Application Of Infrared Spectroscopy Analysis In Quality Detection Of Edible Vegetable Oil

Study on the rapid detection of vegetable oil quality is an attractive and prospective subject for quality supervision of vegetable oil in market. In combination with the chemometrical methods,the attenuated total reflectance fourier transform infrared spectroscopy(FTIR-ATR) analysis technology was used for vegetable oil quantitative and qualitative analysis.In order to provide new reference for the rapid and nondestructive examination for vegetable oil quality detection,the study was carried out by several subjects methods to come up with solution on the abstraction of weak spectral signal and establishing model and the feasibility of the tecnology was demonstrated. The main contents and conclusions are as follows:1. IR spectroscopy combined with partial least square (PLS) was applied to building the quantitative model to quantitatively predict the main fatty acid contents in vegetable oil.In building model, outliers were removed by mahalanobis distance and studentized residual versus leverage methods,the effects on the spectral preprocessing methods were also discussed. The correlation coefficient(R2) between the prdicted and the reference results for the test set is used as an evaluation parameter for the models:the palmitic acid,stearic acid,oleic acid,linoleic acid and linolenic acid results R2=0.963,0.798,0.997,0.996 and 0.991,respectively.It can be concluded that the main fatty acid contents in vegetable oil can be analyzed fast by IR spectroscopy coupled with the appropriate chemometrics methods,and this real-time,at-site measurement will significantly improve the efficiency of quality control and assurance.2. IR spectroscopy combined with pattern recognition based on SIMCA was applied to buliding the qualitative model to qualitatively predict 3 kinds of vegetable oil which have the largest consumption.The infrared spectra which after preprocessing were used to bulid forecasting model,using SIMCA method. The predicting accuracy of rapeseed oil,peanut oil and sesame oil were 100%,100% and 97.5%,respectively.The results showed that different species of vegetable oil can be classified by IR spectroscopy coupled with the appropriate chemometrics methods. 3. IR spectroscopy combined with SIMCA and back-propagation(BP) neural network was applied to buliding the qualitative model to recognize the similar vegetable oil of different processing methods. The predicting accuracy of rapeseed oil,peanut oil and sesame oil were 80%,75% and 95% by SIMCA pattern recognition method. To optimize the predictions, the infrared spectra of rapeseed oil and peanut oil which after preprocessing were used to build forecasting model again,using PLS+BP regression analysis. The optimal model parameters were selected through intensive trial-by-trail analysis, the predicting accuracy of rapeseed oil and peanut oil were 100% and 90%.It has certain feasibility for infrared spectroscopy to identify different processing technology of the similar vegetable oil with the appropriate chemometrics methods.4. Selection of the efficient wavelength regions in FT-IR spectroscopy was used for determination of stearic acid content in vegetable oil.In order to improve the precision and robustness of the stearic acid model, interval partial least-squares(iPLS) and genetic algorithm partial least-squares(GA-PLS) were applied to selecting the efficient spectral regions.Both models were able to produce better prediction models in relation to the full-spectrum model,and the models were simple and easier.Experimental results showed that the performance of GA-PLS model was better than iPLS model,and the optimal model was achieved with correlation coefficient R2=0.8260 in prediction set.