CN109711603B - Method for rapidly predicting infection quantity of rice by aspergillus fungi based on electronic nose - Google Patents
Method for rapidly predicting infection quantity of rice by aspergillus fungi based on electronic nose Download PDFInfo
- Publication number
- CN109711603B CN109711603B CN201811489703.XA CN201811489703A CN109711603B CN 109711603 B CN109711603 B CN 109711603B CN 201811489703 A CN201811489703 A CN 201811489703A CN 109711603 B CN109711603 B CN 109711603B
- Authority
- CN
- China
- Prior art keywords
- rice
- electronic nose
- samples
- aspergillus
- sensor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Apparatus Associated With Microorganisms And Enzymes (AREA)
Abstract
The invention discloses a method for rapidly predicting the infection quantity of rice infected by aspergillus fungi based on an electronic nose. The rice is sterilized by ultraviolet rays and inoculated with a certain amount of Aspergillus fungi. Performing headspace gas detection on the samples inoculated with the rice with different storage times by using an electronic nose; meanwhile, detecting the colony number on the rice sample by adopting a traditional plate counting method; and optimizing the electronic nose sensor array according to the principal component analysis, and extracting the characteristics of the optimized sensor response signals by using a stable value method. And finally, establishing a prediction model based on the electronic nose signal characteristic value and the colony number by adopting a partial least square regression algorithm, and selecting a regression model with a large correlation coefficient and a small root mean square error as a final colony number prediction model so as to obtain the predicted colony number. The method has no damage to rice samples, is simple to operate, has a good prediction effect, and has higher practical application value.
Description
Technical Field
The invention belongs to the field of microorganism detection, and relates to a method for rapidly predicting the infection quantity of rice infected by aspergillus fungi based on an electronic nose.
Background
Rice is one of the most important grain varieties in countries around the world, about 50% of the world population is fed with rice, and more than 20 billion people in asia and rice and products thereof are the main sources of caloric intake. For many years, the yield of paddy in China is stable in the first world, and is about 30% of the total yield of paddy worldwide, and about 1/3 of the total yield of domestic grains. And the consumption of the feed is gradually increased along with the improvement of the living standard of people and the increase of population quantity. However, grains contain abundant nutrients, and are extremely susceptible to deterioration by fungi under suitable conditions of moisture and temperature. It is known that losses of agricultural and industrial materials due to grain mildew or contaminated mycotoxins are hundreds of billions of dollars worldwide. More seriously, humans can poison or induce some diseases, including even cancer, if they eat by mistake foods that are severely contaminated with fungi. Therefore, the method realizes the detection of the fungal pollution and the fungal pollution quantity in the food, and has important significance for guaranteeing the food eating safety and reducing the outbreak of food-borne diseases. The electronic nose is used as a nondestructive and rapid detection method, and has wide application prospect in detecting the quantity of rice polluted by fungi.
Disclosure of Invention
Aiming at the problems of complex and time-consuming, low efficiency, high cost and the like of the existing fungus detection method, the invention provides a method for rapidly predicting the infection degree of rice by aspergillus based on an electronic nose.
A method for rapidly predicting the infection degree of rice by aspergillus based on an electronic nose comprises the following specific steps:
(1) Sterilizing rice samples, inoculating Aspergillus fungus, storing, taking 7 groups of samples (each group of samples is repeated for N times and N is more than 10) at daily intervals for 0-6 days, sealing at room temperature, and sealing volume is not less than 500mL (1 g:25mL of rice to container volume ratio). The sample was allowed to stand for 30 to 60 minutes to saturate the headspace gas in the sealed container, thereby obtaining a headspace gas. Sucking the headspace gas in the sealed container into a sensor array channel of the electronic nose through an electronic nose built-in pump, detecting and recording sensor response signals, and thus obtaining response curves of the sensors on rice samples with different storage times;
(2) Washing, diluting and plate culturing the detection samples with different storage time after the detection of the electronic nose in the step (1) for 5-7 days to obtain the colony numbers of the samples with different storage time;
(3) Extracting signals detected by the sensor in the step (1) to be stable as characteristic values, taking the extracted characteristic values as independent variables, taking the colony numbers of the samples detected in the step (2) at different storage times as dependent variables, establishing a response signal of the electronic nose sensor and a quantitative prediction model of the colony numbers of the samples stored at different times after fungus inoculation through partial least square regression, and selecting a regression model with large correlation coefficient and small root mean square error as a final colony number prediction model;
the partial least squares regression model is: y=a 1 ×X 1 +a 2 ×X 2 +……+a 10 ×X 10 +b, wherein Y is the colony count, a 1 、a 2 ...a 10 And b are both constants;
(4) Detecting rice samples with unknown aspergillus fungi quantity according to the step (1), obtaining a response curve of the electronic nose, extracting a characteristic value by using the method of the step (3), substituting the characteristic value into the quantitative prediction model of the step (3), and predicting the colony number of the unknown rice samples infected by fungi, so that the aim of effectively predicting the aspergillus fungi infection quantity of the rice by the electronic nose is fulfilled.
