棉籽仁Mn含量的近红外测定及其多变量模拟模型

[背景 ]锰(Mn)是棉籽中必不可少的微量元素，其测定方法通常要使用有害试剂，也需要复杂的预处理工艺。因此，需要一种低成本、不使用试剂的快速分析方法来替代。

[结果 ]采用近红外光谱(Near-infrared spectroscopy, NIRS)和化学计量学方法测定了棉籽仁中锰的含量。标准正态变量(Standard normal variate, SNV)与一阶导数(First derivatives, FD)相结合是最佳光谱预处理方法。通过采用蒙特卡罗无信息变量消除法(Monte Carlo uninformative variable elimination, MCUVE)和连续投影算法(Successive projections algorithm method, SPA)从NIR光谱中提取信息变量，分别建立了棉籽锰含量的线性和非线性校准模型。利用MCUVE-SPA-LSSVM最终建立了棉籽锰含量的最优模型，预测均方根误差(Root mean squares error of prediction, RMSEP)为1.994 6，决定系数(R2)为0.949 3，剩余预测偏差(Residual predictive deviation, RPD)为4.370 5。

[结论] MCUVE-SPA-LSSVM模型对棉籽仁中锰含量的测定具有较高的准确性，可以替代传统的分析方法。

[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 (R2) 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.

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

[Authors] YU En, ZHAO Rubing, CAI Yunfei, HUANG Jieqiong, LI Cheng, LI Cong, MEI Lei, BAO Lisheng, CHEN Jinhong, ZHU Shuijin

Journal of Cotton Research. 2019, 2: 12

https://doi.org/10.1186/s42397-019-0030-5