Quality Detection Of Several Kinds Of Agricultural Products Based On Gas Sensor Array

In the recent 20 years, the researches of electronic nose have made big progress. There are several companies to be allowed to produce the electronic nose overseas. The device has the advantage of high portability for measurements with lower costs and good reliability. They are particularly useful for the analysis of headspace of liquid or solid samples. Recently, electronic noses have been extensively used to test the food quality. But, the device is difficult to popularize in Chinese which of developing country due to the price is expensive. The research and application of electronic nose at the start stage in our country. So, the research and development of new device with portable, low power and low cost has become a popular research direction in recent years. Development of electronic nose is able to recognize the agricultural product based on the complex fragrance ingredient of agricultural product. The development of electronic nose and its applications to classify of agricultural product were investigated in this paper. An electronic nose was developed and applied to classify and quantitative analysis of different degree of moldy grain, different degrees of insect damage grain and fruit quality by the responses of the sensors to volatiles in the samples. The main conclusion is as follows:The electronic nose system consists of sampling unit, data acquisition unit and signal processing unit. The electronic nose software system was developed by using the concept of virtual instrument in LabVIEW development platform. The sensor array is composed of eight commercial metal oxide sensors, so long as replaces or several sensors then composes the new sensor array then may facilitate detection other new samples.An electronic nose was applied to classify different degree of moldy grain and the percentage of adulteration in grain. A few of redundancy sensors were removed by multivariate analysis of variance and principal component analysis. Finally, responses signals of residual sensor were chose for the different pattern recognition. The results obtained indicated that the electronic nose could predict aerobic bacterial count of moldy grain and the percentage of adulteration in grain with a high accuracy.The nondestructive measurement used to fruit firmness, sugar content and acidity was studied, and the fruit interior qualities of mathematic models were developed. The relationship between sensor signals and the firmness, sugar content and acidity of "dabai" peach were developed using multiple linear regressions with stepwise procedure, quadratic polynomial step regression and BP network. The results exhibits a very good ability in describing the quality indices of the selected three set of peach in training and prediction, as witnessed by the high correlation coefficients and the relatively low average percent error. The multivariate calibration methods, multiple linear regression (MLR), principal component regression (PCR) and partial least-squares regressions (PLS) were applied to predict the quality indices of"xueqing" pear from different picking dates based on the signal of electronic nose. All models for firmness and soluble solids content show a good prediction performance. However the acidity, there was a very poor correlation with the signal of the electronic nose. It was found that MLR led to more precise predictions than the other multivariate calibration methods. The relationship between sensor signals and the quality indices of pear were developed using quadratic polynomial step regression and BP network also. It was found that the forecast result of multiple linear regressions with stepwise procedure, quadratic polynomial step regression and BP for fruit firmness, sugar content and acidity were very precise. The results indicate that it is possible to use this electronic nose technique for measuring fruit quality characteristics.Electronic nose was applied to classify different degrees of insect damage grain and the percentage of adulteration in grain. Principal-component analysis (PCA) and linear-discriminant analysis (LDA) were applied to the generated patterns to discriminate successfully different degrees of insect damage grain. Multiple linear regressions with stepwise procedure, quadratic polynomial step regression and BP network were applied to predict the time of insect damage for grain and the percentage of adulteration in grain. It was found that quadratic polynomial step regression and BP network led to more precise predictions for the time of insect damage than for the percentage of adulteration in grain. Generally, the fresh rice and insect damage rice adulteration in fresh rice, fresh wheat and insect damage wheat adulteration in fresh wheat, flesh maize and insect damage maize adulteration in fresh maize were able to differentiate clearly. The result showed that forecast precision to the time of insect damage from three kind of models were very high, the standard error prediction is also small. Therefore three kind of models all suit use electronic nose signal of the insect damage grain to establishment model for prediction the time of insect damage.