CN114813976A - Method for quickly detecting early stage gray mold of strawberries after picking - Google Patents
Method for quickly detecting early stage gray mold of strawberries after picking Download PDFInfo
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Abstract
The invention discloses a method for quickly detecting early gray mold of picked strawberries, which comprises the following steps: (1) selecting strawberries with similar sizes and no damage on the surfaces, performing aseptic treatment, inoculating botrytis cinerea, storing in an aseptic constant-temperature and constant-humidity box, and sampling for detection and analysis after 0, 24, 48, 72, 96 and 120 hours of storage morbidity. (2) Adopting headspace automatic sampling, collecting GC-IMS three-dimensional signal maps under different storage time, and counting the change of microorganism content. (3) Analyzing the difference of the odor fingerprints of the strawberry fruits at different disease stages from multiple dimensions. (4) And (3) comparing and screening GC-IMS signal changes of specific volatile substances generated in the process of the onset of diseases of samples at different disease stages, and analyzing. (5) And combining a dynamic principal component analysis algorithm to realize the unsupervised rapid detection of the gray mold of the strawberries after the strawberries are picked. The method can realize nondestructive detection of the gray mold of the picked strawberries, ensure the healthy development of the strawberry fruit industry and provide technical support for the research and development of related industrial equipment.
Description
Technical Field
The research content of the project belongs to the field of food safety and quality detection, mainly aims at the prominent detection problem of the fungal diseases in the strawberry industry, combines the knowledge in the fields of postharvest physiology, metabonomics and chemometrics, researches the relevant mechanism between the metabolism and the lossless signals of the strawberry disease components, and constructs a post-harvest physiological detection model of fruits with strong timeliness.
Background
The strawberry fruit is a berry fruit with bright color and high economic value, and is rich in various substances beneficial to human bodies, such as carotene, VE, VC, phenols and the like. According to statistics, the loss of the fresh strawberries after being picked can reach about 30 percent, mainly because the strawberries have thin fruit peels and high water content, the strawberries after being picked are metabolized actively and are easily damaged by machines such as bruise, extrusion, vibration and the like, so that pathogenic fungi are infected, and the fruits are rotten and deteriorated. The gray mold is a main fungal disease of the picked strawberries, greatly shortens the shelf life of the strawberries, and is a development bottleneck which needs to be broken through urgently in the current strawberry industry.
Gas chromatography ion mobility spectrometry (GC-IMS) is a detection technique that combines both ion mobility and gas chromatography techniques. The detection technology overcomes the limitation of poor separation degree of an ion mobility spectrometry technology, so that the quality of an ion mobility chromatography signal is remarkably improved after the ion mobility chromatography signal is correspondingly subjected to gas phase pre-separation, and the ion mobility chromatography makes chemical information obtained after the gas phase chromatography separation richer through drift time information; the GC-IMS technology gives full play to the respective advantages of different instruments, and has the advantage that the advantages are mutually superposed. The technology has the advantages of rapidness, sensitivity, no need of pretreatment and simplicity, and is applied to a plurality of fields of food flavor analysis, quality detection and the like.
In conclusion, the metabolic rule of the representative component after the strawberry is picked is tracked, and the chemometrics and the characteristic three-dimensional odor fingerprint are combined to form a rapid detection technology for early stage infection of the strawberry with the gray mold after the strawberry is picked.
Disclosure of Invention
The invention aims to provide a method for quickly detecting early gray mold of strawberries, which solves the problems in the prior art and the defects in the prior art.
The invention is realized in such a way that a method for rapidly detecting early gray mold of strawberries comprises the following steps:
(1) selecting strawberries with similar sizes and no damage on the surfaces, performing aseptic treatment, inoculating botrytis cinerea, storing in an aseptic constant-temperature constant-humidity box (20 +/-1 ℃ and 85 +/-5%), and sampling for detection and analysis after 0, 24, 48, 72, 96 and 120 hours of storage and disease attack respectively.
(2) Adopting headspace automatic sampling, collecting GC-IMS three-dimensional signal maps under different storage time, and counting the change of microorganism content in different time periods.
