Detection Of Adulteration And Quantilation Analysis Of Edible Vegetable Oil By Near Infrared Spectrcosopy

Near-infrared spectroscopy (NIRS) is a fast, non-destructive and convenient analytical technique which applied in more and more fields. Chemometrics is a rising Cross-disciplinary with many advantages in the aspects of extracting, analyzing information, spectra pretreatments and building the models. To afford new methods to solve the problem for food security and detection, near infrared spectroscopy combined with chemometrics are very important applications for the qualitative and quantitative analysis.. This paper is divided into seven chapters, with the major study on qualitative discriminate for adulteration of edible vegetable oil and categorys of edible vegetable oil, quantitative detection for adulteration of edible vegetable oil, acid value and peroxide value by chemometrics and near infrared spectroscopy technology.1. The research on category identification, acid value, peroxide value and adulteration detection of edible vegetable oil were summarized at home and abroad. The principles of near infrared spectroscopy were introduced. At the same time the research status of the qualitative and quantitative detection by using near infrared spectroscopy technology was concluded.2. The category identification of edible vegetable oil, included with soybean oil, corn oil, peanut oil, sesame oil and camellia oil was studied by using near infrared spectroscopy. The methods of mahalanobis distance and self-organizing competitive neural networks were used to classify those collected sampleswhich can build discriminative models. Simultaneously, the effects of the wavelength range and methods of spectra pretreatments on the models were explored. Both methods provided. To predict the validation set of 25 samples, the use of the established model of mahalanobis distance and self-organizing competitive neural networks showed very good discrimination between the oil classes with low classification error, the accuracy rate of which were reached to 100%.3. Quantitative determination of acid value and peroxide value were researched by the buildng of partial least squares (PLS) regression model and PLS-Back Propagation artificital neural network combined with near infrared spectroscopy,and the models were used to predict the validation set of 15 samples, respectively. The coefficient of determination of PLS model for acid value and peroxide value were 0.9837,0.9752, and the root mean square error of prediction (RMSEP) of 0.0752,0.00972, respectively. By using the models of BP network to predict the validation set of 15 samples the coefficient of determination R2 for acid value and peroxide value can reached to 0.9695,0.9744with the RMSEP of 0.595,0.00991 respectively. These two models can satisfy with the detected need of acid value and peroxide value, simultaneously.4. Qualitative and Quantitative determination of the adulterated binary system which was comprised of pure camellia oil samples mixed with various concentrations of soybean oil, corn oil and sunflower seed oil were researched by using near infrared spectroscopy. The discriminative model of between pure camellia oil and camellia oil adulterated, and model of amang three camellia oil samples adulterated were estabulished by mahalanobis distance cluster analysis. The accuracy rate useing the established models reached more than 99.1%. The quantitative determination models to detect concentrations of soybean oil, corn oil, and sunflower seed oil which adulterated into camellia oil binary system by partial least squares were established.The correlation coefficients of PLS were 0.99957,0.99962,0.99975, and RMSEC were 0.300,0.309,0.255. The RMSEP were 0.467,0.272, and 0.410, respectively. The true value and the predictive value of the validation set were compared by using paired t-test, the result showed that it was no significant difference and the research can obtained satisfactory results.5. The peanut oil adulterated binary system mixed with various concentrations of soybean oil, rapeseed oil and palm oil were prepared, and then the qualitative and quantitative determination of the system were studied by back propagation artificital neural network combined with near infrared spectroscopy. The BPnet could be divided into four classes processing 744 epochs. The accurate rate of the established BP neural network model to predict calibration set and validation set were reached to 100% and 96.0%, respectively. At the same time, by using BP-neural network, the quantitative determination models which were used to detect the concentrations of soybean oil, rapeseed oil, and palm oil in the peanut oil adulterated binary system were established, the predictive value of the validation set were obtain, also. The coefficients of determination were 0.9851,0.9901,0.9850, and the RMSEP were 1.05,1.10 and 1.72, respectively. Moreover, the results of the model were compared with the PLS and PCR, which show that PLS prediction model is better than the BP network, and both models can meet the needs of detecting the adulteration of peanut oil.6. The sesame oil samples adulterated binary system mixed with various concentrations of soybean oil, rapeseed oil and peanut oil were prepared. Qualitative determination of adulterate peanut oil was studied by Self-Organizing feature Map (SOM) neural network combined with near infrared spectroscopy. The spectrum datas were compressed by PCA to obtain scores of principal components. The optimal result was obtain when training epochs were reach to approximately 500 epochs with the accurate rate of 100%, and the accuracy rate of validation set 50 samples was reached to 95.6%. Subsequently, the quantitative determination models used to detect concentrations of soybean oil, rapeseed oil, and palm oil in peanut oil adulterated binary system were established by PLS. The RMSEP for validation set using PLS models were 1.05,1.31,0.686, and the R2 were 0.9951,0.9825,0.9944; separately. In this chapter, we also studied sesame oil samples adulterated ternary system mixed with varying concentrations of soybean oil and peanut oil, and the quantitative determination models were established to detect concentrations of soybean oil and peanut oil by partial least squares. The correlation coefficients of models were 0.95714 and 0.97025; The RMSEC were 1.54 and 1.42, respectively. The correlation is poor, which revealed that the method could not get an accurate.quantitation analyzed result.