Kiwi fruit soft rot volatile biomarker identification method and soft rot identification method

Abstract

The invention belongs to the technical field of fruit and vegetable disease detection and identification, and relates to a kiwi fruit soft rot volatile biomarker identification method and a soft rot identification method, the identification method comprises the following steps: S1, selecting kiwi fruit for disinfection and sterilization; s2, inoculating pathogenic fungi: dividing the kiwi fruits into a healthy control group and a germ inoculating group; sterile water is inoculated to the healthy control group, and pathogenic fungi causing soft rot are inoculated to the germ inoculation group; healthy control samples and germ samples are randomly extracted every day during storage; s3, using a GC-IMS analyzer for the extracted sample to obtain a three-dimensional spectrogram of the volatile components; and S4, analyzing and identifying the volatile biomarker through VOCal software. According to the method, the volatile biomarker of the soft rot of the kiwi fruit is identified, and then whether the soft rot of the kiwi fruit is generated or not is identified through the identified volatile biomarker, so that the method has the characteristics of rapidness, accuracy, sensitivity and no damage, and monitoring and early warning of the soft rot of the kiwi fruit can be realized.

Description

Technical Field

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

[0002] Kiwifruit (Actinidia chinensis Planch.) is a member of the genus Actinidia of the family Actinidia. It originated in China and has become one of the important economic forest fruits in my country. It is widely planted in Shaanxi, Sichuan, Guizhou and other places. Kiwifruit is soft in texture, sweet and sour in taste, rich in fruity aroma, and unique in flavor. It is rich in vitamin C, folic acid, carotene, amino acids, trace elements, etc., and its nutritional value far exceeds 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 all over the world. However, as a typical respiratory climacteric fruit and a thin-skinned and juicy berry, kiwifruit ripens significantly during storage and is easily invaded by pathogens, causing the fruit to become soft and rotten, with a very high loss rate. At present, soft rot has become one of the most important diseases causing the corruption of kiwifruit after harvest, mainly caused by pathogenic fungi such as Botryosphaeria dothidea, Diaporthe, and Alternaria alternata. In addition, when the soft rot pathogen infects kiwifruit, it is difficult to diagnose in the early stage. It often occurs in the young fruit stage and then remains dormant for a long time until the kiwifruit gradually shows corresponding symptoms when it ripens after being harvested. The soft rot of post-harvest kiwifruit is very easy to occur and infects quickly, which has become a technical problem that needs to be urgently solved for the healthy and stable development of the kiwifruit industry.

[0003] At present, the main identification methods for kiwifruit soft rot are electronic nose and gas chromatography tandem mass spectrometry (GC-MS) detection methods. However, due to the limited selection specificity and recognition ability of the electronic nose, GC-MS requires complex sample pretreatment operations and the time-consuming and labor-intensive analysis process, and the limited ability to monitor the volatile components of kiwifruit during post-harvest storage, the identification of kiwifruit soft rot has problems such as long detection time, poor accuracy and low sensitivity.

[0004] In view of the above-mentioned defects in the identification of kiwifruit soft rot, seeking a rapid, accurate and sensitive method for identifying kiwifruit postharvest soft rot is a major research topic at present. Summary of the invention

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

[0006] The present invention first identifies the volatile biomarkers of kiwifruit soft rot, and then uses the identified volatile biomarkers to identify whether the kiwifruit has soft rot. The method has the characteristics of being fast, accurate and sensitive, and can realize the monitoring and early warning of kiwifruit soft rot.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] A method for identifying volatile biomarkers of kiwifruit soft rot disease comprises the following steps:

[0009] S1. Kiwifruit selection and processing

[0010] Select a batch of kiwifruits of uniform size, without disease spots and mechanical damage, disinfect and sterilize them, and then dry them;

[0011] S2. Inoculation of pathogenic fungi

[0012] S21, grouping the dried kiwifruits into groups, and setting up a healthy control group and a pathogen-inoculated group;

[0013] S22, pathogen inoculation group, each kiwifruit was inoculated with 1×10 5 / mL, 10μL spore suspension, the spore suspension was prepared from the pathogenic fungus that causes soft rot; the healthy control group, each kiwi fruit was inoculated with 10μL of sterile water; after inoculation, they were placed in a constant temperature and humidity incubator for storage;

[0014] S23. During the storage process, kiwifruits were randomly sampled from the pathogen-inoculated group and the healthy control group every day to obtain healthy control samples and pathogen samples;

[0015] S3, GC-IMS detection

[0016] The healthy control samples and pathogen samples collected every day were introduced into the GC-IMS analyzer by headspace injection, and the 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 were obtained by the GC-IMS analyzer; the parameter conditions of the GC-IMS analyzer were as follows: the gas chromatography column was MXT-5, the column temperature was 60°C, the carrier gas was high-purity nitrogen 99.999%, the carrier gas flow rate was initially 2mL / min, maintained for 2min, and then the linear gradient was increased to 100mL / min within 18min, the drift tube length was 9.8cm, the temperature was 45°C, the drift gas was high-purity nitrogen 99.999%, the flow rate was 150mL / min, and the linear voltage in the tube was 510V / cm;

[0017] S4. Volatile biomarker identification

[0018] 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;

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

[0020] 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 characteristic fingerprints of pathogenic samples respectively;

[0021] 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 healthy control sample from the characteristic fingerprint of the pathogen sample, and complete the identification of volatile biomarkers of kiwifruit soft rot.

[0022] It is further defined that in step S22, the pathogenic fungus causing soft rot is Botrytis cinerea, Aspergillus niger or Alternaria alternata.

[0023] It is further defined that in step S23, the culture temperature is 25±1° C., the culture humidity is 85±5%, and the culture time is 7 days.

[0024] It is further defined that the parameters of the headspace injection method are: weigh 1 to 2 g of the sample to be analyzed and place it in a 20 mL headspace bottle, seal it, incubate it at 40 to 50° C. and a stirring rate of 450 to 550 r / min for 12 to 18 minutes, and then inject 500 μL of the headspace at 85° C.

[0025] It is further defined that when the pathogenic fungus causing 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, methylcyclopentenol ketone, 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;

[0026] 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-pentene-3-ol, 1,1-diethoxyethane, hydroxyacetone, 2-pentanone, ethyl lactate, 1,3-butanediol, styrene, 2,3-pentanedione, 4-methylphenol, 5-methylfurfural, benzaldehyde propanediol acetal, salicylaldehyde, butyl formate, 2,6-dimethylpyrazine, furfuryl alcohol, ethyl 2-methylbutyrate, methylcyclopentenolone, γ-valerolactone and 2,4,5-trimethylthiazole;

[0027] 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.

[0028] It is further defined that the method for identifying volatile biomarkers of kiwifruit soft rot also includes the following steps:

[0029] For the two-dimensional spectra of volatile components in healthy control samples and the two-dimensional spectra of volatile components in pathogenic samples; principal component analysis and Euclidean distance analysis in VOCal software were used to obtain the PC1×PC2 score graph and Euclidean distance graph respectively; by comparing the differences between volatile components at different storage times, the key time points for the onset of disease caused by pathogenic fungi infecting kiwifruit were determined.

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

[0031] A1. Sterilize and air-dry the randomly collected kiwifruit samples.

[0032] A2. The kiwifruit sample to be tested is introduced into the GC-IMS analyzer by headspace injection; the GC-IMS analyzer is used to obtain a three-dimensional spectrum of the volatile components in the kiwifruit sample to be tested; the parameter conditions 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 99.999% high-purity nitrogen, the carrier gas flow rate is initially 2mL / min, maintained for 2min, and then the linear gradient is increased to 100mL / min within 18min, the drift tube length is 9.8cm, the temperature is 45°C, the drift gas is 99.999% high-purity nitrogen, 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 volatile components in the kiwi fruit sample to be tested obtained above to obtain a two-dimensional spectrum of volatile components in the kiwi fruit sample to be tested;

[0035] A32. Qualitative analysis of the volatile components in the two-dimensional spectra of the kiwifruit samples was performed using the NIST database and the IMS database in the VOCal software.

[0036] A33. Use the Gallery Plot plug-in in the VOCal software to rearrange the qualitative volatile components and draw a characteristic fingerprint of the kiwifruit sample to be tested;

[0037] 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 identified that the kiwifruit sample to be tested has soft rot. Otherwise, it is identified that the kiwifruit sample to be tested has not soft rot. The volatile biomarkers of kiwifruit soft rot are identified by the identification method of volatile biomarkers of kiwifruit soft rot.

[0038] It is further defined that the parameters of the headspace injection method are: weigh 1 to 2 g of the sample to be analyzed and place it in a 20 mL headspace bottle, seal it, incubate it at 40 to 50° C. and a stirring rate of 450 to 550 r / min for 12 to 18 minutes, and then inject 500 μL of the headspace at 85° C.

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

[0040] 1. The present invention combines GC-IMS technology with VOCal software analysis to characterize the volatile component fingerprints of pathogenic fungi that cause postharvest soft rot of kiwifruit at different infection periods. Based on the difference in fingerprints between healthy kiwifruit and kiwifruit with soft rot, the characteristic volatile biomarkers of pathogenic fungi are identified, providing a new way to warn of the occurrence of postharvest soft rot in kiwifruit.

[0041] 2. The method provided by the present invention does not require complex sample pretreatment processes such as extraction and purification during identification. The kiwifruit samples are directly sampled by headspace, which retains the original volatile component appearance to the maximum extent. Compared with traditional methods, this method is simple to operate, greatly shortens the detection time, can identify a variety of volatile components, has the characteristics of high sensitivity, high efficiency and rapidity, and can more easily realize the early warning and monitoring of soft rot after kiwifruit is harvested.

[0042] 3. The present invention does not require complicated pre-treatment of fruits and samples, and directly absorbs volatile components in the storage environment to identify soft rot, thereby achieving non-destructive detection of soft rot occurring in kiwifruit after harvest.

[0043] 4. The present invention uses GC-IMS technology to detect the volatile components of kiwifruit soft rot caused by various pathogenic fungi, and draws a fingerprint map of the volatile components, which can realize rapid monitoring and early warning of kiwifruit soft rot, timely prevention and control, and avoid large-scale spread of pathogens. It has great application prospects for real-time identification during the onset of kiwifruit soft rot. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The GC-IMS three-dimensional spectra of volatile components of healthy kiwifruit and kiwifruit soft rot samples caused by three pathogenic fungi at different infection periods (0-7d) (A: healthy kiwifruit inoculated with sterile water, B: kiwifruit inoculated with Botrytis cinerea, C: kiwifruit inoculated with Aspergillus niger, D: kiwifruit inoculated with Alternaria alternata).

[0045] Figure 2 The GC-IMS two-dimensional spectra of volatile components of healthy kiwifruit and kiwifruit soft rot samples caused by three pathogenic fungi at different infection periods (0-7d) (A: healthy kiwifruit inoculated with sterile water, B: kiwifruit inoculated with Botrytis cinerea, C: kiwifruit inoculated with Aspergillus niger, D: kiwifruit inoculated with Alternaria alternata).

[0046] Figure 3 The volatile component fingerprints of healthy kiwifruit and kiwifruit soft rot samples caused by three pathogenic fungi at different infection periods (0-7d) (3A: healthy kiwifruit inoculated with sterile water, 3B: kiwifruit inoculated with Botrytis cinerea, 3C: kiwifruit inoculated with Aspergillus niger, 3D: kiwifruit inoculated with Alternaria alternata).

[0047] Figure 4 PCA analysis (4A, 4C, 4E, 4G) and Euclidean distance analysis (4B, 4D, 4F, 4H) of healthy kiwifruit and kiwifruit soft rot samples caused by three pathogenic fungi at different infection periods (0-7d) (4A and 4B: healthy kiwifruit inoculated with sterile water, 4C and 4D: kiwifruit inoculated with Botrytis cinerea, 4E and 4F: kiwifruit inoculated with Aspergillus niger, 4G and 4H: kiwifruit inoculated with Alternaria alternata). DETAILED DESCRIPTION

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

[0049] The technical idea of ​​the present invention is that since pathogenic fungi will produce volatile organic compounds (VOCs) when infecting fruits, volatile organic compounds with specific odors can be used as early warning indicators for fruit decay.

[0050] The present invention adopts gas phase-ion mobility spectrometry (GC-IMS) technology as a detection method. This technology is a detection technology that has both the adsorption separation ability of GC chromatographic columns and the high resolution and sensitivity of IMS, and has the advantages of simple operation, no need for sample pretreatment, fast detection rate, no need for a vacuum system, and stable and reliable technology.

