Quantitative analysis of the hexamethylenetetramine concentration in a hexamethylenetetramine–acetic acid solution using near infrared spectroscopy: A comprehensive study on preprocessing methods and variable selection techniques

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-03-26 DOI:10.1177/09670335241242659

Hui Chao, Shichuan Qian, Zhi Wang, Xin Sheng, Xinping Zhao, Zhiyan Lu, Xiaoxia Li, Yinguang Xu, Shaohua Jin, Lijie Li, Kun Chen

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引用次数: 0

Abstract

Hexamethylenetetramine (HA) is widely used as a raw material in the medical, chemical, industrial, and military industries, and the fast and quantitative analysis of HA is important for manufacturing processes in these fields. Owing to its efficiency, low cost, nondestructive testing, and convenience, near infrared (NIR) spectroscopy is a powerful technique for quantitatively analyzing the HA concentration in HA–acetic acid (HAc) solutions, demonstrating application potential in the production of hexogen and octogen. A series of preprocessing algorithms and variable selection methods were studied to improve the detection accuracy of the NIR spectroscopic calibration. Forty-six different combinations of standard normal variation (SNV), multiplicative signal correction (MSC), first derivative (1stDer), second derivative (2ndDer), and discrete wavelet transform (DWT) were screened. The effects of four variable selection methods (successive projection algorithm (SPA), uninformed variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and multiverse optimization (MVO)) were compared. Finally, a model (SPXY-SNV-1stDer-DWT-MVO-RF) was developed by combining sample set portioning based on the joint x–y distance (SPXY) algorithm with the random forest (RF) calibration model, and MVO was combined with the NIR technique for the first time. The model achieved a coefficient of determination for the calibration set (R2), root mean square error of the calibration set (RMSEC), coefficient of determination for the prediction set (r2), and root mean square error of the prediction set (RMSEP) of 1.000, 0.04%, 0.999, and 0.05%, respectively. This study demonstrated the novelty and feasibility of HA quantitative detection by NIR spectroscopy and provided valuable insights for optimizing quantitative analysis models by optimizing algorithms, indicating the great application potential of NIR spectroscopy in related fields.

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利用近红外光谱定量分析六亚甲基四胺乙酸溶液中的六亚甲基四胺浓度：关于预处理方法和变量选择技术的综合研究

六亚甲基四胺（HA）作为一种原材料被广泛应用于医疗、化工、工业和军事领域，对其进行快速定量分析对这些领域的生产工艺非常重要。近红外（NIR）光谱技术具有高效、低成本、无损检测和便捷等优点，是定量分析 HA-乙酸（HAc）溶液中 HA 浓度的有力技术，在六元和八元生产中具有应用潜力。为了提高近红外光谱校准的检测精度，研究人员研究了一系列预处理算法和变量选择方法。筛选了标准正态变异（SNV）、乘法信号校正（MSC）、一阶导数（1stDer）、二阶导数（2ndDer）和离散小波变换（DWT）的 46 种不同组合。比较了四种变量选择方法（连续投影算法（SPA）、无信息变量消除（UVE）、竞争性自适应加权采样（CARS）和多元宇宙优化（MVO））的效果。最后，通过将基于联合 x-y 距离（SPXY）算法的样本集分配与随机森林（RF）校准模型相结合，建立了一个模型（SPXY-SNV-1stDer-DWT-MVO-RF），并首次将 MVO 与近红外技术相结合。该模型的定标集决定系数（R2）、定标集均方根误差（RMSEC）、预测集决定系数（r2）和预测集均方根误差（RMSEP）分别为 1.000、0.04%、0.999 和 0.05%。该研究证明了利用近红外光谱对 HA 进行定量检测的新颖性和可行性，并为通过优化算法来优化定量分析模型提供了宝贵的启示，表明近红外光谱技术在相关领域具有巨大的应用潜力。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.