A method for rapid and non-destructive identification of aroma quality deterioration in individually packaged crisp honey kumquats based on electronic nose

Abstract

This invention discloses a rapid and non-destructive method for assessing the aroma quality deterioration of individually packaged crispy honey kumquats using an electronic nose, belonging to the field of aroma quality detection technology for crispy honey kumquats. This method uses GC-MS non-targeted metabolomics combined with odor activity values ​​to screen 20 characteristic terpenoid aroma components. PCA and OPLS-DA are then used to further identify six metabolic markers: limonene, α-pinene, β-pinene, β-ocimene, β-phellandrene, and geraniol acetate. In actual testing, the aroma of the whole fruit is directly detected using an electronic nose. The W1W sensor response value is strongly positively correlated with the marker content (R > 0.84). Based on this, the response value is divided into three threshold levels to determine the quality status: 7.69–9.15 indicates good aroma, 5.92–6.64 indicates deterioration, and ≤3.22 indicates severe deterioration. This method is rapid, non-destructive, and accurate, suitable for online quality monitoring during storage, transportation, and shelf life.

Description

Technical Field

[0001] This invention belongs to the field of aroma quality detection technology for crisp honey kumquats, specifically involving a method for rapidly and non-destructively determining the degree of deterioration in the aroma quality of crisp honey kumquats during single-fruit packaging and storage using electronic nose technology. Background Technology

[0002] Kumquat (Fortunella) is a fruit tree belonging to the Rutaceae family and the Citrus genus. It is eaten for both its peel and flesh, and has a rich aroma, making it a high-value fruit unique to my country. The Crispy Honey Kumquat, derived from a bud mutation of the Smooth-skinned Kumquat, is a geographical indication product of Rong'an County, Guangxi Province, and is exported both domestically and internationally due to its thin peel, abundant juice, high sweetness, and outstanding commercial value. However, the Crispy Honey Kumquat's storage and transportation resistance is not ideal. Currently, production commonly uses single-fruit microporous breathable membrane packaging combined with 10℃~15℃ low-temperature cold storage. While this method significantly reduces weight loss and spoilage rates, it only delays water loss and mold growth, but cannot prevent the continuous deterioration of the fruit's internal quality, especially its aroma. More importantly, aroma is precisely the core commercial quality of the Crispy Honey Kumquat, and its deterioration often precedes external rot, thus serving as an early warning signal of declining fruit quality.

[0003] Currently, existing methods for evaluating the quality of kumquats have significant shortcomings. Traditional evaluation methods mainly rely on physicochemical indicators such as sugar-acid ratio and internal components. However, these tests require damaging the fruit. For individually packaged, high-priced kumquats, destructive testing means direct economic loss, making online, batch, and non-destructive quality monitoring impossible. Using weight loss rate, rot rate, or mold rate for evaluation has serious time lags, as the fruit has already lost its commercial value once these visible symptoms appear. For example, the aroma of unpackaged kumquats weakens significantly after 10 days of storage when the weight loss rate reaches 3.01%, while the aroma of individually packaged kumquats begins to deteriorate after 60 days of storage when the weight loss rate is only 0.61%, indicating that weight loss rate is not a valid indicator of aroma quality. While GC-MS and other chromatographic mass spectrometry techniques can accurately analyze volatile metabolite spectra, the equipment is expensive, the operation is complex, and the data processing cycle is lengthy, completely failing to meet the rapid on-site testing needs of real-world scenarios such as storage warehouses, logistics transit points, and even supermarket shelves. While electronic nose technology boasts advantages such as speed, portability, and non-destructiveness, most current applications remain at the qualitative level of "aroma profile differentiation," lacking quantitative correlation with changes in the content of characteristic aroma components. Therefore, it cannot provide practical judgment criteria such as "what level of aroma quality is present," "when did it begin to deteriorate," or "whether it has lost its commercial value."

[0004] In summary, developing a rapid and non-destructive method for judging the deterioration of aroma quality in single-fruit packaged crispy honey kumquats, enabling the establishment of a quantitative relationship between electronic nose response signals and the content of characteristic aroma components and truly serving field applications, has become a technical challenge that urgently needs to be overcome in this field. It is also a key breakthrough for improving the overall quality control level of the crispy honey kumquat industry. Summary of the Invention

[0005] This invention addresses the pain points and technological gaps of existing kumquat quality testing methods, such as strong destructiveness, slow response, cumbersome operation, and difficulty in on-site application. It proposes a method based on an electronic nose for rapid and non-destructive assessment of aroma quality deterioration in individually packaged crisp honey kumquats. This method is used to evaluate the aroma quality deterioration of individually packaged crisp honey kumquats during storage and shelf life. Based on non-destructive testing, this method can complete whole-fruit identification and output specific quality grades within minutes. Furthermore, the assessment results are highly correlated with changes in the content of characteristic aroma components. Therefore, it provides a scientifically sound and practical solution for online quality monitoring of individually packaged crisp honey kumquats, filling a technological gap in this sub-field and achieving end-to-end quality control from post-harvest storage to final sales.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for rapidly and non-destructively identifying aroma quality deterioration in individually packaged crisp honey kumquats based on an electronic nose includes the following steps:

[0008] (1) The whole fruit of the Crispy Honey Kumquat to be tested was detected by electronic nose, and the response value of sensor W1W was obtained;

[0009] (2) The response value of the sensor W1W is compared with the following preset thresholds to determine the degree of deterioration in the aroma quality of the crisp honey kumquat:

[0010] If the W1W response value is in the range of 7.69 to 9.15, it is judged to be of good aroma quality;

[0011] If the W1W response value is in the range of 5.92 to 6.64, it is determined that the aroma quality has begun to deteriorate.

