作者：Xihui Bian*, Yuxia Liu, Rongling Zhang, Hao Sun, Peng Liu, Xiaoyao Tan
关键字：Adulterated grapeseed oil, Spectral analysis, Multivariate calibration, Whale optimization algorithm, Monte Carlo sampling
论文来源：期刊
具体来源：Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
发表时间：2024年

As a high-quality edible oil, grapeseed oil is often adulterated with low-price/quality

vegetable oils, such as soybean oil. A novel ensemble modeling method named as

MC-WOA-PLS is proposed for quantitative analysis of grapeseed oil adulterations

combined with near-infrared (NIR) spectroscopy. The method combines Monte Carlo

(MC) sampling and whale optimization algorithm (WOA) to build numerous partial least

squares (PLS) sub-models. A total of 80 adulterated grapeseed oil samples were

prepared by mixing grapeseed oil with soybean oil, palm oil, cottonseed oil and corn oil

with the designed mass percentages. NIR spectra of the 80 samples were measured in

a transmittance mode in the range of 12000-4000 cm-1. Parameters in MC-WOA-PLS

including the number of LVs in PLS, iteration number of WOA, whale number, iteration

number of the sub-model, and percentage of training subsets were optimized. To

validate the prediction performance of the model, root mean squared error of prediction

(RMSEP), correlation coefficient (R), residual predictive deviation (RPD) and standard

deviation (S.D.) were used. Compared with PLS and WOA-PLS, MC-WOA-PLS can

achieve the best prediction accuracy and stability for quantification of the five pure oils

in adulterated grapeseed oil samples.