Marking substance and rapid detection method for mildew of wine brewing raw grain wheat

Abstract

The invention discloses a marking substance for wheat mildewing in wine brewing unprocessed grains and a rapid detection method, which are used for rapidly detecting wheat mildewing on the basis of six scientifically screened marking substances including cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, tert-butyl alcohol, 2-butanone and nonanal, and providing an accurate basis for judging a wheat mildewing stage. Wine brewing raw grain wheat mildew stage division is realized, and wheat mildew detection accuracy is improved. Based on the early-stage mildew marker substance, early-stage detection of wheat mildew is realized, and the detection sensitivity is enhanced; based on the signal intensity and combination characteristics of the marker substance, determining the main mould types of wheat mildew; besides, the rapid detection of the mildew of the wine-making raw grain wheat is verified by multiple methods, and the similarity calculation, dynamic principal component analysis, clustering heat map analysis and voting method or weighted scoring model of mildew samples at different mildew stages are combined, so that the cross validation of the multiple methods is realized; and the accuracy and reliability of determining the mildew stage and the mold type are improved.

Description

Technical Field

[0001] This invention belongs to the field of detection technology for raw grains in the wine industry, specifically relating to a marker substance for mold growth in wheat used for brewing and a rapid detection method. Background Technology

[0002] Raw materials for brewing are the fundamental substances that determine the quality of baijiu (Chinese liquor). Commonly used raw materials for brewing include wheat, sorghum, rice, and corn, with wheat being the primary raw material. However, wheat grains are susceptible to contamination by putrefactive or toxin-producing fungi during field and storage, leading to the growth of various molds, causing wheat spoilage, which in turn affects the flavor of baijiu and can even result in excessive levels of toxins, causing food safety issues. Therefore, wheat mold is a crucial indicator that breweries pay close attention to during the raw material acceptance process.

[0003] With the continuous development of chromatography and spectroscopy, the detection of molds in the food industry has shifted towards the detection of mold growth metabolites such as mycotoxins and volatile organic compounds (VOCs). Microbial volatile organic compounds (VOCs) refer to the primary or secondary metabolic volatile compounds produced by bacteria and fungi during their growth and reproduction. During the mold growth process in wheat, molds consume nutrients, thereby producing and releasing various metabolic volatile products. Gas chromatography-ion mobility spectrometry (GC-IMS), as an emerging detection technique for identifying VOCs in complex matrices, offers advantages over other techniques, including simplicity, high sensitivity, no sample pretreatment required, and non-destructive screening. GC-IMS has been applied to the detection of Aspergillus flavus infection in wheat. For example, CN 113341006 A provides a method for distinguishing and predicting the proportion of wheat infected with Aspergillus flavus using GC-IMS. However, the accuracy of this method is not good, and it is only applicable to the detection of Aspergillus flavus infection. It cannot be applied to samples infected with complex molds, and its applicability is low.

[0004] Existing technologies using GC-IMS in the detection of mold in other products mainly focus on determining whether mold has occurred, with limited research on detecting mold stages in wheat. For example, CN 111521708 B provides a specific molecular marker for Aspergillus flavus infection in corn, peanuts, and walnuts, and a method for detecting early mold growth using this marker. The method confirms the presence of early mold growth in stock samples based on the migration spectral characteristic peaks of this specific molecular marker in the GC-IMS spectrum. However, because the selected mold marker is specific to early mold growth, it cannot detect other stages of mold growth, and the detected mold is limited to Aspergillus flavus infection, making it unsuitable for mixed mold infections, thus limiting its applicability.

[0005] The mold growth process involves the infection of multiple molds, such as Aspergillus, Penicillium, and Mucor. Detection methods targeting only a single mold cannot be used to detect mold growth caused by other molds. Therefore, detection methods targeting single molds cannot meet practical needs, and it is necessary to develop mold growth detection methods that can detect mixed mold infections.

[0006] Therefore, it is necessary to study markers that comprehensively cover all stages of mold growth and develop rapid mold detection methods that can accurately detect different stages of mold growth and are also applicable to the detection of mixed mold infections. Summary of the Invention

[0007] To address the technical problem of comprehensively covering the markers of the above-mentioned mold growth stages and developing a rapid mold detection method that can accurately detect the mold growth stages and is also applicable to the detection of mixed mold infections, this application provides a marker for mold growth in brewing grain wheat and a rapid detection method therein.

[0008] Firstly, this application provides a marker for mold growth in wheat used for brewing, comprising six markers: cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, tert-butanol, 2-butanone, and nonanal. The first aspect of this application is that by selecting the aforementioned six biomarkers, the accuracy of mold detection in wheat samples can be improved, reflecting information about wheat mold from multiple dimensions. The signal intensities of cyclopentanone monomer, ethyl 3-hydroxybutyrate, and propyl acetate increase with the duration of mold growth, and their changes can directly indicate the occurrence and development of mold. Tert-butanol, 2-butanone, and nonanal, on the other hand, exhibit significant changes in the early stages of mold growth. Their inclusion provides richer data dimensions for detection, helping to more comprehensively capture the complex changes during the mold growth process. Simultaneous detection of these six biomarkers allows for cross-validation of the data. When the signal intensity of one biomarker is affected by external factors or detection errors, the data from other biomarkers can serve as a reference, helping to confirm the reliability of the detection results and thus reducing the possibility of misjudgment.

[0009] The first aspect of this application is that by selecting tert-butanol, 2-butanone, and nonanal as markers, the detection sensitivity can be improved. Tert-butanol, 2-butanone, and nonanal markers show changes in signal intensity in the early stage of mold growth, and their change trends are relatively obvious. Even when the degree of mold growth is mild and the signal intensity of other markers does not change significantly, these markers can still sensitively reflect the early signs of mold growth, which helps to achieve early warning of mold growth in wheat.

[0010] In the first aspect, this application enables the division of mold growth stages by selecting the aforementioned six markers. Tert-butanol, 2-butanone, and nonanal markers show changes in signal intensity in the early stages of mold growth, and the trend of change is relatively obvious, which can be used to determine the early stages of mold growth in wheat samples. Cyclopentanone monomer, ethyl 3-hydroxybutyrate, and propyl acetate gradually increase in signal intensity with the increase of mold growth time, and are significantly higher than other stages in the later stages of mold growth, which can directly indicate the occurrence and development of mold growth.

[0011] Different molds produce specific volatile organic compounds during their metabolism. In the first aspect of this application, by selecting the aforementioned six marker substances, the identification of mold species causing wheat mold can be aided. Ethyl 3-hydroxybutyrate and cyclopentanone monomer are markers for Mucor genus; propyl acetate and 2-butanone are strongly associated with the reproduction of Penicillium genus; and tert-butanol is also strongly associated with the reproduction of Penicillium genus. By detecting the signal intensity and combination characteristics of these markers, the main mold species causing wheat mold can be preliminarily inferred, providing more accurate information for subsequent targeted prevention and treatment.

[0012] The mold species that caused the mold growth on the wheat used for brewing were Mucor, Aspergillus, and Penicillium.

[0013] The first aspect of this application links marker substances with molds. Mucor, Aspergillus, and Penicillium are the main moldy fungi in wheat, and their metabolites are directly related to the marker substances in the first aspect. Cyclopentanone monomer and ethyl 3-hydroxybutyrate are closely related to the metabolic activities of Mucor, while propyl acetate and 2-butanone are strongly related to the metabolic activities of Penicillium, and tert-butanol is strongly related to the metabolic activities of Aspergillus. Through the marker substances in the first aspect, the main moldy fungi can be inferred, providing a basis for subsequent prevention or treatment measures and for understanding the mold mechanism of wheat mold.

[0014] Among the selected markers, ethyl 3-hydroxybutyrate and cyclopentanone are markers of the metabolism of Mucor molds, which can accurately identify the presence of Mucor molds during the wheat mold process and sensitively reflect the metabolic activities of Mucor molds during the wheat mold process. Secondly, this application rapidly detects mold growth in brewing wheat based on the markers described in the first aspect, including the following steps: S1: Construct a GC-IMS database of signal intensities for six markers—cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, 2-butanone, tert-butanol, and nonanal—in mold samples at different stages of mold growth. S2: Perform GC-IMS detection on the wheat sample to be tested, and extract the signal intensity of six markers from the GC-IMS spectrum of the wheat sample to be tested; S3: Compare the signal intensity of 2-butanone, the marker of wheat sample to be tested, with the signal intensity of 2-butanone in the 0d sample. If the signal intensity of 2-butanone in the wheat sample to be tested is similar to that in the 0d sample, it is determined that the wheat sample to be tested has not undergone mold growth. If the signal intensity of 2-butanone in the wheat sample to be tested is higher than that in the 0d sample, a similarity algorithm is used to calculate the similarity between the wheat sample to be tested and moldy samples at each different mold growth stage in the database. The moldy sample with the highest similarity is selected as the matching result, and the mold growth stage corresponding to the moldy sample with the highest similarity is determined as the mold growth stage of the wheat sample to be tested.

[0015] The second aspect of this application is that by comparing the signal intensity of 2-butanone in the sample to be tested with that in a non-moldy sample (0 days), the non-moldy sample can be immediately excluded, significantly reducing the detection time.

[0016] If the 2-butanone signal is elevated, the full database of six markers is invoked, and the closest mold growth stage is matched using a similarity algorithm, avoiding full database analysis of all samples and shortening the detection cycle (samples without mold growth can be quickly screened out). For suspected moldy samples, multi-marker joint analysis is used to reduce misjudgments caused by fluctuations in single markers and improve accuracy. The synergistic effect of multiple markers enhances the ability to distinguish the stages of mold growth in wheat. 2-Butanone reaches its peak at 2 days of mold growth, and tert-butanol maintains a high signal from 2 to 6 days, jointly indicating early mold growth, mold progression, and Penicillium and Aspergillus molds. The signals of cyclopentanone monomer, propyl acetate, and ethyl 3-hydroxybutyrate continue to rise and reach their maximum value in the late stage of mold growth, jointly indicating mold progression, late stage of mold growth, and succession of Mucor and Penicillium molds. The six markers cover the entire stage of mold growth, avoiding the stage blind spots of traditional single-marker detection. Cross-validation of multiple markers can offset the influence of environmental noise (such as temperature and humidity fluctuations) on single signals.

[0017] This application, based on the biomarker substances described in the second aspect, for the rapid detection of mold in brewing wheat, also includes the following steps: S4: Based on dynamic principal component analysis, cross-validation of the wheat samples to be tested is performed, including the following steps: S41: Perform dynamic principal component analysis on moldy samples at different moldy stages to obtain dynamic principal component score maps and factor loading maps including six marker substances for moldy samples at different moldy stages, and determine the moldy stage regions of moldy samples at different moldy stages. S42: Perform dynamic principal component analysis on the wheat sample to be tested, obtain the PCA score of the wheat sample to be tested, and match it with the mold stage region of the mold sample at different mold stages in S41 to determine the mold stage of the wheat sample to be tested.

[0018] S5: Based on cluster heatmap analysis, cross-validation of the wheat samples to be tested is performed, including: S51: Perform clustering heatmap analysis on the signal peak volumes of substances with a projection importance greater than 1 in moldy samples at different stages of mold growth to obtain clustering heatmaps of marker substances in moldy samples at different stages of mold growth, including six marker substances. S52: Perform cluster heatmap analysis on the signal intensity of the six markers of the wheat sample to be tested. Compare the values ​​of the six markers in the cluster heatmap of the sample to be tested with the values ​​in the cluster heatmaps of the markers at different stages of mold growth in S51 to determine the stage of mold growth and the species of mold in the wheat sample to be tested.

[0019] S6: The mold stage of the wheat sample to be tested, determined by S3, is cross-validated with the mold stage of the wheat sample to be tested based on dynamic principal component analysis in S4, or with the mold stage of the wheat sample to be tested based on cluster heatmap analysis in S5. The mold stage of the wheat sample to be tested is then determined using a simple voting method or a weighted scoring model to determine the mold stage and mold genus of the wheat sample to be tested.

[0020] The second aspect of this application employs multi-method validation, combining dynamic principal component analysis, cluster heatmap analysis, and voting methods or weighted scoring models. This cross-validation of multiple methods improves the accuracy and reliability of determining the mold growth stage and mold species. Dynamic principal component analysis and cluster heatmap analysis comprehensively consider the signal intensity and changing trends of various markers, ensuring a comprehensive assessment of moldy samples. Dynamic principal component score maps, factor loading maps, and cluster heatmaps are used to accurately delineate and match mold growth stage regions, improving the accuracy of mold growth stage determination. A simple voting method or weighted scoring model quickly integrates the results of different analytical methods, achieving efficient determination of moldy samples and saving time and resources. It is applicable to sample detection at different mold growth stages, providing accurate determination results for both early and late mold growth, demonstrating broad applicability.