(3) And analyzing the difference of GC-IMS odor fingerprints of the strawberry fruits at different disease stages in a multi-dimensional manner from the gas phase separation time and the ion migration time respectively.
(4) And (3) comparing and screening GC-IMS signal changes of specific volatile substances generated in the disease process of samples at different disease stages, and qualitatively analyzing.
(5) And combining a dynamic principal component analysis algorithm, and realizing the early-stage rapid detection of gray mold of the picked strawberries based on GC-IMS signal difference of the tops of the strawberries in different disease processes.
1. In a preferred embodiment, in the step (1), strawberry fruits having similar sizes and no damage in appearance are selected, and pathogenic bacteria carried by the strawberry itself are removed by irradiating the strawberry fruits with 1% sodium hypochlorite and ultraviolet rays for 1 hour. Counting the spore suspension obtained by inoculation under a blood counting chamber, and diluting to obtain spore suspension with concentration of 1 × 10 5 /mL。
2. In a preferred embodiment, in the step (1), 10 μ L of spore suspension is inoculated at a position 2mm below the surface skin of the strawberry fruit by using a sterilized gun head, and the strawberry fruit is stored in a sterile constant temperature and humidity box for 0, 24, 48, 72, 96 and 120 h.
3. In a preferred embodiment, in the step (1), a headspace automatic sample injection mode is adopted, and GC-IMS fingerprint spectrum collection is directly carried out; the headspace sampling method comprises placing strawberries of different disease stages in 50mL headspace bottles, incubating at 40 deg.C for 15min, and sampling with a sampling amount of 500 μ L.
4. In a preferred embodiment, in the step (2), the sample injection mode directly adopts automatic headspace sample injection, the strawberry fruit is completely placed in a 50mL headspace bottle, and a FSSE54-CB1 chromatographic column is selected for gas phase separation, wherein the analysis time is 30min, and the temperature of the chromatographic column is 60 ℃. The gas chromatography carrier gas flow rate was initially set at 2mL/min, held for 2min, and then increased to 100mL/min in a linear gradient over 18 min;
5. in a preferred embodiment, in the step (3), the differences of the volatile organic components of the strawberries at different disease times are intuitively known from three dimensions respectively from the retention time, migration time and peak intensity of the data collected by the GC-IMS;
6. in a preferred embodiment, in the step (4), qualitative analysis is performed on the post-harvest volatile components of the strawberries at different disease stages by combining the NIST database and the IMS database, and the key metabolic pathways of the volatile components of the strawberries after infection with botrytis are contrastively analyzed.
7. In a preferred embodiment, in the step (4), the differential markers are screened by: and (3) grouping and screening all volatile organic compounds in the strawberry fruits of the treatment groups inoculated with the gray mold and the treatment groups not inoculated with the gray mold, which are measured in the step (1), by adopting a partial least squares discriminant analysis method, and taking the volatile organic compounds with the difference degree contribution value larger than 1 as difference markers.
8. In a preferred embodiment, in the step (5), the three-dimensional atlas is processed with a dynamic principal component. On the coordinate transformation idea, high-dimensional GC-IMS original data are converted into low-dimensional space for processing, cross correlation among variables and correlation on a time sequence are considered, original variable static data are used for constructing dynamic time data by utilizing the dynamic time sequence, and preprocessing and feature extraction such as denoising, dimension reduction and correlation elimination of process variables are realized. The dynamic data expansion equation is shown in the formula:
the lag factor h calculation in the equation needs to be recursively derived from the static state (h ═ 0) according to the relationship function rnew in the equation:
after acquiring the dynamic data matrix X, converting the dynamic data matrix into a standard data matrix
Analyzing the rows, and calculating a covariance matrix C, an eigenvector Iy and an eigenvalue lambda according to the following formula
And arranging the characteristic values from small to large, forming a characteristic matrix U for dynamic PCA (principal component analysis) processing by using the characteristic vectors as column vectors, carrying out weighted summation on all components in the U, and accumulating the variance to obtain the contribution ratio CR.
