Quantitative Determination of the Components in Corn and Tobacco Samples by Using Near-Infrared Spectroscopy and Multiblock Partial Least Squares

Abstract Multiblock partial least squares (MB-PLS) are applied for determination of corn and tobacco samples by using near-infrared diffuse reflection spectroscopy. In the model, the spectra are separated into several sub-blocks along the wavenumber, and different latent variable number was used for each sub-block. Compared with ordinary PLS, the importance and the contribution of each sub-block can be balanced by super-weights and the usage of different latent variable numbers. Therefore, the prediction obtained by the MB-PLS model is superior to that of the ordinary PLS, especially for the large data sets of tobacco samples with a large number of variables. Keywords: ChemometricsMultiblockNear-infrared (NIR) spectrumPartial least squares (PLS)Quantitative analysis This study is supported by National Natural Science Foundation of China (Nos. 20775036 and 20835002). Notes *The calibration and external validation sets were determined by using KS algorithm.