A feature recognition method and system for volatile aroma substances during fruit and vegetable storage based on multi-sensor fusion

Abstract

The present invention relates to the technical field of food analysis, and discloses a method and system for identifying the characteristics of volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion. The method comprises measuring volatile organic compounds and physiological indicators of fruits and vegetables, and recording environmental data at the same time; using a correction formula to adjust the measured values, taking into account temperature, humidity, light and their interaction; and preliminarily judging the maturity stage of fruits and vegetables by the ratio and concentration of volatile organic compounds. The preliminary judgment is verified or adjusted by physiological indicators; and continuous measurement and application of a time series analysis model to predict the future maturity development trend of fruits and vegetables. The present invention not only improves the accuracy of judging the maturity of fruits and vegetables, but also provides a scientific basis for the storage and processing of fruits and vegetables.

Description

Technical Field

[0001] The present invention relates to the technical field of food analysis, and in particular to a method and system for identifying characteristics of volatile aroma substances in fruits and vegetables during storage based on multi-sensor fusion. Background Art

[0002] In the field of modern food analysis technology, it is of great significance to accurately identify the aroma characteristics of fruits and vegetables during storage. Traditional aroma detection methods mainly rely on manual sensory evaluation, which is not only time-consuming and labor-intensive, but also highly subjective, making it difficult to achieve objective and accurate quantitative analysis.

[0003] With the development of science and technology, electronic nose technology has gradually been applied to the detection of fruit and vegetable aroma, but a single sensor is often difficult to fully capture the characteristics of complex aroma components, so multi-sensor fusion technology came into being. Multi-sensor fusion technology can more comprehensively analyze and identify aroma components by integrating information from multiple sensors, thereby improving the accuracy and reliability of detection. Summary of the invention

[0004] In view of this, the present invention proposes a characteristic recognition method and system for volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion, aiming to overcome the shortcomings of the existing technology and provide a technical solution that can more accurately and objectively identify and analyze the aroma characteristics of fruits and vegetables during the storage period.

[0005] The present invention proposes a feature recognition method for volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion, comprising:

[0006] Determine the volatile organic compounds and physiological indicators of fruits and vegetables, determine environmental data, and correct the measured values ​​of the volatile organic compounds and physiological indicators according to the environmental data; the environmental data includes temperature, humidity and light intensity; the calculation formula for correction is:

[0007]

[0008] Wherein, Xc represents the value of volatile organic compounds or physiological indicators after correction; Xm represents the value of volatile organic compounds or physiological indicators of fruits and vegetables; Tm represents ambient temperature; Tr represents reference temperature; RHm represents ambient relative humidity; RHr represents reference humidity; Tm represents ambient light intensity; Ir represents reference light intensity; α, β, γ represent the influence coefficients of temperature, humidity and light on the measured value respectively; λ represents the influence coefficient of the interaction between temperature and humidity on the measured value;

[0009] Preliminarily judging the maturity stage of fruits and vegetables according to the relative proportions and concentrations of the calibrated volatile organic compounds; wherein the volatile organic compounds include aldehydes, esters, alcohols, terpenes, ketones, acids and sulfides; and the maturity stages include unripe stage, mature stage, overripe stage and spoilage stage;

[0010] Verifying and / or adjusting the preliminary judgment results according to the corrected physiological indicators; wherein the physiological indicators include sugar content, acidity, pectin content, ethylene production and bioactive substance content;

[0011] The volatile organic compounds and physiological indicators are continuously measured, and a time series analysis model is applied to predict the maturity trend of fruits and vegetables in a future time period.

[0012] Preferably, the volatile organic compounds of fruits and vegetables are measured, and the maturity stage of the fruits and vegetables is preliminarily determined according to the relative proportions and concentrations of the several volatile organic compounds, including:

[0013] The aldehydes include acetaldehyde and 2-hexenal;

[0014] The alcohols include hexanol, ethanol and 2-propanol;

[0015] The ketones include 6-methyl-5-heptene-2-one, γ-decalactone and β-ionone;

[0016] The esters include ethyl acetate and acetyl acetate;

[0017] The acids include acetic acid and butyric acid;

[0018] The terpenes include limonene and β-pinene;

[0019] The sulfides include dimethyl disulfide.

[0020] Preferably, when determining the volatile organic compounds of fruits and vegetables and preliminarily determining the maturity stage of the fruits and vegetables based on the relative proportions and concentrations of the several volatile organic compounds, the method further includes:

[0021] The judgment parameters of the immature stage include: the aldehydes include acetaldehyde and 2-hexenal, the alcohols include hexanol, and the ketones include 6-methyl-5-heptene-2-one;

[0022] The acetaldehyde / 2-hexenal ratio threshold, the hexanol / acetaldehyde ratio threshold, the hexanol / acetaldehyde difference threshold, and the 6-methyl-5-heptene-2-one threshold are preset and are denoted as Z1, Z2, Z3, and Z4, respectively;

[0023] Calculate the concentration ratio of acetaldehyde and 2-hexenal, denoted as Zx1;

[0024] Calculate the concentration ratio of hexanol and acetaldehyde, denoted as Zx2;

[0025] The concentration of 6-methyl-5-hepten-2-one was measured and recorded as Zx3;

[0026] When the following conditions are met at the same time, the initial judgment result of the maturity stage of fruits and vegetables is the immature stage:

[0027] A1) Zx1>Z1;

[0028] A2) Zx2-Z2>Z3;

[0029] A3)Zx3>Z4.

[0030] Preferably, when determining the volatile organic compounds of fruits and vegetables and preliminarily determining the maturity stage of the fruits and vegetables based on the relative proportions and concentrations of the several volatile organic compounds, the method further includes:

[0031] The judgment parameters of the maturity stage include: the esters include ethyl acetate and acetyl acetate, the terpenes include limonene and β-pinene, and the ketones include γ-decanolide and β-ionone;

[0032] The ethyl acetate / limonene ratio threshold, the γ-decalactone / β-ionone ratio threshold, the β-ionone / limonene ratio threshold, the ethyl acetate absolute concentration threshold, and the γ-decalactone absolute concentration threshold are preset and denoted as P1, P2, P3, P4, and P5, respectively;

[0033] Calculate the concentration ratio of ethyl acetate and limonene, denoted as Px1;

[0034] Calculate the concentration ratio of limonene and β-ionone, denoted as Px2;

[0035] Calculate the concentration ratio of γ-decanolide and β-ionone, denoted as Px3;

[0036] The concentration of ethyl acetate was determined and recorded as Px4;

[0037] The concentration of γ-decanolide was measured and recorded as Px5;

[0038] When the following conditions are met at the same time, the preliminary judgment result of the maturity stage of fruits and vegetables is the mature stage:

[0039] B1)Px1>P1;

[0040] B2) Px2>P2;

[0041] B3) Px3 ≥ P3;

[0042] B4) Px4>P4;

[0043] B5)Px5>P5.

[0044] Preferably, when determining the volatile organic compounds of fruits and vegetables and preliminarily determining the maturity stage of the fruits and vegetables based on the relative proportions and concentrations of the several volatile organic compounds, the method further includes:

[0045] The judgment parameters of the over-ripe stage include: ethanol and 2-propanol, the esters include ethyl acetate and acetyl acetate, the acids include acetic acid and butyric acid, and the sulfides include dimethyl disulfide;

[0046] The acetic acid / butyric acid ratio threshold, the dimethyl disulfide concentration threshold, the acetic acid / butyric acid ratio threshold, the dimethyl disulfide absolute concentration threshold, and the ethyl acetate absolute concentration threshold are preset and denoted as Q1, Q2, Q3, Q4, and Q5, respectively;

[0047] Calculate the concentration ratio of ethanol and 2-propanol, denoted as Qx1;

[0048] Calculate the concentration ratio of ethyl acetate to acetic acid, denoted as Qx2;

[0049] Calculate the concentration ratio of acetic acid to butyric acid, denoted as Qx3;

[0050] Determine the concentration of dimethyl disulfide, denoted as Qx4;

[0051] Determine the concentration of ethyl acetate and record it as Qx5;

[0052] When the following conditions are met at the same time, the initial judgment result of the maturity stage of fruits and vegetables is the over-mature stage:

[0053] C1)Qx1>Q1;

[0054] C2)Qx2>Q2;

[0055] C3)Qx3≥Q3;

[0056] C4)Qx4>Q4;

[0057] C5)Qx5>Q5.

[0058] Preferably, the physiological indicators of fruits and vegetables are measured, and the preliminary determination result is verified and adjusted according to the physiological indicators, including:

[0059] When the preliminary judgment result is the immature stage, the sugar, acidity and pectin content of fruits and vegetables are measured and recorded as bx1, ax1 and px respectively;

[0060] The first sugar threshold range is preset and is denoted as [Br ix min , Brix max ]; pre-set the first pH threshold range, denoted as [Aci dmin , Acid max ]; pre-set the pectin content threshold range, denoted as [Pect min , Pect max ];

[0061] The maturity stage of fruits and vegetables is confirmed to be immature when the following conditions are met:

[0062] D1)bx<Bx min ;

[0063] D2)ax<Acid min ;

[0064] D3) Pect min ≤px≤Pect max ;

[0065] Otherwise, adjust the maturity stage of fruits and vegetables to the mature stage.

[0066] Preferably, when the physiological indicators of fruits and vegetables are measured and the preliminary determination result is verified and adjusted according to the physiological indicators, the method further includes:

[0067] When the preliminary judgment result is the mature stage, the sugar content, acidity and ethylene production of fruits and vegetables are measured and recorded as bx2, ax2 and eth respectively;

[0068] The second sugar threshold range is preset and is denoted as [Br ix min2 , Brix max2 ]; pre-set the second pH threshold range, denoted as [Ac id min2 , Acid max2 ]; the ethylene production threshold range is pre-set, denoted as [Eth min , Eth max ];

[0069] The maturity stage of fruits and vegetables is confirmed to be mature when the following conditions are met at the same time:

[0070] E1)Brix min2 ≤Bx2≤Br ix max2 ;

[0071] E2)Ax2≥Acid min2 And Ax2≤Acid max2 ;

[0072] E3) Eth min ≤Eth≤Eth max ;

[0073] When any one of E1, E2 and E3 does not meet the conditions, and the other two meet the conditions, the pectin content of fruits and vegetables is measured, and the pectin content is recorded as px2;

[0074] When Pect max When px2 or less, the maturity stage of fruits and vegetables is adjusted to the over-mature stage; otherwise, the maturity stage of fruits and vegetables remains at the mature stage;

[0075] Among them, Acid min2 >Ac id min ;Acid max2 >Acid max ;Brix min2 >Brix min ;Brix max2 >Brix max .

[0076] Preferably, when the physiological indicators of fruits and vegetables are measured and the preliminary determination result is verified and adjusted according to the physiological indicators, the method further includes:

[0077] When the preliminary judgment result is the over-mature stage, the sugar content, acidity, ethylene production and microbial content of fruits and vegetables are measured and recorded as bx3, ax3, eth and;

[0078] The third sugar threshold range is preset and is denoted as [Br ix min3 , Brix max3 ]; pre-set the third pH threshold range, denoted as [Acid min3 , Acid max3 ]; the ethylene production threshold range is pre-set, denoted as [Eth min3 , Eth max3 ]; and the microbial content threshold range, denoted as [Micro min ,Micro max ];

[0079] When the following conditions are met at the same time, the maturity stage of fruits and vegetables is confirmed to be over-mature:

[0080] F1)Brix min3 ≤Bx3≤Br ix max3 ;

[0081] F2)Acid min3 ≤Ax3≤Acid max3 ;

[0082] F3) Eth min3 ≤Eth≤Eth max3 ;

[0083] If any of F1, F2 and F3 does not meet the conditions, and the other conditions are met, the microbial content of fruits and vegetables is measured and recorded as mi cro;

[0084] When Micro max ≤micro, the maturity stage of fruits and vegetables is adjusted to the corruption stage;

[0085] When Micro min <micro<M icro max , the judgment result of the maturity stage of fruits and vegetables remains at the over-mature stage;

[0086] When micro≤Micro min , the judgment results of the maturity stage of fruits and vegetables are adjusted to an abnormal state.

[0087] Preferably, the volatile organic compounds and physiological indicators are continuously measured, and a time series analysis model is applied to predict the maturity trend of fruits and vegetables in a future time period, including:

[0088] Collecting historical data of volatile organic compounds and physiological indicators of fruits and vegetables, including ethanol, 2-propanol, ethyl acetate, acetic acid, butyric acid, dimethyl disulfide, sugar, acidity, pectin content and ethylene production;

[0089] A time series analysis model is established based on the historical data, the model is trained using the historical data, and the model parameters are optimized by minimizing the prediction error; after the training is completed, the time series analysis model is used to predict the volatile organic compounds and physiological indicators of fruits and vegetables in the future time period.

[0090] Compared with the prior art, the present invention has the following beneficial effects:

[0091] 1. Improved accuracy of fruit and vegetable maturity judgment: By comprehensively considering multiple volatile organic compounds and physiological indicators, the present invention can more comprehensively evaluate the maturity of fruits and vegetables, thereby reducing the error that may be caused by single indicator judgment.

[0092] 2. Dynamic monitoring of the maturity of fruits and vegetables is achieved. By continuously measuring volatile organic compounds and physiological indicators and applying a time series analysis model for prediction, the present invention can track the maturity process of fruits and vegetables in real time, providing a scientific basis for the harvesting and storage of fruits and vegetables.

[0093] 3. Extend the shelf life of fruits and vegetables. By accurately judging the maturity stage of fruits and vegetables, the shelf life of fruits and vegetables in the market can be extended.

[0094] 4. Improved efficiency of fruit and vegetable quality management. The present invention reduces the workload of manual testing and improves the efficiency and accuracy of fruit and vegetable quality management through an automated and intelligent analysis method.

[0095] 5. Provides guidance for fruit and vegetable processing. By accurately judging the maturity stage of fruits and vegetables, it can provide fruit and vegetable processing companies with more accurate raw material maturity information, thereby optimizing the processing technology and improving the quality and consistency of the product.

[0096] In summary, the present invention not only improves the accuracy of judging the maturity of fruits and vegetables, but also provides a scientific basis for the storage, sale and processing of fruits and vegetables, and has significant economic and social benefits.

[0097] On the other hand, the present application also provides a feature recognition system for volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion, including a data acquisition module, a data processing module, a feature extraction module and a classification module;

[0098] Wherein, the data acquisition module includes a plurality of sensors, and the plurality of sensors are configured to collect volatile organic compounds and / or physiological indicators;

[0099] The data processing module is configured to pre-process the collected raw data of volatile organic compounds and / or physiological indicators;

[0100] The feature extraction module is configured to extract features related to the maturity stage of fruits and vegetables based on the preprocessed data;

[0101] The classification module is configured to analyze the extracted features using a machine learning algorithm to identify the current maturity stage of fruits and vegetables, and to judge and adjust the maturity stage according to pre-set thresholds and rules.

[0102] It is understandable that the above-mentioned method and system for characteristic recognition of volatile aroma substances during storage of fruits and vegetables based on multi-sensor fusion have the same beneficial effects, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0104] Figure 1 This is a flow chart of the feature recognition method of volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion;

[0105] Figure 2This is the functional block diagram of the feature recognition system of volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion. DETAILED DESCRIPTION

[0106] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0107] See also Figure 1 This embodiment provides a method for identifying the characteristics of volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion, including:

[0108] Determine the volatile organic compounds and physiological indicators of fruits and vegetables, determine environmental data, and correct the measured values ​​of the volatile organic compounds and physiological indicators according to the environmental data; the environmental data includes temperature, humidity and light intensity; the calculation formula for correction is:

[0109]

[0110] Wherein, Xc represents the value of volatile organic compounds or physiological indicators after correction; Xm represents the value of volatile organic compounds or physiological indicators of fruits and vegetables; Tm represents ambient temperature; Tr represents reference temperature; RHm represents ambient relative humidity; RHr represents reference humidity; Tm represents ambient light intensity; Ir represents reference light intensity; α, β, γ represent the influence coefficients of temperature, humidity and light on the measured value respectively; λ represents the influence coefficient of the interaction between temperature and humidity on the measured value;

[0111] Preliminarily judging the maturity stage of fruits and vegetables according to the relative proportions and concentrations of the calibrated volatile organic compounds; wherein the volatile organic compounds include aldehydes, esters, alcohols, terpenes, ketones, acids and sulfides; and the maturity stages include unripe stage, mature stage, overripe stage and spoilage stage;

[0112] Verifying and / or adjusting the preliminary judgment results according to the corrected physiological indicators; wherein the physiological indicators include sugar content, acidity, pectin content, ethylene production and bioactive substance content;

[0113] The volatile organic compounds and physiological indicators are continuously measured, and a time series analysis model is applied to predict the maturity trend of fruits and vegetables in a future time period.

[0114] It is understandable that this embodiment proposes an innovative method, which is based on multi-sensor fusion technology and aims to identify and analyze the aroma characteristics released by fruits and vegetables during storage. Specifically, the method involves the following steps:

[0115] First, a series of tests are performed on the fruit and vegetable samples, including the measurement of volatile organic compounds (VOCs) released by the fruits and vegetables and related physiological indicators. These physiological indicators may include sugar content, acidity, pectin content, ethylene production, and bioactive substance content. Through these measurements, detailed information about the current state of the fruits and vegetables can be obtained.

[0116] Secondly, in addition to measuring the fruit and vegetable samples, it is also necessary to collect data related to the growth environment of the fruits and vegetables. These environmental data include temperature, humidity and light intensity. These environmental factors have an important impact on the quality of fruits and vegetables during storage and the formation of aroma characteristics.

[0117] Next, based on the collected environmental data, the previously measured values ​​of volatile organic compounds and physiological indicators are corrected. The purpose of the correction is to eliminate the potential impact of environmental factors on the measurement results, thereby obtaining more accurate and reliable data.

[0118] The corrected data can be used to make a preliminary judgment on the maturity stage of fruits and vegetables. The maturity stage of fruits and vegetables can usually be divided into immature stage, mature stage and over-mature stage. In order to make this judgment, it is necessary to analyze the relative proportions and concentrations of several volatile organic compounds. These volatile organic compounds may include aldehydes, esters, alcohols, terpenes, ketones, acids and sulfides.

[0119] After the initial judgment results, further verification and / or adjustment are required. This step is completed through the corrected physiological indicators. By comparing the changes in physiological indicators, the maturity stage judgment previously made based on volatile organic compounds can be confirmed or corrected.

[0120] Finally, in order to more fully understand the maturity trend of fruits and vegetables, continuous measurement of volatile organic compounds and physiological indicators is required. By applying the time series analysis model, the maturity trend of fruits and vegetables in the future can be predicted. This prediction has important practical significance for the storage of fruits and vegetables and can help relevant personnel better plan production.

[0121] In summary, this embodiment provides a method that integrates multi-sensor data and environmental factors. Through precise calibration and continuous measurement, it can effectively identify and predict the aroma characteristics of fruits and vegetables during storage, thereby providing a scientific basis for the production and management of fruits and vegetables.

[0122] In some embodiments of the present application, the volatile organic compounds of fruits and vegetables are measured, and the maturity stage of the fruits and vegetables is preliminarily determined according to the relative proportions and concentrations of the volatile organic compounds, including:

[0123] The aldehydes include acetaldehyde and 2-hexenal;

[0124] The alcohols include hexanol, ethanol and 2-propanol;

[0125] The ketones include 6-methyl-5-heptene-2-one, γ-decalactone and β-ionone;

[0126] The esters include ethyl acetate and acetyl acetate;

[0127] The acids include acetic acid and butyric acid;

[0128] The terpenes include limonene and β-pinene;

[0129] The sulfides include dimethyl disulfide.

[0130] Preferably, the sensors for detecting the above substances include gas chromatography-mass spectrometry (GC-MS), infrared spectrometer (IR), proton transfer reaction-mass spectrometer (PTR-MS) and electronic nose (E-nose). These sensors can accurately detect and analyze the types and concentrations of different volatile organic compounds.

[0131] In practical applications, these sensors can be used in combination to improve the accuracy and reliability of detection. For example, GC-MS can provide detailed information on the types and concentrations of compounds, while the electronic nose can quickly detect the overall aroma characteristics of the sample. By fusing and analyzing the data from these sensors, we can have a more comprehensive understanding of the characteristics of volatile aroma substances during the storage period of fruits and vegetables.

[0132] In some embodiments of the present application, when the volatile organic compounds of fruits and vegetables are measured and the maturity stage of the fruits and vegetables is preliminarily determined based on the relative proportions and concentrations of the volatile organic compounds, the method further includes:

[0133] The judgment parameters of the immature stage include: the aldehydes include acetaldehyde and 2-hexenal, the alcohols include hexanol, and the ketones include 6-methyl-5-heptene-2-one;

[0134] The acetaldehyde / 2-hexenal ratio threshold, the hexanol / acetaldehyde ratio threshold, the hexanol / acetaldehyde difference threshold, and the 6-methyl-5-heptene-2-one threshold are preset and are denoted as Z1, Z2, Z3, and Z4, respectively;

[0135] Calculate the concentration ratio of acetaldehyde and 2-hexenal, denoted as Zx1;

[0136] Calculate the concentration ratio of hexanol and acetaldehyde, denoted as Zx2;

[0137] The concentration of 6-methyl-5-hepten-2-one was measured and recorded as Zx3;

[0138] When the following conditions are met at the same time, the initial judgment result of the maturity stage of fruits and vegetables is the immature stage:

[0139] A1) Zx1>Z1;

[0140] A2) Zx2-Z2>Z3;

[0141] A3)Zx3>Z4.

[0142] In some embodiments of the present application, when the volatile organic compounds of fruits and vegetables are measured and the maturity stage of the fruits and vegetables is preliminarily determined based on the relative proportions and concentrations of the volatile organic compounds, the method further includes:

[0143] The judgment parameters of the maturity stage include: the esters include ethyl acetate and acetyl acetate, the terpenes include limonene and β-pinene, and the ketones include γ-decanolide and β-ionone;

[0144] The ethyl acetate / limonene ratio threshold, the γ-decalactone / β-ionone ratio threshold, the β-ionone / limonene ratio threshold, the ethyl acetate absolute concentration threshold, and the γ-decalactone absolute concentration threshold are preset and denoted as P1, P2, P3, P4, and P5, respectively;

[0145] Calculate the concentration ratio of ethyl acetate and limonene, denoted as Px1;

[0146] Calculate the concentration ratio of limonene and β-ionone, denoted as Px2;

[0147] Calculate the concentration ratio of γ-decanolide and β-ionone, denoted as Px3;

[0148] The concentration of ethyl acetate was determined and recorded as Px4;

[0149] The concentration of γ-decanolide was measured and recorded as Px5;

[0150] When the following conditions are met at the same time, the preliminary judgment result of the maturity stage of fruits and vegetables is the mature stage:

[0151] B1)Px1>P1;

[0152] B2) Px2>P2;

[0153] B3) Px3 ≥ P3;

[0154] B4) Px4>P4;

[0155] B5)Px5>P5.

[0156] In some embodiments of the present application, when the volatile organic compounds of fruits and vegetables are measured and the maturity stage of the fruits and vegetables is preliminarily determined based on the relative proportions and concentrations of the volatile organic compounds, the method further includes:

[0157] The judgment parameters of the over-ripe stage include: ethanol and 2-propanol, the esters include ethyl acetate and acetyl acetate, the acids include acetic acid and butyric acid, and the sulfides include dimethyl disulfide;

[0158] The acetic acid / butyric acid ratio threshold, the dimethyl disulfide concentration threshold, the acetic acid / butyric acid ratio threshold, the dimethyl disulfide absolute concentration threshold, and the ethyl acetate absolute concentration threshold are preset and denoted as Q1, Q2, Q3, Q4, and Q5, respectively;

[0159] Calculate the concentration ratio of ethanol and 2-propanol, denoted as Qx1;

[0160] Calculate the concentration ratio of ethyl acetate to acetic acid, denoted as Qx2;

[0161] Calculate the concentration ratio of acetic acid to butyric acid, denoted as Qx3;

[0162] Determine the concentration of dimethyl disulfide, denoted as Qx4;

[0163] Determine the concentration of ethyl acetate and record it as Qx5;

[0164] When the following conditions are met at the same time, the initial judgment result of the maturity stage of fruits and vegetables is the over-mature stage:

[0165] C1)Qx1>Q1;

[0166] C2)Qx2>Q2;

[0167] C3)Qx3≥Q3;

[0168] C4)Qx4>Q4;

[0169] C5)Qx5>Q5.

[0170] It can be understood that in the above embodiments, accurate identification and evaluation of the growth stages of fruits and vegetables are achieved through comprehensive analysis of volatile organic compounds and physiological indicators. The comprehensive benefits are as follows:

[0171] Accurately identify the growth stage: Through comprehensive analysis of the relative proportion and concentration of volatile organic compounds in fruits and vegetables, it is possible to accurately distinguish the immature, mature and over-mature stages of fruits and vegetables. This precise growth stage identification helps to formulate scientific and reasonable picking and processing strategies, thereby improving the quality and economic value of fruits and vegetables.

[0172] Multi-dimensional evaluation method: When judging the growth stage of fruits and vegetables, not only the concentration and proportion of volatile organic compounds are considered, but also the physiological indicators of fruits and vegetables, such as sugar content, acidity, pectin content, ethylene production and bioactive substance content. This multi-dimensional evaluation method provides more comprehensive growth status information and enhances the reliability and accuracy of judgment.

[0173] Objective and standardized judgment: The growth stage is judged by setting the threshold and ratio range, ensuring the objectivity and standardization of the judgment process. This method reduces the influence of human factors and improves the consistency and repeatability of judgment.

[0174] Improve production efficiency: Accurate growth stage judgment helps producers formulate more scientific picking plans and storage strategies, avoiding losses caused by picking too early or too late, thereby improving production efficiency and economic benefits.

[0175] Strong adaptability: This method is applicable to different types of fruits and vegetables. By adjusting the threshold and ratio, it can adapt to the characteristics of various fruits and vegetables. Its flexible adaptability makes this method have a wide range of application potential.

[0176] Predict growth trends in advance: By continuously measuring volatile organic compounds and physiological indicators through a time series analysis model, the growth trend of fruits and vegetables in the future can be predicted. This predictive ability helps to formulate countermeasures and optimize management strategies in advance.

[0177] Improved quality control: Through in-depth analysis of the over-ripe stage, especially the detection of microbial content, spoilage can be effectively prevented and controlled to ensure the quality and safety of fruits and vegetables.

[0178] In summary, this method achieves accurate identification of the growth stage of fruits and vegetables through multi-sensor fusion, comprehensive data analysis and standardized judgment, bringing significant advantages of scientific management and optimized production. This comprehensive and systematic evaluation method not only improves the processing efficiency of fruits and vegetables, but also enhances the competitiveness of fruits and vegetables in the market.

[0179] In some embodiments of the present application, when the physiological indicators of fruits and vegetables are measured, and the preliminary judgment results are verified and adjusted according to the physiological indicators, the method includes:

[0180] When the preliminary judgment result is the immature stage, the sugar, acidity and pectin content of fruits and vegetables are measured and recorded as bx1, ax1 and px respectively;

[0181] The first sugar threshold range is preset and is denoted as [Br ix min , Brix max ]; pre-set the first pH threshold range, denoted as [Aci d min , Acid max ]; pre-set the pectin content threshold range, denoted as [Pect min , Pect max ];

[0182] The maturity stage of fruits and vegetables is confirmed to be immature when the following conditions are met:

[0183] D1)bx<Bx min ;

[0184] D2)ax<Acid min ;

[0185] D3) Pect min ≤px≤Pect max ;

[0186] Otherwise, adjust the maturity stage of fruits and vegetables to the mature stage.

[0187] In some embodiments of the present application, when the physiological indicators of fruits and vegetables are measured and the preliminary judgment results are verified and adjusted according to the physiological indicators, the following steps are also included:

[0188] When the preliminary judgment result is the mature stage, the sugar content, acidity and ethylene production of fruits and vegetables are measured and recorded as bx2, ax2 and eth respectively;

[0189] The second sugar threshold range is preset and is denoted as [Br ix min2 , Brix max2 ]; pre-set the second pH threshold range, denoted as [Ac id min2 , Acid max2 ]; the ethylene production threshold range is pre-set, denoted as [Eth min , Eth max ];

[0190] The maturity stage of fruits and vegetables is confirmed to be mature when the following conditions are met at the same time:

[0191] E1)Brix min2 ≤Bx2≤Br ix max2 ;

[0192] E2)Ax2≥Acidmin2 And Ax2≤Acid max2 ;

[0193] E3) Eth min ≤Eth≤Eth max ;

[0194] When any one of E1, E2 and E3 does not meet the conditions, and the other two meet the conditions, the pectin content of fruits and vegetables is measured, and the pectin content is recorded as px2;

[0195] When Pect max When px2 or less, the maturity stage of fruits and vegetables is adjusted to the over-mature stage; otherwise, the maturity stage of fruits and vegetables remains at the mature stage;

[0196] Among them, Acid min2 >Ac id min ;Acid max2 >Acid max ;Brix min2 >Brix min ;Brix max2 >Brix max .

[0197] In some embodiments of the present application, when the physiological indicators of fruits and vegetables are measured and the preliminary judgment results are verified and adjusted according to the physiological indicators, the following steps are also included:

[0198] When the preliminary judgment result is the over-mature stage, the sugar content, acidity, ethylene production and microbial content of fruits and vegetables are measured and recorded as bx3, ax3, eth and;

[0199] The third sugar threshold range is preset and is denoted as [Br ix min3 , Brix max3 ]; pre-set the third pH threshold range, denoted as [Acid min3 , Acid max3 ]; the ethylene production threshold range is pre-set, denoted as [Eth min3 , Eth max3 ]; and the microbial content threshold range, denoted as [Micro min ,Micro max ];

[0200] When the following conditions are met at the same time, the maturity stage of fruits and vegetables is confirmed to be over-mature:

[0201] F1)Brix min3 ≤Bx3≤Br ix max3 ;

[0202] F2)Acid min3 ≤Ax3≤Acid max3 ;

[0203] F3) Eth min3 ≤Eth≤Eth max3 ;

[0204] If any of F1, F2 and F3 does not meet the conditions, and the other conditions are met, the microbial content of fruits and vegetables is measured and recorded as mi cro;

[0205] When Micro max ≤micro, the maturity stage of fruits and vegetables is adjusted to the corruption stage;

[0206] When Micro min <micro<M icro max , the judgment result of the maturity stage of fruits and vegetables remains at the over-mature stage;

[0207] When micro≤Micro min , the judgment results of the maturity stage of fruits and vegetables are adjusted to an abnormal state.

[0208] It is understandable that in the above embodiments, by accurately identifying and comprehensively evaluating the growth stages of fruits and vegetables, effective supervision and control of the quality of fruits and vegetables is achieved, thereby improving production efficiency. Specifically:

[0209] Multi-level verification: Based on the initial judgment, this technology further verifies and adjusts, and combines the measurement of physiological indicators to ensure the accuracy of the growth stage of fruits and vegetables. This multi-level verification mechanism effectively reduces the misjudgment rate and enhances the reliability of the system.

[0210] Strong adaptability: The system makes judgments based on the range and thresholds of physiological indicators at different growth stages, thus adapting to the characteristics of various fruits and vegetables. The adaptability of this design enables the system to be widely used in many types of fruits and vegetables.

[0211] High flexibility: In the judgment of the maturity stage, if the initial result does not fully meet the standard, the system will make adjustments by further measuring the pectin content. This flexibility helps to cope with possible changes in the ripening process of fruits and vegetables and improves the accuracy of judgment.

[0212] Detailed stage distinction: In judging the over-ripe stage, the system not only considers sugar, acidity and ethylene production, but also introduces the determination of microbial content. This detailed stage distinction helps to detect and deal with corruption early and ensure the quality of fruits and vegetables.

[0213] Improve quality control: By monitoring the microbial content of fruits and vegetables in the over-ripe stage, the spoilage problem is effectively controlled and the quality and safety of fruits and vegetables are maintained. This control capability has significantly improved the market competitiveness of fruits and vegetables in practical applications.

[0214] Reduce waste: Accurate growth stage judgment helps producers make scientific picking and processing decisions, thereby reducing waste caused by misjudgment and improving production efficiency.

[0215] Scientific basis provided: The comprehensive analysis of volatile organic compounds and physiological indicators provides a scientific basis to support the rational handling and storage of fruits and vegetables at different growth stages. This method provides effective data support for the handling, processing and marketing of fruits and vegetables.

