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目的:研究不同化学计量学方法对烟草含水率近红外分析准确度的影响。方法:比较不同预处理方法(平滑、一阶、二阶、标准正态变量(SNV)和多元散射校正(MSC)及其组合)以及不同波长筛选方法(基于水分波段、基于波长区间、基于波长点)对预测模型性能的影响。结果:仅对数据进行SNV、MSC、MSC+一阶、MSC+SNV、SNV+一阶预处理的模型能够使不同程度的相对分析误差RPD提高，而其他方法则不同程度下降；在波长筛选方法方面，使用基于波长区间的方法能够获得较好的优化效果，经过变量筛选得到594个波长，为原波长数的27.26%,且能提高0.133 6的RPD值。结论:不同的计量学方法会对烟草含水率分析准确度产生影响，对于此次数据，应采用MSC预处理方法及基于波长区间筛选方法对数据进行处理。
Abstract：Aims: This paper aims to study the impact of different chemometric methods on the accuracy of the near-infrared analysis of tobacco moisture content. Methods: The effects of different preprocessing methods(smoothing, first-order, second-order, standard normal variable[SNV], multivariate scattering correction [MSC] and their combinations) as well as different wavelength screening methods(based on moisture bands, wavelength intervals, and wavelength points) on the performance of prediction models were compared. Results: Models that only performed SNV, MSC, MSC+first-order, MSC+SNV, SNV+first-order preprocessing on the data could achieve varying degrees of RPD improvement while other methods decreased to varying degrees. In terms of wavelength screening methods, using wavelength interval-based methods could achieve good optimization results. After variable screening, 594 wavelengths were obtained, which was 27.26% of the original number of wavelengths, and could improve the RPD value by 0.133 6. Conclusions: Different econometric methods will affect the accuracy of tobacco moisture content analysis. For this data, the MSC preprocessing method and the wavelength interval screening method should be used to process the data.
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基本信息:
DOI：
中图分类号:O657.33;TS41
引用信息:
[1]俞思名,姚燕,刘颖,等.化学计量学方法选取对烟草含水率近红外分析准确度的影响[J].中国计量大学学报,2024,35(01):28-34.
基金信息:
国家自然科学基金项目(No.61673358); 河南中烟工业有限责任公司重点项目(No.A202053,RH202005)