[0021] Thirdly, this application provides a method for obtaining the marker substance described in the first aspect, comprising the following steps: S1': Preparation of moldy samples at different stages of mold growth; under conditions of high temperature and high humidity accelerating mold growth, moldy samples at different stages of mold growth were prepared after different mold growth times. The moldy samples at different stages of mold growth include those with mold growth durations of 0d, 2d, 4d, 6d, 8d, 10d, and 12d. S2': Obtain the dominant mold for wheat mold; obtain the dominant mold for wheat mold from the moldy sample prepared in S1' by microbial isolation method, and culture the dominant mold for wheat mold; S3': The volatile metabolites of the dominant moldy fungus in wheat in S2' were detected by GC-IMS at different stages of mold growth in S1' and S2', respectively. GC-IMS spectra of volatile organic compounds in moldy samples at different stages of mold growth and fingerprint spectra of volatile metabolites of the dominant moldy fungus were obtained. S4': Based on the GC-IMS spectra of volatile organic compounds in moldy samples at different stages of mold growth in S3', characteristic volatile organic compounds in moldy samples at different stages of mold growth are obtained, and the characteristic volatile organic compounds are qualitatively analyzed as candidate wheat mold growth markers. S5': Based on S4', fingerprint spectra of characteristic volatile organic compound signal absorption peaks in moldy samples at different mold growth stages were prepared to perform differential analysis of characteristic volatile organic compounds in moldy samples at different mold growth stages. S6': Based on the fingerprint spectrum of volatile metabolites of dominant molds obtained in S'3, establish fingerprint spectrum of volatile metabolites of single molds, compare the differences in metabolites of single species, and identify the respective marker substances of dominant fungi that cause wheat mold. S7': Initial screening markers; The substances with a material variable projection importance greater than 1 among the candidate wheat mold markers mentioned in S4' are used as initial screening markers for wheat mold. S8': Validate the primary screening marker; Dynamic principal component analysis was used to perform VOC cluster analysis on mold samples at different stages of mold growth using the primary screening marker S7' to verify whether the primary screening marker can accurately distinguish mold samples at different stages of mold growth. S9': The initial screening markers are optimized to obtain wheat mold markers; the wheat mold markers are six markers: cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, tert-butanol, 2-butanone, and nonanal. The primary screening substances in S7' include: cyclopentanone monomer, 2-butanone dimer, ethanol, n-hexanone monomer, ethyl 3-hydroxybutyrate, 2-pentanone, acetic acid monomer, cyclopentanone dimer, tert-butanol, diisobutyl ketone, nonanal, n-butyraldehyde dimer, propyl acetate, isobutanol monomer, isoamyl alcohol monomer, furfural, propionaldehyde monomer, n-propanol monomer, n-butyraldehyde monomer, isobutanol dimer, n-pentanol monomer, and n-butanol monomer.

[0022] This application employs accelerated mold growth testing to prepare samples with different mold growth durations from 0 to 12 days, comprehensively covering all stages from non-moldy to heavily moldy wheat, providing a rich sample base for subsequent analysis and maximally reproducing the true state of wheat mold growth. Microbial isolation methods are used to obtain dominant moldy fungi from the moldy samples and cultivate them, enabling targeted research on major mold-causing species. GC-IMS technology is used to detect volatile metabolites of samples at different mold growth stages and dominant fungi, obtaining detailed GC-IMS spectra and fingerprint spectra, providing precise data support for biomarker screening and analysis. Qualitative analysis is also conducted. By employing differential analysis and combining it with the standard of a projected importance (VIP) value greater than 1 for material variables, preliminary marker substances were scientifically screened, and six representative mold marker substances were further selected to ensure the accuracy and reliability of the markers. Dynamic principal component analysis was used to validate the preliminary marker substances, ensuring that the selected marker substances could accurately distinguish samples at different stages of mold growth, thus improving the reliability of the detection method. The entire process systematically integrates sample preparation, mold isolation, detection and analysis, marker screening and validation, forming a complete and optimized detection method that provides an efficient and accurate solution for the rapid detection of wheat mold.

[0023] This application provides a marker for mold growth in brewing wheat and a rapid detection method. Based on six scientifically screened and validated wheat mold detection markers—cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, tert-butanol, 2-butanone, and nonanal—it provides rapid detection of mold growth in brewing wheat, covering the entire mold growth process and providing accurate criteria for determining the mold growth stage. This enables the segmentation of mold growth stages in brewing wheat and improves the accuracy of mold detection. The addition of early mold growth markers enables early detection of mold growth, enhancing detection sensitivity. Furthermore, based on the signal intensity and combination characteristics of the markers, the main mold species causing the mold growth can be inferred. This application validates the rapid detection of mold growth in brewing wheat using multiple methods, including similarity calculation with mold samples at different stages, dynamic principal component analysis, cluster heatmap analysis, and voting methods or weighted scoring models. This cross-validation of multiple methods improves the accuracy and reliability of determining the mold growth stage and mold species. Attached Figure Description

[0024] The present disclosure will be further described in detail below with reference to the accompanying drawings and preferred embodiments. However, those skilled in the art will appreciate that these drawings are drawn for the purpose of explaining the preferred embodiments only and should therefore not be construed as limiting the scope of the present disclosure. Furthermore, unless specifically indicated, the drawings are only schematic representations of the composition or structure of the described objects and may contain exaggerated depictions, and the drawings are not necessarily drawn to scale.

[0025] Figure 1 Morphological diagram of dominant mold fungi causing wheat mold; Figure 2 GC-IMS spectra of moldy samples at different stages of mold growth (2a. Three-dimensional spectra; 2b. Two-dimensional spectra; 2c. Differential spectra); Figure 3 Fingerprint of volatile metabolites from a single dominant mold fungus causing wheat mold growth; Figure 4 GC-IMS qualitative analysis results of the moldy sample; Figure 5 Fingerprints of volatile organic compounds in moldy samples at different stages of mold growth; Figure 6 PLS-DA analysis of the importance of projected variables; Figure 7 Principal component analysis of substances with VIP > 1 (7a. Score plot; 7b. Factor loading plot); Figure 8 Heatmap of signal peak intensity of marker substances in the initial screening of moldy wheat samples; Detailed Implementation

[0026] The following is in conjunction with the appendix Figures 1 to 8 This disclosure will be explained in detail.

[0027] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this disclosure.

[0028] Materials and reagents: The wheat samples were harvested in Wucheng, Dezhou, Shandong Province in October 2022. The variety was Jimai. The samples were immediately frozen and transported at low temperature after collection.

[0029] Bengal Red Culture Medium (Beijing Aoboxing Biotechnology Co., Ltd.)

[0030] Acetone, 2-Butanone; 2-Pentanone, 2-Hexanone, 2-Hepanone, 2-Octanone, 2-Nonanone (analytical grade, Beijing Sinopharm Chemical Reagent Co., Ltd.)

[0031] Instruments and equipment: FlavourSpec® 1H1-00053 gas ion mobility spectrometer: equipped with LAV, Reporter, Gallery Plot plugins and built-in GC×IMS Library Search NIST and IMS databases (GAS, Germany); CTC-PAL autosampler (CTC Analytics AG, Switzerland); WAX capillary column (30 m × 0.53 mm, 1 μm) (RESTEK, USA); 20 mL headspace vial (Haineng Future Technology Group Co., Ltd.).

[0032] FA224 electronic balance (Shanghai Shunyu Hengping Scientific Instruments Co., Ltd.)

[0033] LHR-250-S Constant Temperature and Humidity Incubator (Shaoguan Taihong Medical Equipment Co., Ltd.)

[0034] G154DWS Vertical Fully Automatic Autoclave (Nanjing Gengchen Scientific Instruments Co., Ltd.)

[0035] SW-CJ-2D Clean Bench (Suzhou Jinda Purification Engineering Equipment Co., Ltd.)

[0036] BCD-452WDPF standard refrigerator (Qingdao Haier Co., Ltd.)

[0037] E124S Electronic Analytical Balance (Sartorius Scientific Instruments (Beijing) Co., Ltd.)

[0038] HH-6 type constant temperature water bath (Jiangsu Jintan Zhongda Instrument Factory).

[0039] Example 1 Preliminary Experiment on Accelerated Mold Growth Example 1 of this application conducts a preliminary accelerated mold growth experiment to examine the conditions for preparing moldy samples.

[0040] The accelerated mold growth pre-experiment involved placing a 25g wheat sample in a constant temperature and humidity incubator (35 ℃, 85% RH) for 12 days. After observing the growth of distinct mold colonies on the surface of the wheat grains, it was determined that the mold growth had reached the late stage. Therefore, the preparation conditions for the moldy sample were set as follows: temperature 35 ℃, humidity 85% RH, and the maximum mold growth time was 12 days.

[0041] Example 2: Establishment of Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) Analytical Conditions The GC-IMS detection and analysis parameters of the moldy wheat samples obtained in the accelerated mold growth test in Example 1 were optimized to improve the detection and separation of volatile metabolites. Through parameter optimization, the drift gas flow rate for GC-IMS detection of the moldy wheat samples was 150 mL / min, and the incubation temperature was 40 ℃~70 ℃; a programmed pressurization flow control was used for the carrier gas in gas chromatography. Preferably, the incubation temperature is 60 ℃; at an incubation temperature of 60 ℃, the intensity of the characteristic absorption peak is the greatest.

[0042] Through parameter optimization, the detection conditions for GC-IMS were established as shown in Table 1.

[0043] Table 1 GC-IMS detection conditions

[0044] According to the accelerated mold growth pre-experiment of Example 1, 25g of wheat samples were weighed into 60mm diameter petri dishes on days 2, 4, 6, 8, 10, and 12, respectively. After marking the sampling date, the samples were placed in a constant temperature and humidity incubator. The remaining samples were sealed and frozen after each weighing. On day 14, all samples were taken out of the constant temperature and humidity incubator and mixed to prepare mold samples at different stages of mold growth. The wheat samples that did not undergo the accelerated mold growth test were taken as day 0 samples and were regarded as non-moldy samples. 2g of each non-moldy sample and each of the mixed mold samples at different stages of mold growth were weighed and placed into 20ml headspace vials, sealed, and prepared in triplicate for later use.

[0045] High temperature and high humidity environments can significantly promote the metabolism and reproduction rate of molds, resulting in more uniform mold growth. Under accelerated conditions, molds can be stimulated to secrete more characteristic metabolites (such as toxins and enzymes), allowing for more intensive data collection (such as toxin concentration and VOCs changes) and analysis of mold growth kinetic models. Furthermore, the synergistic growth of multiple molds (such as Aspergillus, Penicillium, and Mucor) can be promoted, more realistically reflecting the interaction of microbial communities in actual mold growth scenarios and detecting the comprehensive hazards under mixed mold contamination (such as toxin synergistic effects and putrefaction rate).

[0046] Referring to the methods in GB 4789.2-2022 "National Food Safety Standard - Microbiological Examination of Food - Determination of Total Colony Count" and GB4789.15-2016 "National Food Safety Standard - Microbiological Examination of Food - Count of Molds and Yeasts", the total number of molds in moldy samples at different stages of mold growth was counted. Nystatin and chloramphenicol were added to PCA and PDA culture media respectively to prevent contamination by other microorganisms. Each sample was counted three times, and the morphology of the samples was evaluated. The structure is shown in Table 2.

[0047] Table 2. Evaluation of total number and morphology of mold colonies in moldy samples at different stages of mold growth.

[0048] As shown in Table 2, the mold count results clearly show that with the increase of sample preparation time, the total number of molds in the wheat samples increased from (3.2±0.15)×10 in the control group. 2 CFU / g gradually increased to (3.28±0.45)×10 on day 12. 7 CFU / g. Simultaneously, from day 8 onwards during the accelerated mold growth process, changes in wheat quality can be visually observed, including a decrease in plumpness and color, and the disappearance of the pleasant wheat aroma replaced by a slight musty odor. While morphological changes are not obvious within 0-6 days, making it difficult to discern the degree of mold infection from appearance and aroma, the total bacterial count shows an order-of-magnitude increase every two days, highlighting the necessity of establishing a rapid method for identifying early mold growth in wheat.

[0049] Example 4: Isolation of dominant molds causing wheat mold From the moldy samples prepared in Example 3 after 8-12 days, five single mold species with differences observed in colony morphology were obtained by microbial isolation methods such as dilution plating and spot inoculation. These were labeled as F1-F5 (e.g., Figure 1 (as shown); single mold strains of F1 to F5 were cultured respectively.

[0050] like Figure 1 As shown, based on morphology, F1 and F2 belong to the genus Aspergillus, F3 and F5 belong to the genus Penicillium, and F4 belongs to the genus Mucor. Mucor, Aspergillus, and Penicillium are the dominant molds causing mold growth in wheat.

[0051] Example 5: GC-IMS detection of volatile metabolites of dominant moldy fungi in wheat samples at different stages of mold growth. The moldy samples at different stages of mold growth prepared in Example 3 were analyzed by GC-IMS under the gas chromatography-ion mobility spectrometry (GC-IMS) analysis conditions established in Example 2. The GC-IMS three-dimensional, two-dimensional, and differential spectra of volatile organic compounds in the moldy samples at different stages of mold growth were obtained. Figure 2 As shown.

[0052] The dominant wheat mold species F1-F5, isolated and cultured in Example 4, were analyzed using gas chromatography-ion mobility spectrometry (GC-IMS) under the conditions established in Example 2. The volatile metabolites of the dominant molds were detected by GC-IMS to obtain fingerprint chromatograms of the volatile metabolites of the single molds. Figure 3 As shown.

[0053] Example 6: Obtaining markers of wheat mold Obtaining wheat mold markers involves the following steps: Characteristic volatile organic compounds were obtained from mold samples at different stages of mold growth, and the characteristic volatile organic compounds were qualitatively analyzed as candidate markers for wheat mold growth.

[0054] Based on the GC-IMS three-dimensional spectra of moldy samples at different stages of mold growth in Example 5, three-dimensional data of the migration time, retention time, and signal intensity of volatile organic compounds in moldy samples at different stages of mold growth were obtained; then, based on the GC-IMS two-dimensional spectra of Example 5, characteristic signal regions of volatile organic compounds were selected to obtain characteristic volatile organic compounds in moldy samples at different stages of mold growth.

[0055] like Figure 2 This document records GC-IMS spectra of moldy samples at different stages of mold growth. With prolonged storage under conditions of accelerated mold growth at high temperature and humidity, the volatile organic compounds (VOCs), i.e., the flavor profile, of wheat underwent significant changes. Figure 2 a, 2b, and 2c, from left to right, correspond to wheat samples that were accelerated-cultured at 35℃ and 85% RH for 0, 2, 4, 6, 8, 10, and 12 days, respectively.

