A method for identifying a volatile biomarker of soft rot of kiwifruit and a method for identifying soft rot

Abstract

The present application belongs to the technical field of fruit and vegetable disease detection and identification, and relates to a method for identifying volatile biomarkers of soft rot of kiwifruit and a method for identifying soft rot, which comprises the following steps: S1, selecting kiwifruit for disinfection and sterilization; S2, inoculating pathogenic fungi: the kiwifruit is divided into a healthy control group and a pathogenic bacteria group; the healthy control group is inoculated with sterile water, and the pathogenic bacteria group is inoculated with pathogenic fungi causing soft rot; healthy control samples and pathogenic bacteria samples are randomly taken every day during storage; S3, analyzing the extracted samples with a GC-IMS analyzer to obtain a three-dimensional spectrum of volatile components; and S4, identifying volatile biomarkers by VOCal software. The present application identifies volatile biomarkers of soft rot of kiwifruit, and then identifies whether the kiwifruit produces soft rot through the identified volatile biomarkers, which has the characteristics of rapidness, accuracy, sensitivity and non-destructiveness, and can realize monitoring and early warning of soft rot of kiwifruit.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of fruit and vegetable disease detection and identification, and relates to a method for identifying volatile biomarkers of soft rot of kiwifruit and a method for identifying soft rot. BACKGROUND

[0002] Kiwi (Actinidia chinensis Planch.) belongs to the Actinidiaceae family and originated in China. It has become one of the important economic fruits in China and is widely planted in Shaanxi, Sichuan, Guizhou and other places. Kiwi is soft in texture, sweet and sour in taste, rich in fruit aroma, and unique in flavor. It is rich in vitamin C, folic acid, carotene, amino acids, trace elements and other nutrients, and its nutritional value is much higher than that of other fruits. Due to its pleasant sensory characteristics and excellent nutritional quality, it is known as the "crown of vitamin C" and is favored by consumers around the world. However, as a typical fruit of respiratory burst type, kiwi is a berry with thin skin and juicy, and is prone to be infected by pathogenic bacteria during storage, causing fruit to rot and deteriorate, resulting in high loss rate. At present, soft rot has become one of the most important diseases causing postharvest kiwi rot, mainly caused by Botryosphaeria dothidea, Diaporthe, Alternaria alternata and other pathogenic fungi. In addition, it is difficult to diagnose the infection of soft rot pathogen in the early stage, which usually occurs in the young fruit stage, and then remains latent for a long time until the postharvest kiwi ripens and gradually shows the corresponding symptoms. Postharvest kiwi soft rot is prone to occur and infect rapidly, which has become a technical problem to be solved for the healthy and stable development of kiwi industry.

[0003] At present, the identification methods of kiwi soft rot mainly include electronic nose and gas chromatography-mass spectrometry (GC-MS) detection methods. However, due to the limited specificity and recognition ability of electronic nose, GC-MS requires complex sample pretreatment operation and time-consuming and laborious analysis process, which limits the monitoring ability of volatile components during postharvest storage of kiwi, resulting in long detection time, poor accuracy and low sensitivity in the identification of kiwi soft rot.

[0004] In view of the above-mentioned defects in the identification of kiwi soft rot, it is a major research topic to seek a rapid, accurate and sensitive method for identifying postharvest soft rot of kiwi. SUMMARY

[0005] In view of the technical problems in the identification of kiwi soft rot described in the background art, the present application provides a method for identifying volatile biomarkers of soft rot of kiwifruit and a method for identifying soft rot.

[0006] The application first identifies the volatile biomarker of soft rot of kiwifruit, and then identifies the volatile biomarker to identify whether the soft rot of kiwifruit occurs, which is fast, accurate and sensitive, and can realize the monitoring and early warning of the soft rot of kiwifruit.

[0007] In order to achieve the above purpose, the technical scheme adopted by the application is:

[0008] A kind of identification method of volatile biomarker of soft rot of kiwifruit, comprising the following steps:

[0009] S1, kiwifruit selection and treatment

[0010] Select a batch of uniform size, no disease spot and mechanical damage of kiwifruit, disinfect and dry after sterilization;

[0011] S2, inoculation of pathogenic fungi

[0012] S21, the dried kiwifruit is grouped, and a healthy control group and a disease bacteria group are set;

[0013] S22, the disease bacteria group, each kiwifruit is inoculated with 1×10 5 Spore suspension with a concentration of 10 μL, which is prepared from pathogenic fungi causing soft rot;The healthy control group, each kiwifruit is inoculated with 10 μL of sterile water;After inoculation, they are respectively placed in a constant temperature and humidity incubator for storage;

[0014] S23, during storage, kiwifruit is randomly taken from the disease bacteria group and the healthy control group every day to obtain healthy control samples and disease bacteria samples;

[0015] S3, GC-IMS detection

[0016] The healthy control samples and disease bacteria samples taken every day are all introduced into the GC-IMS analyzer by headspace sampling, and the three-dimensional spectrum of the volatile components in the healthy control samples and the three-dimensional spectrum of the volatile components in the disease bacteria samples are obtained by using the GC-IMS analyzer;The parameter conditions of the GC-IMS analyzer are as follows: the gas chromatography column is MXT-5, the column temperature is 60℃, the carrier gas is high-purity nitrogen 99.999%, the carrier gas flow rate is initially 2mL / min, which is maintained for 2min, and then linearly increases to 100mL / min within 18min, the drift tube length is 9.8cm, the temperature is 45℃, the drift gas is high-purity nitrogen 99.999%, the flow rate is 150mL / min, and the linear voltage in the tube is 510V / cm;

[0017] S4, volatile biomarker identification

[0018] S41, projecting the three-dimensional spectrum obtained above, corresponding to obtain the two-dimensional spectrum of volatile components in the healthy control sample and the two-dimensional spectrum of volatile components in the pathogenic sample;

[0019] S42, combining the NIST database and the IMS database in the VOCal software to qualitatively analyze the volatile components on the two-dimensional spectrum obtained above;

[0020] S43, according to different infection periods and repetition times, and using the Gallery Plot plug-in in the VOCal software to rearrange the qualitative volatile components, and respectively drawing the characteristic fingerprint spectrum of the healthy control sample and the characteristic fingerprint spectrum of the pathogenic sample;

[0021] S44, taking the characteristic fingerprint spectrum of the healthy control sample as a reference, identifying the volatile components in the characteristic fingerprint spectrum of the pathogenic sample that are not found in the characteristic fingerprint spectrum of the healthy control sample, and completing the identification of the volatile biomarkers of soft rot disease of kiwi fruit.

[0022] Further limitation, in step S22, the pathogenic fungus causing soft rot is Botryosphaeria dothidea, Diaporthe phaseolorum or Alternaria alternata.

[0023] Further limitation, in step S23, the culture temperature is 25±1℃, the culture humidity is 85±5%, and the culture time is 7 days.

[0024] Further limitation, the parameters of the headspace sampling method are as follows: 1-2g of the sample to be analyzed is placed in a 20mL headspace bottle, sealed, and then incubated at 40-50℃ with stirring at a speed of 450-550r / min for 12-18min, and then 500μL of headspace sample is taken at 85℃.

[0025] Further limitation, when the pathogenic fungus causing soft rot is Botryosphaeria dothidea, the identified volatile biomarkers of soft rot disease of kiwi fruit include: 4-methyl-1-pentanol, isobutyl propionate, 1,3-butanediol, 5-methylfurfural, methylcyclopentenolone, salicylaldehyde, 2,6-dimethylpyrazine, ethyl lactate, 3-heptene-2-ketone, menthol, 2-methylbutanal, 2,4,5-trimethylthiazole, styrene, 2-methylbutyric acid ethyl ester, alpha-terpinene and alpha-terpinolene;

[0026] When the pathogenic fungus causing soft rot is Diaporthe phaseolorum, the identified volatile biomarkers of kiwifruit soft rot include: gamma-terpinene, alpha-terpinene, alpha-terpinolene, menthol, 4-methylbenzaldehyde, butylidene lactone, methyl methacrylate, 3-heptene-2-one, 1-penten-3-ol, 1,1-diethoxyethane, hydroxyacetone, 2-pentanone, ethyl lactate, 1,3-butanediol, styrene, 2,3-pentanedione, 4-methylphenol, 5-methylfurfural, benzaldehyde propylene glycol acetal, salicylaldehyde, butyl formate, 2,6-dimethylpyrazine, furfuryl alcohol, 2-methylbutyric acid ethyl ester, methylcyclopentenolone, gamma-valerolactone and 2,4,5-trimethylthiazole;

[0027] When the pathogenic fungus causing soft rot is Alternaria alternata, the identified volatile biomarkers of kiwifruit soft rot include: isobutyl propionate, 4-methyl-1-pentanol, 3-heptene-2-one, 2-methylbutyric acid ethyl ester, methylcyclopentenolone, salicylaldehyde, 4-methylphenol, ethyl lactate, 2,6-dimethylpyrazine, 1,3-butanediol, styrene and benzaldehyde propylene glycol acetal.

[0028] Further limited, the identification method of the volatile biomarkers of kiwifruit soft rot further comprises the following steps:

[0029] For the two-dimensional spectrum of volatile components in the healthy control sample and the two-dimensional spectrum of volatile components in the pathogen sample; principal component analysis and Euclidean distance analysis in VOCal software are used to obtain PC1xPC2 score map and Euclidean distance map; by comparing the differences between volatile components at different storage times, the key time point of pathogenic fungus infection of kiwifruit is determined.