Drawings
FIG. 1 is a gas phase ion mobility diagram of strawberry fruits with gray mold onset time of 0-120h according to an embodiment of the present invention. FIG. 2 is a gas phase ion mobility spectrum difference diagram of strawberry fruits with gray mold disease in the example of the present invention. Storage time 0h for G1, 24h for G2, 48h for G3, 72h for G4, 96h for G5, and 120h for G6.
FIG. 3 is a fingerprint of a volatile organic compound selected from a gas phase ion mobility spectrum according to an embodiment of the present invention.
Fig. 4 is a diagram of dynamic principal component analysis in an embodiment of the present invention.
FIG. 5 is a gas phase ion mobility pattern of gray mold strawberry fruit in example 4 of the present invention.
Detailed Description
The present invention will now be further illustrated by reference to the following examples, which are provided for the purpose of illustration and description and are not intended to be limiting. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Example 1
1. Raw material selection
The strawberry is selected from the main variety 'Hongyan' strawberry in Jiangsu area as a research object.
2. When selecting strawberry, selecting strawberry fruit with similar size and undamaged appearance, and respectively using 1% sodium hypochlorite and ultraviolet irradiation for 1h to remove pathogenic bacteria carried by strawberry.
The sterilized strawberries were randomly and equally divided into a treatment group and a control group, and were stored in a constant temperature and humidity chamber in an isolated manner. The samples of the treatment group were inoculated with 10. mu.L of spore suspension 2mm below the surface of strawberry fruit using a sterilized pipette tip and stored in a sterile constant temperature and humidity cabinet for 0, 24, 48, 72, 96 and 120 hours. The control group had sterile saline as a control.
Automatically sampling a headspace, and collecting a GC-IMS fingerprint; the working temperature is set to 40 ℃, the equilibration time is set to 15min, the air volume of single sample injection is set to 500 mu L, and the temperature of the sample injection needle is set to 45 ℃. The strawberry fruits are completely put into a 50mL headspace bottle, and gas phase separation is carried out by selecting an FSSE54-CB1 chromatographic column. The gas chromatography carrier gas flow rate was initially set at 2mL/min, held for 2min, and then increased to 100mL/min in a linear gradient over 18 min.
3. Analyzing the difference of volatile organic components in strawberry fruits at different disease times from three dimensions of retention time, migration time and peak intensity, performing qualitative analysis on unknown substances by combining a built-in NIST and IMS database, and performing characteristic chemical substance matching by adopting a standard substance. Data analysis was performed using lav (laboratory analytical viewer) and GC × IMS Library Search analysis software. The data pseudo-color and the dimensionality reduction processing respectively adopt a Reporter plug-in and a Gallery plug-in, and an IMS database built in application software can carry out qualitative analysis on substances.
Example 2
This embodiment is substantially the same as embodiment 1 above, with the difference that:
in the step (1), the processing time is set to 24 h.
Example 3
This embodiment is substantially the same as embodiment 1 above, with the difference that:
in step (1), the processing time is set to 48 h.
Example 4
This embodiment is substantially the same as embodiment 1 above, with the difference that:
in step (1), the processing time is set to 72 h.
Example 5
This embodiment is substantially the same as embodiment 1 above, with the difference that:
in step (1), the processing time was set to 96 h.
Example 6
This embodiment is substantially the same as embodiment 1 above, with the difference that:
in step (1), the processing time is set to 120 h.
Comparative examples
The comparative experiment was carried out on the gray mold of strawberries treated for different periods in the above examples 1 to 6. The results are shown in fig. 1-3, fig. 1 is a gas phase ion migration spectrum of a sample, the difference of volatile substances in headspace gas of strawberries in different disease periods can be visually seen from the graph, and the signal intensity of the volatile substances before and after treatment is compared with fig. 2. Wherein the storage time is 0h after disease inoculation for G1, 24h after disease inoculation for G2, 48h after disease inoculation for G3, 72h after disease inoculation for G4, 96h after disease inoculation for G5, and 120h after disease inoculation for G6. When the gray mold is attacked for 48 and 72 hours, the volatile substances generated by the sample are obviously changed, and the GC-IMS signal intensity corresponding to the ester content is obviously increased. When the incidence time of the gray mold is prolonged to 96 hours and 120 hours, the trend of the volatile substances is weakened. In order to further explore the variation difference of volatile substances of strawberry fruits at different disease stages, characteristic peak signals are further extracted for specific analysis. FIG. 3 is a fingerprint of a selected characteristic volatile substance in a gas phase ion mobility spectrum, wherein darker red indicates higher signal intensity, darker blue indicates lower signal intensity, and the background is set to black by default. From fig. 3, specific information on the volatile matter change of strawberries at different treatment times can be compared. The comparison shows that the contents of propionic acid, nonanal, E-2-octenal, E-2-heptenal, heptanal, propionaldehyde and other substances in the G1 and G2 samples are higher; the contents of esters such as methyl heptanoate, ethyl hexanoate, methyl hexanoate, butyl acetate, isopropyl acetate and the like in the G3 and G4 samples are high; the content of (E, E) -2, 4-heptadienal, (E, Z) -2, 4-heptadienal, benzaldehyde, citronellol, 1-octen-3-ol and the like in the G6 sample is high. Fig. 4 is a diagram of dynamic principal component analysis in an embodiment of the present invention. The different disease stages of the strawberries can be obviously distinguished from the graph, the disease detection can be completed after the strawberries are mildewed and attack for 48 hours after being picked, and the GC-IMS signal of the headspace gas of the sample can be used for judging the attack time of the gray mold. Table 1 shows the volatile difference substances of different onset times of gray mold after strawberry harvest, and the qualitative analysis information of the detected difference substances using NIST and IMS databases includes name, CAS number, retention index RI, retention time Rt, and ion migration time Dt.
TABLE 1
Claims (7)
1. The invention discloses a method for quickly detecting early gray mold of strawberries after picking, which comprises the following steps:
(1) selecting strawberries with similar sizes and no damage on the surfaces, performing aseptic treatment, inoculating botrytis cinerea, storing in an aseptic constant-temperature constant-humidity box (20 +/-1 ℃ and 85 +/-5%), and sampling for detection and analysis after 0, 24, 48, 72, 96 and 120 hours of storage and disease attack respectively.
(2) Adopting headspace automatic sampling, collecting GC-IMS three-dimensional signal maps under different storage time, and counting the change of microorganism content in different time periods.
(3) And analyzing the difference of GC-IMS odor fingerprints of the strawberry fruits at different disease stages in a multi-dimensional manner from the gas phase separation time and the ion migration time respectively.
(4) And (3) comparing and screening GC-IMS signal changes of specific volatile substances generated in the disease process of samples at different disease stages, and qualitatively analyzing.
(5) And combining a dynamic principal component analysis algorithm, and realizing the early-stage rapid detection of gray mold of the picked strawberries based on GC-IMS signal difference of the tops of the strawberries in different disease processes.
2. The method for rapidly detecting early gray mold of strawberry as claimed in claim 1, wherein in step (1), strawberry fruits with similar size and no damage on appearance are selected, and pathogenic bacteria carried by strawberry itself are removed by using 1% sodium hypochlorite and ultraviolet irradiation for 1 h. Counting the obtained spore suspension under a blood counting chamber, and diluting to obtain spore suspension with concentration of 1 × 10 5 and/mL. And inoculating 10 mu L of spore suspension at a position 2mm below the surface skin of the strawberry fruit by adopting a sterilized gun head, and storing for 0, 24, 48, 72, 96 and 120 hours in an aseptic constant temperature and humidity box.
3. The method for rapidly detecting early gray mold of strawberry as claimed in claim 1, wherein in the step (2), a headspace automatic sample injection mode is adopted, and GC-IMS fingerprint collection is directly carried out; the headspace sampling method comprises placing strawberries at different disease stages in 50mL headspace bottles, incubating at 40 deg.C for 15min, and sampling with a sampling amount of 500 μ L. The sample injection mode directly adopts automatic headspace sample injection, the strawberry fruit is completely placed in a 50mL headspace bottle, and a FSSE54-CB1 chromatographic column is selected for gas phase separation, the analysis time is 30min, and the temperature of the chromatographic column is 60 ℃. The gas chromatography carrier gas flow rate was initially set at 2mL/min, held for 2min, and then increased to 100mL/min in a linear gradient over 18 min.
4. The method for rapidly detecting the early stage of gray mold of strawberry according to claim 1, wherein in the step (3), the retention time, migration time and peak intensity of data collected from GC-IMS are respectively used, and the differences of volatile organic components of strawberries at different disease time are respectively visually known from three dimensions; and qualitative analysis is carried out on the volatile components of the strawberries after being picked in different disease stages by combining an NIST database and an IMS database, and the key metabolic pathways of the volatile components of the strawberries after being infected with the botrytis are contrastively analyzed.
5. The method according to claim 1, wherein the differential markers are screened in step (4) by: and (3) grouping and screening all volatile organic compounds in the strawberry fruits of the treatment groups inoculated with the gray mold and the treatment groups not inoculated with the gray mold, which are measured in the step (1), by adopting a partial least squares discriminant analysis method, and taking the volatile organic compounds with the difference degree contribution value larger than 1 as difference markers.
6. The method for rapidly detecting early gray mold of strawberry as claimed in claim 1, wherein in the step (5), the three-dimensional atlas is processed by using dynamic principal components. On the coordinate transformation idea, high-dimensional GC-IMS original data are converted into low-dimensional space for processing, cross correlation among variables and correlation on a time sequence are considered, original variable static data are used for constructing dynamic time data by utilizing the dynamic time sequence, and preprocessing and feature extraction such as denoising, dimension reduction and correlation elimination of process variables are realized. The dynamic data expansion equation is shown in the formula:
the hysteresis factor h in the equation needs to be recursively derived from the static state (h ═ 0) according to the relationship function rnew in the following equation:
after the dynamic data matrix X is obtained, the dynamic data matrix X is converted into a standard data matrix for analysis, and a covariance matrix C, an eigenvector Iy and an eigenvalue lambda are calculated according to the following formula
And arranging the characteristic values from small to large, forming a characteristic matrix U for dynamic PCA (principal component analysis) processing by using the characteristic vectors as column vectors, carrying out weighted summation on all components in the U, and accumulating the variance to obtain the contribution ratio CR.
7. The method for rapidly detecting early gray mold of strawberry as claimed in claim 1, wherein in the step (5), the method provided by the invention can obtain the change of volatile organic matters after the gray mold treatment, the result is visual and accurate, and the method has the advantages of high analysis speed, high sensitivity, easiness in operation and low cost, and is suitable for rapidly identifying and detecting the disease stage of the gray mold of the strawberry after the strawberry is picked in batches.
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| CN102944584A (en) * | 2012-12-07 | 2013-02-27 | 南京农业大学 | Early detection method of common diseases of strawberry by smell sensor |
| US20140127672A1 (en) * | 2011-03-21 | 2014-05-08 | The Regents Of The University Of California | Disease detection in plants |
| KR101695971B1 (en) * | 2016-05-23 | 2017-02-16 | 주식회사 에이치엔엘 | Monitoring method and monitoring system for botrytis cinerea of strawberry |
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| US20140127672A1 (en) * | 2011-03-21 | 2014-05-08 | The Regents Of The University Of California | Disease detection in plants |
| CN102944584A (en) * | 2012-12-07 | 2013-02-27 | 南京农业大学 | Early detection method of common diseases of strawberry by smell sensor |
| KR101695971B1 (en) * | 2016-05-23 | 2017-02-16 | 주식회사 에이치엔엘 | Monitoring method and monitoring system for botrytis cinerea of strawberry |
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| 朱娜;毛淑波;潘磊庆;袁丽佳;屠康;: "电子鼻对草莓采后贮藏早期霉菌感染的检测", 农业工程学报, vol. 29, no. 05, pages 266 - 273 * |
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