Aquaculture product quality tracking method and system based on lipidome fingerprint

Abstract

The present invention belongs to the field of data processing technology and proposes a method and system for tracking the quality of farmed aquatic products based on lipidome fingerprints. Specifically, the method comprises the following steps: first, identifying the distribution process of farmed aquatic products, selecting sampling points in the distribution process, performing HS-SPME-based processing on the farmed aquatic products at each sampling point to obtain aquatic volatile samples, then collecting GC-MS total ion current information of the aquatic volatile samples and constructing peak area vectors, then calculating the quality attenuation density based on the peak area vectors of each sampling point, and finally, using the quality attenuation density, providing feedback to the administrator or management system on the distribution steps at risk of quality attenuation. This method can effectively quantify the degree of inducibility of the risk of aquatic product quality variation in subsequent distribution stages between different sampling points, ultimately locating the distribution steps at which aquatic product quality variation occurs during the distribution process. The quality attenuation density calculation unit not only significantly improves the reliability of abnormal change detection but also has good scenario adaptability.

Description

Technical Field

[0001] The present invention belongs to the technical field of data acquisition and aquatic product quality tracking, and particularly relates to a method and system for tracking the quality of aquacultured aquatic products based on lipidome fingerprints. Background Art

[0002] In the circulation process of farmed aquatic products, ensuring the quality of farmed aquatic products is the key to circulation management. The actual steps involved in circulation management mainly include fishing, storage, transportation and display. In addition, there are still many branching steps in these main steps according to the types of farmed aquatic products. The quality of farmed aquatic products described in these steps usually refers to important sensory perceptions of aquatic products such as taste, flavor or smell. Since these sensory perceptions directly affect consumers' consumption choices, aquaculture management often conducts high-precision analysis of the quality of farmed aquatic products in the circulation process of farmed aquatic products, thereby reducing the risk of quality loss of farmed aquatic products.

[0003] Currently, the methods for detecting the taste, flavor or odor of farmed aquatic products vary. Often, one method only detects one quality characteristic of aquatic products, such as using gas chromatography-mass spectrometry to detect the impact of fishy substances produced by oxidation and microbial metabolism on the odor of aquatic products, using texture analysis to detect the impact of the elasticity and moisture retention capacity of muscle fibers of texture-sensitive species such as shrimp and cod on the taste, and using DHS dynamic headspace analysis to detect the concentration and type of volatile flavor compounds released into the air by aquatic products.

[0004] However, the current quality tracking of farmed aquatic products is often highly targeted, resulting in high development costs and inefficient development processes for quality analysis of different varieties of farmed aquatic products during the distribution process. Therefore, a method and system for tracking the quality of farmed aquatic products based on lipidome fingerprints are urgently needed.

[0005] Lipidomic fingerprinting is used because lipidomics encompasses a wide range of lipid components in aquatic products. These components are susceptible to changes during aquatic processing, directly impacting the product's taste. These include fatty acids such as arachidonic acid (AA), DHA, and EPA, as well as components within the lipidome such as phospholipids and triglycerides, which can lead to unpleasant odors and taste changes. Aldehydes produced during the peroxidation of lipidome components, including hexanal and heptanal, as well as ketones, often contribute to fishy or off-flavor odors, significantly impacting the quality of aquatic products and are often directly linked to changes in farmed aquatic products. Furthermore, the degradation of phospholipids can also affect the product's texture and flavor. Different components exhibit distinct metabolic or degradation characteristics during the distribution process, making lipidomics fingerprinting a viable tool for tracking the quality of farmed aquatic products. Summary of the Invention

[0006] The purpose of the present invention is to propose a method and system for tracking the quality of aquaculture products based on lipidome fingerprints to solve one or more technical problems existing in the prior art and at least provide a beneficial option or create conditions.

[0007] In order to achieve the above object, according to one aspect of the present invention, a method for tracking the quality of aquatic products based on lipidome fingerprint is provided, the method comprising the following steps:

[0008] S100, identifying the aquaculture flow process and selecting sampling points in the aquaculture flow process;

[0009] S200, aquatic volatile samples were obtained by HS-SPME processing of aquaculture products at various sampling points;

[0010] S300, collecting GC-MS total ion current information of aquatic volatile samples and constructing peak area vectors;

[0011] S400, quality attenuation density calculated based on the peak area vector of each sampling point;

[0012] S500: Feedback the circulation steps that may cause quality degradation risk to the administrator or management system through the quality degradation density.

[0013] Furthermore, in step S100, the aquaculture circulation process is identified, and the method for selecting sampling points in the aquaculture circulation process is as follows: the aquaculture circulation process includes an environmental detection device, which is composed of a humidity sensor and a temperature sensor; if the temperature value measured in real time by the temperature sensor is not in the temperature range of 0-4°C, or if the humidity value measured in real time by the humidity sensor is not in the humidity range of 85-95%, then it is defined that a temperature and humidity anomaly occurs at that moment; the time interval GTT of the preset sampling points is 10-30 minutes, and if no temperature and humidity anomaly occurs within the GTT interval, the end moment of the GTT interval is used as the sampling point, otherwise the time point when the temperature and humidity anomaly occurs is recorded as the sampling point of the time period. That is, the time point when the temperature and humidity anomaly occurs replaces the end moment of the GTT interval as a sampling point in a GTT interval. There is one and only one sampling point in each GTT interval. When there are multiple such moments when the temperature and humidity anomaly occurs, the moment when it occurs first is selected as the sampling point, that is, the moment when the temperature and humidity anomaly occurs obtained by the first monitoring is selected.

[0014] The humidity sensor is a capacitive humidity sensor or a resistive humidity sensor, and the temperature sensor is a thermistor sensor or a semiconductor temperature sensor.

[0015] During the fishing and initial storage stages, temperature sensors and humidity sensors should be placed on the top, middle and bottom of the fishing vessel's refrigerated compartment; during transportation and the cold chain, temperature and humidity sensors should be placed on the top, middle and bottom of the refrigerated compartment respectively; during the display refrigerator stage, temperature and humidity sensors should be installed on the top, middle and bottom near the operating table in the processing area; when there are multiple readings of the same type of sensors, the average value is taken as the real-time reading of this type of sensor; the environmental detection equipment sends the sampling point to the server in real time, and the server stores or applies the environmental data.

[0016] Furthermore, in step S200, the method for obtaining aquatic volatile samples by performing HS-SPME processing on aquaculture products at each sampling point is:

[0017] Aquacultured aquatic products were selected at the sampling point for sampling to obtain original samples; aquatic volatile samples were collected from the original samples using the HS-SPME solid phase microextraction method; the extraction temperature in the application of the HS-SPME technology was set between 30-60°C, and the extraction time was set between 10-30 minutes.

[0018] Farmed aquatic products are generally fish. The HS-SPME technology utilizes the principle of solid-phase microextraction to extract volatile organic compounds from the headspace of aquatic products. Specifically, an SPME fiber is selected for the target compound, which is one or more of fatty acids, phospholipids, and triglycerides. The SPME fiber includes PDMS, CAR / PDMS, or PDMS / DVB. The fiber captures volatile substances in the headspace of aquatic products. Volatile substances include fatty acids, aldehydes, ketones, amino acids, and their derivatives. The captured volatile substances are the volatile organic compounds as aquatic volatile samples.

[0019] Furthermore, in step S300, the method for collecting GC-MS total ion current information of the aquatic volatile sample and constructing a peak area vector is: introducing the aquatic volatile sample into the GC-MS gas chromatography-mass spectrometer to obtain a total ion current, reading each aquatic volatile in the total ion current and recording it as a test object, and reading the peak area of ​​each test object, constructing the peak area of ​​each test object in the sampling point into a vector and recording it as a peak area vector.

[0020] The total ion current (TIC) is a spectrum generated by summing the ion intensities of all mass-to-charge ratios detected by the mass spectrometer at each time point. The GC-MS process accurately separates and identifies different volatile compounds in a sample. During the GC-MS process, the chemical components of the sample are separated in the chromatographic column based on their molecular weight, polarity, and other characteristics, forming a series of peaks. The mass spectrometer ionizes these separated components and performs qualitative and quantitative analysis based on the resulting mass spectrum.

[0021] The total ion chromatogram (TIC) obtained by GC-MS provides detailed information about the various chemical components in a sample, including the location of each peak, i.e., retention time, and peak area. Each peak represents a specific chemical component or class of compounds, and the peak area vector is obtained by quantifying the area of ​​each peak in the TIC.

[0022] Furthermore, in step S400, the quality attenuation density is calculated based on the peak area vector of each sampling point in the following manner: the time interval formed by all sampling points is defined as a sampling period, the sampling points of a sampling point in the reverse time direction and along the time direction are defined as its traceback point and extension point respectively; the cosine distance between the peak area vector of a sampling point and each extension point thereof is calculated, and the extension point having the maximum cosine distance is defined as the first marking point; the average cosine distance of each extension point is defined as the extension cosine distance; a first distance condition is set as the cosine distance being greater than the extension cosine distance, and a second distance condition is set as the cosine distance of at least one intermediate extension point between the sampling point and the extension point being less than the extension cosine distance; the first marking point that satisfies the first distance condition and the second distance condition is searched for in reverse time order from the sampling point and recorded as the second marking point; the number of traceback points of the sampling point in the reverse time direction that have the same second marking point as the current sampling point is counted and recorded as the reverse filling number, and a reverse filling coefficient Rc.idx is preset. Rc.idx∈[0.3,1]; the product of the number of inverse charging and the inverse charging coefficient is rounded upwards to the inverse charging value Rc.vl. The third marker of the sampling point is defined as the Rc.vl-th extended point in the time direction of the second marker. The time interval between the sampling point and the third marker is recorded as the reference domain of the sampling point. The ratio of the cosine distance between the second marker and the third marker is recorded as the inverse charging regression ratio Rc.rvt of the sampling point.

[0023] The intermediate extension points refer to the extension points between the sampling point and the extension points participating in the second distance condition judgment. The preset inversion coefficient defaults to 0.5 and is used to adjust the computational model for calculating quality attenuation. When the frequency of the added class objects is high in most sampling points, the value is increased, and vice versa, it is decreased, thereby enhancing the exclusion of the calculated quality attenuation density and preventing overfitting.

[0024] For any sampling point, the step difference vector is obtained by subtracting the peak area vector of the sampling point from the first sampling point in the reverse time direction. The test objects corresponding to the step difference elements with values ​​greater than zero in the step difference vector are classified as increasing objects. Each increasing object is written into a sequence and recorded as an increasing sequence. Otherwise, it is a loss object. The step difference element refers to any element in the step difference vector. The percentile value of the step difference element of the increasing object in the set of step difference elements corresponding to each same increasing object in the reference domain is recorded as the increasing order value. The cumulative value of the increasing order values ​​corresponding to all increasing objects at the sampling point is the increasing cumulative value Icv. The extension point in the reference domain where the increasing cumulative value is less than the increasing cumulative value is recorded as the adaptation extension point. The quality attenuation density Pdt is calculated by the increasing cumulative value and the back-filling regression ratio:

[0025] ;

[0026] Where i0 is the serial number of the sampling point, Pdt(i0) represents the quality attenuation density of the i0th sampling point, i1 is the cumulative variable, len is the number of adaptive extension points corresponding to the sampling point, exp() is the exponential function with the natural constant e as the base, and Icv i0 and Icv i1 is the cumulative value of the i0th sampling point and the i1th adaptation extension point; diff{} is the Euclidean distance function, which returns the Euclidean distance between two call sequences, Pmks i0 and Pmks i1 The increasing class read sequence represents the i0th sampling point and the i1th adaptive extension point respectively. The increasing class read sequence is composed of the corresponding step difference elements of the increasing class sequence at the i0th sampling point and the i1th adaptive extension point. The increasing class sequence of the i1th adaptive extension point is the same as the increasing class sequence of the i0th sampling point.

[0027] Beneficial effects: Since the quality decay density is an analysis result constructed based on a dynamically screened reference domain, it can effectively quantify the degree of induction of the risk of aquatic product quality variation in subsequent circulation stages between different sampling points. This analysis method can not only clearly reflect the time points when lipid group substances undergo significant changes during the circulation of aquatic products, but can also accurately quantify the ability of these inductions to respond continuously to subsequent changes, thereby providing a solid mathematical basis for further identifying the key steps in the risk of sudden quality decline in aquatic products.

[0028] Furthermore, in step S500, the method of feeding back the circulation steps with quality decay risk to the administrator or management system through the quality decay density is as follows: setting a risk management time period RMT, RMT∈[60,120] minutes; taking any sampling point as the current sampling point, and the RMT period in the reverse time direction of the current sampling point as the current risk management time period; recording the quality decay density of the current sampling point as Pdt, and recording the average value of each quality decay density in the current risk management time period as EPdt; presetting the variable risk identification threshold RPRt, and its value range is RPRt∈[1.1,2); when Pdt≥RPRt×EPdt, it is defined that the current sampling point is at quality decay risk, and the flavor of the aquatic product is deteriorated or lost, and each sampling point at quality decay risk is constructed into a sequence and sent to the administrator client.

[0029] Preferably, all undefined variables in the present invention, if not clearly defined, can be manually set thresholds.

[0030] The present invention also provides a system for tracking the quality of aquatic products based on lipidome fingerprints. The system comprises a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for tracking the quality of aquatic products based on lipidome fingerprints are implemented. The system can be run on computing devices such as desktop computers, laptop computers, PDAs, and cloud data centers. The executable system may include, but is not limited to, a processor, a memory, and a server cluster. The processor executes the computer program in the following system units:

[0031] Aquatic sampling point collection unit, used to identify the aquaculture flow process and select sampling points in the aquatic flow process;

[0032] Aquatic volatiles extraction unit, used to process aquacultured aquatic products based on HS-SPME at various sampling points to obtain aquatic volatile samples;

[0033] Peak area vector construction unit, used to collect GC-MS total ion current information of aquatic volatile samples and construct peak area vectors;

[0034] An attenuation density calculation unit, used to calculate the quality attenuation density based on the peak area vector of each sampling point;

[0035] The quality attenuation feedback unit is used to feed back the circulation steps where the quality attenuation risk occurs to the administrator or management system through the quality attenuation density.

[0036] The beneficial effects of the present invention are as follows: since the quality decay density is an analysis result constructed based on a dynamically screened reference domain, it can effectively quantify the degree of inducibility of the risk of aquatic product quality variation in subsequent circulation stages between different sampling points. By extracting the quality decay density, the quality change dynamics of farmed aquatic products during circulation can be accurately quantified, providing a highly targeted means of identifying deterioration stages, and ultimately locating the circulation steps where quality variation occurs in the circulation of aquatic products. The quality decay density calculation unit not only significantly improves the reliability of abnormal change detection, but can also quickly locate problem links in a common manner for a variety of aquatic products, and has good scenario adaptability. At the same time, it supports the construction of an intelligent quality monitoring system, providing scientific support for the digitalization and efficiency of farmed aquatic product circulation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and other features of the present invention will become more apparent through a detailed description of the embodiments shown in conjunction with the accompanying drawings. In the drawings of the present invention, the same reference numerals represent the same or similar elements. Obviously, the drawings described below are only some embodiments of the present invention. It is possible for a person skilled in the art to derive other drawings based on these drawings without inventive effort. In the drawings:

[0038] Figure 1 Shown is a flow chart of a method for tracking the quality of aquacultured aquatic products based on lipidome fingerprinting;

[0039] Figure 2 Shown is the structural diagram of the aquaculture product quality tracking system based on lipidome fingerprint. DETAILED DESCRIPTION

[0040] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other unless there is a conflict.

[0041] like Figure 1 The following is a flow chart of the method for tracking the quality of aquatic products based on lipid group fingerprints. Figure 1 To illustrate a method for tracking the quality of aquatic products based on lipidome fingerprints according to an embodiment of the present invention, the method comprises the following steps:

[0042] S100, identifying the aquaculture flow process and selecting sampling points in the aquaculture flow process;

[0043] S200, aquatic volatile samples were obtained by HS-SPME processing of aquaculture products at various sampling points;

[0044] S300, collecting GC-MS total ion current information of aquatic volatile samples and constructing peak area vectors;

[0045] S400, quality attenuation density calculated based on the peak area vector of each sampling point;

[0046] S500: Feedback the circulation steps that may cause quality degradation risk to the administrator or management system through the quality degradation density.

[0047] Furthermore, in step S100, the aquaculture circulation process is identified, and the method for selecting sampling points in the aquaculture circulation process is: the aquaculture circulation process includes environmental detection equipment, and the environmental detection equipment is composed of a humidity sensor and a temperature sensor; if the temperature value measured in real time by the temperature sensor is not in the temperature range of 0~4°C, or if the humidity value measured in real time by the humidity sensor is not in the humidity range of 85~95%, it is defined that a temperature and humidity anomaly occurs at that moment; the time interval GTT of the preset collection point is 15 minutes, if no temperature and humidity anomaly occurs within the GTT interval, the end moment of the GTT interval is used as the sampling point, otherwise the time point when the temperature and humidity anomaly occurs is recorded as the sampling point of this time period.

[0048] Furthermore, in step S200, the method for obtaining aquatic volatile samples by performing HS-SPME processing on aquaculture products at each sampling point is:

[0049] Aquacultured aquatic products were selected at the sampling point for sampling to obtain original samples; aquatic volatile samples were collected from the original samples using the HS-SPME solid phase microextraction method; the extraction temperature in the application of the HS-SPME technology was set at 40°C, and the extraction time was set at 30 minutes.

[0050] Furthermore, in step S300, the method for collecting GC-MS total ion current information of the aquatic volatile sample and constructing a peak area vector is: introducing the aquatic volatile sample into the GC-MS gas chromatography-mass spectrometer to obtain a total ion current, reading each aquatic volatile in the total ion current and recording it as a test object, and reading the peak area of ​​each test object, constructing the peak area of ​​each test object in the sampling point into a vector and recording it as a peak area vector.

[0051] Furthermore, in step S400, the quality attenuation density is calculated based on the peak area vector of each sampling point in the following manner: the time interval formed by all sampling points is defined as a sampling period, the sampling points of a sampling point in the reverse time direction and along the time direction are defined as its traceback point and extension point respectively; the cosine distance between the peak area vector of a sampling point and each extension point thereof is calculated, and the extension point having the maximum cosine distance is defined as the first marking point; the average cosine distance of each extension point is defined as the extension cosine distance; a first distance condition is set as the cosine distance being greater than the extension cosine distance, and a second distance condition is set as the cosine distance of at least one intermediate extension point between the sampling point and the extension point being less than the extension cosine distance; the first marking point that satisfies the first distance condition and the second distance condition is searched for in reverse time order from the sampling point and recorded as the second marking point; the number of traceback points of the sampling point in the reverse time direction that have the same second marking point as the current sampling point is counted and recorded as the reverse filling number, and a reverse filling coefficient Rc.idx is preset. The value of Rc.idx is 0.5. The product of the number of reverse charging and the reverse charging coefficient is rounded upwards to the nearest integer, and the third marker of the sampling point is defined as the Rc.vl-th extended point in the time direction from the second marker. The time interval between the sampling point and the third marker is recorded as the reference domain of the sampling point. The ratio of the cosine distance between the second marker and the third marker is recorded as the reverse charging regression ratio Rc.rvt of the sampling point.

[0052] For any sampling point, the step difference vector is obtained by subtracting the peak area vector of the sampling point from the first sampling point in the reverse time direction. The test objects corresponding to the step difference elements with values ​​greater than zero in the step difference vector are classified as increasing objects. Each increasing object is written into a sequence and recorded as an increasing sequence. Otherwise, it is a loss object. The step difference element refers to any element in the step difference vector. The percentile value of the step difference element of the increasing object in the set of step difference elements corresponding to each same increasing object in the reference domain is recorded as the increasing order value. The cumulative value of the increasing order values ​​corresponding to all increasing objects at the sampling point is the increasing cumulative value Icv. The extension point in the reference domain where the increasing cumulative value is less than the increasing cumulative value is recorded as the adaptation extension point. The quality attenuation density Pdt is calculated by the increasing cumulative value and the back-filling regression ratio:

[0053] ;

[0054] Where i0 is the serial number of the sampling point, Pdt(i0) represents the quality attenuation density of the i0th sampling point, i1 is the cumulative variable, len is the number of adaptive extension points corresponding to the sampling point, exp() is the exponential function with the natural constant e as the base, and Icv i0 and Icv i1 is the cumulative value of the i0th sampling point and the i1th adaptation extension point; diff{} is the Euclidean distance function, which returns the Euclidean distance between two call sequences, Pmks i0 and Pmks i1Respectively represent the augmented reading sequences of the i0th sampling point and the i1th adaptation extension point, and the augmented reading sequence is composed of the step difference elements corresponding to the augmented sequence at the i0th sampling point and the i1th adaptation extension point.

[0055] Furthermore, in step S500, the method of feeding back the circulation steps with quality decay risk to the administrator or management system through the quality decay density is as follows: setting a risk management time period RMT, with an RMT value of 60 minutes; taking any sampling point as the current sampling point, and the RMT period in the reverse time direction of the current sampling point as the current risk management time period; recording the quality decay density of the current sampling point as Pdt, and recording the average value of each quality decay density in the current risk management time period as EPdt; presetting the variable risk identification threshold RPRt, with a value of 1.5; when Pdt≥RPRt×EPdt, it is defined that the current sampling point is at quality decay risk, and the flavor of the aquatic product is deteriorated or lost, and each sampling point at quality decay risk is constructed into a sequence and sent to the administrator client.

[0056] The embodiment of the present invention provides a system for tracking the quality of aquatic products based on lipidome fingerprints, such as Figure 2 The figure shows a structural diagram of the aquaculture product quality tracking system based on lipidome fingerprint of the present invention. The aquaculture product quality tracking system based on lipidome fingerprint of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned embodiment of the aquaculture product quality tracking method based on lipidome fingerprint are implemented.

[0057] The system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to run in the following units of the system:

[0058] Aquatic sampling point collection unit, used to identify the aquaculture flow process and select sampling points in the aquatic flow process;

[0059] Aquatic volatiles extraction unit, used to process aquacultured aquatic products based on HS-SPME at various sampling points to obtain aquatic volatile samples;

[0060] Peak area vector construction unit, used to collect GC-MS total ion current information of aquatic volatile samples and construct peak area vectors;

[0061] An attenuation density calculation unit, used to calculate the quality attenuation density based on the peak area vector of each sampling point;

[0062] The quality attenuation feedback unit is used to feed back the circulation steps where the quality attenuation risk occurs to the administrator or management system through the quality attenuation density.