[0216] Improve production management: By predicting the growth trend of fruits and vegetables in the future, measures can be taken in advance to optimize production management, reduce potential losses, and improve the overall efficiency of the production process.

[0217] In some embodiments of the present application, the volatile organic compounds and physiological indicators are continuously measured, and a time series analysis model is applied to predict the maturity trend of fruits and vegetables in a future time period, including:

[0218] Collecting historical data of volatile organic compounds and physiological indicators of fruits and vegetables, including ethanol, 2-propanol, ethyl acetate, acetic acid, butyric acid, dimethyl disulfide, sugar, acidity, pectin content and ethylene production;

[0219] A time series analysis model is established based on the historical data, the model is trained using the historical data, and the model parameters are optimized by minimizing the prediction error; after the training is completed, the time series analysis model is used to predict the volatile organic compounds and physiological indicators of fruits and vegetables in the future time period.

[0220] It is understandable that this embodiment can predict the maturity trend of fruits and vegetables in the future by continuously measuring the volatile organic compounds and physiological indicators of fruits and vegetables, combined with the time series analysis model. Specifically, it is necessary to first collect historical data on volatile organic compounds and physiological indicators of fruits and vegetables. These historical data cover a variety of compounds and indicators, including ethanol, 2-propanol, ethyl acetate, acetic acid, butyric acid, dimethyl disulfide, sugar, acidity, pectin content, and ethylene production.

[0221] After collecting these historical data, the next step is to build a time series analysis model based on these data. The purpose of this model is to accurately predict the changing trends of volatile organic compounds and physiological indicators of fruits and vegetables in the future. In order to achieve this goal, it is necessary to train the model using the collected historical data. During the training process, by constantly adjusting the model parameters, we strive to minimize the prediction error and thus optimize the prediction performance of the model.

[0222] After sufficient training and parameter optimization, the time series analysis model will be able to accurately predict the volatile organic compounds and physiological indicators of fruits and vegetables in the future. In this way, it can provide important reference information for the storage, transportation and sales of fruits and vegetables, thereby helping relevant personnel to better manage and control the quality and maturity of fruits and vegetables.

[0223] Preferably, the basic model of the time series analysis model is the autoregressive integrated moving average model (ARIMA), which has a wide range of applications in the field of time series prediction. The ARIMA model combines autoregressive (AR), differential (I) and moving average (MA) to effectively capture the trend and periodicity characteristics in time series data.

[0224] When building an ARIMA model, we first need to determine the order of the model, namely the order of the autoregressive term (p), the difference order (d), and the order of the moving average term (q). These parameters are usually determined by analyzing the autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs of the time series data. By selecting the appropriate order, we can ensure that the model is neither overfitting nor underfitting, thereby improving the accuracy of the prediction.

[0225] After determining the parameters of the ARIMA model, the next step is to perform diagnostic tests on the model to ensure the suitability of the model. This usually includes residual analysis to check whether the residuals are white noise sequences, that is, there is no autocorrelation between the residuals. If the residual analysis shows that the model is insufficient, it may be necessary to readjust the model parameters or choose another model.

[0226] In order to further improve the accuracy of prediction, the ARI MA model can be combined with other machine learning algorithms, preferably support vector machine (SVM) or random forest (RF). These algorithms can capture nonlinear relationships that are difficult for the ARI MA model to handle, thereby improving the overall prediction performance.

[0227] In conclusion, by combining the continuous measurement data of VOCs and physiological indicators with the optimized time series analysis model, the maturity trend of fruits and vegetables in the future can be effectively predicted.

[0228] See also Figure 2 In another preferred embodiment based on the above embodiment, this embodiment provides a feature recognition system for volatile aroma substances during fruit and vegetable storage based on multi-sensor fusion, including a data acquisition module, a data processing module, a feature extraction module and a classification module;

[0229] Wherein, the data acquisition module includes a plurality of sensors, and the plurality of sensors are configured to collect volatile organic compounds and / or physiological indicators;

[0230] The data processing module is configured to pre-process the collected raw data of volatile organic compounds and / or physiological indicators;

[0231] The feature extraction module is configured to extract features related to the maturity stage of fruits and vegetables based on the preprocessed data;

[0232] The classification module is configured to analyze the extracted features using a machine learning algorithm to identify the current maturity stage of fruits and vegetables, and to judge and adjust the maturity stage according to pre-set thresholds and rules.

[0233] It is understandable that in this embodiment, the sensors in the data acquisition module may include but are not limited to gas chromatography-mass spectrometry (GC-MS), infrared spectrometer, electrochemical sensor, etc. These sensors can detect and analyze the types and concentrations of volatile organic compounds (VOCs) released by fruits and vegetables at different maturity stages, thereby providing rich information for subsequent data processing and feature extraction.

[0234] The data processing module includes data filtering, normalization, and denoising to ensure the accuracy and reliability of the data. The preprocessed data will be sent to the feature extraction module, which uses statistical analysis, principal component analysis (PCA) and / or linear discriminant analysis (LDA) to extract features closely related to the maturity stage of fruits and vegetables from the raw data.

[0235] The machine learning algorithms used by the classification module are support vector machines (SVM), random forests, or neural networks. These algorithms can classify and identify the maturity stages of fruits and vegetables based on the extracted features. In order to improve the accuracy and robustness of the system, the classification module can also use ensemble learning methods to comprehensively analyze the prediction results of multiple classifiers to obtain more reliable judgments.

[0236] In order to further optimize the system performance, this embodiment also provides a dynamic adjustment strategy based on a feedback mechanism. The system will continuously adjust the thresholds and rules based on the comparison between the classification results and the actual maturity stage to adapt to the characteristics of fruits and vegetables of different types and varieties. In addition, the system can also receive feedback information input manually through the user interface to achieve more refined adjustments and optimizations.

[0237] In summary, the feature recognition system for volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion provided in this embodiment can realize accurate recognition and dynamic adjustment of the maturity stage of fruits and vegetables, and provide strong technical support for the production and processing of fruits and vegetables.

[0238] Furthermore, the method for constructing a characteristic recognition system for volatile aroma substances during storage of fruits and vegetables based on multi-sensor fusion of the present invention includes the following steps:

[0239] First, the sensors in the data acquisition module are calibrated and synchronized to ensure that they can accurately collect VOC and physiological indicator data. The calibration process includes testing the sensitivity, response time and stability of each sensor and making corresponding adjustments based on the test results.

[0240] Secondly, establish an efficient data transmission network to ensure that the collected data can be transmitted to the data processing module in real time and stably. The data transmission network can use wired or wireless technology, such as Ethernet, Wi-Fi or Bluetooth. In order to ensure the reliability of data transmission, the network design should consider redundancy and fault tolerance mechanisms.

[0241] Then, in the data processing module, the collected raw data is preprocessed. The preprocessing steps include data filtering, normalization, and denoising to eliminate the impact of noise and outliers on subsequent analysis. The preprocessed data will be converted into a format suitable for feature extraction module processing.

[0242] Next, the feature extraction module will use statistical analysis, principal component analysis (PCA) and / or linear discriminant analysis (LDA) to extract features closely related to the maturity stage of fruits and vegetables from the preprocessed data. These features include but are not limited to the types and concentrations of volatile organic compounds, changes in physiological indicators, etc.

[0243] The classification module will use machine learning algorithms such as support vector machines (SVM), random forests or neural networks to analyze and classify the extracted features. In order to improve the accuracy of classification, the classification module can also use ensemble learning methods to combine the prediction results of multiple classifiers to obtain more reliable judgments.

[0244] Finally, the system will continuously adjust the thresholds and rules based on the comparison between the classification results and the actual maturity stage to adapt to the characteristics of different types and varieties of fruits and vegetables. In addition, the system can also receive feedback information from manual input through the user interface to achieve more refined adjustments and optimization.

[0245] In summary, the construction method of the characteristic recognition system of volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion provided by the present invention can realize accurate recognition and dynamic adjustment of the maturity stage of fruits and vegetables, and provide strong technical support for the production and processing of fruits and vegetables. Through continuous optimization and adjustment, the system can adapt to the characteristics of fruits and vegetables of different types and varieties, and improve the efficiency and quality of fruit and vegetable storage and processing.

[0246] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for identifying the characteristics of volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion, characterized in that: include: Determine the volatile organic compounds and physiological indicators of fruits and vegetables, determine environmental data, and correct the measured values ​​of the volatile organic compounds and physiological indicators according to the environmental data; the environmental data includes temperature, humidity and light intensity; the calculation formula for correction is: ； Wherein, Xc represents the value of volatile organic compounds or physiological indicators after correction; Xm represents the value of volatile organic compounds or physiological indicators of fruits and vegetables; Tm represents ambient temperature; Tr represents reference temperature; RHm represents ambient relative humidity; RHr represents reference humidity; Im represents ambient light intensity; Ir represents reference light intensity; α, β, γ represent the influence coefficients of temperature, humidity and light on the measured value respectively; λ represents the influence coefficient of the interaction between temperature and humidity on the measured value; Preliminarily judging the maturity stage of fruits and vegetables according to the relative proportions and concentrations of the calibrated volatile organic compounds; wherein the volatile organic compounds include aldehydes, esters, alcohols, terpenes, ketones, acids and sulfides; and the maturity stages include unripe stage, mature stage, overripe stage and spoilage stage; Verifying and / or adjusting the preliminary judgment results according to the corrected physiological indicators; wherein the physiological indicators include sugar content, acidity, pectin content, ethylene production and bioactive substance content; Continuously measuring the volatile organic compounds and physiological indicators, and applying a time series analysis model to predict the maturity trend of fruits and vegetables in a future time period; Determining the volatile organic compounds of fruits and vegetables and preliminarily judging the maturity stage of fruits and vegetables based on the relative proportions and concentrations of the several volatile organic compounds includes: The aldehydes include acetaldehyde and 2-hexenal; The alcohols include hexanol, ethanol and 2-propanol; The ketones include 6-methyl-5-heptene-2-one, γ-decalactone and β-ionone; The esters include ethyl acetate and acetyl acetate; The acids include acetic acid and butyric acid; The terpenes include limonene and β-pinene; The sulfides include dimethyl disulfide; When the volatile organic compounds of fruits and vegetables are measured and the maturity stage of the fruits and vegetables is preliminarily determined based on the relative proportions and concentrations of the volatile organic compounds, the method further includes: The judgment parameters of the immature stage include: the aldehydes include acetaldehyde and 2-hexenal, the alcohols include hexanol, and the ketones include 6-methyl-5-heptene-2-one; The acetaldehyde / 2-hexenal ratio threshold, the hexanol / acetaldehyde ratio threshold, the hexanol / acetaldehyde difference threshold, and the 6-methyl-5-heptene-2-one threshold are preset and are denoted as Z1, Z2, Z3, and Z4, respectively; Calculate the concentration ratio of acetaldehyde and 2-hexenal, denoted as Zx1; Calculate the concentration ratio of hexanol and acetaldehyde, denoted as Zx2; The concentration of 6-methyl-5-hepten-2-one was measured and recorded as Zx3; When the following conditions are met at the same time, the initial judgment result of the maturity stage of fruits and vegetables is the immature stage: A1) Zx1>Z1; A2) Zx2-Z2>Z3; A3)Zx3>Z4.

2. The method for identifying the characteristics of volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion according to claim 1 is characterized in that: When the volatile organic compounds of fruits and vegetables are measured and the maturity stage of the fruits and vegetables is preliminarily determined based on the relative proportions and concentrations of the volatile organic compounds, the method further includes: The judgment parameters of the maturity stage include: the esters include ethyl acetate and acetyl acetate, the terpenes include limonene and β-pinene, and the ketones include γ-decanolide and β-ionone; The ethyl acetate / limonene ratio threshold, the γ-decalactone / β-ionone ratio threshold, the β-ionone / limonene ratio threshold, the ethyl acetate absolute concentration threshold, and the γ-decalactone absolute concentration threshold are preset and denoted as P1, P2, P3, P4, and P5, respectively; Calculate the concentration ratio of ethyl acetate and limonene, denoted as Px1; Calculate the concentration ratio of limonene and β-ionone, denoted as Px2; Calculate the concentration ratio of γ-decanolide and β-ionone, denoted as Px3; The concentration of ethyl acetate was determined and recorded as Px4; The concentration of γ-decanolide was measured and recorded as Px5; When the following conditions are met at the same time, the preliminary judgment result of the maturity stage of fruits and vegetables is the mature stage: B1)Px1>P1; B2) Px2>P2; B3) Px3 ≥ P3; B4) Px4>P4; B5)Px5>P5.

3. The method for identifying the characteristics of volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion according to claim 2 is characterized in that: When the volatile organic compounds of fruits and vegetables are measured and the maturity stage of the fruits and vegetables is preliminarily determined based on the relative proportions and concentrations of the volatile organic compounds, the method further includes: The judgment parameters of the over-ripe stage include: ethanol and 2-propanol, the esters include ethyl acetate and acetyl acetate, the acids include acetic acid and butyric acid, and the sulfides include dimethyl disulfide; The acetic acid / butyric acid ratio threshold, the dimethyl disulfide concentration threshold, the acetic acid / butyric acid ratio threshold, the dimethyl disulfide absolute concentration threshold, and the ethyl acetate absolute concentration threshold are preset and denoted as Q1, Q2, Q3, Q4, and Q5, respectively; Calculate the concentration ratio of ethanol and 2-propanol, denoted as Qx1; Calculate the concentration ratio of ethyl acetate to acetic acid, denoted as Qx2; Calculate the concentration ratio of acetic acid to butyric acid, denoted as Qx3; Determine the concentration of dimethyl disulfide, denoted as Qx4; Determine the concentration of ethyl acetate and record it as Qx5; When the following conditions are met at the same time, the initial judgment result of the maturity stage of fruits and vegetables is the over-mature stage: C1)Qx1>Q1; C2)Qx2>Q2; C3)Qx3≥Q3; C4)Qx4>Q4; C5)Qx5>Q5.

4. The method for identifying the characteristics of volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion according to claim 3 is characterized in that: Determining the physiological indicators of fruits and vegetables, and verifying and adjusting the preliminary judgment results according to the physiological indicators, including: When the preliminary judgment result is the immature stage, the sugar, acidity and pectin content of fruits and vegetables are measured and recorded as bx1, ax1 and px respectively; The first sugar threshold range is preset, denoted as [Brix min , Brix max ]; pre-set the first pH threshold range, denoted as [Acid min , Acid max ]; pre-set the pectin content threshold range, denoted as [Pect min , Pect max ]; The maturity stage of fruits and vegetables is confirmed to be immature when the following conditions are met: D1)bx1<Brix min ; <h2 style=";text-align:left;direction:ltr">D2)ax1<Acid<h2 style=";text-align:left;direction:ltr"> min <h2 style=";text-align:left;direction:ltr"> ; D3)Pect min ≤px≤Pect max 4 Otherwise, adjust the maturity stage of fruits and vegetables to the mature stage.

5. The method for identifying the characteristics of volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion according to claim 4 is characterized in that: Determining the physiological indicators of fruits and vegetables, and verifying and adjusting the preliminary judgment results according to the physiological indicators, also includes: When the preliminary judgment result is the mature stage, the sugar content, acidity and ethylene production of fruits and vegetables are measured and recorded as bx2, ax2 and eth respectively; The second sugar threshold range is preset and is denoted as [Brix min2 , Brix max2 ]; pre-set the second pH threshold range, denoted as [Acid min2 , Acid max2 ]; preset the ethylene production threshold range, denoted as [Eth min , Eth max ]; The maturity stage of fruits and vegetables is confirmed to be mature when the following conditions are met at the same time: E1)Brix min2 ≤bx2≤Brix max2 ; E2)Ax2≥Acid min2 and ax2≤Acid max2 ; E3)Eth min ≤eth≤Eth max ; When any one of E1, E2 and E3 does not meet the conditions, and the other two meet the conditions, the pectin content of fruits and vegetables is measured, and the pectin content is recorded as px2; When Pect max When px2 or less, the maturity stage of fruits and vegetables is adjusted to the over-mature stage; otherwise, the maturity stage of fruits and vegetables remains at the mature stage; Among them, Acid min2 >Acid min ;Acid max2 >Acid max ;Brix min2 >Brix min ;Brix max2 >Brix max .

6. The method for identifying the characteristics of volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion according to claim 5, characterized in that: Determining the physiological indicators of fruits and vegetables, and verifying and adjusting the preliminary judgment results according to the physiological indicators, also includes: When the preliminary judgment result is the over-mature stage, the sugar content, acidity, ethylene production and microbial content of fruits and vegetables are measured and recorded as bx3, ax3 and eth1 respectively; The third sugar threshold range is preset, denoted as [Brix min3 , Brix max3 ]; pre-set the third pH threshold range, denoted as [Acid min3 , Acid max3 ]; preset the ethylene production threshold range, denoted as [Eth min3 , Eth max3 ]; and the microbial content threshold range, denoted as [Micro min ,Micro max ]; When the following conditions are met at the same time, the maturity stage of fruits and vegetables is confirmed to be over-mature: F1)Brix min3 ≤bx3≤Brix max3 ; <h2 style=";text-align:left;direction:ltr">F2) Acid<h2 style=";text-align:left;direction:ltr"> min3 <h2 style=";text-align:left;direction:ltr"> ≤ax3≤Acid<h2 style=";text-align:left;direction:ltr"> max3 <h2 style=";text-align:left;direction:ltr"> ; F3)Eth min3 ≤eth1≤Eth max3 ; If any of F1, F2, and F3 does not meet the conditions, and the other conditions are met, the microbial content of fruits and vegetables is measured and recorded as micro; When Micro max ≤micro, the maturity stage of fruits and vegetables is adjusted to the corruption stage; When Micro min <micro<Micro max , the judgment result of the maturity stage of fruits and vegetables remains at the over-mature stage; When micro≤Micro min , the judgment results of the maturity stage of fruits and vegetables are adjusted to an abnormal state.

7. The method for identifying the characteristics of volatile aroma substances during the storage period of fruits and vegetables based on multi-sensor fusion according to claim 6, characterized in that: The volatile organic compounds and physiological indicators are continuously measured, and a time series analysis model is applied to predict the maturity trend of fruits and vegetables in the future time period, including: Collecting historical data of volatile organic compounds and physiological indicators of fruits and vegetables, including ethanol, 2-propanol, ethyl acetate, acetic acid, butyric acid, dimethyl disulfide, sugar, acidity, pectin content and ethylene production; A time series analysis model is established based on the historical data, the model is trained using the historical data, and the model parameters are optimized by minimizing the prediction error; after the training is completed, the time series analysis model is used to predict the volatile organic compounds and physiological indicators of fruits and vegetables in the future time period.

8. A feature recognition system for volatile aroma substances during fruit and vegetable storage based on multi-sensor fusion, used to implement the feature recognition method for volatile aroma substances during fruit and vegetable storage based on multi-sensor fusion according to any one of claims 1 to 7, characterized in that: The characteristic recognition system of volatile aroma substances during the storage period of fruits and vegetables comprises a data acquisition module, a data processing module, a characteristic extraction module and a classification module; Wherein, the data acquisition module includes a plurality of sensors, and the plurality of sensors are configured to collect volatile organic compounds and / or physiological indicators; The data processing module is configured to pre-process the collected raw data of volatile organic compounds and / or physiological indicators; The feature extraction module is configured to extract features related to the maturity stage of fruits and vegetables based on the preprocessed data; The classification module is configured to analyze the extracted features using a machine learning algorithm to identify the current maturity stage of fruits and vegetables, and to judge and adjust the maturity stage according to pre-set thresholds and rules.

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