A detection method for processing degree of black sesame

Abstract

The application belongs to the technical field of quality control of traditional Chinese medicine decoction pieces and detection technology, and provides a detection method for processing degree of black sesame. The method detects the relative contents of n-heptanol, 2-methylpyrazine and trans-2-hexen-1-ol in the sample to be detected by GC-IMS; and the relative contents of the three components are substituted into the results of the Bayesian classification discriminant formula of raw and cooked states and processing degree respectively. The method of the application does not need complex sample pretreatment, is simple and environmentally friendly, has low detection cost, and the result is intuitive. To some extent, the method solves the problem that the existing evaluation method is highly subjective and difficult to evaluate the processing degree of black sesame in large quantities, quickly and objectively and accurately, and lays a foundation for the large-scale, intensive, standardized and digitalized production of traditional Chinese medicine decoction pieces.

Description

Technical Field

[0001] This invention belongs to the field of testing technology in the field of quality control technology of traditional Chinese medicine decoction pieces, and specifically relates to a method for detecting the degree of processing of black sesame seeds. Background Technology

[0002] The information disclosed in this background section is intended to enhance understanding of the overall background of the invention and is not necessarily to be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art.

[0003] Black sesame is derived from the plant sesame (Sesamum indicum) of the family Pyrenaceae. Sesamum indicum The dried, mature seeds of *L.* are a time-honored medicinal and edible product. Their medicinal use was first recorded in the *Shennong Bencao Jing* (Shennong's Classic of Materia Medica), where they were listed as a superior-grade herb, possessing the effects of tonifying the liver and kidneys, replenishing essence and blood, and moistening the intestines. Long-term consumption is believed to prolong life. Ancient Chinese medical texts record processing methods such as steaming, stir-frying, grinding, and oil extraction. The 2025 edition of the *Chinese Pharmacopoeia* and the processing standards of various provinces, municipalities, and autonomous regions primarily record the stir-frying method. Ancient physicians believed that raw black sesame seeds could relieve phlegm, cool the blood, and detoxify; stir-fried black sesame seeds, with their rich aroma, were believed to tonify the liver and kidneys, replenish essence and blood, and moisten the intestines to relieve constipation. Nowadays, raw black sesame seeds are rarely used; stir-fried varieties are more favored by clinical patients. After stir-frying, black sesame seeds slightly swell, with little change in shape. Furthermore, the dark color of black sesame seeds makes it difficult to judge the degree of processing by shape and color changes. The aroma of the oil from stir-fried black sesame seeds is significantly enhanced, and aroma can be used as a primary indicator of the degree of stir-frying. However, traditional methods that rely on smell to assess the aroma of roasted black sesame seeds are highly empirical, lack objective quantitative values, and are difficult to replicate. They often require years of practical experience from personnel with extensive expertise. Furthermore, thin-layer chromatography cannot distinguish between different degrees of roasting or between raw and roasted black sesame seeds.

[0004] Gas chromatography-ion mobility spectrometry (GC-IMS) combines the high separation capability of gas chromatography (GC) with the fast analysis speed and high sensitivity of ion mobility spectrometry (IMS). It simplifies sample pretreatment and preserves the original aroma, enabling objective characterization of the aroma of roasted black sesame seeds. This transforms traditional sensory evaluation of aroma into quantifiable odor data. However, current GC-IMS techniques for analyzing flavor differences in black sesame seeds at different roasting levels require the use of 13 flavor compounds for differentiation. Cluster analysis of these 13 differential compounds can only roughly divide black sesame samples at different roasting stages into two categories: unroasted and lightly roasted samples in one category, and moderately roasted and heavily roasted samples in another. It still cannot accurately distinguish between under-roasted, moderately roasted, and over-roasted black sesame seeds. Summary of the Invention

[0005] To address the problems in the existing technology, the present invention provides a method for detecting the degree of processing of black sesame seeds. The degree of processing of black sesame seeds can be quickly determined based on the relative contents of three components: n-heptanol, 2-methylpyrazine, and trans-2-hexen-1-ol.