[0063] The aquaculture product quality tracking system based on lipidomic fingerprints can be run on computing devices such as desktop computers, laptop computers, PDAs, and cloud servers. The aquaculture product quality tracking system based on lipidomic fingerprints can be operated on systems that include, but are not limited to, processors and memories. Those skilled in the art will appreciate that the examples described are merely examples of aquaculture product quality tracking systems based on lipidomic fingerprints and do not constitute a limitation on aquaculture product quality tracking systems based on lipidomic fingerprints. The system can include more or fewer components than the examples, or a combination of certain components, or different components. For example, the aquaculture product quality tracking system based on lipidomic fingerprints can also include input and output devices, network access devices, buses, and the like.

[0064] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the operation system of the aquaculture product quality tracking system based on lipidome fingerprint, and utilizes various interfaces and lines to connect various parts of the entire operation system of the aquaculture product quality tracking system based on lipidome fingerprint.

[0065] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the lipidome fingerprint-based aquaculture product quality tracking system by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback); the data storage area may store data generated based on the use of the mobile phone (such as audio data and a phone book). Furthermore, the memory may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0066] Although the present invention has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively encompass the intended scope of the present invention. In addition, the above description of the present invention is based on foreseeable embodiments for the purpose of providing a useful description, and those insubstantial modifications of the present invention that are not currently foreseen may still represent equivalent modifications of the present invention.

Claims

1. A method for tracking the quality of aquatic products based on lipidome fingerprints, characterized in that: The method comprises the following steps: S100, identifying the aquaculture flow process and selecting sampling points in the aquaculture flow process; S200, aquatic volatile samples were obtained by HS-SPME processing of aquaculture products at various sampling points; S300, collecting GC-MS total ion current information of aquatic volatile samples and constructing peak area vectors; S400, quality attenuation density calculated based on the peak area vector of each sampling point; S500: Feedback the circulation steps that may cause quality degradation risk to the administrator or management system through the quality degradation density; The time interval formed by all sampling points is the sampling period. The sampling points of a sampling point in the reverse time direction and along the time direction are defined as its traceback point and extension point respectively. The cosine distance of the peak area vector between a sampling point and its extension points is calculated. The extension point with the maximum cosine distance is the first marker point. The average cosine distance of each extension point is the extension cosine distance. The first distance condition is set as the cosine distance greater than the extension cosine distance, and the second distance condition is that there is at least one intermediate extension point between the sampling point and the extension point with a cosine distance less than the extension cosine distance. The first marker point that meets the first and second distance conditions is searched in reverse time order from the sampling point and recorded as the second marker point. The number of traceback points in the reverse time direction of the sampling point that have the same second marker point as the current sampling point is counted and recorded as the reverse filling number. The reverse filling coefficient Rc.idx is preset. Rc.idx∈[0.3,1]; the product of the number of inverse charging and the inverse charging coefficient is rounded upwards to the inverse charging value Rc.vl. The third marker of the sampling point is defined as the Rc.vl-th extended point in the time direction of the second marker. The time interval between the sampling point and the third marker is recorded as the reference domain of the sampling point. The ratio of the cosine distance between the second marker and the third marker is recorded as the inverse charging regression ratio Rc.rvt of the sampling point. For any sampling point, the peak area vector of the sampling point is subtracted from the peak area vector of the first sampling point in the reverse time direction to obtain the step difference vector. The test objects corresponding to the step difference elements with values ​​greater than zero in the step difference vector are classified as enhancement objects, otherwise they are loss objects. Each enhancement object is written into a sequence recorded as an enhancement sequence. The step difference element refers to any element in the step difference vector. The percentile value of the step difference element of the enhancement object in the set of step difference elements corresponding to the same enhancement object in the reference domain is recorded as the enhancement order value. The cumulative value of the enhancement order values ​​corresponding to all enhancement objects at the sampling point is the enhancement cumulative value Icv. The extension point in the reference domain where the enhancement cumulative value is less than the enhancement cumulative value corresponding to the current sampling point is recorded as the adaptation extension point. The quality attenuation density Pdt is calculated using the enhancement cumulative value and the back-fill regression ratio: ； Where i0 is the serial number of the sampling point, Pdt(i0) represents the quality attenuation density of the i0th sampling point, i1 is the cumulative variable, len is the number of adaptive extension points corresponding to the sampling point, exp() is the exponential function with the natural constant e as the base, Icv i0 and Icv i1 is the cumulative value of the i0th sampling point and the i1th adaptation extension point; diff{} is the Euclidean distance function, which returns the Euclidean distance between two call sequences, Pmks i0 and Pmks i1 Respectively represent the augmented reading sequences of the i0th sampling point and the i1th adaptation extension point, and the augmented reading sequence is composed of the step difference elements corresponding to the augmented sequence at the i0th sampling point and the i1th adaptation extension point.

