CN102749362A - Method of optimizing peanut freshness detection conditions of electronic nose of gas sensor - Google Patents
Method of optimizing peanut freshness detection conditions of electronic nose of gas sensor Download PDFInfo
- Publication number
- CN102749362A CN102749362A CN2012102505162A CN201210250516A CN102749362A CN 102749362 A CN102749362 A CN 102749362A CN 2012102505162 A CN2012102505162 A CN 2012102505162A CN 201210250516 A CN201210250516 A CN 201210250516A CN 102749362 A CN102749362 A CN 102749362A
- Authority
- CN
- China
- Prior art keywords
- peanut
- electronic nose
- storage
- quality
- sample
- 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.)
- Granted
Links
Images
Landscapes
- Investigating Or Analyzing Materials By The Use Of Fluid Adsorption Or Reactions (AREA)
- Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
Abstract
The invention discloses a method of optimizing peanut freshness detection conditions of an electronic nose of a gas sensor. The method comprises: cleaning and wiping the surfaces of newly produced peanuts, picking up 10-30g of peanut samples with intact shells, putting the peanut samples in a 150-250ml beaker, dividing the peanut samples into 9 groups and sealing the peanut samples, detecting the headspace gases through the electronic nose, selecting the maximal value of the response values of the sensors as the mass-volume original data before storage; storing the detected 9 groups of peanut samples, taking out the peanut samples 2 days later, sealing the peanut samples, detecting the headspace gases through the electronic nose for the second time, and selecting the maximal value or stable value of the response values of the sensors as the mass-volume original data after storage; and introducing the mass-volume original data before storage and the mass-volume original data after storage in a mode identification software SPSS to analyze the main ingredients so as to obtain the optimized detection conditions. The method disclosed by the invention optimizes the conditions of detecting the peanut freshness of the gas sensor in a convenient, objective and intact manner and has good partition degree, so that the method has high generalization and application values.
Description
Technical field
The present invention relates to a kind of method of optimizing the detection by electronic nose peanut freshness condition of gas sensor.
Background technology
The different peanut of freshness almost can't with the naked eye make a distinction it under band shell condition.At present, Chinese scholars generally adopts the method for subjective appreciation or synthesis measuring acid value and peroxide value when the peanut freshness is detected.Yet use above these methods to carry out freshness when detecting, all exist some problems: the subjective appreciation result receives individual and such environmental effects bigger, is difficult to form unified standard; And acid value and peroxide value are measured length consuming time, operation requires height, and instrument is difficult for cleaning.Electronic Nose at field of food wide application prospect is arranged, but the variation of testing conditions can influence testing result as a kind of convenient, objective quality detecting method.
Summary of the invention
The invention provides a kind of method of optimizing the detection by electronic nose peanut freshness condition of gas sensor, purpose is to make testing result can reflect the freshness of peanut more all sidedly, effectively improves the discrimination between the different freshness peanuts.
Optimize the method for the detection by electronic nose peanut freshness condition of gas sensor: the peanut surface with clean water will newly be produced is cleaned; Dry moisture and be placed on shady and cool place; After treating surface moisture evaporation fully; Pick out the intact unabroken peanut sample 10-30g of watchcase and be put in the beaker of 150-250ml, above-mentioned peanut sample is divided into 9 groups by following quality volumetric ratio: 10g-150ml, 10g-200ml, 10g-250ml, 20g-150ml, 20g-200ml, 20g-250ml, 30g-150ml, 30g-200ml, 30g-250ml; 9 groups of samples behind sealing 5min, 15min and 25min, carry out detection by electronic nose to head space gas respectively, and every group of sample done a plurality of parallel laboratory tests, the quality-volume raw data of the maximal value of selecting each sensor response before as storage; 9 groups of peanut samples after the detection are 30 ~ 35 ℃ of temperature; Storage took out after 2 days in the growth cabinet of humidity 85% ~ 90%; Respectively behind sealing 5min, 15min and 25min; Static headspace gas body and function Electronic Nose is carried out the second time detect, every group of sample done a plurality of parallel laboratory tests, the quality-volume raw data of maximal value or the stationary value of selecting each sensor response after as storage; The quality that storage is forward and backward-volume raw data imports mode identificating software SPSS and carries out principal component analysis (PCA), the testing conditions that is optimized.
