A systematic calibration transfer and quantification method based on principal components extreme learning machine for near-infrared spectroscopy

文献类型： 外文期刊

第一作者： Fan, Xiaqiong

作者： Fan, Xiaqiong;Li, Xuefeng;Yang, Tiejun;Fan, Xiaqiong;Gao, Lingling;Lv, Jingjing;Li, Bo;Xu, Kejing;Shao, Yuwen;Chen, Xiaolong;Ji, Hongchao

作者机构：

关键词： Calibration transfer; Near-infrared; Extreme learning machine

期刊名称：ANALYTICA CHIMICA ACTA （ 影响因子：6.0； 五年影响因子：5.7 ）

ISSN： 0003-2670

年卷期： 2025 年 1361 卷

页码：

收录情况： SCI

摘要： Background: Near-infrared spectroscopy (NIR) is widely used in online monitoring and quality control, due to its fast and non-destructive characteristics. Successful NIR analysis often requires accurate calibration models, relating spectral data to sample properties of interest. However, the same sample has different spectral responses in different instruments, preventing quantitative model established on high-quality instruments being directly applied across instruments. Calibration transfer address the above problem and replace the time-consuming and labor-intensive recalibration process. Through calibration transfer, spectra obtained from different instruments can be predicted using established calibration models with ideal performance. Results: This study proposes a systematic calibration transfer method, combing principal component analysis and extreme learning machine, followed by an ELM based quantitative calibration model (PCELM-ELM). Three NIR benchmarking datasets of corn, tobacco and pharmaceutical tablets were used to test the reliability of proposed method. Comparing with competitive methods, PCELM-ELM method demonstrated state-of-the-art transfer capabilities and quantitative capabilities, and it achieved generally smaller root mean square errors of prediction (RMSEPs) than that of other methods. The introduction of partial least squares principal components significantly improves the performance of calibration transfer. The visualized variable importance and the weight of principal components in partial least squares explained the good transfer capabilities of PCELM. Thousands of modeling results with random parameters also demonstrate the robustness of the PCELM method. Significance: The comprehensive results guarantee that PCELM-ELM is an accurate and practical method to transfer the NIR spectra of the slave instrument toward a well-established and maintained calibration model without costly and time-consuming recalibration. Results across diverse datasets confirm PCELM-ELM is a promising calibration transfer and quantitative method in NIR application.

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