[0056] Figure 2 a is the GC-IMS three-dimensional spectrum, from which it can be clearly seen that the intensity and number of signal absorption peaks in the control group (i.e., 0d) are significantly higher than those in the sample after the start of mold growth. Figure 2 b is a two-dimensional top view of 2a, which can further analyze the absorption peaks of volatile organic compounds in each group of samples. It can be seen that most signals appear in the retention time range of 200 ~ 1300 s and the relative migration time range of 1 ~ 1.8 ms. Within the retention time range of 450 ~ 1000 s, the signals of many substances weaken with the extension of storage time. Figure 2 c is a differential spectrum using the control group sample as a reference. After subtracting the control group spectrum from the spectrum on the right, if the concentrations are the same, the subtracted spectrum is white. Red indicates a higher concentration of the substance than the reference, and blue indicates a lower concentration. Darker colors indicate greater variations in concentration. Figure 2 In Figure c, it is easier to see the changing trend of volatile organic compounds in moldy samples at different stages of mold growth. Most of the signal absorption areas are blue after subtraction, while a few areas are red, indicating that the flavor of wheat itself is severely lost after the onset of mold growth, and its flavor profile undergoes regular dynamic changes as the mold growth accelerates and the time is extended.

[0057] Using external standard n-ketones C3~C9 (acetone, 2-butanone, 2-pentanone, 2-hexanone, 2-heptanone, 2-octanone, 2-nonanone) as reference calibration, the retention index of each volatile organic compound was calculated by comparing retention times and ion migration times. The NIST and IMS databases built into the GC-IMS were used for matching and identification, resulting in 64 signal absorption peaks, such as... Figure 4 As shown, the samples include monomers and dimers of 46 organic compounds, including 11 esters, 11 alcohols, 12 aldehydes, 8 ketones, 1 acid, and 1 olefin. The qualitative analysis of the characteristic volatile organic compounds in the moldy samples is shown in Table 3.

[0058] Table 3 Qualitative analysis of characteristic volatile organic compounds in moldy samples

[0059] Note: The suffixes M and D in the same compound represent the monomer and dimer of the volatile organic compound, respectively.

[0060] Fingerprint spectra of characteristic volatile organic compound (VOC) signal absorption peaks in moldy samples at different stages of mold growth were prepared to perform differential analysis of characteristic VOCs in moldy samples at different stages of mold growth; the fingerprint spectra are as follows. Figure 5 As shown, the horizontal axis represents the detected volatile organic compounds (where the Area number indicates an unidentified compound), and the vertical axis, from top to bottom, represents wheat samples from day 0 to day 12 of accelerated mold growth. Each column represents the intensity of the same signal absorption region in different samples, and each row represents the fingerprint spectrum of one sample. Each sample was tested in triplicate. Each dot represents a volatile substance, and the color intensity and area of ​​the dot represent the relative content of that substance; the darker the color and the larger the area, the higher the content of that substance. Figure 5 It can be seen that, Figure 5The peak intensity of the characteristic region selected in box A on the left increases with the severity of mold growth. This includes cyclopentanone monomer and dimer, ethanol, ethyl 3-hydroxybutyrate, propyl acetate, and furfuryl alcohol, as well as three undetermined absorption peaks. Among them, the intensity of cyclopentanone monomer is significantly increased in 10-day wheat samples and the strongest in 12-day wheat samples; the intensity of ethyl 3-hydroxybutyrate is significantly increased in 8-day wheat samples and the strongest in 12-day wheat samples; and the intensity of propyl acetate is the strongest in 12-day wheat samples. The concentration of substances in region B decreases with prolonged mold growth and constitutes the largest proportion among all characteristic signal regions. Qualitatively identified esters include ethyl acetate, methyl furoate, ethyl isobutyrate, isobutyl butyrate, isoamyl butyrate, n-butyl acrylate, and methyl 2-methylbutyrate; ketones include 2-pentanone, 3-pentanone, diisobutyl ketone, 1-penten-3-one, and 4-methyl-3-penten-2-one; alcohols include n-hexanol, n-pentanol, sec-butanol, n-propanol, n-butanol, isopentanol, isopentenol, and isopentanol; aldehydes include propionaldehyde, n-octanol, n-hexanol, n-pentanol, isopentenol, and (E)-2-pentenol. In addition, the content of acetoin, α-pinene, ethylbenzene, and acetic acid also decreases with increasing mold growth. The organic matter content in region C showed a trend of first increasing and then decreasing. Specifically, 2-butanone, isobutyraldehyde-D, and isopentanol were present in low amounts in wheat samples at day 0; tert-butanol, 2-butanone, cyclohexanone, isobutyraldehyde-M, n-butyraldehyde-D, and nonanal were present in higher amounts in wheat samples from day 2 to day 6 compared to other mold stages, and their content decreased significantly after day 6; the response values ​​of n-butyraldehyde, 2-butanone, and tert-butanol reached their highest at day 2 of mold growth, while isopentanol and cyclohexanone had the highest contents in samples at day 6 of mold growth. Figure 5 The fingerprint spectrum shows a significant decrease in the content of most volatile organic compounds (corresponding to region B), indicating that the esters, alcohols, and aldehydes naturally present in wheat are severely lost during the mold growth process. Methyl furoate contributes to cocoa and coffee aromas, n-pentanol provides nutty and baked bread flavors, n-hexanol imparts a fresh, grassy aroma, and n-hexanal provides oily and grassy aromas. The content of these and other pleasant flavor components that make up the wheat aroma profile decreases significantly by the second day after the onset of mold growth. The substances that increase in region A include metabolites accumulated from microbial activity and substances produced by the combination and decomposition reactions of internal substances during storage under high temperature and humidity conditions. Aspergillus can generate various alcohols such as n-pentanol and propanol through pathways such as free amino acid metabolism.

[0061] Based on Example 5, GC-IMS was used to detect the volatile metabolites of dominant molds causing wheat mold, and a fingerprint spectrum of volatile metabolites of a single mold was established. By comparing the differences in metabolites of single fungal species, the respective marker substances of the dominant fungal species causing wheat mold were identified. Figure 3This study recorded the volatile metabolite fingerprints of single mold species. From top to bottom, the relative contents of metabolites qualitatively identified in blank medium (BC) and F1–F5 are shown. Characteristic volatile organic compounds for F1 *Aspergillus* include isoamyl alcohol and propyl butyrate; for F2 *Aspergillus*, markers include n-butyl acrylate and trans-2-hexenal; 2-butanone, propyl acetate, and acetone are present in higher amounts in the headspace fraction of F3 *Penicillium*; cyclopentanone, 2-pentanone, isopropanol, and ethyl 3-hydroxybutyrate are markers for F4 *Mucor*; while F5 *Penicillium* produces more acrolein, isobutanol, propanol, acetic acid, isobutyraldehyde, 2-octanol, isopropyl acetate, and ethyl isovalerate during metabolic activity in the culture medium. By comparing the differences in metabolites of single species, the respective marker substances of the dominant fungal species causing wheat mold were identified. Figure 3 It is known that *Aspergillus* genera F1, F5, F2, and F3 can also metabolize to produce small amounts of ethyl 3-hydroxybutyrate; *Aspergillus* genera F1, F2, and F3 can also metabolize to produce cyclopentanone monomers; *Mucor* genera F4 can also metabolize to produce small amounts of propyl acetate; *Aspergillus* genera F1, F2, and F4 can also metabolize to produce 2-butanone; and *Aspergillus* genera F1 can metabolize to produce tert-butanol; combined with... Figure 5 In regions A and C, Aspergillus and Penicillium are active in the early stages of wheat mold growth, while Mucor is not very active. In the later stages of wheat mold growth, Mucor activity increases significantly, Penicillium activity remains high, and Aspergillus activity decreases. In the late stages of wheat mold growth, Mucor infection and Penicillium infection are the main fungal infections.

[0062] Orthogonal partial least square discriminant analysis (PLS-DA) was established using the qualitative absorption peak volumes of 64 signals. Substances with a variable importance for the projection (VIP) > 1 were used as initial screening markers for wheat mold. Figure 6 As shown.

[0063] Based on the projected importance (VIP) of all known material variables obtained from PLS-DA analysis, substances that contribute significantly to the characterization of the mold growth stage were selected according to their VIP values. Substances with VIP values ​​greater than 1 have a greater impact on flavor and were used as initial screening markers for model building. The list of selected initial screening markers is shown in Table 4.

[0064] Table 4 List of substances with VIP value > 1

[0065] Validate the initial screening marker; use dynamic principal component analysis (PCA) to analyze the VOCs in moldy samples at different stages of mold growth to verify whether the initial screening marker can accurately distinguish between the control group sample and each moldy sample. See [link to relevant documentation]. Figures 7-8 .

[0066] Depend on Figure 7 It can be seen that the variance contribution rates of the two principal components, cyclopentanone-M and 2-butanone-D, are 75% and 14.9%, respectively, totaling 89.9%, which can effectively distinguish samples at different stages of mold growth. From Figure 7 As can be seen from graph a, the control group wheat sample and the wheat sample after the onset of mold are far apart in the score graph, located on the positive and negative half-axis of PC1 respectively. This indicates that the wheat flavor changes immediately after the onset of accelerated mold growth, and according to... Figure 7 In the principal component loading plot, most flavor compounds are located near 0 days, indicating the highest abundance of characteristic volatile organic compounds (VOCs) in wheat before mold growth begins. Flavor compounds near 12 days include ethyl 3-hydroxybutyrate, cyclopentanone monomers and dimers, propyl acetate, 2-butanone, and furfural, which are in the same quadrant as samples from 2 to 6 days, consistent with fingerprint analysis. Furthermore, in the PC2 direction, moldy samples from 2, 4, and 6 days are located on the negative half-axis, while moldy samples from 8, 10, and 12 days are all located on the positive half-axis. These results, combined with fingerprint analysis and principal component loading plots, allow us to divide the accelerated mold growth process into three stages: the unmoldy control group (0 days), 2-6 days, and 8-12 days. Figure 8 Cluster heatmaps were plotted using the signal peak volumes of primary screening markers with VIP>1. Figure 8 It can be visually observed that the contents of 2-butanone, ethanol, propionaldehyde, n-propanol, n-hexanol, acetic acid, and isobutanol are relatively high among the initial screening markers, and are classified into one category by the clustering tree. The remaining esters, aldehydes, and ketones have lower signal intensities and are classified into a second category. Simultaneously, the contents of propionaldehyde, n-hexanol, and n-pentanol gradually decrease with increasing mold growth. This is because as mold growth deepens, nutrients in the wheat are consumed by microbial metabolism, and the content of the aroma and oil flavor components inherent in fresh wheat decreases. Alcohols and ketones originate from the oxidative decomposition of fats and can also be produced by microbial metabolism. The contents of n-propanol, isobutanol, n-butanol, and isobutanol significantly decrease on the second day after the onset of mold growth, while ethanol has the highest content. Furthermore, combined with the wheat mold fingerprint spectrum, its content continuously increases with increasing mold growth.

[0067] The validation results further supported the analysis of volatile organic compound fingerprint spectra of moldy samples at different stages of mold growth and the correlation analysis between samples. The initial screening of marker substances can accurately distinguish between control samples and moldy samples at different stages of mold growth, thus confirming the effectiveness of the screened molecular marker substances.

[0068] The initial screening markers were optimized to obtain wheat mold markers; the wheat mold markers were cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, 2-butanone, tert-butanol, and nonanal. Cyclopentanone monomer, ethyl 3-hydroxybutyrate, and propyl acetate were the main markers of wheat mold; 2-butanone, tert-butanol, and nonanal were early markers of wheat mold.

[0069] As shown in Table 4, the VIP values ​​of cyclopentanone monomer, ethyl 3-hydroxybutyrate, and propyl acetate are all greater than 1. Figure 5 As shown in region A, these three substances are among the few volatile organic compounds in the volatile organic compound fingerprint spectra of moldy samples at different stages of mold growth that increase with the mold content of wheat. Figure 3 As shown, all three markers can be detected in the fingerprint spectrum of volatile metabolites of a single mold. Therefore, cyclopentanone monomer, ethyl 3-hydroxybutyrate, and propyl acetate are the main markers of wheat mold. Cyclopentanone monomer and ethyl 3-hydroxybutyrate are markers for *Mucor* and *Mallotus* species and can be used to determine *Mucor* infection in wheat mold. Propyl acetate is a marker for *Penicillium* and can be used to determine *Penicillium* infection in wheat mold. Figure 5 As shown in area A, mixed infection of *Mucor* and *Penicillium* species was indicated in mold samples at different stages of mold growth. *Penicillium* infection and mild *Mucor* infection occurred in the early stage of mold growth (2-6 days). In the late stage of mold growth (8-12 days), the infection of *Mucor* and *Penicillium* species was significantly enhanced, indicating that the activity of *Mucor* and *Penicillium* species was enhanced in the late stage of mold growth.

[0070] like Figure 5 As shown in region C, the contents of tert-butanol, 2-butanone, and nonanal were higher in wheat samples from 2 to 6 days compared to other mold stages, and decreased significantly after 6 days. The VIP values ​​of tert-butanol, 2-butanone, and nonanal were all greater than 1. Figure 7 In PCA analysis, samples located in the 2-6 day quadrant were positive for tert-butanol, 2-butanone, and nonanal; therefore, these are markers of early-stage mold growth in wheat. Furthermore, based on... Figure 3 2-Butanone is a marker for Penicillium fungi and can be used to determine Penicillium infection in wheat mold. In moldy samples at different stages of mold growth, 2-butanone reached its maximum value at 2 days of mold growth, and the signal intensity decreased significantly after 6 days of mold growth, indicating that Penicillium fungi are highly active in the early stages of mold growth and that early infection by Penicillium fungi occurs. Figure 3 2-Butanone is also a metabolite of Aspergillus and Mucor, which suggests that Penicillium molds are more active than Aspergillus and Mucor molds in the early stages of mold growth.

[0071] This embodiment selects ethyl 3-hydroxybutyrate, cyclopentanone monomer, and propyl acetate, the main markers of wheat mold, which can be effectively applied to the detection of wheat samples at various stages of mold growth. It is not limited to early mold detection or mold presence detection in existing technologies, but enables comprehensive detection of mold growth stages in wheat samples, providing a basis for subsequent wheat grading and processing. Furthermore, unlike existing technologies that determine the degree of mold growth in the late stages through sensory inspection or traditional microbial culture methods, this embodiment uses GC-IMS technology to detect markers such as cyclopentanone monomer, ethyl 3-hydroxybutyrate, and propyl acetate, enabling rapid and efficient determination of the degree of mold growth and mold species.

[0072] This embodiment utilizes the selection of early markers such as tert-butanol, 2-butanone, and nonanal to effectively detect early mold growth in wheat samples. Through the synergistic effect of the main marker and the early marker, the accuracy of early mold growth detection can be improved.

[0073] Example 7 Detection of the mold stage in wheat samples to be tested This embodiment uses the GC-IMS database to detect the signal intensities of cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, 2-butanone, tert-butanol, and nonanal in moldy samples at different stages of mold growth. A similarity algorithm is used to detect the wheat samples under test. Dynamic principal component analysis and cluster heatmap analysis are combined to cross-validate the detection of the wheat samples under test, thereby improving the comprehensiveness and accuracy of the detection of the wheat samples under test.

[0074] First, a GC-IMS database of signal intensities of cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, 2-butanone, tert-butanol, and nonanal in moldy samples at different stages of mold growth was constructed, and the wheat samples to be tested were detected based on a similarity algorithm. Based on the GC-IMS three-dimensional, two-dimensional, and differential spectra of volatile organic compounds from different mold stages obtained in Example 5, characteristic peaks of cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, 2-butanone, tert-butanol, and nonanal were extracted from the GC-IMS spectra. Their retention time, migration time, and signal intensity were recorded. The signal intensities of the six markers of wheat samples at different mold stages were stored in a database, with each sample corresponding to a mold stage (e.g., 0d, 2d, 4d, 6d, 8d, 10d, 12d). Weigh 2g of the wheat sample to be tested into a 20ml headspace vial, seal and set aside for use; prepare 3 parallel samples. Based on Example 2, gas chromatography-ion mobility spectrometry (GC-IMS) analysis conditions were established for the wheat samples to be tested, and GC-IMS detection was performed to obtain three-dimensional spectra, two-dimensional spectra, and differential spectra. Characteristic peaks of six markers—cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, 2-butanone, tert-butanol, and nonanal—were extracted from the GC-IMS spectra of the wheat samples to be tested, and their retention times, migration times, and signal intensities were recorded. The signal intensity of 2-butanone in the wheat sample to be tested is compared with that in the 0d sample. If the signal intensity of 2-butanone in the wheat sample to be tested is similar to that in the 0d sample, it is determined that the wheat sample to be tested has not been moldy. If the signal intensity of 2-butanone in the wheat sample to be tested is higher than that in the 0d sample, then the similarity algorithm is used to calculate the similarity between the wheat sample to be tested and each sample in the database, and the sample with the highest similarity is selected as the matching result. The mold stage corresponding to the sample with the highest similarity is taken as the mold stage of the wheat sample to be tested. If a wheat sample to be tested has the highest similarity to a 6-day moldy sample, then the wheat sample to be tested is determined to be in the early stage of mold growth.

[0075] Based on dynamic principal component analysis (PCA), cross-validation was performed on the wheat samples to be tested. Based on the principal component analysis of substances with VIP>1 in Example 6, PCA score maps and factor loading maps including 6 marker substances were obtained for moldy samples at different moldy stages to determine the moldy stage regions of the samples at different moldy stages. In the early moldy region, 2-butanone, tert-butanol, and nonanal had high loadings, and the scores of early moldy samples were located in the 2-6 day moldy stage region. In the late moldy region, cyclopentanone monomer, ethyl 3-hydroxybutyrate, and propyl acetate had high loadings, and the scores of late moldy samples were located in the 8-12 day moldy stage region. PCA analysis was performed on the signal intensity of each marker in the wheat sample to be tested to obtain the PCA score of the wheat sample to be tested. The score was then matched with the mold stage of the mold sample at different mold stages to determine the mold stage of the wheat sample to be tested. If the PCA score of a wheat sample to be tested is in the 2-6 day mold stage region, then the wheat sample to be tested is determined to be in the early stage of mold.

[0076] Based on cluster heatmap analysis, cross-detection verification was performed on the wheat samples to be tested; based on the signal peak volume cluster heatmap of the primary screening markers VIP>1 in Example 6, cluster heatmaps of markers at different stages of mold growth, including 6 markers, were obtained. Cluster heatmap analysis was performed on the signal intensity of each marker in the wheat sample to be tested. Based on the comparison between the value of each marker in the cluster heatmap and the value of the marker in the cluster heatmap of wheat samples at different mold stages, the mold stage and mold species of the wheat sample to be tested were determined. like Figure 8In the signal peak intensity heatmap shown, the circular clusters represent sample clusters, indicating moldy samples at different stages of mold growth from 0 to 12 days. The circular row clusters represent clusters of markers and other substances with VIP > 1. The 2-butanone signal peak intensity is low in the 0-day sample; the 2-butanone signal peak intensity is strongest in the 2-day moldy sample, indicating early mold growth by *Penicillium* species. In the 2-6 day moldy samples, the signal peak intensities of 2-butanone, tert-butanol, and nonanal are higher than in other moldy samples, indicating early mold growth by *Penicillium* and *Aspergillus* species. In the 4-12 day moldy samples, the signal peak intensity of cyclopentanone monomer gradually increases, indicating slight infection by *Mucor* species in the early stage of mold growth, and the infection intensifies with increasing activity of *Mucor* species over time. In the 12-day moldy sample, the signal peak intensities of ethyl 3-hydroxybutyrate and propyl acetate are the strongest, indicating strong activity by *Mucor* and *Penicillium* species, suggesting late-stage mold growth in the wheat sample.

[0077] If a clustering heatmap analysis is performed on the signal intensity of each marker substance in a wheat sample to be tested, and the row and column clustering of the clustering heatmaps of marker substances at different stages of mold growth is combined, if the signal pattern is similar to that of a 6-day moldy sample, it is determined to be in the early stage of mold growth, with the moldy fungi mainly being Penicillium, Aspergillus and mild Mucor.

[0078] The results of similarity calculation, dynamic principal component analysis, and cluster heatmap analysis based on the GC-IMS database were combined to cross-validate the mold stage of the wheat sample to be tested. The final mold stage and mold species of the wheat sample to be tested were determined by a simple voting method or a weighted scoring model (such as random forest). If a wheat sample is determined to be in the early stage of mold growth based on similarity calculation from the GC-IMS database, dynamic principal component analysis, and cluster heatmap analysis, then the wheat sample is determined to be in the early stage of mold growth. If the cluster heatmap of the wheat sample shows high signal values ​​for 2-butanone and tert-butanol, then the wheat sample is determined to be in the early stage of mold growth caused by Penicillium.

[0079] This embodiment employs a multi-method cross-validation approach, combining similarity calculation based on the GC-IMS database, dynamic principal component analysis, and cluster heatmap analysis. This reduces the risk of misjudgment by a single method and improves detection accuracy. It also enhances the detection capability for complex samples (such as mixed mold growth) and avoids missing crucial information by relying on a single method. By selecting early mold growth markers such as 2-butanone, tert-butanol, and nonanal, and major mold growth markers such as cyclopentanone monomer, ethyl 3-hydroxybutyrate, and propyl acetate, detection of all stages of mold growth in wheat samples can be achieved, with a particular emphasis on enhancing early detection of mold growth in wheat.

[0080] The present application has been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present application. The descriptions of the embodiments above are only for the purpose of helping to understand the present disclosure and its core ideas. It should be noted that those skilled in the art can make several improvements and modifications to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.

Claims

1. A marker for mold growth in wheat used for brewing, characterized in that, The markers for mold growth in the wheat used for brewing include six substances: cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, tert-butanol, 2-butanone, and nonanal.

2. The marker for mold growth in wheat used for brewing as described in claim 1, characterized in that, The mold species that caused the mold growth on the wheat used for brewing were Mucor, Aspergillus, and Penicillium.

3. A method for rapidly detecting mold growth in brewing wheat using the marker substance described in claim 1 or 2, characterized in that, Includes the following steps: S1: Construct a GC-IMS database of signal intensities for six markers—cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, 2-butanone, tert-butanol, and nonanal—in mold samples at different stages of mold growth. S2: Perform GC-IMS detection on the wheat sample to be tested, and extract the signal intensity of six markers from the GC-IMS spectrum of the wheat sample to be tested; S3: Compare the signal intensity of 2-butanone, the marker of wheat sample to be tested, with the signal intensity of 2-butanone in the 0d sample. If the signal intensity of 2-butanone in the wheat sample to be tested is similar to that in the 0d sample, it is determined that the wheat sample to be tested has not undergone mold growth. If the signal intensity of 2-butanone in the wheat sample to be tested is higher than that in the 0d sample, a similarity algorithm is used to calculate the similarity between the wheat sample to be tested and moldy samples at each different mold growth stage in the database. The moldy sample with the highest similarity is selected as the matching result, and the mold growth stage corresponding to the moldy sample with the highest similarity is determined as the mold growth stage of the wheat sample to be tested.

4. The method for rapid detection of mold in brewing wheat as described in claim 3, characterized in that, The method for rapid detection of mold in wheat used for brewing also includes the following steps: S4: Based on dynamic principal component analysis, cross-validation of the wheat samples to be tested is performed, including the following steps: S41: Perform dynamic principal component analysis on moldy samples at different moldy stages to obtain dynamic principal component score maps and factor loading maps including six marker substances for moldy samples at different moldy stages, and determine the moldy stage regions of moldy samples at different moldy stages. S42: Perform dynamic principal component analysis on the wheat sample to be tested, obtain the PCA score of the wheat sample to be tested, and match it with the mold stage region of the mold sample at different mold stages in S41 to determine the mold stage of the wheat sample to be tested.

5. The method for rapid detection of mold in brewing wheat as described in claim 3, characterized in that, The method for rapid detection of mold in wheat used for brewing also includes the following steps: S5: Based on cluster heatmap analysis, cross-validation of the wheat samples to be tested is performed, including: S51: Perform clustering heatmap analysis on the signal peak volumes of substances with a projection importance greater than 1 in moldy samples at different stages of mold growth to obtain clustering heatmaps of marker substances in moldy samples at different stages of mold growth, including six marker substances. S52: Perform cluster heatmap analysis on the signal intensity of the six markers of the wheat sample to be tested. Compare the values ​​of the six markers in the cluster heatmap of the sample to be tested with the values ​​in the cluster heatmaps of the markers at different stages of mold growth in S51 to determine the stage of mold growth and the species of mold in the wheat sample to be tested.

6. The marker substance as described in claim 4 or claim 5 is used for rapid detection of mold in wheat used as raw material for brewing, characterized in that, The rapid detection of mold in the wheat used for brewing also includes the following steps: S6: The mold stage of the wheat sample to be tested, determined by S3, is cross-validated with the mold stage of the wheat sample to be tested based on dynamic principal component analysis in S4, or with the mold stage of the wheat sample to be tested based on cluster heatmap analysis in S5. The mold stage of the wheat sample to be tested is then determined using a simple voting method or a weighted scoring model to determine the mold stage and mold genus of the wheat sample to be tested.

7. A method for obtaining the marker substance for mold growth in wheat used for brewing as described in claim 1, characterized in that, Includes the following steps: S1': Preparation of moldy samples at different stages of mold growth; under conditions of high temperature and high humidity accelerating mold growth, moldy samples at different stages of mold growth were prepared after different mold growth times. S2': Obtain the dominant mold for wheat mold; obtain the dominant mold for wheat mold from the moldy sample prepared in S1' by microbial isolation method, and culture the dominant mold for wheat mold; S3': The volatile metabolites of the dominant moldy fungus in wheat in S2' were detected by GC-IMS at different stages of mold growth in S1' and S2', respectively. GC-IMS spectra of volatile organic compounds in moldy samples at different stages of mold growth and fingerprint spectra of volatile metabolites of the dominant moldy fungus were obtained. S4': Based on the GC-IMS spectra of volatile organic compounds in moldy samples at different stages of mold growth in S3', characteristic volatile organic compounds in moldy samples at different stages of mold growth are obtained, and the characteristic volatile organic compounds are qualitatively analyzed as candidate wheat mold growth markers. S5': Based on S4', fingerprint spectra of characteristic volatile organic compound signal absorption peaks in moldy samples at different mold growth stages were prepared to perform differential analysis of characteristic volatile organic compounds in moldy samples at different mold growth stages. S6': Based on the fingerprint spectrum of volatile metabolites of dominant molds obtained in S'3, establish fingerprint spectrum of volatile metabolites of single molds, compare the differences in metabolites of single species, and identify the respective marker substances of dominant fungi that cause wheat mold. S7': Initial screening markers; The substances with a material variable projection importance greater than 1 among the candidate wheat mold markers mentioned in S4' are used as initial screening markers for wheat mold. S8': Verify the initial screening marker substance; Dynamic principal component analysis was used to perform VOC cluster analysis on mold samples at different stages of mold growth using the S7' primary screening marker to verify whether the primary screening marker can accurately distinguish mold samples at different stages of mold growth. S9': The initial screening markers are optimized to obtain wheat mold markers; the wheat mold markers are six markers: cyclopentanone monomer, ethyl 3-hydroxybutyrate, propyl acetate, tert-butanol, 2-butanone, and nonanal.

8. The method for obtaining the marker substance for mold growth in brewing wheat as described in claim 1, as described in claim 7, is characterized in that, The primary screening substances in S7' include: cyclopentanone monomer, 2-butanone dimer, ethanol, n-hexanone monomer, ethyl 3-hydroxybutyrate, 2-pentanone, acetic acid monomer, cyclopentanone dimer, tert-butanol, diisobutyl ketone, nonanal, n-butyraldehyde dimer, propyl acetate, isobutanol monomer, isoamyl alcohol monomer, furfural, propionaldehyde monomer, n-propanol monomer, n-butyraldehyde monomer, isobutanol dimer, n-pentanol monomer, and n-butanol monomer.

9. The method for obtaining the marker substance for mold growth in brewing wheat as described in claim 1, as described in claim 7, is characterized in that, The moldy samples described in S1' at different stages of mold growth include moldy samples with mold growth durations of 0d, 2d, 4d, 6d, 8d, 10d, and 12d.

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