Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods

The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction (R-p(2)) was 0.9504 and the root mean square error of prediction (RMSEp) was 0.8181. For the stearic acid, R-p(2) and RMSEp were 0.9636 and 0.2965. In the prediction of arachidic acid, R-p(2) and RMSEp were 0.9576 and 0.0577. In the prediction of behenic acid, the R-p(2) and RMSEp were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils.

Automated summary

This study aimed to establish a rapid determination method for quality detection of edible oils based on quantitatively analyzing palmitic acid, stearic acid, arachidic acid, and behenic acid. The support vector machine model with standard normal variate pretreatment showed the best predictive performance for the four fatty acids. Effective wavelengths selected by the successive projections algorithm were useful for establishing simplified prediction models, proving that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils.