Strawberry freshness rapid evaluation method and model based on GC-MS, GC-IMS and electronic nose system

Abstract

The invention belongs to the field of agricultural product detection, and particularly relates to a rapid evaluation method for freshness of strawberries in a storage period based on GC-MS, GC-IMS and an electronic nose system, and a quantitative evaluation model is established. The method can comprehensively, quickly, conveniently, accurately and quantitatively evaluate the freshness state of the strawberries, and avoids the defects of subjectivity, inaccuracy and lack of unified standards of traditional sensory evaluation and time consumption, incompleteness and insensitivity of physical and chemical detection. The method has the advantages of high detection sensitivity, simplicity and convenience in operation and wide adaptability. According to the method, GC-MS, GC-IMS and electronic nose technologies are combined, volatile organic compounds are detected and analyzed, key characteristic volatile compounds are screened based on a partial least squares regression (PLSR) method to construct a model, a strawberry freshness prediction model is established, and the strawberry freshness prediction result is obtained. The characteristic volatile organic substances highly related to the freshness change in the strawberry storage period are determined, and the freshness change in the strawberry storage period can be accurately evaluated.

Description

Technical Field

[0001] The present invention belongs to the field of post - harvest detection of agricultural products, and particularly relates to a method for rapidly evaluating the freshness and establishing a model of strawberries during storage based on GC - MS, GC - IMS and electronic nose systems. Background Technique

[0002] Strawberries are soft in texture, high in moisture and sugar content, and prone to spoilage. The freshness quality deteriorates rapidly after harvest. Existing freshness evaluation methods mainly rely on sensory evaluation and physical - chemical index detection. These methods are either highly subjective, lack quantitative data and are inaccurate, or are complex and time - consuming to operate, involving a large number of organic solvent extraction steps and unable to quickly and comprehensively evaluate the freshness of strawberries. The changes in volatile organic compounds during the storage of strawberries are closely related to their freshness. Therefore, establishing a rapid and accurate detection technology system and evaluation model using advanced GC - MS, GC - IMS technologies and electronic nose systems helps to improve the quality monitoring efficiency and accuracy of strawberries during post - harvest storage, transportation and sales. Summary of the Invention

[0003] To solve the problems in the existing technology, the present invention provides a method for rapidly evaluating the freshness of strawberries based on GC - MS, GC - IMS and electronic nose systems. By comprehensively analyzing VOCs, combining sensory evaluation and physical - chemical indexes to construct a PLSR prediction model, establishing a freshness fingerprint map and a freshness index (FI) of strawberries, and realizing efficient and objective freshness quality evaluation. The method includes the following steps:

[0004] 1. Sample Preparation

[0005] Select Hongyan strawberries (Fragaria×ananassa cv. Hongyan) with consistent maturity, and remove fruits with mechanical damage, pests and diseases or uneven sizes. The samples are stored under the conditions of 24 ± 1°C and relative humidity of 80% for 0 - 5 days, and samples are taken every 24 hours for physical - chemical index determination and volatile compound analysis. The samples are quickly frozen with liquid nitrogen and stored at - 80°C for later measurement.

[0006] 2. Extraction and Detection of Volatile Organic Compounds

[0007] (1) GC - MS Detection:

[0008] Weigh 5 g of strawberry powder ground with liquid nitrogen, place it in a 20 - mL sample bottle, add 3 mL of 0.2 mol / L ethylenediaminetetraacetic acid (EDTA) solution and 3 mL of 20% calcium chloride (CaCl2) solution, add 26 μL of 0.04 mg / L 2 - octanol as an internal standard, and seal it.

[0009] Using an Agilent 7890A gas chromatograph equipped with a 5975C mass spectrometer, a DB-WAX chromatographic column (30m × 0.25mm × 0.25μm) was selected. The carrier gas was helium with a flow rate of 1.0 mL / min. Temperature program: starting at 40°C, rising to 100°C at a rate of 3°C / min, and then rising to 245°C at a rate of 5°C / min.

[0010] (2) GC-IMS detection:

[0011] Weigh 2 grams of freeze-dried strawberry powder and place it in a 20.0 mL headspace vial.

[0012] Headspace injection conditions: incubation time 15 min, incubation temperature 50°C, injection needle temperature 85°C, injection volume 500 μL.

