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| An Evaluation Method of Quantitative Analysis Software for Near-Infrared Spectroscopy |
| LI Rong1, 2, HAO Lu4, YUAN Hong-fu4, HE Gui-mei1, 2, DENG Tian-long1, DU Biao4, 5, GONG Li4, YUE Xin2, 3* |
1. College of Marine and Environmental Sciences, Tianjin University of Science ＆ Technology, Tianjin 300457, China
2. Research Institute for Environmental Innovation (Binhai, Tianjin), Tianjin 300457, China
3. Chinese Research Academy of Environmental Sciences, Beijing 100012, China
4. Beijing Yixingyuan Petrochemical Technology Co., Ltd., Beijing 101300, China
5. China University of Petroleum(Beijing), Beijing 102249, China
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Abstract The commonly used evaluation indexes of multivariate models lack the ability to evaluate many important predictive performance indicators of near-infrared quantitative analysis software. This has become a pain point in evaluating the predictive performance of near-infrared instrument selection and the applicability of models in practical near-infrared analysis applications. Therefore, this study aims to develop an evaluation method for the predictive performance of near-infrared quantitative analysis software. 192 national VI gasoline samples, including 92#, 95#, and 98#, were collected for determination of olefin concentration of gasoline using near-infrared spectroscopy; their near-infrared spectra collected and olefin concentrations were measured as a reference value according to GB/T 30519—2014, and two different multivariate software(one is partial least squares (PLS) modeling software, and the other is non-PLS software) were used to study. It has been found that compared to the reference value, the PLS model has a positive bias in predicting low-concentration samples and a negative bias in predicting high-concentration samples, which is known as the phenomenon of “averaging”. The commonly used performance evaluation indicators for model prediction cannot yet evaluate the degree of the averaging, nor can evaluate (1) the proportion of samples with deviation from the reference value greater than the limit value for the reproducibility of the reference method and (2) the model's generalization ability. In this paper, four new evaluation indicators are proposed to address the above issues, including Averaging Index (AE), Ratio of samples with prediction bias exceeding the limit value (Ratio), Deviation of Abnormal Sample (DAS), and Deviation of Isolated Sample (DIS). The comprehensive use of commonly used evaluation indicators and new ones (12 items) has practical significance in evaluating the predictive performance of near-infrared quantitative analysis software for instrument selection and the applicability of models in practical near-infrared analysis applications. It also has reference significance for academic research in near-infrared analysis.
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Received: 2024-02-02
Accepted: 2024-05-11
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Corresponding Authors:
YUE Xin
E-mail: yuexin@craes.org.cn
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