Method for predicting wheat kernel germination rate based on gc-ims analysis of characteristic volatiles

Abstract

The application discloses a method for predicting the germination rate of wheat kernels based on GC-IMS analysis of characteristic volatile substances, comprising the following steps: step one, detecting the relative content of characteristic volatile substances under the current storage time of the sample to be predicted, wherein the characteristic volatile substances include isoamyl butyrate, cis-3-nonen-1-ol, 1-propanol and propionaldehyde; step two, inputting into a prediction model to output the germination rate of the sample, Y = 126.265*S1-65.982*S2+56.313*S3+6.42*S4+61.332, wherein S1 represents isoamyl butyrate, S2 represents cis-3-nonen-1-ol, S3 represents 1-propanol, and S4 represents propionaldehyde. The application provides a new method for quickly, simply and conveniently predicting the germination rate of wheat kernels, realizes the fast, nondestructive, effective and stable prediction of the germination rate of wheat, and provides scientific and technological support for the pricing of wheat, the management of wheat collection and storage and the like.

Description

Technical Field

[0001] This invention relates to the field of agricultural product storage and food flavor analysis technology. More specifically, this invention relates to a method for predicting wheat grain germination rate based on GC-IMS analysis of characteristic volatile substances. Background Technology

[0002] Wheat is one of my country's important staple crops, playing a vital role in food security. Effective management of wheat storage is an important measure to ensure grain quality and minimize economic losses. A key indicator of wheat storage quality management is the germination rate, which assesses the enzyme activity of wheat and is closely related to the quality of subsequent food processing, directly affecting its market value.

[0003] Traditional methods for determining wheat grain germination rate include manual methods such as soil cultivation and hydroponics, staining methods, conductivity methods, enzymatic methods, and variable temperature germination chamber methods. These methods are labor-intensive, have long measurement cycles, and the reliability and stability of the test results depend on environmental factors and the operator's skill level, posing challenges for large-scale grain harvesting and on-site pricing. In recent years, rapid methods for determining seed germination rate have emerged, such as hyperspectral online monitoring, fluorescence spectroscopy, near-infrared spectroscopy, and machine vision technology. These methods, which collect images or spectra of large numbers of samples for modeling, are widely used. However, these methods are limited by grain appearance, sample size, and model stability. Therefore, further research is needed to explore faster, more accurate, and non-destructive methods for determining wheat grain germination rate to overcome the shortcomings of existing technologies. Summary of the Invention

[0004] One object of the present invention is to solve at least the above-mentioned problems and to provide at least the advantages that will be described later.

[0005] To achieve these objectives and other advantages according to the present invention, a method for predicting wheat grain germination rate based on GC-IMS analysis of characteristic volatile substances is provided, comprising the following steps:

[0006] Step 1: Detect the relative content of characteristic volatile substances in the sample under the current storage time. The content is expressed as the peak volume of each volatile substance reaction peak in IMS. The ratio of the migration time of each volatile substance to the reaction ion peak is normalized. The relative content level of volatile substances is expressed as the relative intensity of the peak volume. Characteristic volatile substances include isoamyl butyrate, cis-3-nonen-1-ol, 1-propanol, and propanal.

[0007] Step 2: Input the relative content of volatile substances detected in Step 1 into the prediction model, and output the germination rate of the sample. The prediction model is shown in Formula 1:

[0008] Y=126.265×S1-65.982×S2+56.313×S3+6.42×S4+61.332 Formula 1

[0009] Where S1 represents the content of isoamyl butyrate, S2 represents the content of cis-3-nonen-1-ol, S3 represents the content of 1-propanol, S4 represents the content of propanal, and Y represents the germination rate, in units of %.

[0010] Preferably, the storage parameters of the wheat grains used to establish the prediction model are the same as those of the sample to be predicted.

[0011] Preferably, in step one, the volatile substances of the sample to be tested are extracted using headspace method, and the content of volatile substances is detected by gas chromatography-ion mobility spectrometry.

[0012] Preferably, the method for establishing a predictive model includes the following steps:

[0013] Volatile substances were collected from wheat grains stored for different times, and the types and contents of volatile substances were determined by gas chromatography-ion mobility spectrometry.

[0014] Germination experiments of wheat grains under different storage times were conducted to obtain the germination rate of wheat grains;

[0015] Based on the germination rate of wheat grains and the types and contents of volatile substances, correlation analysis and stepwise regression analysis were used to screen out key characteristic volatile substances and obtain the prediction model.

[0016] Preferably, the method for detecting the germination rate of wheat grains is as follows: two layers of gauze and two layers of filter paper are placed in a petri dish and moistened. Wheat grains are evenly placed on the filter paper and cultured for 3 days at 20°C and 60% relative humidity, with the grains being moistened 2 to 3 times a day.

[0017] The germination rate was calculated according to Formula 2. Multiple parallel experiments were conducted, and the average value of the results from the multiple parallel experiments was used to obtain the germination rate of wheat grains. Formula 2 is shown below:

[0018]

[0019] In the formula: X represents the wheat grain germination rate, expressed as a percentage (%); M1 represents the number of grains that germinated normally within 3 days; M represents the number of grains in a sample group.

[0020] Preferably, the specific method for detecting the content of volatile substances by gas chromatography-ion mobility spectrometry includes:

[0021] Weigh 5g of wheat grain sample and place it in a 20mL headspace vial, then seal it with a PTFE silicone stopper. Incubate at 75℃ for 20min, then inject 500μL of headspace into a syringe at 85℃. After injection, volatile compounds are passed through an FS-SE-54-CB-1 column (15m capillary, 0.53mm inner diameter, 1μm film thickness) at 60℃. Nitrogen is used for separation at 45℃ and is also used as the carrier gas for GC and the drift gas for IMS. The flow program is as follows: initial carrier gas flow rate 2mL / min, hold for 2min, linearly increase to 15mL / min within 10min, linearly increase to 100mL / min within 10min, and finally increase to 150mL / min within the last 10min. The total analysis time is 25min, and three replicates are set for each sample.

[0022] The present invention has at least the following beneficial effects:

[0023] First, compared to standard methods, the detection of volatile substances allows for the assessment of wheat quality using a small sample size without affecting seed viability, providing a non-destructive method. Furthermore, volatile substances better reflect the intrinsic germination rate of wheat seeds and can avoid germination experiment failures caused by seed mold during hot seasons. Gas chromatography-ion mobility spectrometry (GC-IMS), as an emerging volatile odor characterization technique, is used for the separation and sensitive detection of volatile substances. It offers advantages such as high sensitivity, high resolution, fast response, simple operation, no sample pretreatment required, and detection under normal pressure. GC-IMS can acquire a large number of volatile substance detection signals in a short time, providing rapid and automated analysis.

[0024] Secondly, this invention is the first to correlate volatile components with germination rate, establishing a stable and effective prediction model that reduces the detection time for germination rate from the traditional 3-7 days to 1-2 hours. This invention provides a new method for predicting wheat germination rate, offering technological support for wheat grain pricing, storage management, and other related processes.

[0025] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0026] The present invention will now be described in further detail so that those skilled in the art can implement it based on the description.

[0027] It should be noted that, unless otherwise specified, the experimental methods described in the following implementation plan are all conventional methods, and the reagents and materials described are all commercially available unless otherwise specified.

[0028] 1. Preparation of wheat grain samples with different germination rates

[0029] Grain samples from four wheat varieties were collected: Gaoyou 5766, Fengdecun 5, Zhongmai 578, and Jimai 22. After cleaning, impurity removal, and sun-drying, the samples were numbered 1, 2, 3, and 4. 200g of wheat grain samples were placed flat in mesh bags and subjected to accelerated storage at 40℃ and 65% relative humidity for 28 days, followed by natural storage at room temperature and away from light for 270 days. Samples from the accelerated storage samples were collected on days 0, 7, 14, 21, and 28, while samples from the natural storage samples were collected on days 0, 90, 180, and 270.

[0030] 2. Determination of wheat grain germination rate

[0031] Germination rate determination: Two random samples of intact grains, 100 grains in each sample, were taken. The germination rate was determined according to the method in GB / T 5520-2011. Two layers of gauze and two layers of filter paper were placed in a petri dish and moistened. Wheat grains were evenly placed on the filter paper and cultured at 20℃ and 60% relative humidity for 3 days, with the grains moistened 2-3 times a day. The germination rate was calculated according to Formula 2, and the average value of the three experimental results was calculated.

[0032]

[0033] In the formula: X represents the wheat grain germination rate, expressed as a percentage (%); M1 represents the number of grains that germinated normally within 3 days; M represents the number of grains in a sample group.

[0034] 3. Determination of volatile substances in wheat grains stored for different times

[0035] Volatile components were extracted using headspace analysis, and the volatile substances in wheat samples were determined and analyzed by gas chromatography (GC, Agilent 490, Palo Alto, CA, USA) combined with ion mobility spectrometry (IMS, GAS, Dortmund, Germany). Gas chromatography-Ion mobility spectrometry (GC-IMS) The instrument is equipped with an automated CTC system (Swinghan, Switzerland), which includes an autosampler, syringe, heated splitless sampler, and a radioactive ionization source for headspace (HS) analysis.

[0036] At each sampling time point, 5g of wheat grain sample was weighed and placed in a 20mL headspace vial, which was then sealed with a PTFE silicone stopper. For each treatment at each sampling time, the sample was incubated at 75℃ for 20 min, and then 500μL of headspace gas was injected into a syringe at 85℃. After injection, volatile compounds were separated at 60℃ using an FS-SE-54-CB-1 column (15m capillary, 0.53mm inner diameter, 1μm film thickness). Nitrogen (purity >99.99%) was used for separation at 45℃ and was also used as the carrier gas for GC and the drift gas for IMS. The flow program was as follows: initial carrier gas flow rate 2mL / min, held for 2 min, linearly increased to 15mL / min over 10 min, linearly increased to 100mL / min over 10 min, and finally increased to 150mL / min over the last 10 min. The total analysis time was 25 min, with three replicates per sample.

[0037] Retention indices (RIs) of volatile organic compounds (VOCs) were calculated using C4–C9 N-ketones (Sinopharm Chemical Reagent Beijing Co., Ltd., China) as external standards. Volatile substances were identified by comparing the RIs and drift times (Dt, the time required for ions to reach the collector via the drift tube, in milliseconds) of standards in GC-IMS libraries (GAS, Dortmund, Germany). GC-IMS fingerprint analysis was performed by comparing GC retention times and IMS drift times. The built-in NIST gas-phase retention index database and GASIMS migration time database were used to characterize the VOCs in the samples. These data were derived from two-dimensional spectra, where each point represents a VOC. Data are expressed as the peak volume of each VOC reaction peak in IMS. Normalization was performed by the ratio of the VOC migration time to the reaction ion peak (RIP), and the relative abundance level of VOCs was expressed as the relative intensity of the peak volume.

[0038] 4. Extraction of characteristic volatile substances from the sample

[0039] Correlation analysis was used to extract characteristic volatile substances from wheat grain samples. Wheat grain samples numbered 1, 2, and 3 were used to establish a regression model, and a total of 31 characteristic volatile substances related to germination rate were extracted.

[0040] 5. Establishment of the prediction model

[0041] The germination rate and volatile matter content of wheat grain samples numbered 1, 2, and 3 were determined. SPSS software was used to perform correlation and stepwise regression analyses on the germination rate and volatile matter content to screen key characteristic volatile substances. A wheat grain germination rate prediction model was then established, as detailed below:

[0042] Y=126.265×S1-65.982×S2+56.313×S3+6.42×S4+61.332 Formula 1

[0043] Where Y is the germination rate in %; the independent variable S1 is isoamyl butyrate; S2 is cis-3-nonen-1-ol; S3 is 1-propanol; and S4 is propionaldehyde.

[0044] Table 1. Measured values ​​of germination rate and key characteristic volatile substances.

[0045]

[0046]

[0047] 6. Accuracy evaluation of the prediction model

[0048] The wheat grain sample numbered 4, Jimai 22, was selected and stored under the same conditions of light protection and room temperature for 270 days. Volatile substances were measured every 90 days to test the model, and the measured value of its germination rate was determined.

[0049] Substitute the measured values ​​of isoamyl butyrate S1, cis-3-nonen-1-ol S2, 1-propanol S3, and propanal S4 of the sample into the prediction model: Y = 126.265 × S1 - 65.982 × S2 + 56.313 × S3 + 6.42 × S4 + 61.332, and calculate the predicted germination rate (Y) for the three sampling time points respectively.

[0050] As shown in Table 2, the error between the predicted germination rate and the actual germination rate is within ±2%, indicating high prediction accuracy. Correlation analysis using SPSS statistical software showed that the predicted and measured values ​​of the germination rate at the three sampling points had high correlation coefficients and reached a highly significant level (p<0.01), indicating that the prediction model can quickly predict the germination rate of wheat grains, with the advantages of speed and high accuracy.

[0051] Table 2. Measured and predicted values ​​of the blind sample

[0052]

[0053] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A method for predicting wheat grain germination rate based on GC-IMS analysis of characteristic volatile substances, characterized in that, Includes the following steps: Step 1: Detect the relative content of characteristic volatile substances in the sample under the current storage time. The content is expressed as the peak volume of each volatile substance reaction peak in IMS. The ratio of the migration time of each volatile substance to the reaction ion peak is normalized. The relative content level of volatile substances is expressed as the relative intensity of the peak volume. The characteristic volatile substances are isoamyl butyrate, cis-3-nonen-1-ol, 1-propanol and propanal. Step 2: Input the relative content of volatile substances detected in Step 1 into the prediction model, and output the germination rate of the sample. The prediction model is shown in Formula 1: Official 1 Wherein, S1 represents the content of isoamyl butyrate, S2 represents the content of cis-3-nonen-1-ol, S3 represents the content of 1-propanol, S4 represents the content of propanal, and Y represents the germination rate, in percentages (%). The storage parameters of the wheat grains used to build the prediction model are the same as those of the sample to be predicted. The method for building a predictive model includes the following steps: Volatile substances were collected from wheat grains stored for different times, and the types and contents of volatile substances were determined by gas chromatography-ion mobility spectrometry. Germination experiments of wheat grains under different storage times were conducted to obtain the germination rate of wheat grains; Based on the germination rate of wheat grains and the types and contents of volatile substances, correlation analysis and stepwise regression analysis were used to screen out key characteristic volatile substances and obtain the prediction model. In step one, headspace analysis was used to extract volatile substances from the sample to be tested, and gas chromatography-ion mobility spectrometry was used to detect the relative content of volatile substances. Specific methods for detecting volatile substances using gas chromatography-ion mobility spectrometry include: Weigh 5 g of wheat grain sample and place it in a 20 mL headspace vial, then seal it with a PTFE silicone stopper. Incubate at 75 °C for 20 min, and then inject 500 μL of headspace into a syringe at 85 °C. After injection, volatile compounds are separated at 60 °C using an FS-SE-54-CB-1 column (capillary column length 15 m, inner diameter 0.53 mm, membrane thickness 1 μm). Nitrogen is used for separation at 45 °C and is also used as the carrier gas for GC and the drift gas for IMS. The flow program is as follows: initial carrier gas flow rate 2 mL / min, hold for 2 min, linearly increase to 15 mL / min within 10 min, linearly increase to 100 mL / min within 10 min, and finally increase to 150 mL / min within the last 10 min. The total analysis time is 25 min, and three replicates are set for each sample.

2. The method for predicting wheat grain germination rate based on GC-IMS analysis of characteristic volatile substances as described in claim 1, characterized in that, The specific method for detecting the germination rate of wheat grains is as follows: two layers of gauze and two layers of filter paper are placed in a petri dish and moistened. Wheat grains are evenly placed on the filter paper and cultured for 3 days at 20℃ and 60% relative humidity, with the grains being moistened 2-3 times a day. The germination rate was calculated according to Formula 2. Multiple parallel experiments were conducted, and the average value of the results from the multiple parallel experiments was used to obtain the germination rate of wheat grains. Formula 2 is shown below: Official 2 In the formula: X represents the wheat grain germination rate, expressed as a percentage (%); M1 represents the number of grains that germinated normally within 3 days; M represents the number of grains in a sample group.

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