Quantitative Analysis of Single Component Oils in Quinary Blend Oil by Near-Infrared Spectroscopy Combined With Chemometrics

The rapid and accurate quantitative analysis of blend oil is of great importance for the quality control of blend oil. However, most previous studies on the quantitative analysis of blend oil have focused on binary, ternary and quaternary blends, and few studies have been conducted on more multi-component blend oil, which is difficult to meet the needs of blend oil detection. This study explores the feasibility of near infrared spectroscopy combined with chemometrics for the quantitative analysis of the singlecomponentoil in quinary blend oil. 51 quinary blend oil samples were formulated from corn oil, soybean oil, rice oil, sunflower oil and sesame oil, and their NIR spectra were measured in a transmittance mode in the range of 12 000 similar to 4 000 cm (1). Firstly, the sample set partitioning based on joint x-y distances (SPXY) algorithm was used to divide the sample into 38 calibration and 13 prediction set samples. Secondly, the modeling effect of five multivariate calibration methods, including principal component regression (PCR), partial least squares (PLS), support vector regression (SVR), artificial neural network (ANN), and extreme learning machine (ELM), were examined for the quantitative analysis of each component in quinary blend oil. Then six spectral preprocessing methods including Savitzky Golag smoothing (SG smoothing), standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (1st Der), second derivative (2' Der), and continuous wavelet transform (CWT) were compared based on the best modeling method and the reasons for the effectiveness of the preprocessing methods were discussed. Finally, based on the optimal preprocessing method, the competitive adaptive reweighted sampling (CARS) and Monte Carlo uninformative variable elimination (MCUVE) algorithms were further used to screen the variables associated with the predicted components. The results showed that PLS was the optimal modeling method among the five modeling methods, with root mean square error of the prediction set (RMSEP) of 5. 564 4, 5. 559 2, 3. 592 6, 7. 421 8, and 4. 193 0 for the five components of corn oil, soybean oil, rice oil, sunflower oil, and sesame oil, respectively. After preprocessing-variable selection and then PLS modeling, the RMSEP for the five components were 1. 955 3, 0. 562 4, 1. 145 0, 1. 619 0 and 1. 067 1, respectively and the correlation coefficients of prediction set (R-P)were all higher than 0. 98, indicating that with appropriate spectral preprocessing, variable selection and modeling methods, the accuracy of quantitative analysis of each component in quinary blend oil was greatly improved. This research provided a reference for rapid and nondestructive quantitative detection of multi-component blend oil.