CN102944585A - Detection method of fruit postharvest diseases by smell sensor - Google Patents
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Abstract
The invention discloses a quick nondestructive detection method of fruit postharvest diseases, belonging to the field of postharvest quality of agricultural products. A selected metal oxide smell sensor obtains a smell response signal after fruit harvesting; and by substituting the smell response signal into the established judging model, the method can judge whether the fruit is infected by pathogenic microorganisms and judge the type information of the injection. The method disclosed by the invention can quickly detect whether the fruit is infected by diseases after harvesting in a nondestructive manner and detect the types of diseases; and moreover, the method is simple to operate and quick and accurate, reduces the cost of manual detection, reduces the loss in storage after fruit harvesting, and can be applied to the detection and monitoring in the circulation, storage and sales processes after fruit harvesting.
Description
Technical field
The present invention is the method that a kind of smell sensor detects fruit postharvest diseases, belongs to the technical field that agricultural product are adopted rear Quality Detection and control.
Background technology
World's fruit is of a great variety, mainly contains grape, apple, pears, peach, Lee, strawberry, apricot, persimmon, cherry, Kiwi berry, citrus, banana, pineapple and fig etc.China is world fruit big producing country, is first of the world such as the output of apple, pears, peach, Lee and persimmon.In harvesting, transportation and sales and storage, be huge because of the rotten loss that causes after fruit is adopted.According to document announcement, also about 15～24%, developing country is because shortage refrigerating equipment and sanitary condition are relatively poor in the loss that the postharvest fruit and vegetable of developed country causes because rotting, and the loss of postharvest fruit and vegetable is up to 50%.Main pathogenic effects owing to pathogenic microorganism rots.Owing to fruit product is organized tender succulence and is rich in sugar, plucking, transporting, selling, preserving easy damage and deterioration in the link.Pathogenic microorganism is many from parasitics lower wound or dead tissue invasion, and these pathogenic microorganisms mainly contain grey grape full (Botrytis cinerea), Rhizopus stolonifer (Rhizopus stolonnifer), Penicillium (Penicilliums spp.), mucor (Mueorspp.), the chain lattice are embraced (Alternaria spp.), aspergillus (Aspergillus spp.) and soft rotten bacterium (Erwinia sp.) etc.The common characteristics of these germs are that growth and breeding is rapid, can infect rotting rapidly of Peeling device for fruits choush.Tradition check pathogen method mainly relies on concrete microbiology and Measurement for Biochemistry, as utilize culture method to detect according to bacterium colony characteristic and biochemical reaction characteristics, these methods can both be quantitatively or the qualitative analysis pathogen, but time-consuming, expense is large, and needs the professional and technical personnel.The morbidity of infected fruit presents than manifest symptom, and the fruit loss is more serious, thereby and enters will infect in the packing and close on fruit and cause serious economic loss.Therefore, inquiring into quick, accurate, harmless fruit disease early detection method tool is of great significance and using value.
Summary of the invention
Technical matters
For the problems referred to above, the lossless detection method that the purpose of this invention is to provide a kind of fruit postharvest diseases, the smell information of utilizing gas sensor to obtain to adopt rear fruit is judged the whether kind of pathogenic infection microorganism and infection of fruit, can carry out nondestructive detection, the minimizing economic loss.
Technical scheme
A kind of lossless detection method of fruit postharvest diseases (technology path is seen Fig. 1), to utilize smell sensor to detect fruit to infect the smell that distributes behind the Different Kinds of Pathogens microorganism and determine the whether kind of introduced disease and introduced disease of fruit, the detecting step that it is characterized in that it is as follows
1) with fruit sample surfaces impurity elimination, clean, with 75% alcohol-pickled 30s, treat that the alcohol complete laggard row inoculation of volatilizing processes, all samples is divided into two groups, one group is used for smell sensor and obtains fruit smell information, one group of chemical composition and relative content that is used for gaseous mass spectrum coupling Instrument measuring fruit smell, wherein, the fruit sample is inoculated respectively such fruit and is adopted rear susceptible sex pheromone, simulation fruit is adopted rear cause pathogeny imcrobe infection, inoculates the fruit sample of each class pathogenic microorganism and represent not introduced disease fruit sample size to be 50-80;
2) the fruit sample is preserved, the interval same time adopts the smell of smell sensor and gas phase GC-MS device test fruit, when wherein smell sensor is tested, the fruit sample is placed closed container, when head space gas reached capacity, smell sensor obtained the response signal of smell, and carried out analyzing and processing, simultaneously, utilize gaseous mass spectrum coupling instrument to detect chemical composition and the relative content of analyzing smell;
3) after gas sensor and gas phase GC-MS detect and finish, calculate the index that rots, the index that rots then shows pathogenic infection microorganism of fruit greater than 0, wherein, by the fruit rot size fruit is divided into 3 grades: 0 grade, without rotting; 1 grade, the fruit face has 1～3 tawny scab, and the pathological tissues area is no more than 25% of fruit total surface area; 2 grades, mycelia appears in fruit surface, or the pathological tissues area reaches more than 25% of fruit total surface area, is calculated as follows rotten index: the index=∑ that rots [(rank * this grade fruit number rots)/(the highest rotten rank * total fruit number)] * 100%;
4) data of analysis gaseous mass spectrum coupling instrument, according to the composition of gas componant and the difference degree of content, determine the storage time of fruit smell difference maximum, simultaneously the smell sensor response data of obtaining is carried out principal component analysis (PCA) according to storage time and pathogenic infection microbe species respectively, comprehensive gaseous mass spectrum coupling instrument information and smell sensor information, according to the principle that realizes simultaneously judging as early as possible the fruit introduced disease and being easy to distinguish the pathogenic infection microbe species, determine the better test duration, the variance significance analysis result of combined sensor response determines better smell sensor combination;
5) on selected sensor combinations basis, set up following Fisher linear discriminant analysis model based on mahalanobis distance,
y
0＝b
0+a
10×S
1+a
20×S
2+a
30×S
3+…+a
n0×s
n
y
1＝b
1+a
11×S
1+a
21×S
2+a
31×S
3+…+a
n1×S
n
y
2＝b
2+a
12×S
1+a
22×S
2+a
32×S
3+…+a
n2×S
n
……
y
k＝b
k+a
1k×S
1+a
2k×S
2+a
3k×S
3+…+a
nk×S
n(1)
In the following formula, S is the response of smell sensor, is to touch resistance G behind the sample volatile matter and sensor at the resistance G through standard activity carbon filtering gas according to sensor
0Ratio G/G
0, the lower sensor reference numeral that is designated as, value from 1,2,3......n; B and a are respectively constant term and the independent variable coefficient of discriminant, and the subscript of y represents the type of pathogenic infection microorganism, and wherein 0 representative does not have the pathogenic infection microorganism;
6) unknown fruit sample is put into closed container, adopt smell sensor to obtain response, in the substitution formula (1), all y values relatively, its subscript of group of y value maximum represents the pathogenic microorganism type of this fruit infection.
Beneficial effect
The present invention utilizes the smell information after smell sensor acquisition fruit is adopted, can not destroy in the situation of fruit integrality, the smell information that gives out by fruit, judge and adopt the whether kind of pathogenic infection microorganism and infection of rear fruit, can shift to an earlier date the quality information that obtains fast after fruit is adopted, carry out timely early warning to adopting rear fruit disease situation, provide effective information for the operator further processes, avoid serious economic loss.Cultivate the methods such as detection with respect to traditional microorganism, not only save time, and avoided the use of chemical reagent.Technology and method are novel, and achievement in research not only can be used for express-analysis and the detection in laboratory, and can by exploitation online detection instrument and portable instrument, be used for fruit and adopt the links such as rear processing, storage and sale.
Four, description of drawings
Fig. 1: technology path
Fig. 2: index rots;
Fig. 3: different disposal group strawberry fruit response PCA analysis chart, the principal component analysis (PCA) figure of (a) control group strawberry response signal wherein, (b) the principal component analysis (PCA) figure of inoculation gray mold strawberry response signal, (c) the principal component analysis (PCA) figure of inoculation penicillium expansum strawberry response signal, (d) the principal component analysis (PCA) figure of inoculation head mold strawberry response signal;
Fig. 4: different disposal strawberry sensor response PCA analysis result, wherein the principal component analysis (PCA) figure of (a) the 0th day different disposal group response signal, (b) the principal component analysis (PCA) figure of the 2nd day different disposal group response signal
Five, embodiment
A kind of smell sensor detects the method for fruit disease, and take strawberry as example, embodiment is as follows:
1. test material
Test used strawberry cultivars and be " beauty ".On April 14th, 2012 plucked in Nanjing middle bar strawberry field, and the selection color and luster is even, in the same size, without the strawberry of surface damage.
Select strawberry to adopt three kinds of common pathogens of postoperative infection: grey mold (Botrytis sp., BC), penicillium expansum (Penicillium sp., PE) and head mold (Rhizopus sp., RH), under upper 24 ℃ of potato dextrose agar (Potato dextrose agar, PDA), 85% relative humidity condition, activate 7d before using.
2. instrument
Portable electric nose (AIRSENSE, PEN3).Used Electronic Nose sensor array comprises 10 sensors, is respectively W1C (S
1:, detectability 10ppm responsive to the aroma type compound), W5S (S
2:, detectability 1ppm responsive to oxides of nitrogen), W3C (S
3:, detectability 10ppm responsive to Ammonia, aroma type compound), W6S (S
4:, detectability 100ppm responsive to hydrogen), W5C (S
5:, detectability 1ppm responsive to alkane, aroma type compound), W1S (S
6:, detectability 100ppm responsive to hydrocarbons), W1W (S
7:, detectability 1ppm responsive to sulfuretted hydrogen, terpenes), W2S (S
8:, detectability 100ppm responsive to alcohols, part aroma type compound), W2W (S
9:, detectability 1ppm responsive to organic sulfide to fragrance ingredient), W3S (S
10: to concentration, alkane sensitivity, and have selectivity, detectability 100ppm).During mensuration, the volatile matter in the sensor coatings adsorption sample produces conductivity variations, and the resistivity G behind the record sensor adsorption sample volatile matter and sensor absorption are through the air conductivity G of active carbon filtration
0Ratio G/G
0, the response gas concentration is larger, G/G
0Value more depart from 1 (being greater than or less than 1), if concentration is lower than detectability or does not respond to gas, then this ratio approaches even equals 1.
3. test method
The strawberry of picking out is equally divided into 4 groups.75% alcohol-pickled 30s treats that the alcohol complete laggard row inoculation of volatilizing processes.The processed group fruit is respectively at 4 * 10
5Soak 30s in the spore suspension of the grey mold of individual/mL (BC), mould (PE) and head mold (RH), control group is processed with sterilized water (CK), and (50 ± 5g) is 1 group, places 5 ℃ of refrigerations of 100mL dixie cup to dry rear per 3 fruits.
The dixie cup that strawberry is housed taken out from refrigerator put into the 150mL beaker, in 24 ℃, the 85% relative humidity condition lower open mouth 2h that rises again, make the fruit internal temperature reach 24 ℃, seal in 24 ℃ with tinfoil and leave standstill 10min.Test parameters is: flow velocity 120mL/min, minute 60s, gas washing time 80s, preparation of samples time 5s, automatic zero set (AZS) time 5s.Test finds that the sensor response tends towards stability about 25s, select the response at 30s place to be used for data analysis.
By the fruit rot size fruit is divided into 3 grades: 0 grade, without rotting; 1 grade, the fruit face has 1～3 tawny scab, and the pathological tissues area is no more than 25% of fruit total surface area; 2 grades, mycelia appears in fruit surface, or the pathological tissues area reaches more than 25% of fruit total surface area.Be calculated as follows rotten index: the index=∑ that rots [(rank * this grade fruit number rots)/(the highest rotten rank * total fruit number)] * 100%.
Volatile matter is measured in gaseous mass spectrum coupling (GC-MS): each processed group is got 5 strawberries, and the chopping mixing is deposited stand-by for-18 ℃ after liquid nitrogen is processed.Solid-phase micro-extracting device is inserted and release fiber head to wear out; Get the 10g pulp in the 20mL sample bottle, 40 ℃ of water-baths are with the extraction equipment extraction 40min that wore out.Gas chromatography mass spectrometry (GC-MS) is measured the research method with reference to [27] such as Thomas RH.The gas chromatograph injection port temperature is made as 240 ℃, and the PDMS extracting head is resolved 3min in injection port; Column temperature: 50 ℃ keep 5min, then rise to 200 ℃ with 2 ℃ of per minutes, keep 10min; Fid detector, 240 ℃; 230 ℃ of ion source temperatures; Carrier gas He, flow velocity 1mL/min; Sweep limit: m/z30-450.Each component determines through the NIST library searching, only selects matching degree greater than 80% component, and relative content accounts for peak area and detects gas total peak area number percent and represent.
4. sensor is to the response of strawberry volatile matter
The Electronic Nose response signal tends towards stability behind the certain hour gradually measuring starting stage relative conductivity fast rise, in addition, and S
3(to Ammonia, aroma type compound sensitivity), S
5(to alkane, aroma type compound sensitivity), S
6(responsive to hydrocarbons), S
7(to sulfuretted hydrogen, terpenes sensitivity), S
8(to alcohols, part aroma type compound sensitivity) has higher relative conductivity than other sensors.By the Electronic Nose sensor to the response test of strawberry fruit volatile matter as can be known, Electronic Nose has obvious response to the volatile matter of strawberry fruit, and each sensor is different to its response.Based on the hypothesis of harm influence strawberry fruit volatile flavor substance, show that to utilize PEN3 electric nasus system identification Diseases of Strawberry feasible.
5. disease is on the impact of the rotten index of storage period strawberry
The Storage of Strawberry phase that various pathogenic bacteria is processed rot index variation as shown in Figure 2, as can be seen from the figure with the prolongation of storage time, each rotten index of organizing fruit becomes large gradually.CK organizes the strawberry morbidity at the latest, still disease-free generation in the time of the 4th day, and the rotten index of preserving latter stage is minimum, and the processed group fruit namely begins to fall ill behind refrigeration 2d, and storage rotten index in latter stage is organized far above CK.
6. the strawberry fruit sensor response principal component analysis (PCA) of different disposal
Strawberry fruit principal component analysis (PCA) (PCA) analysis result of different disposal group is seen Fig. 3, can find out, each organizes first, second major component contribution rate of accumulative total of fruit all more than 90%, can represent the most information of original variable.Sample spot during CK group fruit storage 4d distributes and notable difference is all arranged in storage early stage (0,2d), later stage (6,8,10d); The principal component analysis (PCA) result of each processed group is similar, and the sample spot of preserving 0d, 2d days distributes and to be different from anaphase storage (4,6,8,10d), and the sample spot during storage 2d distributes maximum with the anaphase storage difference.
7. Electronic Nose is to the differentiation of Diseases of Strawberry kind
Utilize PCA respectively the fruit sensor response of processing rear 0d, 2d to be analyzed, the result as shown in Figure 4.As seen from the figure, after processing finishes, detect at once, can't distinguish its Species of Pathogens, and after processing rear 5 ℃ of storage 2d, the different disposal sample spot is gathered in diverse location.Show after processing under-effected to be used for the differentiation of its kind to Fruit volatile substances of pathogen in a short time, and its otherness of 2d just shows after processing, can distinguish in early days in morbidity, therefore, the early stage differentiation that utilizes principal component analysis (PCA) to carry out Diseases of Strawberry is feasible.
The PCA analysis result can demonstrate the difference between each group intuitively, illustrate accurately that its difference need carry out variance analysis.Wilks ' Lambda is the ratio of quadratic sum and total sum of squares in the group.When the class mean of all observations equated, Wilks ' Lambda value was 1; Make a variation in group and compare with total variation hour, Wilks ' Lambda value is close to 0.Therefore, Wilks ' Lambda value is large, represents that the average of each group is substantially equal; Variant between the little expression group of Wilks ' Lambda
[28], utilize the level of significance (P＜0.05) of F check analysis difference.By table 1 analysis result as can be known, in the time of the 2nd day, significant difference between response between control group and processed group, also there were significant differences for response between the various pathogenic bacteria processed group.
The sensor response variance analysis of 2d different diseases strawberry after table 1 is processed
8.GC-MS analyze
The characteristic perfume material of strawberry fruit is hexyl acetate, methyl caproate, ethyl hexanoate, monooctyl ester class, nerolidol etc.
[30], the sample of processing rear 2d is measured, it is different that the result shows that different disposal group features of fruits aroma substance accounts for the ratio that detects the material total peak area, CK, BC, PE, RH group are respectively 88.99%, 76.49%, 89.88%, 89.36%.In addition, pathogen is processed and also butyric acid-other 12 kinds of materials such as 2-hexene ester has been produced impact, the results are shown in Table 2.Contrast CK group and pathogen processed group, find that volatile component difference is mainly reflected on n-octyl isovalerate, n-hexyl isovalerate, styron acetic acid esters and the carvene, above-mentioned ester class all has in various degree embodiment in the processed group fruit, and the control group fruit does not detect, and carvene (0.94%) is then only measured in the CK group; The component of volatile matter also is not quite similar in the various pathogenic bacteria processed group fruit, butyric acid-2-hexene ester (0.62%), 5-hydroxyl first furfural (9.13%), maltol (1.51%) only detect in the BC group, methyl cinnamate (0.10%), benzocyclobutene (1.63%) and carypohyllene (0.16%) only occur in PE group fruit, contain 3-carene and octyl acetate in the RH group volatile constituent but without 5-hydroxyl first furfural.In three kinds of processing, detected altogether the six species specificity components such as styrene, thujopsene, eucalyptol, undecane, thought and to carry out accordingly the detection of fruit infection pathogen.Originally studies show that fungal infection is outwardness on the impact of strawberry fruit volatile matter, but the determining of the characteristic gas of indication Species of Pathogens also needs further research, can determine in the variation of storage period by measuring behind the different amount of the inoculation pathogens volatile constituent of fruit.
Table 2 strawberry fruit part volatile matter relative content
Table?3?Relative?contents?of?some?aromas?in?strawberry?after?different?treatment
9. Diseases of Strawberry is distinguished foundation and the checking of model
Fisher linear discriminant analysis method is a kind of effective feature extracting method in the pattern-recognition, by original variable being projected on the best direction, distinguishes to realize the best of distinguishing sample different classes of in the training set
[29]The fruit sensor responses of processing rear 5 ℃ of refrigeration 2d are analyzed, utilized method of gradual regression (variable is introduced in P＜0.05 o'clock, and this variable o'clock is rejected in P＞0.1) to carry out the discriminatory analysis based on mahalanobis distance.Discriminant is:
Model is carried out Willks λ check, and its P value of each variable of introducing is all less than 0.0001, and the whole P value of model shows that also less than 0.0001 institute's established model has statistical significance.With unknown sample Electronic Nose response signal value S
3, S
5, S
6, S
7, S
8, S
9Respective value is brought discriminant into, draws the maximum group of y value and is the Species of Pathogens that it infects.The gained model is carried out the result see Table 3, the overall accuracy rate of modeling group is 96.7%, and it is 94.2% that overall accuracy rate is organized in checking, illustrates that this model can distinguish strawberry different diseases infection type preferably.
Table 3 is based on foundation and the result of the disease forecast model of smell
Claims (1)
1. the fast non-destructive detection method of a fruit postharvest diseases is to utilize smell sensor to detect fruit to infect the smell that distributes behind the Different Kinds of Pathogens microorganism and determine the whether kind of introduced disease and introduced disease of fruit, it is characterized in that its detecting step is as follows,
1) with fruit sample surfaces impurity elimination, clean, with 75% alcohol-pickled 30s, treat that the alcohol complete laggard row inoculation of volatilizing processes, all samples is divided into two groups, one group is used for smell sensor and obtains fruit smell information, another group is used for chemical composition and the relative content of gaseous mass spectrum coupling Instrument measuring fruit smell, wherein, the fruit sample is inoculated respectively such fruit and is adopted rear susceptible sex pheromone, simulation fruit is adopted rear cause pathogeny imcrobe infection, inoculates the fruit sample of each class pathogenic microorganism and represent not introduced disease fruit sample size to be 50-80;
2) the fruit sample is preserved, the interval same time adopts the smell of smell sensor and gas phase GC-MS device test fruit, when wherein smell sensor is tested, the fruit sample is placed closed container, when head space gas reaches capacity, obtain the smell response signal by smell sensor, and carry out analyzing and processing, simultaneously, utilize chemical composition and the relative content of gaseous mass spectrum coupling instrument analyzing and testing smell;
3) after gas sensor and gas phase GC-MS detect and finish, calculate the index that rots, the index that rots then shows pathogenic infection microorganism of fruit greater than 0, wherein, by the fruit rot size fruit is divided into 3 grades: 0 grade, without rotting; 1 grade, the fruit face has 1～3 tawny scab, and the pathological tissues area is no more than 25% of fruit total surface area; 2 grades, mycelia appears in fruit surface, or the pathological tissues area reaches more than 25% of fruit total surface area, is calculated as follows rotten index: the index=∑ that rots [(rank * this grade fruit number rots)/(the highest rotten rank * total fruit number)] * 100%;
4) data of analysis gaseous mass spectrum coupling instrument, according to the composition of gas componant and the difference degree of content, determine the storage time of fruit smell difference maximum, simultaneously the smell sensor response data of obtaining is carried out principal component analysis (PCA) according to storage time and pathogenic infection microbe species respectively, comprehensive gaseous mass spectrum coupling instrument information and smell sensor information, according to the principle that realizes simultaneously judging as early as possible the fruit introduced disease and being easy to distinguish the pathogenic infection microbe species, determine the better test duration, the variance significance analysis result of combined sensor response determines better smell sensor combination;
5) on selected sensor combinations basis, set up following Fisher linear discriminant analysis model based on mahalanobis distance,
y
0＝b
0+a
10×S
1+a
20×S
2+a
30×S
3+…+a
n0×S
n
y
1＝b
1+a
11×S
1+a
21×S
2+a
31×S
3+…+a
n1×S
n
y
2＝b
2+a
22×S
1+a
22×S
2+a
22×S
3+…+a
n2×S
n
……
y
k＝b
k+a
1k×S
1+a
2k×S
2+a
3k×S
3+…+a
nk×S
n(1)
In the following formula, S is the response of smell sensor, is to touch resistance G behind the sample volatile matter and sensor at the resistance G through standard activity carbon filtering gas according to sensor
0Ratio G/G
0, the lower sensor reference numeral that is designated as, value is 1,2,3......n; B and a are respectively constant term and the independent variable coefficient of discriminant, and the subscript of y represents the type of pathogenic infection microorganism, and wherein 0 representative does not have the pathogenic infection microorganism;
6) unknown fruit sample is put into closed container, adopt smell sensor to obtain response, in the substitution formula (1), all y values relatively, its subscript of group of y value maximum represents the pathogenic microorganism type of this fruit infection.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103344672A (en) * | 2013-06-13 | 2013-10-09 | 南京中医药大学 | Nondestructive testing method for rapid discrimination of sulphur-fumigated traditional Chinese medicinal materials by using gas sensors and application of gas sensors |
| CN103499616A (en) * | 2013-07-30 | 2014-01-08 | 中国标准化研究院 | Selection method of sensors in producing area models for quality detection of Longjing tea on basis of genetic algorithm |
| CN103499615A (en) * | 2013-07-30 | 2014-01-08 | 中国标准化研究院 | Selection method of sensors in tree species model for quality detection of Longjing tea based on genetic algorithm |
| CN103616414A (en) * | 2013-12-10 | 2014-03-05 | 江苏大学 | Rapid identification method of aerogenic bacterium variety |
| CN104267067A (en) * | 2014-10-28 | 2015-01-07 | 南京农业大学 | Method based on smell sensor and used for predicting growth stage of pseudomonas aeruginosa as meat typical putrefying bacterium |
| CN104833780A (en) * | 2015-01-19 | 2015-08-12 | 南京农业大学 | Method of predicting quality grade of strawberries on the basis of ethanol sensor |
| CN114586546A (en) * | 2022-03-14 | 2022-06-07 | 西南大学 | Automatic strawberry picking device based on electronic nose and image recognition and control method thereof |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101620195A (en) * | 2009-08-12 | 2010-01-06 | 南京农业大学 | Method for detecting internal quality of jirou sweet persimmon by smell sensor |
| CN101655471A (en) * | 2009-09-23 | 2010-02-24 | 南京农业大学 | Method for detecting egg freshness by using gas sensor |
-
2012
- 2012-12-07 CN CN2012105207004A patent/CN102944585A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101620195A (en) * | 2009-08-12 | 2010-01-06 | 南京农业大学 | Method for detecting internal quality of jirou sweet persimmon by smell sensor |
| CN101655471A (en) * | 2009-09-23 | 2010-02-24 | 南京农业大学 | Method for detecting egg freshness by using gas sensor |
Non-Patent Citations (4)
| Title |
|---|
| I.CONCINA ET AL.: "Early detection of microbial contamination in processed tomatoes by electronic nose", 《FOOD CONTROL》 * |
| S.BALASUBRAMANIAN ET AL.: "Evaluation of an artificial olfactory system for grain quality discrimination", 《LWT》 * |
| 唐琳 等: "基于气味与颜色的脊尾白虾新鲜度评价", 《农业工程学报》 * |
| 陈学红 等: "高氧处理对草莓果实采后活性氧代谢和腐烂的影响", 《南京农业大学学报》 * |
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| CN103344672A (en) * | 2013-06-13 | 2013-10-09 | 南京中医药大学 | Nondestructive testing method for rapid discrimination of sulphur-fumigated traditional Chinese medicinal materials by using gas sensors and application of gas sensors |
| CN103499616A (en) * | 2013-07-30 | 2014-01-08 | 中国标准化研究院 | Selection method of sensors in producing area models for quality detection of Longjing tea on basis of genetic algorithm |
| CN103499615A (en) * | 2013-07-30 | 2014-01-08 | 中国标准化研究院 | Selection method of sensors in tree species model for quality detection of Longjing tea based on genetic algorithm |
| CN103616414A (en) * | 2013-12-10 | 2014-03-05 | 江苏大学 | Rapid identification method of aerogenic bacterium variety |
| CN104267067A (en) * | 2014-10-28 | 2015-01-07 | 南京农业大学 | Method based on smell sensor and used for predicting growth stage of pseudomonas aeruginosa as meat typical putrefying bacterium |
| CN104833780A (en) * | 2015-01-19 | 2015-08-12 | 南京农业大学 | Method of predicting quality grade of strawberries on the basis of ethanol sensor |
| CN114586546A (en) * | 2022-03-14 | 2022-06-07 | 西南大学 | Automatic strawberry picking device based on electronic nose and image recognition and control method thereof |
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