[0030] A method for identifying kiwifruit soft rot, comprising the following steps:

[0031] A1, after disinfection and sterilization, the randomly collected kiwifruit to be tested is dried;

[0032] A2, the kiwifruit to be tested is introduced into the GC-IMS analyzer by headspace sampling; the three-dimensional spectrum of volatile components in the kiwifruit to be tested is obtained by using the GC-IMS analyzer; the parameter conditions of the GC-IMS analyzer are as follows: the gas chromatography column is MXT-5, the column temperature is 60℃, the carrier gas is high-purity nitrogen 99.999%, the carrier gas flow rate is initially 2mL / min, maintained for 2min, then linearly increased to 100mL / min within 18min, the drift tube length is 9.8cm, the temperature is 45℃, the drift gas is high-purity nitrogen 99.999%, the flow rate is 150mL / min, and the linear voltage in the tube is 510V / cm;

[0033] A3, identification of kiwifruit soft rot

[0034] A31, projecting the three-dimensional spectrum of the volatile components in the above obtained kiwi fruit sample to be tested to obtain a two-dimensional spectrum of the volatile components in the kiwi fruit sample to be tested;

[0035] A32, qualitatively analyzing the volatile components in the two-dimensional spectrum of the volatile components in the above obtained kiwi fruit sample to be tested by combining the NIST database and the IMS database in the VOCal software;

[0036] A33, re-arranging the qualitative volatile components by using the Gallery Plot plug-in in the VOCal software to draw a characteristic fingerprint spectrum of the kiwi fruit sample to be tested;

[0037] A34, identifying the tested volatile components from the characteristic fingerprint spectrum, and comparing the tested volatile components with the volatile biomarkers of the kiwi fruit soft rot, if the tested volatile components are consistent with the volatile biomarkers of the kiwi fruit soft rot, it is identified that the kiwi fruit sample to be tested has soft rot, otherwise, it is identified that the kiwi fruit sample to be tested does not have soft rot; the volatile biomarkers of the kiwi fruit soft rot are identified by the identification method of the volatile biomarkers of the kiwi fruit soft rot.

[0038] Further limited, the parameters of the headspace sampling mode are as follows: 1-2 g of the sample to be analyzed is placed in a 20 mL headspace bottle, and after being sealed, it is incubated at 40-50 DEG C under the condition of 450-550 r / min stirring speed for 12-18 min, and then 500 mu L of the headspace sample is taken at 85 DEG C.

[0039] Compared with the prior art, the beneficial effects of the present application are:

[0040] 1. The present application combines GC-IMS technology with VOCal software analysis means to characterize the volatile component fingerprint spectrum of the pathogenic fungus at different infection stages of the postharvest soft rot of kiwi fruit, and according to the difference between the fingerprint spectrum of healthy kiwi fruit and the fingerprint spectrum of soft rot kiwi fruit, the characteristic volatile biomarkers of the pathogenic fungus are determined, which provides a new way for the early warning of the postharvest storage of kiwi fruit.

[0041] 2. The method provided by the present application does not need complex sample pretreatment processes such as extraction and purification in identification, the kiwi fruit sample is directly sampled by headspace, which maximizes the retention of the original volatile component appearance, compared with the traditional method, this method is simple in operation, greatly shortens the detection time, can identify a variety of volatile components, has the characteristics of high sensitivity, high efficiency and rapidness, and is more easy to realize the early warning and monitoring of the postharvest soft rot of kiwi fruit.

[0042] 3、The present application does not need to carry out complex pretreatment process on the fruit and sample, directly absorbs the volatile components in the storage environment to identify the soft rot, so as to realize the nondestructive detection of the soft rot of the kiwi fruit after harvesting.

[0043] 4、The present application detects the volatile components of the kiwi fruit soft rot caused by various pathogenic fungi by using the GC-IMS technology, draws the fingerprint of the volatile components, can realize the rapid monitoring and early warning of the kiwi fruit soft rot, timely prevention and control, avoids the large-scale spread of the pathogen, and has great application prospect for the real-time identification in the disease process of the kiwi fruit soft rot. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The GC-IMS three-dimensional spectrum of the volatile components of healthy kiwi fruit and the kiwi fruit soft rot samples caused by three kinds of pathogenic fungi in different infection periods (0-7d) (A: the healthy kiwi fruit treatment group inoculated with sterile water, B: the kiwi fruit treatment group inoculated with Botryosphaeria dothidea, C: the kiwi fruit treatment group inoculated with Diaporthe phaseolorum, D: the kiwi fruit treatment group inoculated with Alternaria alternate);

[0045] Figure 2 The GC-IMS two-dimensional spectrum of the volatile components of healthy kiwi fruit and the kiwi fruit soft rot samples caused by three kinds of pathogenic fungi in different infection periods (0-7d) (A: the healthy kiwi fruit treatment group inoculated with sterile water, B: the kiwi fruit treatment group inoculated with Botryosphaeria dothidea, C: the kiwi fruit treatment group inoculated with Diaporthe phaseolorum, D: the kiwi fruit treatment group inoculated with Alternaria alternate);

[0046] Figure 3 The fingerprint of the volatile components of healthy kiwi fruit and the kiwi fruit soft rot samples caused by three kinds of pathogenic fungi in different infection periods (0-7d) (3A: the healthy kiwi fruit treatment group inoculated with sterile water, 3B: the kiwi fruit treatment group inoculated with Botryosphaeria dothidea, 3C: the kiwi fruit treatment group inoculated with Diaporthe phaseolorum, 3D: the kiwi fruit treatment group inoculated with Alternaria alternate);

[0047] Figure 4 The PCA analysis (4A, 4C, 4E, 4G) and Euclidean distance analysis (4B, 4D, 4F, 4H) of healthy kiwi fruit and the kiwi fruit soft rot samples caused by three kinds of pathogenic fungi in different infection periods (0-7d) (4A and 4B: the healthy kiwi fruit treatment group inoculated with sterile water, 4C and 4D: the kiwi fruit treatment group inoculated with Botryosphaeria dothidea, 4E and 4F: the kiwi fruit treatment group inoculated with Diaporthe phaseolorum, 4G and 4H: the kiwi fruit treatment group inoculated with Alternaria alternate); DETAILED DESCRIPTION

[0048] In order for those skilled in the art to better understand the present application, the present application is further described in conjunction with the embodiments and drawings.

[0049] The technical idea of the present application is that when pathogenic fungi infect fruits, volatile organic compounds (VOCs) are produced, and volatile organic compounds with specific odors can be used as early warning indicators for fruit rot.

[0050] The present application adopts gas chromatography-ion mobility spectrometry (GC-IMS) technology as a detection method. This technology is a detection technology that simultaneously has the separation capacity of a GC chromatographic column and the high resolution and sensitivity of IMS, and has the technical advantages of simple operation, no sample pretreatment, fast detection rate, no vacuum system, and stability and reliability.

[0051] The identification method and the identification method provided by the present application will be described in detail below with specific examples.

[0052] It should be noted that, unless otherwise specified, the chemical drugs, chemical reagents and instrument equipment used in the following examples are commercially available products in the art.

[0053] Example 1

[0054] The identification method of the volatile biomarker of soft rot of kiwifruit provided in this embodiment is as follows:

[0055] S1, raw material selection and treatment

[0056] The kiwifruit used is a local variety of Shaanxi, "Cui Xiang", which is harvested and used immediately, with a soluble solids content of 6.5%, uniform size, no disease spots and mechanical damage. It is disinfected by soaking in 2% sodium hypochlorite for 3 minutes to remove the pathogenic bacteria carried by itself, and then naturally dried.

[0057] S2, inoculation of pathogenic fungi

[0058] In this step, in order to identify the volatile biomarker after the occurrence of soft rot, the pathogenic fungi causing soft rot are inoculated into kiwifruit, so that the pathogenic fungi infect the kiwifruit to produce soft rot.

[0059] S21, group the dried kiwifruit, and set up a healthy control group and a disease bacteria group;

[0060] S22, in the disease bacteria group, each kiwifruit is inoculated with 10 μL of spore suspension with a concentration of 1×10 5 / mL, and the spore suspension is prepared from pathogenic fungi causing soft rot; in the healthy control group, each kiwifruit is inoculated with 10 μL of sterile water; after inoculation, they are respectively placed in a constant temperature and humidity incubator for storage;

[0061] S23, during the storage process, kiwifruit is randomly taken from the disease bacteria group and the healthy control group every day to obtain healthy control samples and disease bacteria samples.

[0062] Preferably, the pathogenic fungi causing soft rot are Botryosphaeria dothidea, Diaporthe citri or Alternaria alternata.

[0063] In the implementation, since three pathogenic fungi are selected, the pathogen inoculation groups are three groups, corresponding to inoculation of spore suspensions of Botryosphaeria dothidea, Diaporthe citri and Alternaria alternata.

[0064] Specifically, the kiwifruits are divided into four groups, one of which is a healthy control group, denoted as CK; the pathogen inoculation groups correspond to inoculation of spore suspensions of Botryosphaeria dothidea, Diaporthe citri and Alternaria alternata, respectively, with a concentration of 1×10 5 The healthy control group is inoculated with an equal amount of sterile water to remove the background of changes in volatile components due to the respiratory burst of the kiwifruits during storage.

[0065] After inoculation, the four groups are placed in a constant temperature and humidity incubator, respectively, and stored at a temperature of 25±1℃ and a humidity of 85±5%; random sampling is performed at 0, 1, 2, 3, 4, 5, 6 and 7 days of storage, and the healthy control samples and pathogen samples are extracted every day.

[0066] To ensure the accuracy of the analysis results, each group has three biological replicates, and each biological replicate contains five kiwifruits.

[0067] S3, GC-IMS injection detection

[0068] For the healthy control samples and pathogen samples extracted every day, the GC-IMS analyzer is used to obtain three-dimensional spectra of the volatile components in the healthy control samples and the three-dimensional spectra of the volatile components in the pathogen samples.

[0069] In this embodiment, all the extracted samples are injected into the GC-IMS analyzer in a headspace injection mode.

[0070] In this embodiment, the parameters of headspace injection are as follows: 1.5 g of the sample to be tested is weighed into a 20 mL headspace bottle, sealed, and incubated at 45℃ with stirring at a speed of 500 r / min for 15 min, and then 500 μL of headspace injection is performed at 85℃. The subsequent detection in this embodiment is based on this.

[0071] The parameter conditions of the GC-IMS instrument are as follows: the gas chromatography column is MXT-5, the column temperature is 60℃, the carrier gas is high-purity nitrogen 99.999%, the carrier gas flow rate is initially 2 mL / min, maintained for 2 min, and then linearly increased to 100 mL / min within 18 min, the drift tube length is 9.8 cm and the temperature is 45℃, the drift gas is high-purity nitrogen 99.999% with a flow rate of 150 mL / min, and the linear voltage in the tube is 510 V / cm.

[0072] In this embodiment, VOCal software is used for mapping.

[0073] In implementation, the test results of the GC-IMS analyzer are transmitted to the VOCal software, and the GC-IMS three-dimensional spectra of volatile components of healthy control kiwi fruit and kiwi fruit samples infected with three pathogenic fungi at different infection times (0-7d) are made by using the Reporter plug-in in the VOCal software, as shown in FIG. 2. Figure 1

[0074] Referring to FIG. 2, the difference in volatile components of different samples at different times can be directly compared and analyzed by comparing the presence or absence of peaks. The background is blue, the x-axis represents ion migration time, the y-axis represents retention time, and the z-axis represents signal strength. Figure 1

[0075] S4, identification of volatile biomarkers

[0076] In this step, the VOCal software of the GC-IMS analyzer is used to analyze the three-dimensional spectra described above to identify volatile biomarkers. The specific steps are as follows:

[0077] S41, the three-dimensional spectra obtained above are projected to obtain two-dimensional spectra of volatile components in healthy control samples and two-dimensional spectra of volatile components in pathogen samples.

[0078] All the three-dimensional spectra obtained in step S41 are projected into two-dimensional spectra, and the results are shown in FIG. 3. Figure 1 Figure 2

[0079] Referring to FIG. 3, the background is blue, and the red vertical line at 1.0 on the abscissa is the RIP peak (reaction ion peak after normalization processing). Each bright spot on both sides of the RIP peak represents a volatile component. The darker (red) the color, the higher the peak signal intensity, and the lighter (white) the color, the lower the peak signal intensity. Figure 2

[0080] S42, the volatile components on the two-dimensional spectra obtained above are qualitatively analyzed by combining the NIST database and the IMS database in the VOCal software.

[0081] S43, according to different infection times and repetition times, and by using the Gallery Plot plug-in in the VOCal software, the qualitative volatile components are rearranged to draw characteristic fingerprint spectra of healthy control samples and characteristic fingerprint spectra of pathogen samples, respectively.

[0082] The qualitative analysis of the volatile components in all spectra is combined with the NIST database and the IMS database, and the matched substance information (including CAS number, retention index, retention time, drift time, etc.), as shown in Table 1.​​​​​

[0083] S44. Using the characteristic fingerprint of the healthy control sample as a benchmark, identify the volatile components not found in the characteristic fingerprint of the pathogen sample from the characteristic fingerprint of the healthy control sample, and complete the identification of the volatile biomarkers of kiwifruit soft rot.

[0084] In this example, the qualitative volatile components were rearranged according to different infection periods and repetition numbers based on the Gallery Plot plug-in in the VOCal software to draw the characteristic fingerprint of each treatment group. Each row represents the volatile component composition of each sample, and each column represents the signal strength comparison of each volatile component at different infection periods and three biological repetitions in each treatment group. The results are shown in Figure 2. Figure 3 shown.

[0085] See also Figure 3 It can be seen that a total of 66 volatile components of known compounds were detected in the healthy kiwifruit group; 80 volatile components of known compounds were detected in the group infected with Botrytis cinerea; a total of 83 volatile components of known compounds were detected in the group infected with Aspergillus niger; and a total of 75 volatile components of known compounds were detected in the group infected with Alternaria alternata.

[0086] Table 1 Volatile components identified by GC-IMS in all treatment groups

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] according to Figure 3 According to the qualitative results in Table 1, the fingerprints of volatile components released by the three pathogenic fungi were compared with those on day 0. Then, the volatile components that were obviously produced and stably present and not found in the healthy kiwifruit group were screened out from the volatile component fingerprints (because kiwifruit releases volatile components during storage and ripening, the background influence of its own volatile components needs to be removed). The volatile biomarkers of soft rot in kiwifruit caused by the three pathogenic fungi were clearly identified, also known as characteristic volatile biomarkers.

[0093] Specifically, 16 characteristic volatile biomarkers are released by the grapevine pathogen Botryosphaeria dothidea when it infects kiwifruit to produce soft rot, and the specific compounds are: 4-Methyl-1-pentanol, Isobutyl propanoate, 1,3-Butanediol, 5-Methylfurfural, 2-Hydroxy-3-methyl-2-cyclopenten-1-one (Cyclotene), 2-Hydroxybenzaldehyde, 2,6-Dimethyl pyrazine, Ethyl 2-hydroxypropanoate, 3-Hepten-2-one, 3-p-Menthanol, 2-Methylbutanal, 2,4,5-Trimethylthiazole, Styrene, Ethyl 2-methylbutanoate, α-Terpinene, and α-Terpinolene.

[0094] Specifically, 27 characteristic volatile biomarkers were released by the soft rot disease caused by Diaporthe phaseolorum infection in kiwifruit, specifically: γ-Terpinene, α-Terpinene, α-Terpinolene, 3-p-Menthanol, 4-Methylbenzaldehyde, δ-Hexalactone, Methyl methacrylate, 3-Hepten-2-one, 1-Penten-3-ol, 1,1-Diethoxyethane, Hydroxyacetone, 2-Pentanone, Ethyl 2-hydroxypropanoate, 1,3-Butanediol, Styrene, 2,3-Pentanedione, 4-Methylphenol, 5-Methylfurfural, 4-Methyl-2-phenyl-1,3-dioxolane, 2-Hydroxybenzaldehyde, Butyl formate, 2,6-Dimethyl pyrazine, 2-Furanmethanol, Ethyl 2-methylbutanoate, 2-Hydroxy-3-methyl-2-cyclopenten-1-one, Dihydro-5-methyl-2(3H)-furanone, 2,4,5-Trimethylthiazole.

[0095] Specifically, the characteristic volatile biomarkers released by the infection of kiwifruit by Alternaria alternata to cause soft rot are 12 compounds, specifically: Isobutyl propanoate, 4-Methyl-1-pentanol, 3-Hepten-2-one, Ethyl 2-methylbutanoate, 2-Hydroxy-3-methyl-2-cyclopenten-1-one (Cyclotene), 2-Hydroxybenzaldehyde, 4-Methylphenol, Ethyl 2-hydroxypropanoate, 2,6-Dimethyl pyrazine, 1,3-Butanediol, Styrene, 4-Methyl-2-phenyl-1,3-dioxolane.

[0096] From the above results, it can be concluded that the characteristic volatile biomarkers after the infection of kiwifruit by the three pathogenic fungi to cause soft rot are 34 compounds.

[0097] In this embodiment, in order to compare the change trend and similarity between different time periods of each treatment group, the two-dimensional spectrum of the volatile components of all samples obtained by projection in step S41 (the results of each sample repeated 3 times per day) is subjected to Dynamic PCA and Euclidean distance plug-ins in VOCal software, and principal component analysis and Euclidean distance analysis are performed on the volatile components to obtain a PC1xPC2 score plot and a Euclidean distance plot, as shown in Figure 4

[0098] When the distance between samples is far, the similarity is low, and the difference is obvious; when the distance between samples is close, the similarity is high, and the difference is not obvious. See Figure 4 It can be seen that the volatile components of each sample change obviously when healthy kiwifruit is infected with different pathogenic fungi and at different infection times, indicating that the volatile components of each group at different infection times have obvious differences. This indicates that the identification method provided by the present application has high reliability and good accuracy.

[0099] Example 2

[0100] The present embodiment provides a method for identifying soft rot of kiwifruit, comprising the following steps:

[0101] A1, disinfect and sterilize the randomly collected kiwifruit samples to be tested, and then dry them;​

[0102] A2, the kiwi fruit to be tested sample is introduced into the GC-IMS analyzer in a headspace sampling mode, and a three-dimensional spectrum of the volatile components in the kiwi fruit to be tested sample is obtained by using the GC-IMS analyzer.

[0103] In this embodiment, the parameters of the headspace sampling mode are as follows: 1-2 g of the sample to be analyzed is weighed and placed in a 20 mL headspace bottle, and after being sealed, the sample is incubated at 40-50 °C under stirring at a stirring rate of 450-550 r / min for 12-18 min, and then 500 μL of the sample is introduced into the headspace at 85 °C.

[0104] In this embodiment, the parameters of the GC-IMS analyzer are as follows: the gas chromatography column is MXT-5, the column temperature is 60 °C, the carrier gas is high-purity nitrogen 99.999%, the initial carrier gas flow rate is 2 mL / min, which is maintained for 2 min, and then linearly increased to 100 mL / min within 18 min, the drift tube length is 9.8 cm, the temperature is 45 °C, the drift gas is high-purity nitrogen 99.999%, the flow rate is 150 mL / min, and the linear voltage in the tube is 510 V / cm;

[0105] A3, identification of the soft rot of kiwi fruit

[0106] A31, the three-dimensional spectrum of the volatile components in the kiwi fruit to be tested sample obtained above is projected to obtain a two-dimensional spectrum of the volatile components in the kiwi fruit to be tested sample;

[0107] A32, the volatile components in the two-dimensional spectrum of the volatile components in the kiwi fruit to be tested sample are qualitatively analyzed in combination with the NIST database and the IMS database in the GC-IMS analyzer;

[0108] A33, the qualitative volatile components are rearranged by using the Gallery Plot plug-in in the GC-IMS analyzer to obtain a characteristic fingerprint spectrum of the kiwi fruit to be tested sample;

[0109] A34, the tested volatile components are identified from the characteristic fingerprint spectrum, and compared with the volatile biomarkers of the soft rot of kiwi fruit, if the tested volatile components are consistent with the volatile biomarkers of the soft rot of kiwi fruit, it is identified that the kiwi fruit to be tested sample has the soft rot, otherwise, it is identified that the kiwi fruit to be tested sample does not have the soft rot; the volatile biomarkers of the soft rot of kiwi fruit are identified by using the identification method of the volatile biomarkers of the soft rot of kiwi fruit in embodiment 1.

[0110] When the actually detected volatile components are one or more of the volatile biomarkers (a total of 34 compounds) identified in Example 1, it indicates that the kiwifruit has soft rot; when the actually detected volatile components are the same as the characteristic volatile biomarkers (a total of 16 compounds) released by the Botryosphaeria dothidea infection kiwifruit to produce soft rot, it indicates that the soft rot of the kiwifruit is caused by Botryosphaeria dothidea; when the actually detected volatile components are the same as the characteristic volatile biomarkers (a total of 27 compounds) released by the Diaporthe infection kiwifruit to produce soft rot, it indicates that the soft rot of the kiwifruit is caused by Diaporthe; when the actually detected volatile components are the same as the characteristic volatile biomarkers (a total of 12 compounds) released by the Alternaria infection kiwifruit to produce soft rot, it indicates that the soft rot of the kiwifruit is caused by Alternaria. It can be seen that through the actually detected volatile components, not only can the occurrence of soft rot of kiwifruit be detected, but also the cause of the occurrence of soft rot can be identified, so that early warning can be carried out in time during the young fruit stage of kiwifruit.

[0111] In the above examples, the parameters of headspace sampling can be replaced in the following ranges: 1-2 g of the sample to be analyzed is weighed, and after sealing, it is incubated at 40-50°C under the condition of stirring rate of 450-550 r / min for 12-18 min. When the parameters of headspace sampling are changed according to the above ranges, the rapid, efficient and accurate identification of soft rot can be achieved.

[0112] In the identification of volatile markers, the three pathogenic fungi (Botryosphaeria, Diaporthe and Alternaria) are preferably used to infect healthy kiwifruit to identify the volatile biomarkers causing soft rot. The pathogenic fungi can also be replaced by other pathogenic fungi causing soft rot to infect healthy kiwifruit, and more volatile biomarkers can be identified by using the identification method provided by the present application, and then the occurrence of soft rot of kiwifruit can be identified.

[0113] The above-described embodiments are only used to illustrate the specific implementation of the present application, and are not intended to limit the scope of the present application. Those skilled in the art should understand that the above examples can be modified or some technical features can be replaced by equivalents without departing from the scope and technology of the present application.

Claims

1. A method for identifying volatile biomarkers of kiwifruit soft rot, characterized in that: The following steps are involved: S1. Kiwifruit selection and processing Select a batch of kiwifruits that are uniform in size, free of disease spots and mechanical damage, disinfect and sterilize them, and then air dry them; S2. Inoculation of pathogenic fungi S21, grouping the dried kiwifruit into groups, and setting up a healthy control group and a pathogen-inoculated group; S22, pathogen-inoculated group, each kiwifruit was inoculated with a concentration of 1×10 5 Each kiwifruit was inoculated with 10 μL of sterile water, and the spore suspension was prepared from the pathogenic fungus that causes soft rot. In the healthy control group, each kiwifruit was inoculated with 10 μL of sterile water. After inoculation, the kiwifruits were placed in a constant temperature and humidity incubator for storage. S23. During the culture process, kiwifruits were randomly sampled from the healthy control group and the pathogen-inoculated group every day to obtain healthy control samples and pathogen samples; S3, GC-IMS detection The healthy control samples and pathogen samples collected daily were sampled using headspace injection, and three-dimensional spectra of the volatile components in the healthy control samples and the pathogen samples were obtained using a GC-IMS analyzer. The GC-IMS analyzer parameters were as follows: a MXT-5 gas chromatographic column, a column temperature of 60°C, a carrier gas of 99.999% high-purity nitrogen, an initial carrier gas flow rate of 2 mL / min, maintained for 2 minutes, and then linearly increased to 100 mL / min over 18 minutes. The drift tube length was 9.8 cm, the temperature was 45°C, the drift gas was 99.999% high-purity nitrogen, the flow rate was 150 mL / min, and the linear voltage in the tube was 510 V / cm. S4. Identification of Volatile Biomarkers S41, projecting the three-dimensional spectra obtained above to obtain corresponding two-dimensional spectra of volatile components in the healthy control sample and two-dimensional spectra of volatile components in the pathogen sample; S42, qualitatively analyzing the volatile components on the two-dimensional spectrum obtained above using the NIST database and the IMS database in the VOCal software; S43. According to different infection periods and repetition times, the qualitative volatile components were rearranged using the Gallery Plot plug-in in the VOCal software to draw characteristic fingerprints of healthy control samples and pathogen samples respectively; S44. Using the characteristic fingerprint of the healthy control sample as a benchmark, identify the volatile components not found in the characteristic fingerprint of the pathogen sample from the characteristic fingerprint of the healthy control sample, and complete the identification of the volatile biomarkers of kiwifruit soft rot.

2. The method for identifying volatile biomarkers of kiwifruit soft rot according to claim 1, characterized in that: In step S22, the pathogenic fungus causing soft rot is Botrytis cinerea, Metaspora spp. or Alternaria alternata.

3. The method for identifying volatile biomarkers of kiwifruit soft rot according to claim 1, characterized in that: In step S23, the culture temperature is 25±1° C., the culture humidity is 85±5%, and the culture time is 7 days.

4. The method for identifying volatile biomarkers of kiwifruit soft rot according to claim 1, characterized in that: The parameters of the headspace sampling method are as follows: 1-2 g of the sample to be analyzed is weighed and placed in a 20 mL headspace bottle, which is sealed and incubated at 40-50° C. and a stirring rate of 450-550 r / min for 12-18 min, and then 500 μL of the sample is injected into the headspace at 85° C.

5. The method for identifying volatile biomarkers of kiwifruit soft rot according to claim 2, characterized in that: When the pathogenic fungus causing the soft rot is Botrytis cinerea, the identified volatile biomarkers of kiwifruit soft rot include: 4-methyl-1-pentanol, isobutyl propionate, 1,3-butanediol, 5-methylfurfural, methylcyclopentenolone, salicylaldehyde, 2,6-dimethylpyrazine, ethyl lactate, 3-heptene-2-one, menthol, 2-methylbutanal, 2,4,5-trimethylthiazole, styrene, ethyl 2-methylbutyrate, α-terpinene and α-terpinolene; When the pathogenic fungus causing the soft rot is Aspergillus niger, the identified volatile biomarkers of kiwifruit soft rot include: γ-terpinene, α-terpinene, α-terpinolene, menthol, 4-methylbenzaldehyde, butyl hexalactone, methyl methacrylate, 3-heptene-2-one, 1-penten-3-ol, 1,1-diethoxyethane, hydroxyacetone, 2-pentanone, ethyl lactate, 1,3-butanediol, styrene, 2,3-pentanedione, 4-methylphenol, 5-methylfurfural, benzaldehyde propylene glycol acetal, salicylicaldehyde, butyl formate, 2,6-dimethylpyrazine, furfuryl alcohol, ethyl 2-methylbutyrate, methylcyclopentenolone, γ-valerolactone and 2,4,5-trimethylthiazole; When the pathogenic fungus causing the soft rot is Alternaria alternata, the identified volatile biomarkers of kiwifruit soft rot include: isobutyl propionate, 4-methyl-1-pentanol, 3-heptene-2-one, ethyl 2-methylbutyrate, methylcyclopentenolone, salicylaldehyde, 4-methylphenol, ethyl lactate, 2,6-dimethylpyrazine, 1,3-butanediol, styrene and benzaldehyde propylene glycol acetal.

6. The method for identifying volatile biomarkers of kiwifruit soft rot according to claim 2, characterized in that: The method for identifying volatile biomarkers of kiwifruit soft rot also includes the following steps: For the two-dimensional spectra of volatile components in healthy control samples and the two-dimensional spectra of volatile components in pathogen samples; principal component analysis and Euclidean distance analysis in VOCal software were used to obtain the corresponding PC1×PC2 score graph and Euclidean distance graph; by comparing the differences between volatile components under different storage times, the key time point for pathogen infection of kiwifruit was determined.

7. A method for identifying kiwifruit soft rot, characterized in that: The following steps are involved: A1. Sterilize and air-dry randomly collected kiwifruit samples. A2. The kiwifruit sample was introduced into the GC-IMS analyzer using a headspace injection method, and a three-dimensional spectrum of the volatile components in the kiwifruit sample was obtained using the GC-IMS analyzer. The GC-IMS analyzer parameters were as follows: a MXT-5 gas chromatography column, a column temperature of 60°C, a carrier gas of 99.999% high-purity nitrogen, an initial carrier gas flow rate of 2 mL / min, maintained for 2 minutes, and then increased linearly to 100 mL / min over 18 minutes. The drift tube length was 9.8 cm, the temperature was 45°C, the drift gas was 99.999% high-purity nitrogen at a flow rate of 150 mL / min, and the in-tube linear voltage was 510 V / cm. A3. Identification of kiwifruit soft rot A31. Projecting the three-dimensional spectrum of volatile components in the kiwifruit sample obtained above to obtain a two-dimensional spectrum of volatile components in the kiwifruit sample; A32. Qualitatively analyze the volatile components in the two-dimensional spectrum of the kiwifruit sample using the NIST database and the IMS database in the VOCal software. A33. Use the Gallery Plot plug-in in the VOCal software to rearrange the qualitatively identified volatile components and draw a characteristic fingerprint of the kiwifruit sample to be tested; A34. Identify the tested volatile components from the characteristic fingerprint and compare them with the volatile biomarkers of kiwifruit soft rot. If the tested volatile components are consistent with the volatile biomarkers of kiwifruit soft rot, it is determined that the kiwifruit sample to be tested has soft rot. Otherwise, it is determined that the kiwifruit sample to be tested has not soft rot. The volatile biomarker of kiwifruit soft rot is identified using the method for identifying volatile biomarkers of kiwifruit soft rot according to claim 5.

8. The method for identifying kiwifruit soft rot according to claim 7, characterized in that: The parameters of the headspace sampling method are as follows: 1-2 g of the sample to be analyzed is weighed and placed in a 20 mL headspace bottle, which is sealed and incubated at 40-50° C. and a stirring rate of 450-550 r / min for 12-18 min, and then 500 μL of the sample is injected into the headspace at 85° C.

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