Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430205, China; School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2020 Dec 5;242:118718. doi: 10.1016/j.saa.2020.118718. Epub 2020 Jul 20.
Based on near-infrared spectrum and interval random forest, a fast quantitative analysis method for the content of sunset yellow content was established. The spectra of 132 cream pigment samples were obtained by FT-NIR spectrometer, and various preprocessing methods such as standard normal variable (SNV), wavelet transform (WT), and SG (Savitzky-Golay) were used to smooth and denoise the original spectrum. In this paper, WT and first-order differentiation were used as pretreatment and the Kennard-Stone algorithm was used to divide the data set. Finally interval partial least squares, partial least squares, interval random forest and random forest were used to construct an optimal quantitative analysis model. The experimental results show that the interval random forest can find the best sub-interval to achieve the prediction ability of the model. The R (the coefficient of determination) and RMSEP (root mean square error of the prediction) of the prediction set are 0.8965 and 0.2454, respectively. The research results show that near-infrared spectroscopy combined with interval random forest algorithm is a fast and non-destructive method to detect the content of sunset yellow in cream.
基于近红外光谱和区间随机森林，建立了一种快速定量分析日落黄含量的方法。采用傅里叶变换近红外光谱仪获得 132 个奶油颜料样品的光谱，采用标准正态变量（SNV）、小波变换（WT）和 SG（Savitzky-Golay）等多种预处理方法对原始光谱进行平滑和去噪。本文采用 WT 和一阶微分作为预处理，采用 Kennard-Stone 算法对数据集进行划分。最后，采用区间偏最小二乘、偏最小二乘、区间随机森林和随机森林构建最优定量分析模型。实验结果表明，区间随机森林可以找到最佳子区间，从而实现模型的预测能力。预测集的 R（决定系数）和 RMSEP（预测值的均方根误差）分别为 0.8965 和 0.2454。研究结果表明，近红外光谱结合区间随机森林算法是一种快速、无损的检测奶油中日落黄含量的方法。