[0051] The identification method and authentication method provided by the present invention are described in detail below with reference to specific embodiments.

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

[0053] Example 1

[0054] This embodiment provides a method for identifying volatile biomarkers of kiwifruit soft rot, and the specific steps are as follows:

[0055] S1. Raw material selection and processing

[0056] The kiwifruit used is the Shaanxi local variety "Cui Xiang". It is picked and used immediately, with a soluble solids content of 6.5%. It is uniform in size, free of disease spots and mechanical damage. It is soaked in 2% sodium hypochlorite for 3 minutes for disinfection to remove the pathogens it carries, and then dried naturally.

[0057] S2. Inoculation of pathogenic fungi

[0058] In this step, in order to identify volatile biomarkers after the occurrence of soft rot, pathogenic fungi that cause soft rot are inoculated into kiwi fruit, so that the pathogenic fungi infect kiwi fruit and cause soft rot.

[0059] S21, grouping the dried kiwifruits into groups, and setting up a healthy control group and a pathogen-inoculated group;

[0060] S22, pathogen inoculation group, each kiwifruit was inoculated with 1×10 5 / mL, 10μL spore suspension, the spore suspension was prepared from the pathogenic fungus that causes soft rot; the healthy control group, each kiwi fruit was inoculated with 10μL of sterile water; after inoculation, they were placed in a constant temperature and humidity incubator for storage;

[0061] S23. During the storage process, kiwifruits were randomly sampled from the pathogen-infected group and the healthy control group every day to obtain healthy control samples and pathogen samples.

[0062] Preferably, the pathogenic fungus causing soft rot is Botrytis cinerea, Metarella serrata or Alternaria alternata.

[0063] During implementation, since three pathogenic fungi were selected, the inoculation groups were divided into three groups, corresponding to the inoculation of Botrytis cinerea, Aspergillus niger and Alternaria alternata.

[0064] Specifically, kiwifruit was divided into 4 groups, one of which was a healthy control group, denoted as CK; the pathogen-inoculated groups were inoculated with spore suspensions of Botrytis cinerea, Aspergillus niger, and Alternaria alternata, with a concentration of 1×10 5 / mL, numbered B, D, A, and the inoculation volume was 10μL. The healthy control group was inoculated with an equal amount of sterile water to remove the background of changes in volatile components of kiwifruit during storage.

[0065] After inoculation, the above four groups were placed in a constant temperature and humidity incubator, stored and cultured at a temperature of 25±1°C and a humidity of 85±5%, respectively; and random sampling was carried out at 0, 1, 2, 3, 4, 5, 6, and 7 days of storage, and healthy control samples and pathogen samples were taken every day.

[0066] To ensure the accuracy of the analysis results, there were 3 biological replicates in each group, and each biological replicate contained 5 kiwifruits.

[0067] S3, GC-IMS injection detection

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

[0069] In this embodiment, all the extracted samples are introduced into the GC-IMS analyzer by headspace injection.

[0070] In this embodiment, the parameters of headspace sampling are: weigh 1.5 g of the sample to be tested and place it in a 20 mL headspace bottle, seal it, incubate it at 45°C and stirring rate of 500 r / min for 15 min, and then inject 500 μL of headspace at 85°C. Subsequent detection in this embodiment is based on this.

[0071] The parameters of the GC-IMS instrument are as follows: the gas chromatography column is MXT-5, the column temperature is 60°C, the carrier gas is 99.999% high-purity nitrogen, the initial carrier gas flow rate is 2 mL / min, maintained for 2 min, and then the linear gradient is 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 99.999% high-purity nitrogen, the flow rate is 150 mL / min, and the linear voltage in the tube is 510 V / cm.

[0072] In this embodiment, VOCal software is used to draw the spectrum.

[0073] During implementation, the test results of the GC-IMS analyzer were transferred to the VOCal software, and the Reporter plug-in in the VOCal software was used to generate GC-IMS three-dimensional spectra of the volatile components of the healthy control kiwifruit and the kiwifruit soft rot samples caused by the three pathogenic fungi at different infection periods (0-7 days), such as Figure 1 shown.

[0074] See also Figure 1 , the difference of volatile components in different periods and samples can be directly analyzed by comparing the presence or absence of peaks. Blue is the background, the x-axis represents the ion migration time, the y-axis represents the retention time, and the z-axis represents the signal strength.

[0075] S4. Volatile biomarker identification

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

[0077] S41, projecting the three-dimensional spectra obtained above, and correspondingly obtaining two-dimensional spectra of volatile components in healthy control samples and two-dimensional spectra of volatile components in pathogen samples.

[0078] right Figure 1 All three-dimensional spectra obtained in are projected into two-dimensional spectra, and the results are as follows Figure 2 shown.

[0079] See also Figure 2 , the background is blue, and the red vertical line at the horizontal axis 1.0 is the RIP peak (reactive ion peak, normalized). Each bright spot on both sides of the RIP peak represents a volatile component. The darker the color (red), the higher the peak signal intensity, and the lighter the color (white), the lower the peak signal intensity.

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

[0081] 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.

[0082] The volatile components in all spectra were qualitatively analyzed by combining the NIST database and the IMS database. The matched substance information (including CAS number, retention index, retention time, drift time, etc.) is 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 healthy control sample from the characteristic fingerprint of the pathogen sample, and complete the identification of volatile biomarkers of kiwifruit soft rot.

[0084] In this example, the qualitative volatile components were rearranged according to different infection periods and repetition times 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 3 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 Information on 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 the 0th day. Then, the volatile components that were obviously produced and stably existed and were not found in the healthy kiwifruit group were screened out from the volatile component fingerprints (because kiwifruit releases volatile components during the ripening process after storage, 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 called characteristic volatile biomarkers.

[0093] Specifically, there are 16 characteristic volatile biomarkers released by Botrytis cinerea to produce soft rot in kiwifruit, including: 4-Methyl-1-pentanol (4-methyl-1-pentanol), Isobutyl propanoate (isobutyl propanoate), 1,3-Butanediol (1,3-butanediol), 5-Methylfurfural (5-methylfurfural), 2-Hydroxy-3-methyl-2-cyclopenten-1-one (Cyclotene) (methylcyclopentenolone), 2-Hydroxybenzaldehyde (salicylaldehyde), 2,6-Dimethyl pyrazine(2,6-dimethylpyrazine), Ethyl2-hydroxypropanoate(ethyl lactate), 3-Hepten-2-one(3-heptene-2-one), 3-p-Menthanol(menthol), 2-Methylbutanal(2-methylbutanal), 2,4,5-Trimethylthiazole(2,4,5-trimethylthiazole), Styrene(styrene), Ethyl 2-methylbutanoate(ethyl 2-methylbutanoate), α-Terpinene(α-terpinene), α-Terpinolene(α-terpinolene).

[0094] Specifically, there are 27 characteristic volatile biomarkers released by kiwifruit soft rot caused by Aspergillus niger infection, including: 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 methacrylate, 1,2-Diethoxyethane ... 2-hydroxypropanoate(ethyl lactate), 1,3-Butanediol(1,3-butylene glycol), Styrene(styrene), 2,3-Pentanedione(2,3-pentanedione), 4-Methylphenol(4-methylphenol), 5-Methylfurfural(5-methylfurfural), 4-Methyl-2-phenyl-1,3-dioxolane(benzaldehyde propylene glycol acetal), 2-Hydroxybenzaldehyde(salicylic aldehyde), Butyl formate(butyl formate), 2,6-Dimethyl pyrazine(2,6-dimethylpyrazine), 2-Furanmethanol(furfuryl alcohol), Ethyl 2-methylbutanoate(2-methylbutanoic acid ethyl ester), 2-Hydroxy-3-methyl-2-cyclopenten-1-one(Cyclotene)(methylcyclopentenolone), Dihydro-5-methyl-2(3H)-furanone(γ-valerolactone), 2,4,5-Trimethylthiazole(2,4,5-trimethylthiazole).

[0095] Specifically, there are 12 characteristic volatile biomarkers released by Alternaria alternata infection of kiwifruit to produce soft rot, including: 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, Ethyl2-hydroxypropanoate, 2,6-Dimethyl pyrazine(2,6-dimethylpyrazine), 1,3-Butanediol(1,3-butanediol), Styrene(styrene), 4-Methyl-2-phenyl-1,3-dioxolane(benzaldehyde propylene glycol acetal).

[0096] From the above results, it can be concluded that there are 34 compounds in total that are characteristic volatile biomarkers of kiwifruit soft rot caused by the three pathogenic fungi.

[0097] In this embodiment, in order to compare the change trends and similarities between different treatment groups at different times, the two-dimensional spectra of the volatile components of all samples obtained by projection in step S41 (the result of repeating each sample 3 times per day) were used to perform principal component analysis and Euclidean distance analysis on the volatile components, and a PC1×PC2 score map and an Euclidean distance map were obtained. The results are shown in FIG. Figure 4 shown.

[0098] When the distance between samples is far, it means the similarity is low and the difference is obvious; when the distance between samples is close, it means the similarity is high and the difference is not obvious. Figure 4 It can be seen that when healthy kiwifruit was infected with different pathogenic fungi and at different infecting times, the volatile components of each sample changed significantly, indicating that the volatile components of each group at different infecting times were significantly different. This shows that the identification method provided by the present invention has high reliability and good accuracy.

[0099] Example 2

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

[0101] A1. Sterilize and air-dry the randomly collected kiwifruit samples.

[0102] A2. The kiwifruit sample to be tested is introduced into the GC-IMS analyzer by headspace injection, and the GC-IMS analyzer is used to obtain a three-dimensional spectrum of the volatile components in the kiwifruit sample to be tested.

[0103] In this embodiment, the parameters of the headspace injection 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 headspace is injected at 85° C.

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

[0105] A3. Identification of kiwifruit soft rot

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

[0107] A32, qualitatively analyzing the volatile components in the two-dimensional spectrum of the volatile components in the kiwifruit sample according to the NIST database and the IMS database in the GC-IMS analyzer;

[0108] A33, using the Gallery Plot plug-in in the GC-IMS analyzer to rearrange the qualitative volatile components, and draw a characteristic fingerprint of the kiwifruit sample to be tested;

[0109] 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 identified that the kiwifruit sample to be tested has soft rot. Otherwise, it is identified that the kiwifruit sample to be tested has not soft rot. The volatile biomarkers of kiwifruit soft rot are identified using the identification method of the volatile biomarkers of kiwifruit soft rot in Example 1.

[0110] In this embodiment, when the volatile components actually detected are one or more of the volatile biomarkers identified in Example 1 (a total of 34 compounds), it indicates that soft rot occurs in kiwifruit; when the volatile components actually detected are the same as the characteristic volatile biomarkers (a total of 16 compounds) released by the Botrytis cinerea infection of kiwifruit to produce soft rot identified in Example 1, it indicates that the soft rot of kiwifruit is caused by Botrytis cinerea; when the volatile components actually detected are the same as the characteristic volatile biomarkers released by the Aspergillus niger infection of kiwifruit identified in Example 1 (a total of 27 compounds), it indicates that the soft rot of kiwifruit is caused by Aspergillus niger; when the volatile components actually detected are the same as the characteristic volatile biomarkers released by the Alternaria alternata infection of kiwifruit identified in Example 1 (a total of 12 compounds), it indicates that the soft rot of kiwifruit is caused by Alternaria alternata. It can be seen that this embodiment can not only detect whether kiwifruit has soft rot through the actual detection of volatile components, but also identify which pathogen causes the soft rot, so that early warning can be given in time during the young fruit stage of kiwifruit.

[0111] In the above embodiment, the parameters of the headspace sampling can be replaced within the following range: weigh 1-2 g of the sample to be analyzed, seal it, and incubate it for 12-18 min at 40-50° C. and a stirring rate of 450-550 r / min. When the parameters of the headspace sampling are changed within the above range, rapid, efficient, and accurate identification of soft rot can be achieved.

[0112] When identifying volatile markers, the present invention preferably uses three pathogenic fungi (Botrytis cinerea, Metaspora spp. and Alternaria alternata) to infect healthy kiwifruit, and identifies volatile biomarkers that cause soft rot. The pathogenic fungi can also be replaced with other pathogenic fungi that cause soft rot to infect healthy kiwifruit, and more volatile biomarkers can be identified using the identification method provided by the present invention, thereby identifying whether the kiwifruit has soft rot.

[0113] The embodiments described above are only used to illustrate the specific implementation of the present invention, rather than to limit the scope of the present invention. Those skilled in the art should understand that the above embodiments may be modified or some technical features may be replaced by equivalents without departing from the scope and technology of the present invention.

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 of uniform size, without disease spots and mechanical damage, disinfect and sterilize them, and then dry them; S2. Inoculation of pathogenic fungi S21, grouping the dried kiwifruits into groups, and setting up a healthy control group and a pathogen-inoculated group; S22, pathogen inoculation group, each kiwifruit was inoculated with 1×10 5 / mL, 10μL spore suspension, the spore suspension was prepared from the pathogenic fungus that causes soft rot; the healthy control group, each kiwi fruit was inoculated with 10μL of sterile water; after inoculation, they 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 every day were sampled by headspace injection, and then the 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 were obtained by GC-IMS analyzer; the parameter conditions of the GC-IMS analyzer were as follows: the gas chromatography column was MXT-5, the column temperature was 60°C, the carrier gas was 99.999% high-purity nitrogen, the carrier gas flow rate was initially 2mL / min, maintained for 2min, and then the linear gradient was increased to 100mL / min within 18min, the drift tube length was 9.8cm, the temperature was 45°C, the drift gas was 99.999% high-purity nitrogen, the flow rate was 150mL / min, and the linear voltage in the tube was 510V / cm; S4. Volatile biomarker identification 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 respectively by combining 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 characteristic fingerprints of pathogenic 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 healthy control sample from the characteristic fingerprint of the pathogen sample, and complete the identification of 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 the step S22, the pathogenic fungus causing soft rot is Botrytis cinerea, Metarhinia 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 injection method are: weigh 1-2 g of the sample to be analyzed and place it in a 20 mL headspace bottle, seal it, incubate it at 40-50° C. and stirring speed 450-550 r / min for 12-18 min, and then inject 500 μL of 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, methylcyclopentenol ketone, 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-pentene-3-ol, 1,1-diethoxyethane, hydroxyacetone, 2-pentanone, ethyl lactate, 1,3-butanediol, styrene, 2,3-pentanedione, 4-methylphenol, 5-methylfurfural, benzaldehyde propanediol acetal, salicylaldehyde, 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 pathogenic samples; principal component analysis and Euclidean distance analysis in VOCal software were used to obtain the PC1×PC2 score graph and Euclidean distance graph respectively; by comparing the differences between volatile components at different storage times, the key time point for the pathogen to infect kiwifruit was determined.

7. A method for identifying kiwifruit soft rot, characterized in that: The following steps are involved: A1. Sterilize and air-dry the randomly collected kiwifruit samples. A2. The kiwifruit sample to be tested was introduced into the GC-IMS analyzer by headspace injection, and the GC-IMS analyzer was used to obtain a three-dimensional spectrum of the volatile components in the kiwifruit sample to be tested; the parameter conditions of the GC-IMS analyzer were as follows: the gas chromatography column was MXT-5, the column temperature was 60°C, the carrier gas was 99.999% high-purity nitrogen, the carrier gas flow rate was initially 2mL / min, maintained for 2min, and then the linear gradient was increased to 100mL / min within 18min, the drift tube length was 9.8cm, the temperature was 45°C, the drift gas was 99.999% high-purity nitrogen, the flow rate was 150mL / min, and the linear voltage in the tube was 510V / cm; A3. Identification of kiwifruit soft rot A31, projecting the three-dimensional spectrum of volatile components in the kiwi fruit sample to be tested obtained above to obtain a two-dimensional spectrum of volatile components in the kiwi fruit sample to be tested; A32. Qualitative analysis of the volatile components in the two-dimensional spectra of the kiwifruit samples was performed 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 qualitative volatile components and draw a characteristic fingerprint of the kiwifruit sample to be tested; A34. Identify the tested volatile components from the characteristic fingerprint spectrum 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 identified that the kiwifruit sample to be tested has soft rot. Otherwise, it is identified 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 described in claim 5.

8. The method for identifying kiwifruit soft rot according to claim 7, characterized in that: The parameters of the headspace injection method are: weigh 1-2 g of the sample to be analyzed and place it in a 20 mL headspace bottle, seal it, incubate it at 40-50° C. and stirring speed 450-550 r / min for 12-18 min, and then inject 500 μL of headspace at 85° C.

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