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Manganese (Mn) is an essential microelement in cottonseeds, which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures. Therefore a rapid, low-cost, and reagent-free analytical way is demanded to substitute the traditional analytical method. The Mn content in cottonseed meal was investigated by near-infrared spectroscopy (NIRS) and chemometrics techniques. Standard normal variate (SNV) combined with first derivatives (FD) was the optimal spectra pre-treatment method. Monte Carlo uninformative variable elimination (MCUVE) and successive projections algorithm method (SPA) were employed to extract the informative variables from the full NIR spectra. The linear and nonlinear calibration models for cottonseed Mn content were developed. Finally, the optimal model for cottonseed Mn content was obtained by MCUVE-SPA-LSSVM, with root mean squares error of prediction (RMSEP) of 1.994 6, coefficient of determination (R2) of 0.949 3, and the residual predictive deviation (RPD) of 4.370 5, respectively. The MCUVE-SPA-LSSVM model is accuracy enough to measure the Mn content in cottonseed meal, which can be used as an alternative way to substitute for traditional analytical method.

Determination of manganese content in cottonseed meal using near-infrared spectrometry and multivariate calibration