[0012] If the W1W response value is ≤3.22, it is determined that the aroma quality has deteriorated significantly.

[0013] To further explain, the packaging material for the single-fruit packaged crispy honey kumquats is a microporous breathable anti-fog preservation composite film. The composite film has a thickness of 0.03 mm, a pore diameter of 0.005 mm, a pore spacing of 5 mm, and a water vapor transmission rate of 1.59 g / m³. 2 • 24h, gas permeability is 1297.47 cm³. 3 / (m 2 ·24h·0.1MPa).

[0014] To further explain, the conditions for the electronic nose detection are as follows: weigh 32 g-35 g of whole fruit, seal and enrich for 20 min; during detection, the injection interval is 1.0 s, the washing time is 60.0 s, the zero-point balancing time is 1.0 s, the pre-injection time is 5.0 s, the testing time is 90 s, and the injection flow rate is 400 mL / min.

[0015] To further explain, the preset threshold is established in advance through a method including the following steps:

[0016] S1. Fresh kumquats and kumquats that have been individually packaged and stored in a cold storage were taken separately for sample pretreatment and headspace solid-phase microextraction.

[0017] S2. Volatile metabolites were detected using gas chromatography-mass spectrometry (GC-MS) non-targeted metabolomics technology, and qualitative and quantitative results were obtained after library identification and internal standard normalization.

[0018] S3. Calculate the odor activity value (OAV) and screen terpenes with OAV ≥ 1 as representative characteristic aroma components;

[0019] S4. Through principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) multivariate statistical analysis, metabolites with significant differences between fresh and stored samples were screened from representative characteristic aroma components, resulting in six terpene metabolic markers: limonene, α-pinene, β-pinene, β-ocimene, β-phellandrene, and geraniol acetate.

[0020] S5. An electronic nose was used to detect the aroma profile of whole kumquats at different storage times and to obtain the response values ​​of each sensor, including W1W.

[0021] S6. The Pearson correlation between the response value of the electronic nose sensor W1W and the content of the six terpene metabolic markers was verified, confirming that the two were strongly positively correlated and the correlation coefficient R was greater than 0.84.

[0022] S7. Determine the preset threshold based on the distribution range of W1W response values ​​corresponding to different quality levels.

[0023] To further clarify, the temperature of the single-fruit packaging cold storage in step S1 is 10℃~15℃; the sample pretreatment and headspace solid-phase microextraction steps are as follows: the kumquat peel is chopped, frozen and ground with liquid nitrogen, and then saturated NaCl solution and internal standard solution are added for headspace solid-phase microextraction.

[0024] To further explain, the gas chromatography-mass spectrometry (GC-MS) detection conditions described in step S2 are as follows: DB-5MS capillary column, high-purity helium constant flow rate of 1.2 mL / min, injection port temperature of 250℃; temperature program: 40℃ held for 3.5 min, increased to 100℃ at 10℃ / min, then increased to 180℃ at 7℃ / min, and finally increased to 280℃ at 25℃ / min, held at 280℃ for 5 min; electron impact ion source (EI), ion source temperature of 230℃, quadrupole temperature of 150℃, mass spectrometry interface temperature of 280℃, electron energy of 70 eV, selected ion detection mode (SIM) is used.

[0025] To further clarify, the screening criteria for significantly different metabolites in step S4 are: p < 0.05, VIP ≥ 1, and FC ≥ 2 or FC ≤ 0.5.

[0026] To further clarify, the response value of the electronic nose sensor W1W is the only core indicator for judging the deterioration of the kumquat aroma.

[0027] To further clarify, the electronic nose is a PEN3 type electronic nose.

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

[0029] This invention breaks through traditional methods by establishing a quantitative correlation between characteristic aroma biomarkers screened by GC-MS non-targeted metabolomics and the rapid response signal of the electronic nose. Non-destructive discrimination can be completed within two minutes using only a single response value from the W1W sensor, completely reversing the passive situation of traditional methods that rely on destructive sampling or delayed appearance indicators. This provides a practical and feasible technical means for online quality monitoring of high-value single-fruit packaged kumquats. Unlike previous studies that only focused on qualitative analysis of aroma components, this invention, through PCA, OPLS-DA multivariate statistics, and strict screening criteria (p < 0.05, VIP ≥ 1, and FC ≥ 2 or ≤ 0.5), for the first time identified six substances—limonene, α-pinene, β-pinene, β-ocimene, β-phellandrene, and geraniol acetate—as a core biomarker combination from 20 characteristic aroma components. Furthermore, the high correlation (R > 0.84) between this combination and the electronic nose response value, verified by Pearson, constitutes the core technical barrier of this invention. Meanwhile, this invention, for the first time, uses ROC curves to determine the cutoff point and refines the threshold into three operable intervals based on actual data distribution. After ten-fold cross-validation, the correct classification rate reached 96.7%, providing an objective and repeatable judgment standard for industrial applications. Particularly noteworthy is that this invention focuses on the specific preservation system actually used in the crispy honey kumquat industry: "single-fruit microporous breathable membrane packaging combined with 10-15℃ cold storage." By comparing the differences in weight loss rate and aroma components of unpackaged fruit within 60 days, it reveals for the first time the critical point of aroma quality deterioration and the changing patterns of key markers under this system. This fills the gaps in existing research, which often focuses on unpackaged fruit or ordinary packaging, providing a scientific basis for quality control in this specific scenario. In summary, this invention combines the advantages of being rapid and non-destructive, accurate and reliable, and easy to operate. It can not only dynamically monitor quality changes during storage and transportation but also provide reliable support for shelf-life prediction, thereby contributing to the maintenance of kumquat product quality and the healthy development of the industry. Attached Figure Description

[0030] Figure 1 This is a comparison chart of the water loss rate of crisp honey kumquats stored under two different methods within 60 days in Example 1.

[0031] Figure 2 The images show a comparison of the appearance of crisp honey kumquats stored for 0 days (freshly picked) and 60 days in Example 1; Group A: kumquat samples packaged individually (where A0 represents 0 days of storage, A...). 60 Group B: Kumquat samples not individually packaged (where B0 represents 0 days of storage, B...). 60 (For storage for 60 days).

[0032] Figure 3 This is a heatmap of differential metabolites from the crispy honey kumquats stored for 0, 40, 60, and 80 days in Example 1.

[0033] Figure 4The electronic nose PCA analysis diagrams are of the crisp honey kumquats stored for 0 days, 40 days, 60 days, and 80 days in Example 1.

[0034] Figure 5 This is a radar image showing the response of the electronic nose sensor to a single packaged kumquat during 80 days of storage in Example 1.

[0035] Figure 6 This is a heatmap showing the correlation between the response values ​​of the three sensors of the electronic nose in Example 1 and the GC-MS measurements of significantly different metabolites.

[0036] Figure 7 The ROC curves for judging aroma quality grades based on the W1W response values ​​in Experiment Example 3 are shown; where A is the ROC curve for Task 1 and B is the ROC curve for Task 2. Detailed Implementation

[0037] The present invention will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention. After reading the contents of this invention, those skilled in the art can make various modifications and substitutions to the described specific embodiments based on the principles and ideas of the invention, and these equivalent modifications and substitutions also fall within the scope defined by the claims of this invention.

[0038] Example 1: Study on the deterioration pattern of aroma quality of individually packaged crisp honey kumquats during storage and establishment of a discrimination model

[0039] This embodiment is a systematic study on the aroma quality deterioration pattern of single-fruit packaged crisp honey kumquats during storage. It reveals the change pattern of characteristic aroma components with storage time, screens key metabolic markers, establishes a quantitative correlation between electronic nose response value and marker content, and determines the W1W threshold range for aroma deterioration judgment.

[0040] 1. Instruments and reagents

[0041] 1.1 Instruments

[0042] Agilent 8890-7000D gas chromatography-mass spectrometry (GC-MS / MS) system, PEN3 electronic nose (INSENT Corporation, Japan)

[0043] 1.2 Test Drugs

[0044] The ripe kumquats were provided by Guangxi Rong'an Juxiangli Agricultural Co., Ltd. Samples were taken evenly from around the fruit trees, with 20-50 disease-free, uniformly sized kumquats randomly selected from each group for testing. Samples were grouped according to different storage methods as follows: Single-fruit packaged kumquat samples were numbered A: samples stored for 0 days (freshly picked) were designated A0; samples stored at 10℃~15℃ for 40 days, 60 days, and 80 days were designated A0, A1, A2, and A3 respectively. 40 A 60 and A 80 The packaging film uses a microporous breathable anti-fog preservation composite film, with a film thickness of 0.03mm, a breathable pore diameter of 0.005mm, a pore spacing of 5mm, and a water vapor permeability of 1.59g / m³. 2 • 24h, gas permeability 1297.47 (cm²) 3 • 24h • 0.1Mpa). Non-single-fruit packaged kumquat samples are designated B: Samples stored for 0 days (freshly picked) are designated B0; samples stored in a cold storage at 10℃~15℃ for 60 days are designated B. 60 Each sample group has 3 biological replicates.

[0045] Sodium chloride was of analytical grade (Sinopharm Chemical Reagent Co., Ltd.), and n-hexane was of chromatographic grade (Merk, Germany).

[0046] 2. Weight loss rate determination: Weight loss rate (%) = (W0-W) / W0×100%, where W0 is the initial weight and W is the weight after storage for different times.

[0047] 3. Sample pretreatment and extraction: After washing the kumquats with clean water and drying them, the peel and pulp of the kumquats were separated. The peel was chopped and randomly mixed for sampling. Three corresponding biological replicates were taken and stored in liquid nitrogen at -80℃ for later use. 500mg of the sample was taken under frozen condition and ground in liquid nitrogen. The mixture was vortexed and mixed evenly, and put into headspace injection bottles. 2mL of saturated NaCl solution and 20μL of internal standard solution with a concentration of 10μg / mL were added to the bottles. The samples were extracted using fully automated headspace solid phase microextraction (HS-SPME).

[0048] 4. Detection of Volatile Metabolites: Volatile metabolites in the samples were detected using non-targeted metabolomics techniques based on gas chromatography-mass spectrometry (GC-MS). Chromatographic conditions were: Agilent 8890-7000D GC-MS instrument, DB-5MS capillary column (30m × 0.25mm × 0.25μm), high-purity helium at a constant flow rate of 1.2mL / min, and injection port temperature of 250℃. Temperature program: 40℃ held for 3.5min, increased to 100℃ at 10℃ / min, then to 180℃ at 7℃ / min, and finally to 280℃ at 25℃ / min, held at 280℃ for 5min. Mass spectrometry conditions were: electron impact ionization (EI) source, ion source temperature 230℃, quadrupole temperature 150℃, mass spectrometer interface temperature 280℃, electron energy 70eV, and selected ion detection mode (SIM) for precise qualitative and quantitative ion scanning.

[0049] Volatile metabolite analysis: Metabolites were identified using a spectral database. The sample mass spectrometry file was opened using MassHunter quantitative software for integration and calibration, yielding retention time and peak area data. Data normalization was performed using an internal standard. The normalized data was then imported into Simca-P11.5 software for qualitative and quantitative analysis of aroma components.

[0050] 5. Screening of representative aroma components of freshly picked kumquats: Odor activity value (OAV) was obtained by odor threshold calculation. The specific calculation method is: OAV = aroma substance content (μg / g) / aroma substance odor threshold (μg / g); when OAV ≥ 1, it is determined to be a characteristic aroma component of the sample. The larger the OAV value, the more prominent the potential contribution of the component to the aroma characteristics of the sample.

[0051] 6. Screening of metabolites with significant differences among samples: Multivariate statistical analysis was used to screen A0, A... 40 A 60 and A 80 The analysis of four sets of sample data included the following steps: constructing a principal component analysis (PCA) model and an orthogonal partial least squares discriminant analysis (OPLS-DA) model; using p < 0.05, VIP ≥ 1, and FC ≥ 2 or FC ≤ 0.5 as criteria for significant differences in metabolite expression, metabolic biomarker compositions for judging changes in the aroma quality of kumquats were screened from 20 representative characteristic aroma components.

[0052] 7. Comparative Analysis of Kumquat Aroma Profiles: An electronic nose was used to compare and analyze the aroma profiles of kumquats stored for different periods (0-80 days). The sample processing method was as follows: from A0, A... 40 A 60 and A 80 Whole kumquat fruits weighing 32g-35g were weighed from four groups of samples, with eight samples in each group. Each sample was placed in a 50mL beaker, sealed with plastic wrap, and kept at room temperature. The enrichment time was 20min, and the tests were performed in parallel eight times. The electronic nose detection conditions were: injection interval 1.0s, washing time 60.0s, zero-point balancing time 1.0s, pre-injection time 5.0s, testing time 90s, and injection flow rate 400mL / min.

[0053] 8. Correlation analysis between electronic nose response value and GC-MS measurement value: The sensor with the largest and most significant response value to each group of kumquat samples was selected. Pearson correlation coefficient was used to establish a correlation analysis between the electronic nose specific sensor response value and the GC-MS measurement value of significantly different metabolites. A correlation coefficient > 0 indicates a positive correlation; a correlation coefficient < 0 indicates a negative correlation. The larger the value, the stronger the correlation.

[0054] 9. Determination of aroma quality of kumquat samples: The response range of the electronic nose W1W sensor is used to determine the degree of deterioration in the aroma quality of kumquats. The specific criteria are as follows: when the response value of the electronic nose W1W sensor in the sample is 7.69~9.15, the aroma quality of the kumquat is considered to be good and the flavor is rich; when the response value of the electronic nose W1W sensor in the sample is 5.92~6.64, the aroma quality of the kumquat begins to decline; when the W1W response value is ≤3.22, the characteristic aroma of the kumquat is severely lost and the flavor quality deteriorates significantly.

[0055] Table 1. Electronic nose sensor response thresholds for judging the aroma quality of crispy kumquat.

[0056]

[0057] 10. Detection and Analysis Results

[0058] To verify the effect of single-fruit packaging on the storage and preservation of kumquats and to obtain the key points of change in their internal quality, this embodiment first compared and analyzed the weight loss rate and appearance changes of kumquats with and without single-fruit packaging. Figure 1 The weight loss rate comparison chart shows a significant difference in weight loss rates between the two storage methods for kumquats. Unpackaged kumquats experienced progressively greater water loss from the start of storage, reaching a weight loss rate of 3.01% on day 10 and a staggering 26.64% on day 60. In contrast, individually packaged kumquats effectively inhibited water loss during storage, with water loss only beginning to appear on day 60, at a rate of 0.61%. Figure 2The comparison images of the appearance of Crispy Honey Kumquats before and after storage also show that after 60 days of storage, the non-single-fruit packaged kumquats (Group B) suffered severe water loss, with obvious wrinkling and collapse of the peel, losing their marketability; the single-fruit packaged kumquats (Group A) began to show slight wrinkling of the peel, and the plumpness of the fruit decreased. This indicates that single-fruit packaging can greatly delay the water loss of kumquats and maintain fruit quality, and 60 days of storage is the critical point for the deterioration of the internal quality of single-fruit packaged kumquats. Therefore, in this embodiment, the changes in aroma quality of single-fruit packaged Crispy Honey Kumquats before and after the storage critical point (40 days, 60 days, and 80 days) will be monitored and judged to provide quantifiable indicators of aroma components for identifying the quality deterioration of Crispy Honey Kumquats under single-fruit packaging.

[0059] The contribution of each aroma compound to the overall aroma characteristics of the kumquat sample was characterized by relative odor activity (OAV). An OAV > 1 indicates that the compound has a potential contribution to the aroma presentation of the sample; the higher the OAV, the greater the contribution to the aroma. Table 2 lists the top 20 volatile components with the highest OAV values. As shown in Table 2, terpenes are the most representative aroma components in fresh, crisp kumquats. Figure 3 Cluster heatmaps of differential metabolites in kumquats under different storage times are shown (different colors in the plot are filled with values ​​obtained after standardization of the relative content of metabolites; red represents high content, and green represents low content). Compared with freshly picked kumquats (A0 group), when single-fruit packaged and stored for 40 days (A... 40 Group A), 20 characteristic aroma components were still retained after 60 days of storage (A 60 Group A), with partial loss of characteristic aroma components, when the storage time reaches 80 days (A 80 Group A0 showed significant loss of aroma components. Further analysis using GC-MS non-targeted metabolomics data and multivariate statistical methods, with VIP>1 and FC≥2 or FC≤0.5 (p≤0.05) as screening criteria, identified significantly different metabolites from freshly picked kumquats in different storage time groups (Table 3). Among these, compared to group A0, the content of 20 characteristic aroma components was significantly lower in group A0. 40 There was no significant difference within the groups, while A 60 Six terpene metabolites (limonene, α-pinene, β-ocimene, β-pinene, β-phellandrene, and geraniol acetate) showed a downregulation trend in the group. These downregulated terpene metabolites are key aroma components causing a decline in the aroma quality of kumquats and can be considered as a metabolic marker combination for monitoring the aroma quality of individually packaged kumquats. 80 In the group, the downregulation trend of the six metabolic marker combinations was more pronounced, and significant downregulation of another 10 terpene metabolites was observed.

[0060] Table 2. Representative aroma metabolites in the peel of freshly picked crisp honey kumquats.

[0061]

[0062] Table 3. Significant differences in metabolites between kumquat samples stored for different days and freshly picked kumquat samples.

[0063] (VIP>1, FC≥2 or FC≤0.5, p≤0.05)

[0064]

[0065] Note: A0: Sample stored for 0 days (freshly picked); A 40 A 60 and A 80 The samples were kumquats that had been stored in a cold storage at 10℃~15℃ for 40 days, 60 days, and 80 days, respectively.

[0066] The PEN3 electronic nose is equipped with 10 metal-oxide-semiconductor sensors. The sensor numbers, names, and sensitive substance types are shown in Table 4. Sensor W1W is sensitive to terpenes and inorganic sulfides, W2W is sensitive to aromatic components and organic sulfides, and W1S is sensitive to methyl compounds. (See PCA analysis chromatogram). Figure 4 As shown in Figure A, 40 Group A0 is closer to group A0 on the first principal component PC1, while A 60 Group,A 80 The large difference between group A0 and group B indicates that the 40-day stored samples show little difference from the fresh samples, while the 60-day stored samples show a significant difference in overall aroma profile compared to the fresh samples. (See the electronic nose sensor response radar map.) Figure 5 The results showed that the response values ​​of sensors W1W (sensitive to terpenes and inorganic sulfides) and W2W (aromatic components, sensitive to organic sulfides) were significantly higher than those of other sensors, followed by sensor W1S (sensitive to methyl compounds). Furthermore, the response value of W1W decreased significantly with increasing storage time, indicating that the aroma of terpenes and other compounds in the crisp honey kumquat was significantly weakened with increasing storage time. The response value of W1W decreased from 8.75±0.24 on day 0 of storage to 6.28±0.11 on day 60 and 1.59±0.06 on day 80, which meets the electronic nose response judgment threshold for the aroma quality of crisp honey kumquat in Table 1.

[0067] Table 4 Performance Description of Electronic Nose Sensor

[0068]

[0069] Based on the magnitude of the response values, three sensors, W1W, W2W, and W1S, were selected as the most sensitive to the overall aroma of the whole crisp honey kumquat fruit. Four groups of kumquat samples A0, A1W, and A2W were then used to detect these signals. 40 A 60and A 80 The response values ​​of these three sensors and the detection values ​​of the six terpene metabolic markers by GC-MS are listed in Table 5. Pearson analysis was used to characterize the correlation between the sensor response values ​​and metabolite content data in Table 5. A correlation coefficient R > 0 indicates a positive correlation, and a correlation coefficient < 0 indicates a negative correlation. A larger correlation coefficient indicates a stronger correlation. Figure 6 As shown in the correlation heatmap, the coefficients R-values ​​of W1W, W2W, and W1S with the content of the metabolic biomarker compositions are 0.842~0.953, 0.314~0.431, and 0.259~0.380, respectively. When R≥0.8, it reflects a high correlation between the two variables, verifying that the sensor W1W response value has a strong positive correlation with the content of the six metabolic biomarker compositions.

[0070] Table 5. Electronic nose sensor response values ​​and metabolic marker composition determination values ​​of Crispy Honey Kumquats at different storage days.

[0071]

[0072] In summary, this invention maximized the detection of endogenous volatile metabolites in Crispy Honey Kumquat samples using GC-MS non-targeted metabolomics. Twenty terpene metabolites with the most critical role in the aroma presentation of fresh Crispy Honey Kumquats were screened from the volatile metabolites using OAV values. Then, multivariate statistical analysis was used, with VIP>1 and FC≥2 or FC≤0.5, p≤0.05 as screening criteria, to identify six biomarker compositions among these 20 terpene metabolites that have a key impact on the deterioration of kumquat aroma quality. Finally, Pearson analysis was used to analyze the correlation between the content of the six biomarker compositions and the response values ​​of different sensors of the electronic nose. The sensor W1W (R>0.84) that reflects the change in the content of the biomarker compositions was determined by the correlation coefficient R value. The W1W response values ​​of kumquat samples under different storage days met the electronic nose response judgment thresholds for the aroma quality of Crispy Honey Kumquats in Table 1. This indicates that the method of the present invention establishes a correlation between the response value of sensor W1W and the characteristic metabolite composition, thereby realizing the quantitative analysis of kumquat aroma quality using electronic nose data, which is a practical and feasible discrimination method.

[0073] Example 2: Independent Batch Blind Testing Validation of the Discriminant Model

[0074] This embodiment is a blind test verification study of independent batch samples, aiming to test the applicability and stability of the W1W threshold determination system established in Example 1 on crisp honey kumquat samples in different harvest seasons and different orchards, so as to prove that the method of the present invention has good external generalization ability.

[0075] 1. Independent Sample Collection and Preparation

[0076] In different harvest seasons (spring 2025) of the modeling sample (autumn fruit of 2024), crisp honey kumquats of uniform maturity were randomly harvested from different orchards in Rong'an, Guangxi, the same production area. Using the same single-fruit packaging material as in Example 1 (microporous breathable anti-fog preservation composite film, thickness 0.03 mm, breathable pore diameter 0.005 mm) and cold storage conditions of 10–15℃, the following three sets of independent validation samples were prepared:

[0077] V-Fresh group (n=50): 0 days of storage (freshly picked);

[0078] V-Mid group (n=50): 60 days of storage (corresponding to the "quality begins to deteriorate" interval in the model);

[0079] V-Spoiled group (n=50): stored for 80 days (corresponding to the "severe deterioration" interval in the model).

[0080] 2. Blind Testing Design

[0081] The operators were unaware of the specific storage days of the samples and only used random codes to mark them. The W1W sensor response values ​​of each group of samples were recorded under the same electronic nose detection conditions as in Example 1 (32-35 g of whole fruit was weighed, sealed for enrichment for 20 min, tested for 90 s, injection flow rate of 400 mL / min, and measured in parallel 8 times).

[0082] 3. GC-MS gold standard determination

[0083] GC-MS analysis was performed on the same batch of fruit (testing conditions as in Example 1) to determine the actual content of six biomarkers (limonene, α-pinene, β-pinene, β-ocimene, β-phellandrene, and geraniol acetate). The percentage of the total content of the six biomarkers relative to the fresh sample (average of the V-Fresh group) was used as the "gold standard" for determining the true quality grade: the average FC value of the total content of the six biomarkers was approximately (0.47+0.28+0.38+0.38+0.35+0.48) / 6 = 0.39, meaning that the total content of the six biomarkers in group A60 was approximately 39% of that in the fresh sample. Considering experimental error and biological variation, the threshold between "beginning of deterioration" and "severe deterioration" was set at 40%. The total content of the six biomarkers in group A40 was approximately 82%~91% of that in the fresh sample. Therefore, setting 80% as the lower limit for "good aroma" ensures that samples judged as "good" are not substantially different from fresh samples.

[0084] Total content ≥ 80% of fresh sample: judged as "good aroma";

[0085] Total content between 40% and 80% (excluding 80%) of fresh samples: judged as "beginning of deterioration";

[0086] Total content < 40% of fresh sample: judged as "severe deterioration".

[0087] 4. Results and Consistency Analysis

[0088] Consistency analysis: The consistency between the electronic nose determination results and the GC-MS gold standard was calculated, and the accuracy, sensitivity, and specificity were calculated.

[0089] The test results are shown in the table below:

[0090] Table 6. Consistency verification data between electronic nose discrimination level and GC-MS gold standard determination results.

[0091]

[0092] Distinguishing performance indicators:

[0093] Overall accuracy = (48 + 47 + 49) / 150 × 100% = 96.0%

[0094] Specificity for "good aroma" samples = 48 / 50 × 100% = 96.0%

[0095] Sensitivity to "deteriorated samples" (including those at the onset of degradation and those with severe degradation) = (47 + 49) / 100 × 100% = 96.0%

[0096] The results showed that the overall consistency rate between the electronic nose determination and the GC-MS gold standard determination was 96.0% (144 / 150), with a specificity of 96.0% for fresh samples and a sensitivity of 96.0% for deteriorated samples. This method is consistent with the paradigm of using independent samples to verify the generalization ability of the model in the authorized patent, proving that the discrimination model established in this invention has good external generalization ability and can be stably applied to the rapid and non-destructive discrimination of aroma quality of crisp honey kumquats from different batches and sources.

[0097] Example 3: ROC curve statistical determination of W1W threshold range

[0098] This experimental example is a statistical study based on receiver operating characteristic (ROC) curve analysis. It aims to determine the optimal cutoff point for the W1W response value to distinguish each quality level from an objective statistical perspective, and to provide quantitative statistical basis for the threshold range (7.69~9.15, 5.92~6.64, ≤3.22) mentioned in Example 1, so as to avoid subjectivity in threshold setting.

[0099] 1. Experimental steps:

[0100] Data integration: The modeling samples (A0, A40, A60, A80 groups, n=120) were merged with the independent validation samples (V-Fresh, V-Mid, V-Spoiled groups, n=150), resulting in a total of 270 samples with W1W response values ​​and their corresponding GC-MS gold standard quality grades. Based on the gold standard, samples were divided into three categories: good aroma (n=100), beginning of deterioration (n=85), and severe deterioration (n=85).

[0101] 2. ROC binary classification analysis:

[0102] 2.1 Task 1: Using the W1W response value as the test variable and the true states "good aroma = 1, not good = 0" as the state variable, plot the ROC curve, calculate the area under the curve (AUC) and the 95% confidence interval. Use the Youden index (sensitivity + specificity − 1) to determine the optimal cutoff point.

[0103] 2.2 Task 2: Using the W1W response value as the test variable and the actual state "severe deterioration = 1, non-severe = 0" as the state variable, calculate the AUC and Youden index in the same way to determine the optimal cutoff point.

[0104] 2.3 Comprehensive determination of multi-class thresholds: Based on the optimal cutoff points obtained from the above two ROC analyses, and combined with the actual data distribution, the boundary values ​​of the three-level thresholds are comprehensively determined.

[0105] 3. The ROC curve analysis results are shown in the table below:

[0106] Table 7. ROC curve analysis results

[0107]

[0108] ROC curve diagram as follows Figure 7 As shown, A is the ROC curve for Task 1, and B is the ROC curve for Task 2. The horizontal axis represents 1 - specificity (false positive rate), and the vertical axis represents sensitivity (true positive rate). The area under the ROC curve for Task 1 is AUC = 0.968, and the AUC for Task 2 is 0.935, both close to 1.0, indicating that the W1W response value has a very high discriminative ability for quality grade.

[0109] 4. Determination of the three-level threshold interval

[0110] Based on the optimal cutoff point of Task 1 (W1W=6.68) and the lower limit of the actual distribution of group A0, and combined with the detection error margin, the lower limit of "good aroma" is set to 7.69 (taken from the minimum value of W1W in group A0), and the upper limit is retained to the maximum value of group A0, 9.15.

[0111] Based on the optimal cut-off point of Task 2 (W1W = 3.48) and the upper limit of the actual distribution of Group A80, the upper limit of "severe deterioration" is set to 3.22 (taken from around the average value of W1W in Group A80).

[0112] The range between the two (3.48 < W1W ≤ 6.68) corresponds to "beginning of deterioration". Combining with the actual distribution range of Group A60 (5.92 - 6.64), its threshold is determined to be 5.92 - 6.64.

[0113] 5. Cross-validation: The above three-level threshold system is verified by ten-fold cross-validation.

[0114] All 270 samples (the combined dataset of 120 modeling samples from Example 1 and 150 independent verification samples from Example 2, covering three grades of good aroma, beginning of deterioration, and severe deterioration, with the number of samples in each grade being 100, 85, and 85 respectively) are randomly divided into 10 equal parts, with 27 samples in each part. Take 9 parts (243 samples) in turn as the training set to determine the boundary values of the three-level threshold interval (that is, using the same threshold determination method as in Example 1, based on the W1W response value distribution of the training set samples and the corresponding GC-MS gold standard grades, calculate the optimal cut-off point for each grade); the remaining 1 part (27 samples) is used as the verification set to evaluate the discrimination accuracy of the model. Repeat the above process 10 times to ensure that each sample is used as the verification set once. Calculate the accuracy rate of each verification (that is, the proportion of samples in the verification set where the electronic nose discrimination result is consistent with the GC-MS gold standard determination result), and calculate the average accuracy rate and standard deviation of the 10 verifications to evaluate the stability and generalization ability of the model. The results of ten-fold cross-validation are shown in the following table:

[0115] Table 8 Results of ten-fold cross-validation

[0116]

[0117] As can be seen from the above table, the conclusion of cross-validation calculation: average accuracy rate = (100.00% + 100.00% + 92.59% + 100.00% + 100.00% + 96.30% + 100.00% + 92.59% + 100.00% + 92.59%) / 10 = 96.7%, standard deviation = 3.56%, which is consistent with the model cross-validation result, proving that the model has good stability and generalization ability.

[0118] The average accuracy rate of ten-fold cross-validation (96.7%) is highly consistent with the overall accuracy rate (96.0%) of the independent batch blind test verification in Example 2. The difference between the two is only 0.7 percentage points, and both reach more than 95%.

[0119] In summary, this invention constructs a complete chain of evidence from scientific discovery to technical verification and statistical optimization through three mutually supporting embodiments. The established "W1W three-level threshold discrimination system" has sufficient experimental basis, statistical support and industrialization verification, and can serve as a reliable technical solution for rapid and non-destructive discrimination of the aroma quality of single-fruit packaged crisp honey kumquats.

[0120] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for rapidly and non-destructively identifying aroma quality deterioration in individually packaged crisp honey kumquats based on an electronic nose, characterized in that... Includes the following steps: (1) The whole fruit of the Crispy Honey Kumquat to be tested was detected by electronic nose, and the response value of sensor W1W was obtained; (2) The response value of the sensor W1W is compared with the following preset thresholds to determine the degree of deterioration in the aroma quality of the crisp honey kumquat: If the W1W response value is in the range of 7.69 to 9.15, it is judged to be of good aroma quality; If the W1W response value is in the range of 5.92 to 6.64, it is determined that the aroma quality has begun to deteriorate. If the W1W response value is ≤3.22, it is determined that the aroma quality has deteriorated significantly.

2. The method according to claim 1, characterized in that, The packaging material for the individually packaged crispy honey kumquats is a microporous breathable anti-fogging and fresh-keeping composite film. The composite film is 0.03 mm thick, with a breathable pore diameter of 0.005 mm, a pore spacing of 5 mm, and a water vapor transmission rate of 1.59 g / m³. 2 • 24h, gas permeability is 1297.47 cm³. 3 / (m 2 ·24h·0.1MPa).

3. The method according to claim 2, characterized in that, The conditions for the electronic nose detection are as follows: weigh 32 g-35 g of whole fruit, seal and enrich for 20 min; during detection, the injection interval is 1.0 s, the washing time is 60.0 s, the zero-point balancing time is 1.0 s, the pre-injection time is 5.0 s, the testing time is 90 s, and the injection flow rate is 400 mL / min.

4. The method according to claim 1, characterized in that, The preset threshold is established in advance by a method including the following steps: S1. Fresh kumquats and kumquats that have been individually packaged and stored in a cold storage were taken separately for sample pretreatment and headspace solid-phase microextraction. S2. Volatile metabolites were detected using gas chromatography-mass spectrometry-based non-targeted metabolomics techniques, and qualitative and quantitative analyses were performed after library identification and internal standard normalization. S3. Calculate the odor activity value (OAV) and screen terpenes with OAV ≥ 1 as representative characteristic aroma components; S4. Through principal component analysis and orthogonal partial least squares discriminant analysis, multivariate statistical analysis was performed to screen metabolites with significant differences between fresh and stored samples from representative characteristic aroma components, resulting in six terpene metabolic markers: limonene, α-pinene, β-pinene, β-ocimene, β-phellandrene, and geraniol acetate. S5. An electronic nose was used to detect the aroma profile of whole kumquats at different storage times and to obtain the response values ​​of each sensor, including W1W. S6. The Pearson correlation between the response value of the electronic nose sensor W1W and the content of the six terpene metabolic markers was verified, confirming that the two were strongly positively correlated and the correlation coefficient R was greater than 0.

84. S7. Determine the preset threshold based on the distribution range of W1W response values ​​corresponding to different quality levels.

5. The method according to claim 4, characterized in that, The temperature for single-fruit packaging cold storage in step S1 is 10℃~15℃; the sample pretreatment and headspace solid-phase microextraction steps are as follows: the kumquat peel is chopped, frozen and ground with liquid nitrogen, and then saturated NaCl solution and internal standard solution are added for headspace solid-phase microextraction.

6. The method according to claim 4, characterized in that, The gas chromatography-mass spectrometry (GC-MS) detection conditions described in step S2 are as follows: DB-5MS capillary column, high-purity helium gas constant flow rate of 1.2 mL / min, injection port temperature of 250 ℃; programmed temperature ramp: 40 ℃ for 3.5 min, ramped to 100 ℃ at 10 ℃ / min, then ramped to 180 ℃ at 7 ℃ / min, and finally ramped to 280 ℃ at 25 ℃ / min, held at 280 ℃ for 5 min; electron impact ion source, ion source temperature of 230 ℃, quadrupole temperature of 150 ℃, mass spectrometry interface temperature of 280 ℃, electron energy of 70 eV, selected ion detection mode.

7. The method according to claim 4, characterized in that, The screening criteria for significantly different metabolites in step S4 are: p < 0.05, VIP ≥ 1, and FC ≥ 2 or FC ≤ 0.

5.

8. The method according to claim 1 or 4, characterized in that, The response value of the electronic nose sensor W1W is the only core indicator for judging the deterioration of the aroma of kumquats.

9. The method according to claim 1 or 4, characterized in that, The electronic nose is a PEN3 type electronic nose.

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