2. The method for tracking the quality of aquatic products based on lipidome fingerprint according to claim 1, characterized in that: In step S100, the aquaculture circulation process is identified, and the method for selecting sampling points in the aquaculture circulation process is: the aquaculture circulation process includes environmental detection equipment, and the environmental detection equipment is composed of a humidity sensor and a temperature sensor; if the temperature value measured in real time by the temperature sensor is not in the temperature range of 0~4°C, or if the humidity value measured in real time by the humidity sensor is not in the humidity range of 85~95%, it is defined that a temperature and humidity abnormality occurs at that moment; the time interval GTT of the preset collection point is 10-30 minutes, if no temperature and humidity abnormality occurs within the GTT interval, the end moment of the GTT interval is used as the sampling point, otherwise the time point when the temperature and humidity abnormality occurs is recorded as the sampling point.

3. The method for tracking the quality of aquaculture products based on lipidome fingerprint according to claim 1, characterized in that: In step S200, the method for obtaining aquatic volatile samples by HS-SPME processing of aquaculture products at various sampling points is as follows: Aquacultured aquatic products were selected at the sampling point for sampling to obtain original samples; aquatic volatile samples were collected from the original samples using the HS-SPME solid phase microextraction method; the extraction temperature in the application of the HS-SPME technology was set between 30-60°C, and the extraction time was set between 10-30 minutes.

4. The method for tracking the quality of aquaculture products based on lipidome fingerprint according to claim 1, characterized in that: In step S300, the method for collecting GC-MS total ion current information of the aquatic volatile sample and constructing a peak area vector is: introducing the aquatic volatile sample into the GC-MS gas chromatography-mass spectrometer to obtain a total ion current, reading each aquatic volatile in the total ion current and recording it as a test object, and reading the peak area of ​​each test object, constructing the peak area of ​​each test object in the sampling point into a vector and recording it as a peak area vector.

5. The method for tracking the quality of aquaculture products based on lipidome fingerprint according to claim 1, characterized in that: In step S500, the method for using the quality decay density to report the circulation step of the quality decay risk to the administrator or management system is as follows: a risk management time period RMT is set, RMT∈[60,120] minutes; any sampling point is used as the current sampling point, and the RMT period in the reverse time direction of the current sampling point is used as the current risk management time period; the quality decay density of the current sampling point is recorded as Pdt, and the average value of the quality decay densities in the current risk management time period is recorded as EPdt; The variable risk identification threshold RPRt is preset, and its value range is RPRt∈[1.1,2); when Pdt≥RPRt×EPdt, it is defined that the current sampling point is at risk of quality degradation, and the flavor of the aquatic product is deteriorating or lost. The sampling points at risk of quality degradation are constructed into a sequence and sent to the administrator client.

6. Aquaculture product quality tracking system based on lipidome fingerprint, characterized by: The aquaculture product quality tracking system based on lipidome fingerprint includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the aquaculture product quality tracking method based on lipidome fingerprint according to any one of claims 1 to 5 are implemented. The aquaculture product quality tracking system based on lipidome fingerprint runs on a desktop computer, a laptop computer, a PDA, or a computing device in a cloud data center.

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