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Quantitative Analysis of Adulterations in Oat Flour by FT-NIR Spectroscopy Incomplete Unbalanced Randomized Block Design and Partial Least Squares

机译:FT-NIR光谱不完全不平衡随机区组设计和偏最小二乘定量分析燕麦粉中的掺假

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摘要

This paper developed a rapid and nondestructive method for quantitative analysis of a cheaper adulterant (wheat flour) in oat flour by NIR spectroscopy and chemometrics. Reflectance FT-NIR spectra in the range of 4000 to 12000 cm−1 of 300 oat flour objects adulterated with wheat flour were measured. The doping levels of wheat flour ranged from 5% to 50% (w/w). To ensure the generalization performance of the method, both the oat and the wheat flour samples were collected from different producing areas and an incomplete unbalanced randomized block (IURB) design was performed to include the significant variations that may be encountered in future samples. Partial least squares regression (PLSR) was used to develop calibration models for predicting the levels of wheat flour. Different preprocessing methods including smoothing, taking second-order derivative (D2), and standard normal variate (SNV) transformation were investigated to improve the model accuracy of PLS. The root mean squared error of Monte Carlo cross-validation (RMSEMCCV) and root mean squared error of prediction (RMSEP) were 1.921 and 1.975 (%, w/w) by D2-PLS, respectively. The results indicate that NIR and chemometrics can provide a rapid method for quantitative analysis of wheat flour in oat flour.
机译:本文开发了一种快速,无损的方法,用于通过近红外光谱和化学计量学定量分析燕麦粉中较便宜的掺假品(小麦粉)。测量了掺假小麦粉的300个燕麦粉对象在4000至12000 cm -1 范围内的FT-NIR反射光谱。小麦粉的掺杂水平为5%至50%(w / w)。为确保该方法的通用性,燕麦和小麦粉样品均从不同产地收集,并进行了不完全不平衡随机区组(IURB)设计,以包括未来样品中可能遇到的重大差异。偏最小二乘回归(PLSR)用于建立校准模型以预测小麦粉的水平。研究了包括平滑,采用二阶导数(D2)和标准正态变量(SNV)变换在内的各种预处理方法,以提高PLS的模型准确性。 D2-PLS的蒙特卡罗交叉验证的均方根误差(RMSEMCCV)和预测的均方根误差(RMSEP)分别为1.921和1.975(%,w / w)。结果表明,近红外光谱和化学计量学可以为定量分析燕麦粉中的小麦粉提供快速的方法。

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