Further, the calculation formula of the correlation coefficient and the root mean square error in the step (3) is as follows:
r is a correlation coefficient; RMSE is root mean square error; n is the number of samples with known infection degree of aspergillus fungi on rice used in the process of establishing a predictive model; x is X i The actual value of the ith sample colony number in the process of establishing the prediction model is obtained;
establishing an average value of true values of all sample colony numbers in the process of establishing a prediction model; y is Y i A predicted value of the ith sample colony number in the process of establishing a prediction model; />
Is the average of all sample colony count predictions during the predictive model.
The beneficial effects of the invention are as follows: predicting the infection degree of aspergillus fungi on rice by using an electronic nose, using a sensor response curve stable value as a characteristic value, adopting a Logistic equation to simulate a fungus growth curve according to an X-axis gravity center coordinate of a two-dimensional score in principal component analysis, and establishing a prediction model based on the electronic nose signal characteristic value and the colony number by using a partial least squares regression algorithm, thereby obtaining the predicted colony number. The method realizes the quantitative prediction of the infection quantity of aspergillus fungi on rice directly by using an electronic nose, has the characteristic of quick and nondestructive, and provides a new method for predicting the degree of rice, even agricultural products.
Drawings
FIG. 1 is a sensor response signal of an electronic nose for detecting the degree of infection of rice by Aspergillus candidus;
FIG. 2 is a regression model plot between Aspergillus candidus infection level and actual value;
FIG. 3 is a sensor response signal of the electronic nose for detecting the degree of infection of rice with Aspergillus fumigatus;
FIG. 4 is a regression model plot of Aspergillus fumigatus infection level versus actual value.
Detailed Description
The invention is further described below with reference to the drawings and examples.
The invention adopts a method for rapidly predicting the infection quantity of aspergillus fungi on rice based on an electronic nose, and establishes an effective prediction model based on electronic nose data, and the method comprises the following specific steps:
(1) Placing commercial rice at 110mW s/cm 2 Sterilizing under ultraviolet lamp for 30-60min, inoculating Aspergillus fungus at a certain concentration, and storing at 28+ -1deg.C and 85% relative humidity. The rice samples stored for 0-6 days were taken out at daily intervals for 7 groups of samples (each group of samples was repeated N times, N > 10), sealed at room temperature, and the sealed volume was not less than 500mL (1 g:25mL in terms of the weight of rice to the volume of the container). Standing the sample for 30-60 minutes to saturate the headspace gas in the sealed container, thereby obtaining the headspace gas; before the detection of the electronic nose is started, cleaning the electronic nose system by using dry clean air, setting the cleaning flow rate to be 400-600 mL/min, and cleaning time to be 60-80 seconds; after the cleaning is finished, sucking the headspace gas in the sealed container into a sensor array channel of the electronic nose through an electronic nose built-in pump, and reacting an electronic nose sensor with sample gas to generate a sensor signal; the sensor signal is the ratio of the conductivity G of the sensor when in contact with the sample gas to the conductivity G0 of the sensor when passing through the calibration gas, i.e. G/G0; the flow rate of the gas of the built-in pump is 200-300mL/min, and the detection time is 70-90 seconds; detecting and recording sensor array response signals to obtain a sensorResponse curves of the array to rice samples at different storage times;
(2) Washing, diluting and plate culturing the detection samples with different storage time after the detection of the electronic nose in the step (1) for 5-7 days to obtain the colony numbers of the samples with different storage time;
(3) Extracting signals detected by the sensor in the step (1) to be stable as characteristic values, taking the extracted characteristic values as independent variables, taking the colony numbers of the samples detected in the step (2) at different storage times as dependent variables, establishing a response signal of the electronic nose sensor and a quantitative prediction model of the colony numbers of the samples stored at different times after fungus inoculation through partial least square regression, and selecting a regression model with large correlation coefficient and small root mean square error as a final colony number prediction model;
the partial least squares regression model is: y=a 1 ×X 1 +a 2 ×X 2 +……+a 10 ×X 10 +b, wherein Y is the colony count, a 1 、a 2 ...a 10 And b are both constants;
(4) Detecting rice samples with unknown aspergillus fungi quantity according to the step (1), obtaining a response curve of the electronic nose, extracting a characteristic value by using the method of the step (3), substituting the characteristic value into the quantitative prediction model of the step (3), and predicting the colony number of the unknown rice samples infected by fungi, so that the aim of effectively predicting the aspergillus fungi infection quantity of the rice by the electronic nose is fulfilled.
Further, the calculation formula of the correlation coefficient and the root mean square error in the step (3) is as follows:
r is a correlation coefficient; RMSE is root mean square error; n is a sample number with known infection degree of rice used in the process of establishing a predictive model by aspergillus fungiA number; x is X i The actual value of the ith sample colony number in the process of establishing the prediction model is obtained;
establishing an average value of true values of all sample colony numbers in the process of establishing a prediction model; y is Y i A predicted value of the ith sample colony number in the process of establishing a prediction model; />
Is the average of all sample colony count predictions during the predictive model.
The method is suitable for rapidly predicting the infection quantity of the rice, the wheat, the corn and other agricultural products by different aspergillus fungi, and is mainly suitable for detecting an electronic nose and processing the result data thereof. The following examples facilitate a better understanding of the present invention, but are not intended to limit the present invention.
Example 1
A method for rapidly predicting the infection degree of rice by aspergillus candidus based on an electronic nose comprises the following steps:
(1) Taking commercially available Jiangsu star-good rice as an experimental object, and placing the rice at 110mW s/cm 2 After sterilization for 30-60min under ultraviolet lamp, 7 batches of rice samples are selected and respectively inoculated with 0.2mL of rice samples with the concentration of 10 7 CFU/mL of Aspergillus candidus spore suspension was stored at 28.+ -. 1 ℃ and 85% relative humidity. A batch of rice samples were taken at 24h intervals and sealed in a container at room temperature, and 7 total groups of samples (21 replicates for each group) were taken, each group numbered 0d,1d,2d,3d,4d,5d,6d. The volume of the container is 500mL, and after the sample is kept stand for 60 minutes, the headspace gas in the sealed container is saturated, so that the headspace gas is obtained; before the detection of the electronic nose is started, cleaning the electronic nose system by using dry clean air, setting the cleaning flow rate to be 600ml/min, and the cleaning time to be 60 seconds; after the cleaning is finished, sucking the headspace gas in the sealed container into a sensor array channel of the electronic nose through an electronic nose built-in pump, and reacting an electronic nose sensor with sample gas to generate a sensor signal; the sensor signal is the conductivity G of the sensor when contacting the sample gas and the sensor when passing through the calibration gasThe ratio of the conductivities G0, i.e. G/G0; the flow rate of the gas of the built-in pump is 200ml/min, and the detection time is 90 seconds; detecting and recording response signals of the sensor array, so as to obtain response curves of the sensor array to rice samples with different storage times;
in this case, a PEN2 type electronic nose of the company AIRSENSE in germany is used as a detection instrument, and the electronic nose system is composed of 10 metal oxide sensors, and the model and corresponding characteristics of the electronic nose system are shown in table 1:
TABLE 1 PEN2 electronic nose sensor array and sensor response characteristics
After the output result of the electronic nose is obtained, the characteristic extraction is carried out, the response curve is observed, and the stability is found to be achieved after 75 seconds, so that the numerical value of the response curve at 75 seconds is used as a characteristic value, and fig. 1 is an electronic nose response curve graph of different storage time after rice is inoculated with aspergillus candidus.
(2) The total colony count measurement of the rice inoculated aspergillus candidus with different storage times is carried out according to the national food safety standard GB4789.15-2010, and the colony count measurement is repeated three times;
(3) And (3) extracting signals detected by the sensor to be stable in the step (1) as characteristic values, taking the extracted characteristic values as independent variables, and carrying out partial least square regression modeling by taking the colony numbers of the samples detected in the step (2) at different storage times as the dependent variables. Its correlation coefficient R 2 0.894, the expression of which is: y= -32.1256-22.7202X 1 +1.9015×X 2 +68.9305×X 3 +22.9134×X 4 -25.5406×X 5 +3.1017×X 6 -0.0337×X 7 -0.1137×X 8 -24.339×X 9 +11.0814×X 10 Wherein Y is colony number, X 1 -X 10 All are stable values of the sensors of the electronic nose.
(4) To verify the accuracy of the model, substituting the sensor response value of the prediction set into the prediction model, calculating the predicted infection quantity of the rice aspergillus candidus, and establishing a regression model with the actual infection quantityThe result is shown in fig. 2, and the model formula is as follows: y=0.846 x+0.415, where y is the predicted value, x is the actual value, and the correlation coefficient R 2 Rmse=0.886, rmse=0.195 indicates that the model predicts better.
Example 2
A method for rapidly predicting the infection degree of rice with aspergillus fumigatus based on an electronic nose comprises the following steps:
(1) Taking commercially available Jiangsu star-good rice as an experimental object, and placing the rice at 110mW s/cm 2 After sterilization for 30-60min under ultraviolet lamp, 7 batches of rice samples are selected and respectively inoculated with 0.2mL of rice samples with the concentration of 10 7 CFU/mL Aspergillus fumigatus spore suspension, and placed at 28+ -1deg.C, 85% relative humidity for storage. A batch of rice samples were taken at 24h intervals and sealed in a container at room temperature, and 7 total groups of samples (21 replicates for each group) were taken, each group numbered 0d,1d,2d,3d,4d,5d,6d. The volume of the container is 500mL, and after the sample is kept stand for 60 minutes, the headspace gas in the sealed container is saturated, so that the headspace gas is obtained; before the detection of the electronic nose is started, cleaning the electronic nose system by using dry clean air, setting the cleaning flow rate to be 600ml/min, and the cleaning time to be 60 seconds; after the cleaning is finished, sucking the headspace gas in the sealed container into a sensor array channel of the electronic nose through an electronic nose built-in pump, and reacting an electronic nose sensor with sample gas to generate a sensor signal; the sensor signal is the ratio of the conductivity G of the sensor when in contact with the sample gas to the conductivity G0 of the sensor when passing through the calibration gas, i.e. G/G0; the flow rate of the gas of the built-in pump is 200ml/min, and the detection time is 90 seconds; detecting and recording response signals of the sensor array, so as to obtain response curves of the sensor array to rice samples with different storage times;
in this case, a PEN2 type electronic nose of the company AIRSENSE in germany is used as a detection instrument, and the electronic nose system is composed of 10 metal oxide sensors, and the model and corresponding characteristics of the electronic nose system are shown in table 2:
TABLE 2 PEN2 electronic nose sensor array and sensor response characteristics
After the output result of the electronic nose is obtained, the characteristic extraction is carried out, the response curve is observed, and the stability is found to be achieved after 75 seconds, so that the numerical value of the response curve at 75 seconds is used as a characteristic value, and fig. 3 is an electronic nose response curve graph of rice inoculated with aspergillus fumigatus at different storage times.
(2) The total colony count of rice inoculated samples with different storage times is determined according to food safety national standard GB4789.15-2010, and the colony count determination is repeated three times.
(3) And (3) extracting signals detected by the sensor to be stable in the step (1) as characteristic values, taking the extracted characteristic values as independent variables, and carrying out partial least square regression modeling by taking the number of sample colonies detected in the step (2) at different storage times as the dependent variables. Its correlation coefficient R 2 0.938, expressed as: y= -98.1099+2.0643×x 1 -4.376×X 2 +72.6379×X 3 -11.3324×X 4 -45.5001×X 5 +94.2517×X 6 +20.0076×X 7 -97.1027×X 8 +77.745×X 9 -7.2422×X 10 Wherein Y is colony number, X 1 -X 10 All are stable values of the sensors of the electronic nose.
(4) In order to verify the accuracy of the model, substituting the sensor response value of the prediction set into the prediction model, calculating the predicted infection degree of the rice with aspergillus fumigatus, and establishing a regression model with the actual infection degree, wherein the result is shown in fig. 4, and the model formula is as follows: y=0.86×x+0.284, where y is the predicted value, x is the actual value, and the correlation coefficient R 2 =0.911, rmse=0.172, indicating that the model predicts better.
Through the detailed description of the method for rapidly predicting the infection quantity of the aspergillus on the rice based on the electronic nose, the established model for predicting the infection quantity of the aspergillus on the rice has higher prediction performance, and further shows that the method disclosed by the invention has higher application value and is worthy of being widely popularized.
Claims (1)
1. A method for rapidly predicting the infection quantity of rice by aspergillus fungi based on an electronic nose is characterized by comprising the following steps:
step 1, sterilizing rice samples, inoculating and storing Aspergillus fungi, taking out 7 groups of samples from the rice samples stored for 0-6 days at intervals of each day, repeating the steps of N times, N > 10, sealing at room temperature, keeping the sealed volume at least 500mL, keeping the sample stand for 30-60 minutes according to the volume ratio of rice to the container of 1 g:25: 25mL, enabling the headspace gas in the sealed container to be saturated, thus obtaining headspace gas, sucking the headspace gas in the sealed container into a sensor array channel of an electronic nose through an electronic nose built-in pump, detecting and recording sensor response signals, and thus obtaining response curves of the sensor to the rice samples with different storage times;
step 2, carrying out rinsing, dilution and plate culture on 7 samples in the step 1 for 5-7 days, and counting aspergillus fungus colonies on a plate so as to obtain sample colony numbers with different storage times;
step 3, extracting signals detected by the sensor in the step 1 to be stable as characteristic values, taking the extracted characteristic values as independent variables, taking the colony numbers of the samples detected in the step 2 at different storage times as dependent variables, establishing a quantitative prediction model of response signals of the electronic nose sensor and the colony numbers of the samples stored at different times after fungus inoculation by partial least squares regression, and selecting a regression model with large correlation coefficient and small root mean square error as a final colony number prediction model, wherein the partial least squares regression model is as follows: y=a 1 ×X 1 +a 2 ×X 2 +……+a 10 ×X 10 +b, wherein Y is the colony count, a 1 、a 2 ...a 10 And b are both constants;
step 4, detecting rice samples with unknown aspergillus fungi quantity according to the step 1, obtaining a response curve of the electronic nose, extracting a characteristic value by using the method of the step 3, substituting the characteristic value into the quantitative prediction model of the step 3, and predicting the colony number of the unknown rice samples infected by fungi, thereby achieving the purpose of effectively predicting the aspergillus fungi infection quantity of the rice by the electronic nose;
in the step 1, the aspergillus fungi are dominant aspergillus fungi in rice mildew, including aspergillus candidus, aspergillus fumigatus, aspergillus clavatus and aspergillus niger;
in step 1, the rice sample is placed at 110mW s/cm 2 Sterilizing under ultraviolet lamp for 30-60min, storing at 28+ -1deg.C and 85% relative humidity, cleaning the electronic nose system with dry clean air before each detection, setting cleaning flow rate at 400ml/min-600ml/min, and cleaning for 60-90 seconds; after the cleaning is finished, sucking the headspace gas in the sealed container into a sensor array channel of the electronic nose through an electronic nose built-in pump, and reacting an electronic nose sensor with sample gas to generate a sensor signal; the sensor signal is the ratio of the conductivity G of the sensor when in contact with the sample gas to the conductivity G0 of the sensor when passing through the calibration gas, i.e. G/G0; the flow rate of the gas of the built-in pump is 200-300ml/min, and the detection time is 70-90 seconds; detecting and recording response signals of the sensor array, so as to obtain response curves of the sensor array to rice samples with different storage times;
in step 3, the calculation formula of the correlation coefficient and the root mean square error is as follows:
r is a correlation coefficient; RMSE is root mean square error; n is the number of samples with known infection degree of aspergillus fungi on rice used in the process of establishing a predictive model; x is X i The actual value of the ith sample colony number in the process of establishing the prediction model is obtained;
establishing an average value of true values of all sample colony numbers in the process of establishing a prediction model; y is Y i A predicted value of the ith sample colony number in the process of establishing a prediction model; />
Predicting values for colony numbers of all samples in the process of predicting modelAverage value of (2).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811489703.XA CN109711603B (en) | 2018-12-06 | 2018-12-06 | Method for rapidly predicting infection quantity of rice by aspergillus fungi based on electronic nose |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811489703.XA CN109711603B (en) | 2018-12-06 | 2018-12-06 | Method for rapidly predicting infection quantity of rice by aspergillus fungi based on electronic nose |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109711603A CN109711603A (en) | 2019-05-03 |
| CN109711603B true CN109711603B (en) | 2023-06-16 |
Family
ID=66254694
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811489703.XA Active CN109711603B (en) | 2018-12-06 | 2018-12-06 | Method for rapidly predicting infection quantity of rice by aspergillus fungi based on electronic nose |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109711603B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111122658B (en) * | 2019-12-24 | 2021-06-29 | 浙江大学 | Method and device for detecting infection degree of fruit botrytis cinerea |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105572305B (en) * | 2016-01-11 | 2017-08-04 | 浙江大学 | A kind of method of fast prediction tea tree based on electronic nose by insect Invasive degree |
| CN105738422A (en) * | 2016-02-01 | 2016-07-06 | 浙江大学 | Method for quickly detecting storage time of walnuts based on electronic nose |
-
2018
- 2018-12-06 CN CN201811489703.XA patent/CN109711603B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109711603A (en) | 2019-05-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhan et al. | Measuring immigration and sexual reproduction in field populations of Mycosphaerella graminicola | |
| Hou et al. | Thermal inactivation of fungi in chestnuts by hot air assisted radio frequency treatments | |
| Lysøe et al. | The transcription factor FgStuAp influences spore development, pathogenicity, and secondary metabolism in Fusarium graminearum | |
| Dobolyi et al. | Occurrence of aflatoxin producing Aspergillus flavus isolates in maize kernel in Hungary | |
| Degola et al. | Laboratory tests for assessing efficacy of atoxigenic Aspergillus flavus strains as biocontrol agents | |
| Wei et al. | Molecular characterization of atoxigenic Aspergillus flavus isolates collected in China | |
| Fan et al. | Occurrence of fungicide resistance in Botrytis cinerea from greenhouse tomato in Hubei Province, China | |
| Scruggs et al. | Etiology and epidemiological conditions promoting Fusarium root rot in sweetpotato | |
| Christ et al. | Characterization and mycotoxigenic potential of Fusarium species in freshly harvested and stored sugar beet in Europe | |
| Hocking et al. | Advances in food mycology | |
| Garcia-Cela et al. | Carbon dioxide production as an indicator of Aspergillus flavus colonisation and aflatoxins/cyclopiazonic acid contamination in shelled peanuts stored under different interacting abiotic factors | |
| CN109711603B (en) | Method for rapidly predicting infection quantity of rice by aspergillus fungi based on electronic nose | |
| Smits et al. | Accurate determination of process variables in a solid-state fermentation system | |
| Kuhnem et al. | Trichothecene genotype composition of Fusarium graminearum not differentiated among isolates from maize stubble, maize ears, wheat spikes, and the atmosphere in New York | |
| CN109666717A (en) | The prediction technique of degree is infected by aspergillus fungi based on the rice of electronic nose | |
| CN109486898B (en) | Method for rapidly predicting growth condition of aspergillus fungi in rice based on electronic nose | |
| He et al. | Analysis of rice microbial communities under different storage conditions using culture-dependent and-independent techniques | |
| Gu et al. | Early identification of Aspergillus spp. contamination in milled rice by E‐nose combined with chemometrics | |
| Kolawole et al. | Postharvest pathogenic fungi of wheat circulating in Lagos State, Nigeria | |
| Jurick et al. | Carbon, nitrogen and pH regulate the production and activity of a polygalacturonase isozyme produced by Penicillium expansum | |
| Shen et al. | Recognition of harmful fungal species and quantitative detection of fungal contamination in peanuts based on electronic nose technology | |
| Pitt et al. | Methods for enumeration, isolation and identification | |
| Jayachandran et al. | Modulation of mycobiota associated with rice stored in bamboo reinforced concrete silo-molecular identification and phenotypic characterization | |
| Hashimoto et al. | Distribution of Aspergillus section Nigri at shochu fermenting places in Japan | |
| Mateo et al. | Environmental temperature and relative humidity, two Key factors in maize technology affecting ochratoxin a production and growth of ochratoxigenic species |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |