国内精品国产三级国产在线专_欧美日韩在线播放一区二区_成人xxxx视频_欧美激情视频一区_色女人综合av_久久久亚洲影院_久久久久久九九_成人免费网站在线_日韩美女毛茸茸_狠狠干一区二区

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 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, named MC-WOA-PLS. 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 latent variables (LVs) in PLS, iteration number of WOA, whale number, number of PLS sub-models, and percentage of training subsets were optimized. To validate the prediction performance of the model, root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean squared error of prediction (RMSEP), correlation coefficient (R), residual predictive deviation (RPD), and standard deviation (S.D.) were used. Compared with PLS, standard normal variate-PLS (SNV-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS), randomization test-PLS (RT-PLS), variable importance in projection-PLS (VIP-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.