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

[0007] A method for detecting the degree of processing of black sesame seeds includes the following steps: The relative contents of n-heptanol, 2-methylpyrazine and trans-2-hexen-1-ol in the sample were determined by GC-IMS. The relative contents of the above three components were first substituted into the Bayesian classification discriminant formula for raw and roasted black sesame seeds to calculate the values. If the value of roasted black sesame seeds is larger, then it is roasted black sesame seeds; if the value of black sesame seeds is larger, then it is raw black sesame seeds. When it is determined to be roasted black sesame, the Bayesian classification discriminant of black sesame with different roasting degrees is substituted into the value to further determine the degree of processing of roasted black sesame. The value with the largest value is the degree of processing of black sesame.

[0008] Preferably, the GC-IMS conditions are as follows: 1 g of the sample powder to be tested is accurately weighed and placed in a 20 mL headspace vial, and incubated at 65°C for 20 min at an incubation speed of 500 rpm / min; then 500 µL of headspace phase is injected at 70°C; separation is performed using an MXT-5 column, with the IMS temperature set to 45°C; high-purity nitrogen is used as the drift gas and carrier gas, and the gas flow rate program is as follows: the flow rate is 2 mL / min for the first 2 min, then linearly increased to 100 mL / min within 10 min, then linearly increased to 150 mL / min within 20 min, and finally maintained at 150 mL / min.

[0009] The relative content of each volatile component was calculated using the peak area normalization method, expressed as a percentage of peak area (%). Specifically, the peak area values ​​corresponding to different volatile components in the spectrum were first measured. Then, the peak area of ​​each volatile component was calculated by adding the peak areas of all identified volatile components to the total peak area, thus determining the proportion of that volatile component's peak area in the total peak area. The relative content of each component was expressed as a percentage.

[0010] The Bayesian classification discriminant formulas for raw and roasted black sesame seeds and for black sesame seeds of different roasting degrees are used to determine whether the black sesame seeds are raw or roasted; and to determine the roasted black sesame seeds of three roasting degrees: under-roasted, moderately roasted, and over-roasted.

[0011] The method for obtaining the Bayesian classification discriminant includes the following steps: Using the relative contents of all identified volatile components as independent variables, a discriminant model was established, and a stepwise method was used for discriminant analysis. Optimize the parameters of the statistical method and the step method: select Box's M for statistical descriptive analysis, select Wilks' lambda for the step method, select to use the F frequency as the judgment criterion, set it to 0.05 for entry and 0.1 for deletion; The F-value is used to determine whether the independent variable has a significant impact on the model's decision: an F-value less than 0.05 indicates that the independent variable has a significant impact on the model's decision; an F-value greater than 0.1 indicates that the independent variable has no significant impact on the model's decision. Variables with an F-value between 0.05 and 0.1 will not appear in the final model during the stepwise selection process. These variables may contribute to the decision, but their collinearity is not strong enough to reach the set significance level.

[0012] The volatile components are: 3-(Methylthio)propanal, vanillin, 5-methylfurfural, and trans-2-octenal. trans -2-octenal), benzaldehyde, 3-methoxy-4-hydroxybenzaldehyde, trans-2-heptenal trans -2-heptenal), n-Octanal, Heptanal, trans-2-nonenal trans -2-nonenal), trans-2-hexenal ( trans -2-Hexenal), trans-2-heptenal dimer ( trans -2-heptenal (M)), trans-1-pentenal ( trans -2-Pentenal), trans-1-pentenal dimer ( trans -2-Pentenal (M)), cis-4-heptenal ((Z)-4-Heptenal), 2-butoxyethanol, trans-2-hexen-1-ol ( trans-2-Hexen-1-ol), 2-hexen-1-ol, 2-hexen-1-ol dimer (2-hexen-1-ol (M)), 1-hexanol, 2-ethyl-1-hexanol, 2-methyl-1-butanol, 1-butanol, 1-penten-3-ol, trans-2-hexen-1-ol dimer ( trans -2-Hexen-1-ol (M)), 1-Heptanol, 4-methyl-1-pentanol, ethyl 3-hydroxybutanoate, γ-nonanolide ( γ -nonalacton), ethyl hexanoate, hexyl acetate, β-phenylacetic acid ethyl acetate βPhenylethyl acetate, cis-3-vinyl lactate, propyl hexanoic acid ester, methyl phenylacetic acid ester, hexanoic acid hexyl ester, allyl isothiocyanate, propyl propionate, methyl hexanoic acid ester, 2-methylpropyl butyrate butanoate, cis-geranylacetone, 3-octanone, cyclohexanone, 1-octen-3-one, 2-hexanone, 6-methyl-5-hepten-2-one, 5-nonanone, 1-penen-3-one, 3-Hydroxy-2-methyl-4H-pyran-4-one, 1-hydroxy-2-propanone, 2-isopropyl-3-methoxypyrazine, 2-ethyl-5-methylpyrazine, 2-methylpyrazine, 2-methylpyrazine dimer (M)), 2,6-dimethylpyrazine, 2,3-diethyl-5-methylpyrazine (2,3-diethyl-5-methyl pyrazine), 2-acetylpyrazine, 2-ethylfuran, 2-pentylfuran, 2-acetylpyrrole, 2-acetyl-1-pyrrole, 1-ethyl-1H-pyrrole, eugenol, 4-vinyl-2-methoxyphenol, butanoic acid, acetic acid, 3-octenic acid, 4-ethylphenol, 2-methoxyphenol, linalool oxide, dipropyldisulfide, dipropyl disulfide dimer (M)), butylbenzene, thiazole, isoquinoline, disulfide dimer (M)), limonene, disulfide, rose oxide, α-limonene (, α -Limonene).

[0013] In some embodiments, the Bayesian classification discriminant for raw and roasted black sesame seeds is: Black sesame seeds: 3.089 a +7.989 b +2.117 c -45.209; Roasted black sesame seeds: 8.902 a +15.210 b +5.981 c -120.890; The Bayesian discriminant for black sesame seeds roasted to different degrees is as follows: Inadequate cooking: 14.755 a + 11.62 b + 6.229 c - 35.055; Cooked to medium doneness: 15.142 a + 11.488 b + 7.434 c - 36.146; Fried: 12.164 a + 12.667 b + 5.163 c - 36.41; Where a is the relative content of n-heptanol, b is the relative content of 2-methylpyrazine, and c is the relative content of trans-2-hexen-1-ol.

[0014] A method for processing black sesame seeds includes the following steps: The black sesame seeds were heated and roasted, and the roasting parameters were adjusted so that the roasted black sesame seeds were judged to be moderately roasted after testing using the above-mentioned testing method.

[0015] The stir-frying parameters are selected from, but are not limited to, stir-frying temperature, stir-frying time, dosage of medicine, or equipment parameters; the equipment parameters are selected from, but are not limited to, rotation speed, air volume, and temperature control probe position.

[0016] The present invention has the following advantages: The method provided by this invention can not only distinguish between raw and roasted black sesame seeds, but also judge the degree of roasting of roasted black sesame seeds. It requires no complex sample pretreatment, is simple and environmentally friendly, has low testing costs, and provides intuitive results. It solves the problems of existing evaluation methods being highly subjective and unable to quickly, objectively, and accurately evaluate the degree of processing of black sesame seeds in large quantities, laying the foundation for the large-scale, intensive, standardized, and digitalized production of traditional Chinese medicine decoction pieces. Attached Figure Description

[0017] Figure 1 These are two-dimensional top views of the volatile components of 10 batches of raw black sesame (a) and roasted black sesame (b) by GC-IMS. Figure 2 This is a comparison chart showing the differences in volatile components between raw and roasted black sesame seeds; Figure 3 These are the fingerprint spectra of volatile components from 10 batches of raw and roasted black sesame samples; Figure 4 The figures are OPLS-DA (a) and substitution test (b) plots for raw and roasted black sesame seeds; Figure 5 These are the appearance characteristics of black sesame samples with different degrees of roasting; Figure 6 This is a graph showing the differences in volatile components of black sesame seeds at different roasting levels. Detailed Implementation

[0018] The present invention will be further described below with reference to the embodiments and accompanying drawings, but the present invention is not limited to the following embodiments.

[0019] Example 1: Establishment and Validation of the Detection Method 1. Sample collection Ten batches of black sesame slices produced by different Chinese herbal medicine manufacturers were collected and identified by Professor Zhang Fang of the School of Pharmacy, Shandong University of Traditional Chinese Medicine, as *Sesamum indicum*, a plant in the family Erythraceae. Sesamum indicum The dried, mature seeds of L. are numbered SP1-SP1 sequentially. 10 Experienced pharmacists and clinical pharmacists were invited to provide guidance on the processing of roasted black sesame seeds. Following the method for roasting black sesame seeds as described in Part I of the 2025 edition of the Chinese Pharmacopoeia, and incorporating the experience of these experienced pharmacists and clinical pharmacists, samples of roasted black sesame seeds with appropriate roasting temperatures were prepared and sequentially numbered CP1-CP. 10 The relevant information collected regarding raw black sesame slices is shown in Table 1.

[0020] Table 1. Relevant information on 10 batches of raw black sesame slices. 2. GC-IMS analysis of volatile components in raw and roasted black sesame seeds Using GC-IMS (Flavor Spec) ® The volatile components were determined using a method in Germany. Raw and roasted black sesame seeds were pulverized and passed through a No. 2 sieve. 1 g of the sample powder was accurately weighed and placed in a 20 mL headspace vial, incubated at 65°C for 20 min at a rotation speed of 500 rpm / min. Then, 500 µL of the headspace phase was injected using a 70°C syringe. Separation was performed using an MXT-5 column with an IMS temperature set to 45°C. High-purity nitrogen was used as both the drift gas and the carrier gas, with the following flow rate program: 2 mL / min for the first 2 min, then linearly increased to 100 mL / min over 10 min, then linearly increased to 150 mL / min over 20 min, and finally maintained at 150 mL / min.

[0021] 3. Establishment of GC-IMS spectra of raw and roasted black sesame seeds and identification of volatile components. In the GC-IMS detection results, each spot corresponds to a specific volatile component, and the color intensity of the spot is positively correlated with the signal response intensity. Comparison revealed a significant difference in the signal response intensity of volatile components between black sesame seeds and roasted black sesame seeds. Figure 1 To more intuitively present the differences in volatile components between the two, the spectral information of the black sesame sample was used as a reference. By subtracting the signals of the same substances from the results of the corresponding roasted black sesame sample, a difference graph between raw and roasted black sesame was obtained. Figure 2 In this differential spectrum, the vast majority of signals appear red, indicating that the volatile components of black sesame seeds undergo significant changes after being roasted at high temperatures.

[0022] Identification of volatile components: Each volatile compound is represented by a two-dimensional characteristic peak consisting of retention time and migration time in the GC-IMS spectrum. Using a mixed standard solution of C4–C9 n-ketones as a reference, under conditions identical to the samples, logarithmic retention time curves were constructed using VOCal software (including the GC×IMS Library Search module), mapping the retention times of each peak in the sample to retention indices. Combining retention indices and migration times, and matching with the NIST database and the built-in IMS database, qualitative analysis of unknown volatile components was performed. A total of 80 volatile components were identified in raw and roasted black sesame seeds, including 15 aldehydes (18.75%), 12 alcohols (15.00%), 13 esters (16.25%), 9 ketones (11.25%), 7 pyrazines (8.75%), 3 furans (3.75%), 3 pyrroles (3.75%), 7 acids (8.75%), and 11 other components (13.75%) (Table 2).

[0023] Table 2. Identification results of volatile components in raw and roasted black sesame seeds. Note: "D" indicates monomer; "M" indicates dimer; "-" indicates flavor is currently unknown.

[0024] Based on the qualitative analysis results of volatile components, fingerprint spectra of volatile components in raw and roasted black sesame seeds were constructed. The results are shown in [Figure number missing]. Figure 3 The diagram is divided into four regions from left to right, labeled A, B, C, and D. Figure 3 It can be seen that the content of the 10 volatile components did not change significantly before and after black sesame roasting. These substances correspond to region A in the figure, such as limonene. After roasting, the relative content of volatile components in region B decreased significantly, such as trans-2-hexen-1-ol. After black sesame roasting, the relative content of volatile components in regions C and D both showed an increasing trend. Among them, the volatile components in region D were mainly detected in the roasted black sesame samples, such as 2-methylpyrazine and trans-2-heptenal.

[0025] 4. Bayesian qualitative discriminant analysis of raw and roasted black sesame seeds. The relative contents of 80 volatile components in 10 batches of raw and roasted black sesame seeds were calculated using the peak area normalization method, expressed as peak area percentage (%). A Bayesian discriminant was constructed for rapid identification of raw and roasted black sesame seeds, and the discriminant coefficients for n-heptanol (a), 2-methylpyrazine (b), and trans-2-hexen-1-ol (c) were finally determined. P<0.05 indicates that it has significant significance for classification. The Bayesian determination formulas for raw and roasted black sesame seeds are shown in equations (1) and (2): Y 黑芝麻 =3.089 a +7.989 b +2.117 c -45.209 (1); Y 炒黑芝麻 =8.902 a +15.210 b +5.981 c -120.890 (2).

[0026] Substitute the relative contents of n-heptanol, 2-methylpyrazine, and trans-2-hexen-1-ol in the sample to be tested into the above formula, if Y 黑芝麻 > Y 炒黑芝麻 If the result is positive, the sample is black sesame; otherwise, it is roasted black sesame.

[0027] The predictive ability of the Bayesian discriminant was tested using cross-validation. The results in Table 3 show that the qualitative discrimination accuracy for both raw and roasted black sesame seeds is 100%, which means that the Bayesian discriminant can be used to quickly identify raw and roasted black sesame seeds.

[0028] Table 3 Bayesian Discriminant Analysis Cross-Validation Bayesian discriminant analysis results indicate significant differences in the volatile components of raw and roasted black sesame seeds. Therefore, the OPLS-DA model was used to visualize these differences. Figure 4 a. Among them, R 2 X =0.856, R 2 Y =0.978, Q 2 =0.973, indicating that the model has good predictive ability. 200 permutation tests ( Figure 4 b) Verification results Q 2 The regression line intersects the ordinate at a negative value, indicating that the OPLS-DA model does not overfit, thus confirming that the model is suitable for studying the volatile components in raw and roasted black sesame seeds.

[0029] 5. Preparation of black sesame samples with different roasting degrees The same black sesame sample (SP1) was stir-fried at different temperatures (160, 180, 200, 220℃) and times (5, 10, 15, 20, 25 min) to prepare 20 batches of black sesame seeds with different roasting degrees (numbered S1-S). 20 (See Table 4).

[0030] Table 4. Black sesame samples with different roasting degrees (n = 20) Experienced pharmacists and clinical pharmacists were invited to evaluate black sesame seeds of different roasting levels based on traditional experience (color, aroma, etc.). They unanimously agreed that S1, S2, S3, S6, S7, and S... 11 and S 16 The samples belong to the category of insufficiently roasted samples; S4, S5, S8, S9, S 10 S 12 S 13 and S 17 The appearance of the sample is basically consistent with the pharmacopoeia description, belonging to the category of moderately roasted samples; S 14 S 15 S 18 S 19 and S 20 The sample had a distinct burnt, bitter taste, indicating it was over-roasted. The colors of the black sesame powder (passed through a No. 2 sieve) at different roasting levels are shown in the image. Figure 5 .

[0031] 6. GC-IMS analysis of black sesame seeds roasted to different degrees Black sesame seeds of varying roasting degrees were pulverized and passed through a No. 2 sieve. 1 g of sample powder was accurately weighed and placed in a 20 mL headspace vial, incubated at 65℃ for 20 min at a rotation speed of 500 rpm / min. Then, 500 µL of the headspace phase was injected using a 70℃ syringe. Separation was performed using an MXT-5 column with the IMS temperature set to 45℃. High-purity nitrogen was used as both the drift gas and carrier gas, with the following flow rate program: 2 mL / min for the first 2 min, then linearly increased to 100 mL / min over 10 min, then linearly increased to 150 mL / min over 20 min, and finally maintained at 150 mL / min. GC-IMS (Flavor Spectrometry) was used. ® (Germany) determined the volatile components.

[0032] In the GC-IMS detection results, each spot corresponds to a specific volatile component, and the color intensity of the spot is positively correlated with the signal response intensity.

[0033] Spectral information from black sesame seeds at different roasting levels, collected by GC-IMS, was processed. Using the raw product spectrum as a reference, signals of corresponding substances in black sesame seeds at different roasting levels were subtracted to obtain difference spectra. In these spectra, the color of the signal peaks reflects the difference in VOC content between raw and roasted black sesame seeds; the darker the color, the greater the difference. White indicates that the content of a certain component is the same in both, red indicates that the content of that component in the sample is higher than in the raw product, and blue indicates that the content of that component in the sample is lower than in the raw product. Figure 6 ).

[0034] 7. Bayesian qualitative discriminant analysis of black sesame seeds roasted to different degrees. The relative contents of 80 volatile components in 10 batches of raw and roasted black sesame seeds were calculated using the peak area normalization method and expressed as peak area percentage (%). Then, a Bayesian algorithm combined with stepwise discriminant analysis was used to classify 20 types of black sesame seeds with different roasting degrees into three categories: under-roasted, moderately roasted, and over-roasted. The results identified three variables most favorable for discrimination: n-heptanol (a), 2-methylpyrazine (b), and trans-2-hexen-1-ol (c). Based on these three key variables, a Bayesian discriminant formula for distinguishing black sesame seeds with different roasting degrees was constructed. Y 炒制不及 = 14.755 a + 11.62 b + 6.229 c - 35.055 (3) Y 炒制适中 = 15.142 a + 11.488 b + 7.434 c - 36.146 (4) Y 炒过 = 12.164 a + 12.667 b + 5.163 c - 36.41 (5) The discrimination coefficients of these three components were tested. P <0.05, which fully demonstrates that the above variables have significant statistical significance in the process of classifying the degree of roasting of black sesame seeds.

[0035] Substitute the relative contents of n-heptanol, 2-methylpyrazine, and trans-2-hexen-1-ol in the sample into formulas (3), (4), and (5). When Y 炒制不及 When the maximum value is reached, the sample is determined to be undercooked. This process is repeated to determine the sample's classification.

[0036] Table 5. Bayesian cross-validation results of black sesame seeds with different roasting levels. The predictive ability of Bayesian discrimination was tested using cross-validation. Table 6 shows that the qualitative identification accuracy reached 100% for all three types of samples: under-roasted, moderately roasted, and over-roasted. This result fully demonstrates that the Bayesian discrimination method can be applied to identify black sesame seeds of different roasting degrees.

[0037] Example 2: Practical Application of the Detection Method 1. Sample testing Volatile components were determined using GC-IMS (Flavour Spec®, Germany). Twelve batches of commercially available roasted black sesame seeds (sample information shown in Table 6) were pulverized and passed through a No. 2 sieve. 1 g of sample powder was accurately weighed and placed in a 20 mL headspace vial, incubated at 65°C for 20 min at a rotation speed of 500 rpm / min. Then, 500 µL of headspace phase was injected using a 70°C syringe. Separation was performed using an MXT-5 column, with the IMS temperature set to 45°C. High-purity nitrogen was used as both drift and carrier gas, with the following flow rate program: 2 mL / min for the first 2 min, then linearly increased to 100 mL / min over 10 min, followed by a linear increase to 150 mL / min over 20 min, and finally maintained at 150 mL / min.

[0038] Table 6. Relevant information for 12 batches of commercially available roasted black sesame seeds. 2. Quantitative Analysis Peak area data of 80 volatile components in 12 batches of commercially available black sesame samples were exported. The relative contents of three characteristic components, n-heptanol, 2-methylpyrazine and trans-2-hexen-1-ol, were calculated using the normalization method and expressed as peak area percentage (%).

[0039] 3. Identification Results in Practical Application The relative contents of n-heptanol (a), 2-methylpyrazine (b), and trans-2-hexen-1-ol (c) in 12 batches of commercially available roasted black sesame samples (Table 7) were imported into the Bayesian discriminant formulas (1) and (2) for raw and roasted black sesame obtained in Example 1. All 12 batches of samples were identified as roasted black sesame, indicating that the qualitative discrimination accuracy of raw and roasted black sesame was 100%. Further analysis of the degree of roasting of black sesame was conducted. Substituting the samples into the Bayesian discriminant formulas (3), (4), and (5) for black sesame with different degrees of processing, it was found that 3 batches of commercially available roasted black sesame belonged to the over-roasted category, while the remaining 9 batches belonged to the moderately roasted category, with a quality pass rate of 75%.

[0040] Table 7. Discriminative model results for identifying three volatile components in 12 batches of commercially available roasted black sesame seeds. The results above show that the method for detecting the degree of processing of black sesame seeds constructed in this invention can accurately identify the degree of processing of black sesame seeds, and solves the problem that existing evaluation methods are highly subjective and difficult to evaluate the degree of processing of black sesame seeds quickly, objectively and accurately in large quantities. It can be applied to the large-scale production and testing of traditional Chinese medicine decoction pieces.

[0041] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for detecting the degree of processing of black sesame seeds, characterized in that, Includes the following steps: The relative contents of n-heptanol, 2-methylpyrazine and trans-2-hexen-1-ol in the sample were determined by GC-IMS. The relative contents of the above three components were first substituted into the Bayesian classification discriminant formula for raw and roasted black sesame seeds to calculate the values. If the value of roasted black sesame seeds is larger, then it is roasted black sesame seeds; if the value of black sesame seeds is larger, then it is raw black sesame seeds. When it is determined to be roasted black sesame, the Bayesian classification discriminant of black sesame with different roasting degrees is substituted into the value to further determine the degree of roasting of black sesame. The value with the largest value is the degree of roasting of black sesame. The Bayesian classification discriminant for black sesame seeds with different roasting degrees is used to determine the roasting degree of black sesame seeds as under-roasted, moderately roasted, and over-roasted.

2. The detection method according to claim 1, characterized in that, The GC-IMS conditions were as follows: 1 g of the sample powder to be tested was accurately weighed and placed in a 20 mL headspace vial, and incubated at 65 °C for 20 min at an incubation speed of 500 rpm / min; then 500 µL of headspace phase was injected at 70 °C; separation was performed using an MXT-5 column, with the IMS temperature set at 45 °C; high-purity nitrogen was used as the drift gas and carrier gas, and the gas flow rate program was as follows: the flow rate was 2 mL / min for the first 2 min, then linearly increased to 100 mL / min within 10 min, then linearly increased to 150 mL / min within 20 min, and finally maintained at 150 mL / min.

3. The detection method according to claim 1, characterized in that, The relative content of each volatile component was calculated using the peak area normalization method and expressed as a percentage of peak area.

4. The detection method according to claim 1, characterized in that, The method for obtaining the Bayesian classification discriminant includes the following steps: Using the relative contents of all identified volatile components as independent variables, a discriminant model was established, and a stepwise method was used for discriminant analysis. Optimize the parameters of the statistical method and the step method: select Box's M for statistical descriptive analysis, select Wilks' lambda for the step method, select to use the F frequency as the judgment criterion, set it to 0.05 for entry and 0.1 for deletion; The F-value is used to determine whether the independent variable has a significant impact on the model's discrimination: an F-value less than 0.05 indicates that the independent variable has a significant impact on the model's discrimination; an F-value greater than 0.1 indicates that the independent variable has no significant impact on the model's discrimination; variables with an F-value between 0.05 and 0.1 indicate that the variable has a certain contribution to the discrimination, but the collinearity is not strong enough and does not reach the set significance level. The volatile components are: 3-(methylthio)propanal, vanillin, 5-methylfurfural, trans-2-octenal, benzaldehyde, 3-methoxy-4-hydroxybenzaldehyde, trans-2-heptenal, n-octanal, heptenal, trans-2-nonenal, trans-2-hexenal, trans-2-heptenal dimer, trans-1-pentenal, trans-1-pentenal dimer, cis-4-heptenal, 2-butoxyethanol, trans-2-hexen-1-ol, 2 -Hexen-1-ol, 2-Hexen-1-ol dimer, 1-hexanol, 2-ethyl-1-hexanol, 2-methyl-1-butanol, 1-butanol, 1-pentanol-3-ol, trans-2-hexen-1-ol dimer, 1-heptanol, 4-methyl-1-pentanol, ethyl 3-hydroxybutyrate, γ-nonanolide, ethyl hexanoate, hexyl acetate, ethyl β-phenylacetate, cis-3-vinyl lactate, propyl hexanoate, methyl phenylacetate, hexyl hexanoate, allyl isopropyl alcohol Thiocyanate, propyl propionate, methyl hexanoate, isobutyl butyrate, cis-geranione, 3-octanone, cyclohexanone, 1-octen-3-one, 2-hexanone, methylheptenone, 5-nonanone, 1-penten-3-one, maltol, hydroxyacetone, 2-isopropyl-3-methoxypyrazine, 2-ethyl-5-methylpyrazine, 2-methylpyrazine, 2-methylpyrazine dimer, 2,6-dimethylpyrazine, 2,3-diethyl-5-methylpyrazine, 2-acetylpyrazine Azides, 2-ethylfuran, 2-n-pentylfuran, 2-acetylpyrrole, 2-ethylpyrrole, 1-ethyl-1H-pyrrole, eugenol, 4-vinyl-2-methoxyphenol, butyric acid, acetic acid, 3-octenic acid, 4-ethylphenol, 2-methoxyphenol, linalool oxide, dipropyl disulfide, dipropyl disulfide dimer, butylbenzene, thiazole, isoquinoline, disulfide dimer, limonene, disulfide, rose oxide, α-limonene.

5. The detection method according to claim 1, characterized in that, The Bayesian discriminant for raw and roasted black sesame seeds is: Black sesame seeds: 3.089 a +7.989 b +2.117 c -45.209; Roasted black sesame seeds: 8.902 a +15.210 b +5.981 c -120.890; The Bayesian discriminant for black sesame seeds roasted to different degrees is as follows: Inadequate cooking: 14.755 a + 11.62 b + 6.229 c - 35.055; Cooked to medium doneness: 15.142 a + 11.488 b + 7.434 c - 36.146; Fried: 12.164 a + 12.667 b + 5.163 c - 36.41; Where a is the relative content of n-heptanol, b is the relative content of 2-methylpyrazine, and c is the relative content of trans-2-hexen-1-ol.

6. A method for processing black sesame seeds, characterized in that, Includes the following steps: The black sesame seeds are heated and roasted, and the roasting parameters are adjusted so that the roasted black sesame seeds are judged to be moderately roasted after being tested by the detection method described in any one of claims 1-5.

7. The method for processing black sesame seeds according to claim 6, characterized in that, The stir-frying parameters are selected from at least one of stir-frying temperature, stir-frying time, dosage of medicine, and equipment parameters; the equipment parameters are selected from at least one of rotation speed, air volume, and temperature control probe position.

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