[0013] Gas chromatographic conditions: using 99.99% pure nitrogen as the carrier gas, the flow rate program was: maintaining at 2 mL / min for 2 min, rising uniformly to 10 mL / min within 8 min, rising to 100 mL / min within 20 min, rising to 150 mL / min within 25 min, and the total analysis time was 25 min.

[0014] Ion mobility spectrometry (IMS) conditions: drift tube temperature 45°C, drift gas flow rate 150 mL / min.

[0015] By comparing the retention indices (RI) and ion migration times in the GC-IMS library and the NIST database, volatile compounds were preliminarily identified. Each sample was measured in parallel 3 times, and the retention index was calculated by the laboratory analyzer (LAV).

[0016] (3) Electronic nose detection:

[0017] Take out the strawberry sample from -80°C and place it in a container at 24 ± 1°C and relative humidity 85% to equilibrate for 2 h, then seal and equilibrate the headspace volatile compounds for 10 min.

[0018] Use a PEN 3 electronic nose (Win Muster Air-sense Analytics Inc.), equipped with 10 metal oxide sensors. The substances corresponding to the sensors were cleaned with activated carbon-filtered air for 80 s before detection.

[0019] Pump the headspace gas to the sensor chamber at a constant rate of 120 mL / min through a 3 mm Teflon tube, the detection time was 60 s, and the sensor response was recorded as the G / G0 ratio.

[0020] The data was recorded by a computer, the response curve stabilized after 25 s, and the value at 30 s was taken for analysis.

[0021] 4. Data analysis

[0022] The electronic nose data was used to draw radar charts and load analysis charts using Origin 2018 software (attached Figure 1 ), to show the changes in odor characteristics at different storage times.

[0023] The GC-MS data was subjected to principal component analysis (PCA) using SPSS software, and heat map PCA charts and factor loading charts were generated (attached Figure 2 ).

[0024] The GC-IMS data was used to generate two-dimensional spectra and fingerprint spectra through the Reporter Gallery plot and LAV plug-ins (attached Figure 3 ), for rapid screening of key VOCs.

[0025] (2) Freshness index (FI) and PLSR model:

[0026] Based on the contents of key VOCs (such as ethyl hexanoate, 2-heptanone, isohexanol) with different freshness at different storage times, combined with the VIP values of OPLS-DA (VIP>1), the FI formula was constructed.

[0027] The PLSR method was used to construct a freshness prediction model, and 10 key VOCs (including ethyl hexanoate, isohexanol, methyl butyrate, 2-methylbutanol, benzyl alcohol, etc.) were selected as prediction variables. The model formula is: Freshness=(0.1523×A)+(-0.0876×B)+(-0.0954×C)+(0.1347×D)+(-0.0789×E)+(0.1012×F)+(-0.0653×G)+(0.0897×H)+(-0.0456×I)+(0.1124×J). Among them, A to J correspond to key VOCs respectively.

[0028] The present invention has the following beneficial effects:

[0029] 1. Compared with traditional sensory evaluation, the present invention generates objective chemical fingerprint spectra through GC-MS, GC-IMS and electronic nose, and combined with the PLSR model, the repeatability of the FI value is increased by 50%, and the variability is significantly reduced.

[0030] 2. The detection accuracy of using GC-IMS or GC-MS alone is lower than the comprehensive analysis of this method. After combining with the electronic nose, the accuracy of the overall odor characteristic evaluation is improved by about 30%.

[0031] 3. The detection is fast, without complex pretreatment, and is suitable for on-site rapid screening and industrial application.

[0032] 4. The freshness grading and PLSR model provide a basis for optimizing storage technologies (such as low temperature, controlled atmosphere), and extend the shelf life of strawberries by about 2-3 days.

[0033] 5. By screening 10 VOCs with high VIP values, the model has strong predictive ability, providing reliable scientific support for strawberry quality monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0035] Figure 1 : Radar chart of the electronic nose response values of strawberry samples, showing the response changes of sensors to VOCs during the storage period of 0 - 5 days, reflecting the increase of inorganic sulfides and aromatic compounds.

[0036] Figure 2 : Heat map and PCA map of volatile substances determined by GC - MS, revealing the transformation trend of ester volatile substances into acids and ketones during the process from fresh fruits to non - fresh fruits.

[0037] Figure 3 : GC - IMS fingerprint spectra of strawberries with different freshness levels, showing the dynamic changes of VOCs and distinguishing fresh, sub - fresh and non - fresh stages. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The various exemplary embodiments of the present invention will now be described in detail. Unless otherwise specified, the methods in the embodiments are all conventional methods, and unless otherwise specified, the reagents used are all conventional commercially available reagents or reagents prepared by conventional methods. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, characteristics and implementation schemes of the present invention.

[0039] It should be understood that the terms described in the present invention are only for describing specific embodiments and are not used to limit the present invention. Additionally, for the numerical ranges in the present invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any intermediate value within any stated value or stated range, as well as each smaller range between any other stated value or intermediate value within the stated range, is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded from the range.

[0040] Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although this invention only describes preferred methods and materials, any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of this invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In case of conflict with any incorporated document, the content of this specification shall prevail.

[0041] Without departing from the scope or spirit of this invention, various improvements and changes can be made to the specific embodiments of the specification of this invention, which are obvious to those skilled in the art. Other embodiments obtained from the specification of this invention are obvious to those skilled in the art. The specification and examples of this invention are merely exemplary.

[0042] Regarding the use of "comprising", "including", "having", "containing", etc. herein, they are all open-ended terms, meaning including but not limited to.

[0043] Example 1: Determination of Aroma Substances and Freshness Grading during the Change of Strawberry Freshness Based on GC-MS

[0044] Sample Preparation: Fresh 'Hongyan' strawberries were collected, and damaged or pest-diseased fruits were removed, and then stored at 24 ± 1°C and a relative humidity of 80% for 0 - 5 days. Samples were taken daily, quickly frozen in liquid nitrogen, and stored at -80°C.

[0045] Weighed 5 grams of strawberry powder ground in liquid nitrogen, placed it in a 20 mL sample bottle, added 3 mL of 0.2 mol / L ethylenediaminetetraacetic acid (EDTA) solution and 3 mL of 20% calcium chloride (CaCl2) solution, and added 26 microliters of 0.04 mg / L 2-octanol as an internal standard, and sealed it.

[0046] An Agilent 7890A gas chromatograph equipped with a 5975C mass spectrometer was used, and a DB-WAX chromatographic column (30 m × 0.25 mm × 0.25 μm) was selected. The carrier gas was helium, and the flow rate was 1.0 mL / min. Temperature program: Starting at 40°C, rising to 100°C at a rate of 3°C / min, and then rising to 245°C at a rate of 5°C / min.

[0047] 3. Result Analysis

[0048] The GC-MS detection results (Table 1) indicate that the dynamic changes of VOCs during strawberry storage significantly affect freshness. From 0 to 1 day, esters (such as ethyl hexanoate, methyl hexanoate) are the main components, endowing the fruity aroma. The concentration of ethyl hexanoate is 320.64 ng / g, and that of methyl hexanoate is 127.26 ± 34.44 ng / g. The ester content significantly decreases during storage for 2 - 3 days. For example, ethyl hexanoate drops to 116.68 ± 7.55 ng / g (a decrease of 63.6%), while alcohols (such as linalool, nerolidol) and ketones (such as 2-heptanone) increase significantly. Linalool increases from 186.45 ng / g to 9506.46 ng / g (an increase of about 50 times), and 2-heptanone increases from not detected to 1260.9 ± 321.76 ng / g. From 4 to 5 days, acids (such as hexanoic acid) and ketones are dominant. Hexanoic acid increases from 28.92 ± 8.92 ng / g to 2315.14 ± 380.56 ng / g (an increase of 79 times), reflecting the intensification of fatty acid oxidation and microbial metabolism.

[0049] Principal component analysis (PCA, attached Figure 2 , PC1 = 31.6%, PC2 = 18.3%) shows that samples from 0 to 1 day are clustered in the high-ester content area, indicating the fresh fruity aroma characteristics; samples from 2 to 3 days transition to the alcohol and ketone areas, reflecting the decline in quality; samples from 4 to 5 days are clustered in the acid and ketone areas, indicating the accumulation of deteriorated odors. OPLS-DA analysis screened out markers with VIP > 1, including ethyl hexanoate (VIP = 1.52), 2-heptanone (VIP = 1.35), and hexanoic acid (VIP = 1.45). Pearson correlation analysis shows that esters (such as ethyl hexanoate) are positively correlated with freshness (r = 0.87, p < 0.01), while acids and ketones are negatively correlated with freshness (r = -0.83, p < 0.01), which is consistent with the metabolic processes of pectin degradation, sugar consumption, and oxidative stress. The decrease in esters may be related to the enhanced activity of carboxylesterase, while the accumulation of acids and ketones reflects the intensification of fatty acid β-oxidation and microbial metabolism.

[0050] The freshness grading is determined according to the change trend of VOCs: fresh from 0 to 1 day, dominated by esters, FI value 0.20–0.092; sub-fresh from 2 to 3 days, with the increase of alcohols and ketones, FI value 0.057–0.045; not fresh from 4 to 5 days, dominated by acids and ketones, FI value -0.015 to -0.081. These results verify the correspondence between the VOCs fingerprint and freshness grading, providing a reliable chemical basis for rapid evaluation.

[0051] Table 1 Comparison of VOCs Concentrations in Strawberries Detected by GC-MS at Different Times

[0052]

[0053]

[0054] Example 2: Detection and Screening of Characteristic Volatile Markers of Postharvest Strawberries

[0055] 1. Preparation of Strawberry Samples

[0056] Collect fresh 'Benihoppe' strawberries and store them at room temperature for 0 - 5 days. Take samples by freeze-drying with liquid nitrogen every day. Grind the samples into powder after freeze-drying every day. Weigh 2 g of the powder and place it in a 20 mL headspace vial, seal it for later measurement.

[0057] 2. Detection Method

[0058] The samples are subjected to headspace injection and analysis under specific conditions, and GC-MS, GC-IMS and electronic nose systems are used to measure volatile organic compounds respectively to obtain the chemical fingerprint spectra of strawberry samples.

[0059] 3. Result Analysis

[0060] The change of VOCs is the core marker of the deterioration of strawberry freshness. GC-MS detection (Table 1) shows that esters (such as ethyl hexanoate, methyl hexanoate) are the main components from 0 to 1 day, giving fruity aroma; alcohols (such as linalool, isohexanol) and ketones (such as 2-heptanone) increase from 2 to 3 days; acids (such as hexanoic acid) and ketones dominate from 4 to 5 days. The content of hexanoic acid increases from 28.92 ± 8.92 ng / g to 2315.14 ± 380.56 ng / g (an increase of 7905%), and 2-heptanone increases from not detected to 428.69 ± 43.39 ng / g, reflecting the enhanced fatty acid oxidation and microbial metabolism. 53 compounds are detected by GC-IMS (Table 2), and Table 3 shows the change of relative content: the content of ethyl hexanoate decreases from 0.4528% on day 0 to 0.1487% on day 5 (a decrease of 67.2%), the content of methyl hexanoate decreases from 1.44% to 1.37% (a decrease of 4.7%), while the content of 2-heptanone increases from 0.40% to 1.76% (an increase of 340.5%), and the content of isohexanol increases from 0.82% to 3.84% (an increase of 366.3%). These changes indicate the reduction of ester synthesis and the accumulation of alcohols and ketones related to overripeness and microbial metabolism.

[0061] The electronic nose radar chart (attached Figure 1 ) shows that the response value of S7 sensor (sulfide / terpene) increases by 45.3% from day 2 and reaches the peak on day 5, and the response increase of S2 sensor (nitrogen oxide) is 61.4%, reflecting the accumulation of deteriorated odor. The GC-IMS fingerprint spectrum (attached Figure 3)Clearly distinguish the fresh (ester-dominated), transitional (increasing alcohols / ketones), and deteriorated (acid / ketone-dominated) stages. PCA analysis (PC1 = 65.5%, PC2 = 21.3%) showed that samples from 0 to 1 day clustered in the ester region, those from 2 to 3 days transitioned to the alcohol / ketone region, and those from 4 to 5 days clustered in the acid / ketone region, with a cumulative variance contribution rate of 86.8%. OPLS-DA (R2Y = 0.994, Q2 = 0.986) screened out markers with VIP > 1, including ethyl hexanoate (VIP = 1.52), isohexanol (VIP = 1.38), and 2-heptanone (VIP = 1.35). Pearson correlation analysis showed that the decrease in esters was related to the loss of fruity aroma (r = 0.89, p < 0.01), and the increase in acids and ketones was consistent with enhanced oxidative stress and the tricarboxylic acid cycle (TCA) (r = -0.86, p < 0.01). The decrease in esters might be caused by the hydrolysis of carboxylesterase, and the accumulation of acids was related to microbial metabolism and fatty acid decomposition. These VOC changes provided a chemical basis for freshness grading and prediction models.

[0062] Table 2 Volatile flavor compounds of Hongyan strawberries based on GC-IMS

[0063]

[0064]

[0065] Table 3 Contents of volatile flavor compounds of strawberries with different freshness levels based on GC-IMS

[0066]

[0067]

[0068]

[0069] Example 3: Establish a strawberry freshness prediction model based on volatile organic compounds.

[0070] 1. Determination of strawberry freshness:

[0071] Among them, the freshness of strawberries was determined based on the storage time and the results of the electronic nose. It was stipulated that the freshness of strawberries with a storage time of 0 - 1 day was "fresh", 2 - 3 days was "sub-fresh", and 4 - 5 days was "not fresh".

[0072] 2. Establishment of the strawberry freshness prediction model:

[0073] The partial least squares regression (PLSR) method was used to construct a freshness prediction model based on the relative contents of various volatile organic compounds in strawberry samples. The specific steps are as follows:

[0074] (1) Use the volatile organic compound data obtained by GC-IMS detection as the independent variable, and standardize the data.

[0075] (2) Use the actual freshness value of the sample as the dependent variable (storage time corresponds to "fresh" = 1, "sub-fresh" = 0, "not fresh" = -1), and also standardize it.

[0076] (3) Divide the processed data into a calibration set and a prediction set, establish a model by the PLSR method, and screen out 10 most important volatile markers as key prediction variables. Statistical analysis and regression results show that the regression coefficients and absolute values of these 10 markers are at a relatively high level.

[0077] 3. Model analysis and verification:

[0078] (1) After establishing the PLSR model based on the calibration set, statistically analyze the VIP values of each variable. The results show that the indicators with VIP values greater than 1 are as follows: ethyl hexanoate, isohexanol, 2-heptanone, methyl butanoate, 2-methylbutan-1-ol, isopropyl acetate (VIP = 1.22), 1-octen-3-ol, hexanal, benzyl alcohol, and ethyl acetate. The VIP values of these markers being greater than 1 indicates that they have a relatively high explanatory ability for predicting the freshness of strawberries.

[0079] (2) Finally, use the following optimized model formula to predict the freshness of strawberries:

[0080] Freshness = (0.1523 × A) + (-0.0876 × B) + (-0.0954 × C) + (0.1347 × D) + (-0.0789 × E) + (0.1012 × F) + (-0.0653 × G) + (0.0897 × H) + (-0.0456 × I) + (0.1124 × J). Where:

[0081] A: ethyl hexanoate

[0082] B: isohexanol

[0083] C: 2-heptanone

[0084] D: methyl butanoate

[0085] E: 2-methylbutan-1-ol

[0086] F: isopropyl acetate

[0087] G: 1-octen-3-ol

[0088] H: hexanal

[0089] I: benzyl alcohol

[0090] J: ethyl acetate

[0091] The model was established based on the calibration set. Linear regression analysis showed a coefficient of determination (R^2 = 0.7356), indicating a high correlation between the predicted value and the actual freshness. Cross-validation showed high model robustness, with Q2 = 0.6984. Applied to the prediction set, the Freshness values were highly consistent with the storage time: 0 - 1 day (excellent, Freshness = 0.85 - 1.0), 2 - 3 days (average, Freshness = 0.10 - 0.45), 4 - 5 days (not for sale, Freshness = -0.75 to -0.20). Pearson correlation analysis showed that the Freshness values were negatively correlated with the storage time (r = -0.92, p < 0.01), consistent with the decrease in esters (such as ethyl hexanoate) (r = 0.89, p < 0.01) and the increase in ketones (such as 2-heptanone) (r = -0.87, p < 0.01) in Table 3. The Figure 3 fingerprint further verified the model's ability to distinguish fresh, transitional, and deteriorated stages. The ester-dominated region (0 - 1 day) corresponded to high Freshness values, and the ketone- and alcohol-dominated region (4 - 5 days) corresponded to low Freshness values. [[ID=]15]

[0092] 4. Verification and application:

[0093] By screening 10 VOCs with high VIP values, the model accurately reflected the changing trend of strawberry freshness. The prediction results were highly consistent with the storage time and freshness grading, providing a reliable scientific basis for strawberry quality monitoring and grading. The model parameters and variable selection can be optimized according to actual needs without departing from the scope of the present invention.

[0094] In summary, by selecting 10 most predictive volatile markers and using the PLSR method to establish a strawberry freshness prediction model, it can accurately reflect the freshness changes of strawberries at different storage times, providing a reliable scientific basis for strawberry grading and quality monitoring.

[0095] Although the strawberry freshness prediction model described in Example 3 has been disclosed in a preferred manner, those of ordinary skill in the art can make various changes and modifications to the model parameters, variable selection, and data processing methods without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be defined by the claims.

[0096] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A rapid evaluation method and evaluation model establishment for strawberry freshness based on GC-MS, GC-IMS and electronic nose systems, characterized in that, It includes the following steps: S1 Sample preparation Select 'Benihoppe' strawberries with consistent maturity, and remove fruits with mechanical damage, pests and diseases, or uneven size; the samples are stored at 24±2°C and a relative humidity of 80% for 0-5 days, taking pictures and sampling every day to measure physical and chemical indexes such as hardness, sugar and acid; some samples are quickly frozen with liquid nitrogen and stored at -80°C for volatile substance analysis; S2 Extraction and detection of volatile organic compounds (1) Detect the samples using GC-MS Weigh 5 grams of strawberry fruit powder ground with liquid nitrogen, place it in a 20 mL sample bottle, add 3 mL of 0.2 mol / L ethylenediaminetetraacetic acid solution and 3 mL of 20% calcium chloride solution, add 26 μL of 2-octanol (0.04 mg / L) as an internal standard, and seal it; An Agilent 7890A gas chromatograph is equipped with a 5975C mass spectrometer, and a DB-WAX chromatographic column is selected; the carrier gas is helium with a flow rate of 1.0 mL / min; the temperature program: starting at 40°C, rising to 100°C at a rate of 3°C / min, and then rising to 245°C at a rate of 5°C / min; (2) Detect the samples using GC-IMS After grinding 2 g of frozen samples into powder with liquid nitrogen, transfer them to a 20.0 mL headspace bottle; The headspace injection conditions are set as follows: incubation time 15 min, incubation temperature 50°C, injection needle temperature 85°C, injection volume 500 μL; Gas chromatograph flow rate conditions: Use nitrogen with a purity of 99.99% as the carrier gas, and the program flow rate is as follows: run at 2 mL / min for 2 min, rise uniformly to 10 mL / min within 8 min, rise to 100 mL / min within 20 min, rise to 150 mL / min within 25 min, and the analysis time is 25 min; IMS conditions: The drift tube temperature is 45°C, and the drift gas velocity is 150 mL / min; Preliminarily identify volatile compounds by comparing the RI and ion migration time of reference substances in the GC-IMS library and the NIST database, and measure each sample in parallel 3 times; (3) Electronic nose detection method After grinding 2 g of frozen samples into powder with liquid nitrogen, transfer them to a 20.0 mL headspace bottle, place it at 24±1°C, and after 2 hours, seal it and equilibrate the volatile compounds in the headspace for 10 min; Connect a 3 mm Teflon tube to the needle and pump the headspace gas in the sensor chamber at a constant rate of 120 mL / min; The detection time is set to 60 s, and the sensor response is obtained from the G / G0 ratio; The data is recorded and stored by a computer, the response curve stabilizes after 25 s, and the value at 30 s is used; S3. Data analysis and freshness evaluation (1) Data processing: Use Origin 2018 software to draw a radar chart and a load analysis chart for the electronic nose data; Generate two-dimensional spectra and fingerprint spectra for the GC-IMS data through the Reporter Galleryplot and LAV plugins equipped with the GC-IMS operating system; Use the built-in workstation of the GC-MS data to compare with the NIST08 database to determine the measured substances, and use SPSS software for principal component analysis and orthogonal partial least squares discriminant analysis to generate a heat map; (2) Freshness grading: According to the storage time and the change of VOCs, the freshness of strawberries is divided into three grades: Fresh: 0 - 1d, esters are dominant, FI value is 0.20–0.092; Sub - fresh: 2 - 3d, esters decrease, alcohols and ketones increase, FI value is 0.057–0.045; Not fresh: 4 - 5d, acids and ketones are dominant, FI value is -0.015 to -0.081; (3) Freshness index and PLSR model: Based on the content changes of key VOCs in strawberries with different storage times and different freshness levels, combined with the VIP values under the OPLS - DA model, an FI formula was constructed, and a freshness prediction model was constructed using the PLSR method. Finally, 10 key VOCs were selected as prediction variables.

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