用于烟草成分多元定量分析的近红外和中红外光谱特征信息融合策略

IF 3.7 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-08-23 DOI:10.1016/j.chemolab.2024.105222
Honghong Wang , Qiong Wu , Wuye Yang , Jie Yu , Ting Wu , Zhixin Xiong , Yiping Du
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引用次数: 0

摘要

烟叶中总烟碱、总糖、还原糖和总氮含量的测定对烟叶质量评价和配方设计具有重要意义。为了快速检测烟草中 4 种成分的含量,利用上海烟草集团有限责任公司提供的 129 个烟草粉末固体样品的近红外和中红外光谱数据,研究了两种近红外-中红外光谱融合技术,即融合技术 1 是在对每个光谱进行变量选择后,通过融合特征变量建立模型。融合技术 2 是先融合近红外-红外光谱数据,然后选择变量建立模型。两种融合技术都使用了连续预测算法(SPA)、竞争性自适应加权采样(CARS)、后向区间PLS(biPLS)、前向区间PLS(fiPLS)、协同区间PLS(siPLS)和区间交互移动窗偏最小二乘法(iMWPLS)算法来筛选波长变量。结果表明,对于总尼古丁和总糖,融合技术方法 2 结合 iMWPLS 算法建立的 PLSR 模型效果最好,与全光谱融合方法相比,其 RMSEP 分别从 0.2314 到 1.3225 下降到 0.0821 和 0.8079,优于单一的近红外和中红外模型以及近红外-中红外融合技术 1。对于还原糖,简单的全谱融合模型的分析能力最强,RMSEP 最低,优于单一的近红外-中红外模型和所有由两种光谱融合技术结合六种波长选择算法建立的模型。对于总氮,融合技术 1 结合 iMWPLS 算法模型的预测效果较单一近红外和中红外模型以及近红外-中红外融合技术 2 有显著提高,其 RMSEP 为 0.0634。结果表明,两种近红外-近红外光谱融合技术充分利用了近红外光谱和近红外光谱提供的互补信息,成功地应用于烟草中总烟碱、总糖、还原糖和总氮含量的快速检测,为烟草成分的快速检测提供了一种新的方法和思路。
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NIR and MIR spectral feature information fusion strategy for multivariate quantitative analysis of tobacco components

The determination of total nicotine, total sugar, reducing sugar and total nitrogen contents in tobacco is of great significance to tobacco quality evaluation and formulation design. To quickly detect the content of 4 components of tobacco, using near-infrared (NIR) and mid-infrared (MIR) spectral data from 129 solid samples of tobacco powder provided by Shanghai Tobacco Group Co., Ltd., Two NIR-MIR spectral fusion techniques are studied, that is, fusion technology 1 is to establish a model by fusing feature variables after variable selection of each spectrum. The fusion technology 2 is to first fuse the NIR-MIR spectral data and then select the variables to establish the model. Both fusion technologies use successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), backward interval PLS (biPLS), forward interval PLS (fiPLS), synergy interval PLS (siPLS), and interval interaction moving window partial least squares (iMWPLS) algorithms to filter wavelength variables. The results showed that for total nicotine and total sugar, the PLSR model established by fusion technology method 2 combined with iMWPLS algorithm is the best, and its RMSEP decreases from 0.2314 to 1.3225 to 0.0821 and 0.8079 respectively compared with the full spectrum fusion method, which is superior to the single NIR and MIR models and NIR-MIR fusion technology 1. For reducing sugars, the simple full-spectrum fusion model has the best analytical ability and the lowest RMSEP, which is superior to the single NIR-MIR models and all models established by two spectral fusion techniques combined with six wavelength selection algorithms. For total nitrogen, the prediction effect of fusion technology 1 combined with iMWPLS algorithm model was significantly improved compared with single NIR and MIR models and NIR-MIR fusion technology 2, and its RMSEP was 0.0634. The results showed that the two NIR-MIR spectral fusion techniques made full use of the complementary information provided by NIR and MIR spectroscopy, and successfully applied them to the rapid detection of total nicotine, total sugar, reducing sugar and total nitrogen content in tobacco, which provided a new method and idea for the rapid detection of tobacco components.

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来源期刊
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.
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