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

Abstract

Background: 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. Results: 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 (R 2 ) of 0.949 3, and the residual predictive deviation (RPD) of 4.370 5, respectively. Conclusions: 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.

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy

“…This constraint reduces our ability to fully understand the drivers of phenotypic diversity, as well as to identify new and ecologically meaningful axes of phenotypic variation. In this perspective, NIRS allows us to detect a large variety of commonly measured chemical compounds such as phosphorus (P)—a key element of the leaf economics spectrum— and base cations [calcium (Ca), potassium (K), and magnesium (Mg)], and other micronutrients ( Cozzolino et al, 2001 ; Ortiz-Monasterio et al, 2007 ; Galvez-Sola et al, 2015 ; Ercioglu et al, 2018 ; de Oliveira et al, 2019 ; Yu et al, 2019 ; Prananto et al, 2021 ). This opens new avenues to link resource-use strategies with plant elemental composition, fluxes, stoichiometry, and beyond, with nutrient cycling in ecosystems ( Ustin et al, 2004 ).…”

The supply of macro- and microelements to cotton plants at different distances from the pipes of fertilizer production factory

“…Manganese is an essential microelement for cotton (Yu, et al, 2019). Foliar exposure of Mn nanoparticles can improved Mn contents in shoot and grain (Dimkpa et al, 2018).…”