The present invention has optimized the condition of gas sensor detection peanut freshness, and is convenient, objective, harmless, and has good discrimination, has higher popularization and using value.
Description of drawings
The sensor response signal of FOX4000 type Electronic Nose in Fig. 1 embodiment of the invention 1;
The principal component analysis (PCA) result of different quality in Fig. 2 embodiment of the invention 1-volume sample;
Principal component analysis (PCA) result when sample quality is 30g in Fig. 3 embodiment of the invention 1;
Principal component analysis (PCA) result when sample quality is 20g in Fig. 4 embodiment of the invention 1;
The principal component analysis (PCA) result of different sealing time sample in Fig. 5 embodiment of the invention 1;
The sensor response signal of PEN 2 type Electronic Nose in Fig. 6 embodiment of the invention 2;
The principal component analysis (PCA) result of different quality in Fig. 7 embodiment of the invention 2-volume sample;
Principal component analysis (PCA) result when sample quality is 30g in Fig. 8 embodiment of the invention 2;
Principal component analysis (PCA) result when sample quality is 20g in Fig. 9 embodiment of the invention 2;
The principal component analysis (PCA) result of different sealing time sample in Figure 10 embodiment of the invention 2.
Embodiment
Optimize the method for the detection by electronic nose peanut freshness condition of gas sensor: the peanut surface with clean water will newly be produced is cleaned; Dry moisture and be placed on shady and cool place; After treating surface moisture evaporation fully; Pick out the intact unabroken peanut sample 10-30g of watchcase and be put in the beaker of 150-250ml, above-mentioned peanut sample is divided into 9 groups by following quality volumetric ratio: 10g-150ml (quality volumetric ratio 1:15), 10g-200ml (1:20), 10g-250ml (1:25), 20g-150ml (1:7.5), 20g-200ml (1:10), 20g-250ml (1:12.5), 30g-150ml (1:5), 30g-200ml (1:6.67), 30g-250ml (1:8.33); 9 groups of samples behind sealing 5min, 15min and 25min, carry out detection by electronic nose to head space gas respectively, and every group of sample done a plurality of parallel laboratory tests, the quality-volume raw data of the maximal value of selecting each sensor response before as storage; 9 groups of peanut samples after the detection are 30 ~ 35 ℃ of temperature; Storage took out after 2 days in the growth cabinet of humidity 85% ~ 90%; Respectively behind sealing 5min, 15min and 25min; Static headspace gas body and function Electronic Nose is carried out the second time detect, every group of sample done a plurality of parallel laboratory tests, the quality-volume raw data of maximal value or the stationary value of selecting each sensor response after as storage; The quality that storage is forward and backward-volume raw data imports mode identificating software SPSS and carries out principal component analysis (PCA), the testing conditions that is optimized.
FOX4000 type Electronic Nose with French Alpha MOS company is that detecting instrument elaborates, and this electric nasus system is made up of 18 metal oxide sensors, and its model and response characteristic are as shown in table 1:
Each sensor's response characteristic of table 1 FOX4000 type Electronic Nose
Little white sand peanut surface with clean water will newly be produced is cleaned; Dry moisture and be placed on shady and cool place; After treating surface moisture evaporation fully; Pick out the intact unabroken peanut of watchcase and be put in the not isometric beaker, can above-mentioned sample be divided into 9 groups: 10g-150ml (quality volumetric ratio 1:15), 10g-200ml (1:20), 10g-250ml (1:25), 20g-150ml (1:7.5), 20g-200ml (1:10), 20g-250ml (1:12.5), 30g-150ml (1:5), 30g-200ml (1:6.67), 30g-250ml (1:8.33) according to peanut quality and beaker volume by different quality.9 groups of samples behind sealing 5min, 15min and 25min, extract 2ml head space gas and carry out detection by electronic nose respectively, and be 180 seconds detection time, and scavenging period is 160 seconds.Every group of sample done 6 parallel laboratory tests.When selecting sealing 25min, the maximal value of each sensor response is as the preceding quality-volume raw data of storage.
9 groups of peanut samples after the detection are 30 ~ 35 ℃ of temperature, and storage took out after 2 days in the growth cabinet of humidity 85% ~ 90%, respectively behind sealing 5min, 15min and 25min; Extract 2ml head space gas; Carry out the second time with Electronic Nose and detect, be made as detection time 180 seconds, scavenging period was made as 150 seconds.Every group of sample done 6 parallel laboratory tests.When selecting sealing 25min, the maximal value of each sensor response is as the quality volume raw data after storing.Detect to observe and find that repeatedly the sensor response signal figure of experiment is similar, all about 120 seconds, begin to tend towards stability, as shown in Figure 1.
The quality volume raw data that storage is forward and backward imports mode identificating software and carries out principal component analysis (PCA), and analysis result is as shown in Figure 2.As can be seen from Figure 2 the storage before with the storage after 9 groups of peanut samples all can be made a distinction.
When selecting the sample of detection by electronic nose 30g and 20g respectively, the maximal value of sensor response imports mode identificating software and carries out principal component analysis (PCA), analysis result such as Fig. 3 and shown in Figure 4.From Fig. 3 and Fig. 4, can find out; Sample quality is that 30g, beaker volume are when being 250ml (quality volumetric ratio 1:8.33); The peanut sample quality is 20g, beaker volume when being 200ml (quality volumetric ratio 1:10), and Electronic Nose can both be distinguished storing forward and backward sample preferably under this two testing conditions.Wherein quality is that 30g, beaker volume PC1 and the PC2 coordinate contribution rate when being 250ml (quality volumetric ratio 1:8.33) is 94.216%; PC1 during specific mass volumetric ratio 1:10 and PC2 coordinate contribution rate are 88.125% height, and the quantity of information that includes high sensor signal on the coordinate is described.Therefore think that it is better that sample quality is that 30g, beaker volume are distinguished effect when being 250ml (quality volumetric ratio 1:8.33).
Selecting storage front and back sample quality is that 30g, beaker volume are 250ml (quality volumetric ratio 1:8.33); The maximal value of each sensor response was as the forward and backward sealing time raw data of storage when the sealing time was 5min, 15min and 25min; Carry out principal component analysis (PCA), analysis result is as shown in Figure 5.From Fig. 5, can find out when the sealing time is 25min that Electronic Nose is distinguished best results to the forward and backward sample of its storage.
The PEN2 type Electronic Nose of Germany AIRSENSE company is that detecting instrument elaborates, and this electric nasus system is made up of 10 metal oxide sensors, and its model and response characteristic are as shown in table 2:
Each sensor's response characteristic of table 2 PEN2 type Electronic Nose
Little white sand peanut surface with clean water will newly be produced is cleaned; Dry moisture and be placed on shady and cool place; After treating that water evaporates fully; Pick out the intact unabroken peanut of watchcase and be put in the not isometric beaker, can above-mentioned peanut sample be divided into 9 groups: 10g-150ml (quality volumetric ratio 1:15), 10g-200ml (1:20), 10g-250ml (1:25), 20g-150ml (1:7.5), 20g-200ml (1:10), 20g-250ml (1:12.5), 30g-150ml (1:5), 30g-200ml (1:6.67), 30g-250ml (1:8.33) according to peanut quality and beaker volume by different quality.9 groups of samples detect with Electronic Nose behind sealing 5min, 15min and 25min respectively, are made as detection time 70 seconds, and scavenging period was made as 60 seconds.Every group of sample done 8 parallel laboratory tests.When selecting the sealing time to be 25min, the 70th second response of each sensor is as the preceding quality-volume raw data of storage.
9 groups of peanut samples after the detection are 30 ~ 35 ℃ of temperature; Storage took out after 2 days in the growth cabinet of humidity 85% ~ 90%; Behind sealing 5min, 15min and 25min, carry out the second time with Electronic Nose respectively and detect, be made as detection time 70 seconds, scavenging period was made as 60 seconds.Every group of sample done 8 parallel laboratory tests.When selecting the sealing time to be 25min, the 70th second response of each sensor is as the quality-volume raw data after storing.Detect to observe and find that repeatedly the sensor response signal figure of experiment is similar, all about 60 seconds, begin to tend towards stability, as shown in Figure 6.
The quality volume raw data that storage is forward and backward imports mode identificating software and carries out principal component analysis (PCA), and analysis result is as shown in Figure 7.As can be seen from Figure 7 the storage before with the storage after 9 groups of peanut samples all can be made a distinction; When wherein quality was 30g, Electronic Nose was distinguished best results to the forward and backward sample of its storage; When quality was 20g, secondly Electronic Nose distinguished effect to the forward and backward sample of its storage.
When selecting the sample of detection by electronic nose 30g and 20g respectively, the 70th second response of sensor imports mode identificating software and carries out principal component analysis (PCA), analysis result such as Fig. 8 and shown in Figure 9.Can find out from Fig. 8 and Fig. 9 no matter sample quality is 30g or 20g, when the beaker volume was 250ml, Electronic Nose was distinguished best results to storing forward and backward sample; When the beaker volume is 150ml and 200ml, distinguish effect secondly.
More than comprehensive, can find out that obtaining sample quality is that 30g, beaker volume are that Electronic Nose can both be distinguished storing forward and backward sample preferably under the testing conditions of 250ml (quality volumetric ratio 1:8.33).
Selecting storage front and back sample quality is that 30g, beaker volume are 250ml (the quality volumetric ratio is 1:8.33); The 70th second response of Electronic Nose sensor was as the forward and backward sealing time raw data of storage when the sealing time was 5min, 15min and 25min; Carry out principal component analysis (PCA), analysis result is shown in figure 10.From Figure 10, can find out when the sealing time is 5min that Electronic Nose can not make a distinction the forward and backward sample of storage; The sealing time, Electronic Nose was distinguished best results to the forward and backward sample of its storage when being 25min.
Certainly, if along with the variation of the peanut sample place of production or kind, the detection by electronic nose peanut freshness condition of the optimization gas sensor that obtains is difference to some extent.
Claims (1)
1. method of optimizing the detection by electronic nose peanut freshness condition of gas sensor; It is characterized in that: the peanut surface with clean water will newly be produced is cleaned; Dry moisture and be placed on shady and cool place; After treating surface moisture evaporation fully; Pick out the intact unabroken peanut sample 10-30g of watchcase and be put in the beaker of 150-250ml, above-mentioned peanut sample is divided into 9 groups by following quality volumetric ratio: 10g-150ml, 10g-200ml, 10g-250ml, 20g-150ml, 20g-200ml, 20g-250ml, 30g-150ml, 30g-200ml, 30g-250ml; 9 groups of samples behind sealing 5min, 15min and 25min, carry out detection by electronic nose to head space gas respectively, and every group of sample done a plurality of parallel laboratory tests, the quality-volume raw data of the maximal value of selecting each sensor response before as storage; 9 groups of peanut samples after the detection are 30 ~ 35 ℃ of temperature; Storage took out after 2 days in the growth cabinet of humidity 85% ~ 90%; Respectively behind sealing 5min, 15min and 25min; Static headspace gas body and function Electronic Nose is carried out the second time detect, every group of sample done a plurality of parallel laboratory tests, the quality-volume raw data of maximal value or the stationary value of selecting each sensor response after as storage; The quality that storage is forward and backward-volume raw data imports mode identificating software SPSS and carries out principal component analysis (PCA), the testing conditions that is optimized.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210250516.2A CN102749362B (en) | 2012-07-19 | 2012-07-19 | Method of optimizing peanut freshness detection conditions of electronic nose of gas sensor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210250516.2A CN102749362B (en) | 2012-07-19 | 2012-07-19 | Method of optimizing peanut freshness detection conditions of electronic nose of gas sensor |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102749362A true CN102749362A (en) | 2012-10-24 |
