, and to quantitatively score the odor, taste, texture and overall acceptability to obtain sensory evaluation data. The summary module integrates physical index data, chemical index data, and sensory evaluation data, and uses multivariate statistical analysis methods to establish quality characteristic maps of razor clams from different production areas, so as to obtain real-time and effective identification and traceability of production area quality based on controlled variable conditions.

[0058] The following are examples of sensory evaluation experiments on razor clam samples from different production areas: I. Experimental Background and Objectives This experiment, based on the principle of controlled variables, selected standardized samples from three main razor clam producing areas: Area A, Area B, and Area C. Through a unified sensory evaluation process, quantitative data on aroma, taste, texture, and overall acceptability were obtained, providing reliable sensory indicators for multi-source data fusion and production area identification. The experiment strictly adhered to the control requirements of the same batch of samples, the same processing procedure, and the same evaluation standards to eliminate interference from non-originating factors.

[0059] II. Experimental Preparation 2.1 Sample Preparation Ten samples of razor clam muscle (15g each) in the same condition were selected from the standardized sample sets of the three production areas. The samples were processed using a standardized cooking procedure: the constant temperature water bath was set to 95℃, the samples were placed in a covered ceramic container and heated for 8 minutes, and then naturally cooled to 25℃. They were then placed in disposable tasting cups numbered A1-A10, B1-B10, and C1-C10. The cups were only marked with the numbers and no information about the production area.

[0060] 2.2 Evaluation Team Formation Twenty evaluators aged 22-45 with no history of seafood allergies were recruited and underwent 40 hours of systematic training. The training included the definition of sensory indicators for razor clams (e.g., "umami" refers to the unique sweet and fresh taste of razor clams without any bitter aftertaste), a 10-point scoring system (1 point = very weak / unacceptable, 10 points = very strong / very acceptable), and tasting guidelines (e.g., rinsing the mouth with purified water for 30 seconds after each tasting). Following the training, ten qualified evaluators (numbered P1-P10) were selected through a blind taste test to form an evaluation team.

[0061] 2.3 Evaluation of Environmental Control The sensory evaluation room is maintained at a temperature of 23±1℃ and a relative humidity of 55±5%, illuminated with 4000K warm white light, with ventilation equipment turned off and activated carbon placed to absorb odors. Each evaluator is provided with an independent evaluation station, purified water, a spittoon, and an electronic scoring terminal; communication between evaluators is prohibited.

[0062] III. Experimental Procedure and Grading Criteria 3.1 Evaluation Process The evaluators received tasting cups in random order and completed the odor, taste, texture and overall acceptability scores in turn. After each sample was evaluated, they rinsed their mouths three times and waited two minutes before evaluating the next sample.

[0063] 3.2 Specific scoring criteria Aroma: Based on the unique and rich seafood aroma of razor clams, 1 point (no obvious odor / off-odor), 10 points (rich and pure seafood aroma, no off-odors). Taste: Divided into 4 dimensions: umami, sweetness, saltiness, and bitterness. Each dimension is scored independently. The core evaluation is the purity and harmony of the taste, with 1 point (corresponding to extremely weak taste / extremely strong bitterness) and 10 points (outstanding umami / sweetness, no bitterness). Texture: It is evaluated in three dimensions: hardness, elasticity, and chewiness. The firmness and chewability are rated as 1 point (too soft / too hard, no elasticity) and 10 points (firm and elastic, easy to chew). Overall Acceptability: Based on the preceding indicators, this evaluates the product's acceptability as a consumer product, with a score of 1 (completely unacceptable) and 10 (very acceptable).

[0064] IV. Recording of Experimental Raw Data The following are the raw data (in points) of the scores given by 10 evaluators to 30 samples. The blanks are outliers (exceeding the mean ± 2 standard deviations within the group) and have been temporarily marked. They will be processed later.

[0065] 4.1 Table 1 Scoring Table for Samples from Production Area A Sample number evaluator odor Umami sweet Salty bitter taste hardness elasticity chewing Overall acceptability A1 P1 8.2 8.5 7.8 6.2 1.2 7.5 8.0 7.6 8.3 P2 8.0 8.3 7.6 6.0 1.0 7.3 7.8 7.4 8.1 P3 8.5 8.8 8.0 6.3 1.1 7.6 8.2 7.8 8.5 P4 7.8 8.2 7.5 5.9 1.3 7.2 7.7 7.3 8.0 P5 8.3 8.6 7.9 6.1 1.0 7.4 8.1 7.7 8.4 P6 8.1 8.4 7.7 6.2 1.2 7.5 7.9 7.5 8.2 P7 7.9 8.3 7.6 6.0 1.1 7.3 7.8 7.4 8.1 P8 8.4 8.7 7.9 6.3 1.0 7.6 8.1 7.8 8.4 P9 8.2 8.5 7.8 6.1 1.2 7.4 8.0 7.6 8.3 P10 8.0 8.3 7.7 6.0 1.1 7.3 7.8 7.5 8.2 A2 P1 8.1 8.4 7.7 6.1 1.1 7.4 7.9 7.5 8.2 P2 7.9 8.2 7.5 5.9 1.2 7.2 7.7 7.3 8.0 P3 8.3 8.6 7.9 6.2 1.0 7.5 8.1 7.7 8.4 P4 7.7 8.1 7.4 5.8 1.3 7.1 7.6 7.2 7.9 P5 8.2 8.5 7.8 6.1 1.1 7.4 8.0 7.6 8.3 P6 8.0 8.3 7.6 6.0 1.2 7.3 7.8 7.4 8.1 P7 7.8 8.2 7.5 5.9 1.1 7.2 7.7 7.3 8.0 P8 8.3 8.6 7.9 6.2 1.0 7.5 8.1 7.7 8.4 P9 8.1 8.4 7.7 6.1 1.1 7.4 7.9 7.5 8.2 P10 7.9 8.2 7.6 6.0 1.2 7.3 7.8 7.4 8.1 Note: The original data structure of samples A3-A10 is the same as that of A1-A2. Due to space limitations, it is not fully listed. Its score distribution is similar to that of A1-A2, with an average odor score of 8.0-8.3 and an average overall acceptability score of 8.0-8.4.

[0066] 4.2 Table 2 Scoring Table for Samples from Production Area B Sample number evaluator odor Umami sweet Salty bitter taste hardness elasticity chewing Overall acceptability B1 P1 7.3 7.6 7.0 6.3 1.5 7.0 7.3 7.1 7.5 P2 7.1 7.4 6.8 6.1 1.6 6.8 7.1 6.9 7.3 P3 7.5 7.8 7.2 6.4 1.4 7.1 7.4 7.2 7.7 P4 7.0 7.3 6.7 6.0 1.7 6.7 7.0 6.8 7.2 P5 7.4 7.7 7.1 6.3 1.5 7.0 7.3 7.1 7.6 P6 7.2 7.5 6.9 6.2 1.6 6.9 7.2 7.0 7.4 P7 7.1 7.4 6.8 6.1 1.5 6.8 7.1 6.9 7.3 P8 7.5 7.8 7.2 6.4 1.4 7.1 7.4 7.2 7.7 P9 7.3 7.6 7.0 6.3 1.5 7.0 7.3 7.1 7.5 P10 7.2 7.5 6.9 6.2 1.6 6.9 7.2 7.0 7.4 Note: The original data structure of samples B2-B10 is the same as that of B1. The average odor score is 7.0-7.5, and the average overall acceptability score is 7.2-7.7, which is slightly lower than that of production area A.

[0067] 4.3 Table 3 Scoring Table for Samples from Production Area C Sample number evaluator odor Umami sweet Salty bitter taste hardness elasticity chewing Overall acceptability C1 P1 6.5 6.8 6.2 6.4 2.0 6.3 6.5 6.4 6.6 P2 6.3 6.6 6.0 6.2 2.2 6.1 6.3 6.2 6.4 P3 6.7 7.0 6.4 6.5 1.8 6.4 6.6 6.5 6.8 P4 6.2 6.5 5.9 6.1 2.3 6.0 6.2 6.1 6.3 P5 6.6 6.9 6.3 6.4 1.9 6.3 6.5 6.4 6.7 P6 6.4 6.7 6.1 6.3 2.1 6.2 6.4 6.3 6.5 P7 6.3 6.6 6.0 6.2 2.2 6.1 6.3 6.2 6.4 P8 6.7 7.0 6.4 6.5 1.8 6.4 6.6 6.5 6.8 P9 6.5 6.8 6.2 6.4 2.0 6.3 6.5 6.4 6.6 P10 6.4 6.7 6.1 6.3 2.1 6.2 6.4 6.3 6.5 Note: The original data structure of samples C2-C10 is the same as that of C1. The average odor score is 6.2-6.7, and the average overall acceptability score is 6.3-6.8, which is the lowest among the three production areas.

[0068] V. Data Processing and Result Analysis 5.1 Abnormal Data Handling Outlier data was identified using an intragroup consistency test: the mean μ and standard deviation σ of the scores given by 10 evaluators for the same sample and the same indicator were calculated, and scores exceeding the range of μ ± 2σ were identified as outliers. In this experiment, three outliers were detected (a hardness score of 4.2 for sample A5 P6, a umami score of 9.5 for sample B7 P3, and a bitterness score of 4.0 for sample C3 P8). All outliers were replaced with the intragroup mean for the same indicator from the same production area (e.g., the outlier hardness score for sample A5 was replaced with 7.4).

[0069] 5.2 Data Standardization Processing To eliminate differences in evaluators' scoring scales, all raw scores were normalized using the formula: Score = (Raw Score - Minimum Indicator Value) / (Maximum Indicator Value - Minimum Indicator Value) × 10, ensuring that all indicator scores fall within the range of 1-10. The average core indicators for each production area after normalization are shown in Table 4 below. Table 4. Average values ​​of core indicators for each production area after treatment. production area Odor mean Average umami Average sweetness Average bitterness level (the lower the better) Elastic mean Overall Acceptability Mean A 8.12 8.43 7.75 1.12 7.94 8.26 B 7.28 7.56 6.92 1.55 7.23 7.45 C 6.45 6.78 6.18 2.03 6.42 6.58 5.3 Results Analysis Significant differences exist between production areas: Area A performed best in all sensory indicators, especially aroma intensity, umami, and elasticity, with an overall acceptability score exceeding 8 points; Area C had the lowest scores in all indicators, with the highest score in bitterness; Area B was at an intermediate level, forming a clear gradient of superiority in the south and secondary in the north, which is consistent with the differences in natural environment such as water temperature and salinity among the production areas.

[0070] The indicators showed strong correlation: the correlation coefficient between umami and overall acceptability reached 0.89, indicating that the umami of razor clams is the core factor affecting consumer acceptance; bitterness and astringency were negatively correlated with overall acceptability (correlation coefficient -0.76), and the higher bitterness in region C was the main reason for its lower score.

[0071] Good consistency in evaluation: The intragroup consistency coefficient (Crunbach α coefficient) of samples from each production area is greater than 0.85, indicating that the evaluation group scores have high reliability and the data can be used for subsequent multi-source data fusion analysis.

[0072] VI. Experimental Conclusions This experiment, through a standardized sensory evaluation process, obtained quantitative sensory data from razor clam samples from three production areas, clarifying the sensory quality characteristics of each area: razor clams from area A are characterized by their rich aroma, prominent umami flavor, and firm texture; razor clams from area B are of medium quality; and razor clams from area C have a weaker umami flavor and a slightly bitter taste. The processed experimental data exhibits good reliability and comparability, and can be integrated with physical index data (such as muscle elastic modulus) and chemical index data (such as amino acid composition), providing crucial support for constructing quality characteristic maps and production area discrimination models for razor clams from different production areas.

[0073] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for quality testing of razor clam samples from different production areas under controlled variables, characterized in that, The method includes: Step 1: Based on the traceability information of different production areas, representative live razor clams were collected. The harvesting time window of all samples from all production areas was synchronized and controlled. The samples were purified in a standardized environment with standardized temperature, salinity and temporary holding time. Uniform cleaning, shelling and tissue separation pretreatment operations were performed to obtain standardized samples with controlled variables. Step 2: Perform morphological and textural analysis on the standardized samples to obtain physical index data including individual size, shell color characteristics, shell-to-meat mass ratio, and muscle elastic modulus. Step 3: Based on the standardized samples of the same batch corresponding to the physical index data, perform qualitative and quantitative analysis of biochemical components, and determine chemical index data including crude protein, total sugar, amino acid composition and content, fatty acid composition and content, and mineral element content. Step 4: Organize trained sensory evaluators to perform standardized cooking treatment on the same standardized sample that has completed physical and chemical testing, and quantitatively score the odor, taste, texture and overall acceptability to obtain sensory evaluation data. Step 5: Integrate physical index data, chemical index data, and sensory evaluation data, and use multivariate statistical analysis to establish quality characteristic maps of razor clams from different production areas, so as to obtain real-time and effective identification and traceability of production area quality based on controlled variable conditions.

2. The method for quality detection of razor clam samples from different production areas under controlled variables according to claim 1, characterized in that, Based on traceability information from different production areas, representative live razor clams were collected. Harvesting time windows were synchronized across all production areas. Samples were purified in standardized environments with standardized temperature, salinity, and holding times, undergoing uniform washing, shelling, and tissue separation pretreatment procedures to obtain standardized samples with controlled variables, including: Based on the predetermined geographical coordinates of each production area, live razor clam samples with representative production areas are collected synchronously within the same tidal cycle. Live razor clam samples with representative origins were placed in a temporary holding tank with controlled temperature and salinity, and standardized purification treatment was carried out according to uniform temporary holding time and light conditions to eliminate environmental residues and metabolic interference factors, resulting in purified samples. After purification, the samples were mechanically cleaned, shelled to a uniform size, and separated into muscle tissues to obtain razor clam muscle samples with consistent morphology and free from external contamination. Samples of razor clam muscle with consistent morphology and free from external contamination were aliquoted, labeled, and stored in a low-temperature environment to obtain a standardized sample set for multidimensional detection.

3. The method for quality detection of razor clam samples from different production areas under controlled variables according to claim 2, characterized in that, Morphological and textural analyses were performed on standardized samples to obtain physical parameters including individual size, shell color characteristics, shell-to-meat mass ratio, and muscle elastic modulus, including: The morphological measurements were performed on samples taken from the standardized sample set. Two-dimensional images of the dorsal, ventral and lateral views of the samples were acquired using an image acquisition device. The body length, body width and projected area of ​​the individual were quantitatively calculated based on the image analysis algorithm to obtain the specification parameter set. Based on the same sample to be tested corresponding to the specification parameter set, the color is measured. The L, a, b color space values ​​of a specific area on the shell surface are measured using a colorimeter, and the color characteristics are quantified according to the standard colorimetric card to obtain the shell surface color data. The samples that have completed the morphology and color determination were dissected and separated. The shell weight and meat weight were weighed separately using a precision balance. The shell-meat mass ratio was calculated based on the weighed weights. Based on the shell-to-meat mass ratio, the elastic modulus and resilience parameters of the razor clam muscle tissue were determined to obtain textural property data. By integrating and fusing textural property data, shell-to-meat mass ratio, shell surface color data, and specification parameter sets, a physical index dataset is obtained for chemical composition analysis and multi-source data fusion.

4. The method for quality detection of razor clam samples from different production areas under controlled variables according to claim 3, characterized in that, Based on standardized samples from the same batch corresponding to the physical index data, qualitative and quantitative analysis of biochemical components was performed, determining chemical index data including crude protein, total sugar, amino acid composition and content, fatty acid composition and content, and mineral element content, including: Based on the standardized samples of the same batch corresponding to the physical index dataset, samples were taken and subjected to homogenization and freeze-drying pretreatment to prepare samples for component analysis. Based on the sample used for component analysis, its crude protein content and total sugar content were determined to obtain basic nutritional data. For the same sample after the basic nutritional composition data were determined, an amino acid analyzer was used to separate and determine the content of amino acids from protein hydrolysis, and the amino acid composition and content data were obtained. Lipids were extracted from the same sample after the basic nutritional components were determined. The fatty acids were separated, identified and quantitatively analyzed by gas chromatography and mass spectrometry to obtain fatty acid composition and content data. The sample solution, after lipid extraction and separation and digestion of amino acids through protein hydrolysis, was subjected to quantitative analysis of multiple mineral elements to obtain mineral element content data. Data on mineral element content, basic nutrient composition, amino acid composition and content, and fatty acid composition and content are integrated to obtain a chemical index dataset for integration with sensory evaluation data.

5. The method for quality detection of razor clam samples from different production areas under controlled variables according to claim 4, characterized in that, Trained sensory evaluators conducted standardized cooking treatments on the same standardized samples that had undergone physical and chemical testing. They then quantitatively scored the odor, taste, texture, and overall acceptability to obtain sensory evaluation data, including: Based on the same batch of standardized samples corresponding to the chemical index dataset, samples were taken and heat-treated according to a preset standardized cooking procedure to prepare samples for sensory evaluation. Based on the samples used for sensory evaluation, a trained sensory evaluation team was organized to conduct independent evaluations in a sensory evaluation room with controlled environmental conditions. The evaluators in the sensory evaluation team first standardized the intensity of the odor attributes of the samples used for sensory evaluation to obtain odor characteristic score data; Based on the same sample corresponding to the odor characteristic score data, the evaluator is required to assess the intensity of the taste attributes, including umami, sweetness, saltiness and bitterness, to obtain the taste characteristic score data. Based on the same evaluation process corresponding to the taste feature scoring data, the evaluator is required to continue to perceive and score the sample texture characteristics, including hardness, elasticity, and chewiness, to obtain texture perception scoring data. Based on the texture perception score data, taste characteristic score data, and odor characteristic score data, the evaluators are required to give a comprehensive score for the overall acceptability of the sample in order to obtain the comprehensive score data. Based on the comprehensive scoring data, after intragroup consistency testing and data standardization, a sensory evaluation dataset for multi-source data fusion is obtained.

6. The method for quality detection of razor clam samples from different production areas under controlled variables according to claim 5, characterized in that, By integrating physical, chemical, and sensory evaluation data, multivariate statistical analysis was used to establish quality characteristic profiles of razor clams from different production areas. This enabled real-time and effective identification and traceability of production area quality based on controlled variables, including: The system receives and preprocesses physical index datasets, chemical index datasets, and sensory evaluation datasets, performs checks and imputations on missing and outlier values, and standardizes the data in each dimension to obtain a multi-source heterogeneous fusion dataset. Based on a multi-source heterogeneous fusion dataset, principal component analysis is used to perform feature dimensionality reduction and visualization, extracting principal component features that characterize the quality differences of different production areas, and obtaining the principal component feature space. Based on the principal component feature space, the discriminant analysis method is used to calculate the classification boundary and discrimination coefficient of samples from different production areas in the feature space, and the production area discrimination rule is obtained. The multi-source detection data of samples from unknown production areas are calculated using production area discrimination rules. By comparing the distance and discrimination value of the samples with the feature space center of each production area, the probability and quality feature similarity of the production area to which the sample belongs are obtained. Based on the principal component feature space, production area discrimination rules, and the probability and quality feature similarity of the production areas, a visual fingerprint map reflecting the inherent quality characteristics of razor clams from different production areas is constructed. By integrating quality characteristic fingerprinting with production area discrimination rules, the quality of razor clam samples from different production areas can be effectively judged in real time and traced to their place of origin under controlled variable conditions.

7. The method for quality detection of razor clam samples from different production areas under controlled variables according to claim 6, characterized in that, Based on a multi-source heterogeneous fusion dataset, principal component analysis (PCA) is used for feature dimensionality reduction and visualization. Principal component features representing quality differences across different production areas are extracted, resulting in a principal component feature space, including: Receive a multi-source heterogeneous fusion dataset and calculate the covariance matrix between the dimensions of each variable in the dataset. The covariance matrix is ​​decomposed into eigenvalues ​​to calculate each eigenvalue and its corresponding eigenvector. The feature vectors are sorted in descending order of their feature values, and the top k feature vectors whose cumulative contribution rate exceeds a preset threshold are selected as the principal component directions. The multi-source heterogeneous fusion dataset is projected onto the principal component direction, and the score of each sample on the principal component is calculated to obtain the principal component score matrix. Based on the principal component score matrix, a principal component feature space is constructed using the first and second principal components as coordinate axes to visualize the distribution characteristics of samples from different production areas.

8. A quality testing system for razor clam samples from different production areas under controlled variables, the system implementing the method as described in any one of claims 1 to 7, characterized in that, include: The acquisition module is used to collect representative live razor clam samples based on traceability information from different production areas, and to implement synchronized control of the harvesting time window for samples from all production areas. The samples are purified in a standardized environment with standardized temperature, salinity and temporary holding time, and uniform cleaning, shelling and tissue separation pretreatment operations are performed to obtain standardized samples with controlled variables. The reasoning module performs morphological and textural analysis on standardized samples to obtain physical index data including individual size, shell color characteristics, shell-to-meat mass ratio and muscle elastic modulus. The calculation module performs qualitative and quantitative analysis of biochemical components based on standardized samples from the same batch corresponding to physical index data, and measures chemical index data including crude protein, total sugar, amino acid composition and content, fatty acid composition and content, and mineral element content. The processing module organizes trained sensory evaluators to perform standardized cooking treatment on the same standardized sample that has completed physical and chemical testing, and quantitatively scores the odor, taste, texture and overall acceptability to obtain sensory evaluation data. The summary module integrates physical index data, chemical index data, and sensory evaluation data, and uses multivariate statistical analysis to establish quality characteristic maps of razor clams from different production areas, so as to obtain real-time and effective identification and traceability of production area quality based on controlled variable conditions.

Images