Diverse local calibration approaches for chemometric predictive analysis of large near-infrared spectroscopy (NIRS) multi-product datasets

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-07-01 DOI:10.1016/j.chemolab.2024.105173

Xueping Yang , Fuyu Yang , Matthieu Lesnoff , Paolo Berzaghi , Alessandro Ferragina

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引用次数: 0

Abstract

This study aimed to assess the predictive accuracy of Near-Infrared Spectroscopy (NIRS) across a large multi-product library, employing novel local calibration methodologies. Three local strategies were examined: LOCAL Algorithm, Locally Weighted Regression predicted on k-nearest neighbor selection (kNN-LWPLSR), along with a newly proposed algorithm within this study called Hybrid Local. These strategies were applied to an extensive multi-product dataset. When compared with Global PLS models, the results exhibited significant reductions in RMSEP values for all local strategies. Particularly, the kNN-LWPLSR demonstrated proficient prediction for the constituents of ADF and DM. The newly proposed method [Hybrid Local] exhibits comparable performance to the LOCAL Algorithm; however, it notably reduces the prediction time by half compared to the latter, representing a significant advancement for the practical implementation of NIRS technology within industrial processing scenarios.

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用于大型近红外光谱（NIRS）多产品数据集化学计量预测分析的多种局部校准方法

本研究旨在采用新颖的局部校准方法，评估近红外光谱（NIRS）在大型多产品库中的预测准确性。研究考察了三种局部策略：LOCAL 算法、基于 k 近邻选择的局部加权回归预测 (kNN-LWPLSR) 以及本研究中新提出的混合局部算法。这些策略被应用于一个广泛的多产品数据集。与全局 PLS 模型相比，所有本地策略的 RMSEP 值都有显著降低。特别是，kNN-LWPLSR 对 ADF 和 DM 的成分进行了出色的预测。新提出的[混合本地]方法与 LOCAL 算法的性能相当，但与后者相比，它明显缩短了一半的预测时间，这对于在工业加工场景中实际应用近红外光谱技术来说是一个重大进步。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.