| CN102749362B CN102749362B (en) | 2014-06-11 |
Family
ID=47029724
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210250516.2A Active CN102749362B (en) | 2012-07-19 | 2012-07-19 | Method of optimizing peanut freshness detection conditions of electronic nose of gas sensor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102749362B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103471952A (en) * | 2013-09-17 | 2013-12-25 | 中安高科检测科技（北京）有限公司 | Preparation method of gas sensor array for detecting Jinhua hams |
| CN104730140A (en) * | 2013-07-30 | 2015-06-24 | 中国标准化研究院 | Parameter optimization method for honey detection by electronic nose |
-
2012
- 2012-07-19 CN CN201210250516.2A patent/CN102749362B/en active Active
Non-Patent Citations (3)
| Title |
|---|
| 周博等: "鸡蛋贮藏时间和新鲜度的电子鼻检测", 《浙江大学学报(工学版)》 * |
| 张红梅: "基于气体传感器阵列的几种农产品品质检测研究", 《万方学位论文》 * |
| 蒋德云等: "电子鼻对花生异味测定的初步研究", 《安徽农业大学学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104730140A (en) * | 2013-07-30 | 2015-06-24 | 中国标准化研究院 | Parameter optimization method for honey detection by electronic nose |
| CN103471952A (en) * | 2013-09-17 | 2013-12-25 | 中安高科检测科技（北京）有限公司 | Preparation method of gas sensor array for detecting Jinhua hams |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102749362B (en) | 2014-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102749370B (en) | Nondestructive rapid detection method of quality index of shell agricultural products | |
| CN102590283B (en) | Method for detecting freshness of grass carp by using electronic nose | |
| CN102297930A (en) | Method for identifying and predicting freshness of meat | |
| CN102879436A (en) | Method of using electronic nose for detecting freshness of river crucian carp | |
| CN103134850A (en) | Tea quality rapid detection apparatus and detection method based on characteristic fragrance | |
| CN104111274A (en) | Method for identifying producing area of red bayberry juice by using gas sensor array type electronic nose fingerprint analysis system | |
| CN103389323B (en) | Method for evaluating ages of precious medicinal materials quickly and losslessly | |
| CN105738581B (en) | A kind of method for quick identification of the different freshness hickory nuts based on electronic nose | |
| CN109298134A (en) | A kind of embedded electronic nose detection system and detection method | |
| CN108195895A (en) | A kind of tea leaf nitrogen content rapid detection method based on electronic nose and spectrophotometric color measurement instrument | |
| CN102507800A (en) | Rapid aroma fingerprint identification method for geographical indication protection product of vinegar | |
| CN105092750B (en) | Method for judging quality of fresh tobacco leaf sample in tobacco metabonomics research and kit | |
| CN102749362B (en) | Method of optimizing peanut freshness detection conditions of electronic nose of gas sensor | |
| CN105954412A (en) | Sensor array optimization method for Carya cathayensis freshness detection | |
| CN110596080A (en) | Mineral element-based golden pomfret origin identification method | |
| CN203758946U (en) | Device capable of simultaneously determining combustion heat and combustion rate of cigarette | |
| CN103604505A (en) | Test representation method of cigarette and reconstituted tobacco combustion temperature distribution | |
| CN102998350A (en) | Method for distinguishing edible oil from swill-cooked dirty oil by electrochemical fingerprints | |
| CN103913550A (en) | Method for tracing and verifying origin of pure natural fruit products | |
| CN102749426B (en) | Method for rapidly and qualitatively detecting freshness of peanuts without damage | |
| CN103412099B (en) | Device and method for detecting pawpaw storage time | |
| CN103454320A (en) | Electronic nose system for detecting freshness of carya cathayensis | |
| CN104849350A (en) | Method for identifying and classifying wood defects based on multiple features | |
| CN109657733B (en) | Variety discriminating method and system based on constituent structure feature | |
| CN104090082B (en) | The Forecasting Methodology of the full smoke pH of a kind of cigarette |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |