应用紫外高光谱技术,通过PCA和PLS-R模型定量分析原棉蜜露含量

Mona Knoblich, Mohammad Al Ktash, F. Wackenhut, Volker Jehle, E. Ostertag, M. Brecht
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引用次数: 0

摘要

棉花被蜜露污染被认为是影响纺织品质量的重要问题之一,因为它在生产过程中会引起粘连。因此,每年数百万美元的损失归因于蜜瓜污染。这项工作介绍了使用紫外高光谱成像(225-300 nm)来表征原棉样品上的蜜露污染。作为参考样品,棉花样品浸泡在含有不同浓度的糖和蛋白质的溶液中以模拟蜂蜜露。采用主成分分析(PCA)和偏最小二乘回归(PLS-R)等多变量技术对原棉样品高光谱图像中每个像素处的蜜露量进行预测和分类。结果表明,PCA模型能够根据棉花样品的糖浓度进行区分。前两个主成分(pc)解释了近91.0%的总方差。建立PLS-R模型,验证决定系数(R2cv) = 0.91,交叉验证均方根误差(RMSECV) = 0.036 g。该PLS-R模型能够预测原棉样品中每个像素的蜜露含量(以克为单位)。综上所述,紫外高光谱成像与多变量数据分析相结合,在纺织品质量控制方面具有很大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying UV Hyperspectral Imaging for the Quantification of Honeydew Content on Raw Cotton via PCA and PLS-R Models
Cotton contamination by honeydew is considered one of the significant problems for quality in textiles as it causes stickiness during manufacturing. Therefore, millions of dollars in losses are attributed to honeydew contamination each year. This work presents the use of UV hyperspectral imaging (225–300 nm) to characterize honeydew contamination on raw cotton samples. As reference samples, cotton samples were soaked in solutions containing sugar and proteins at different concentrations to mimic honeydew. Multivariate techniques such as a principal component analysis (PCA) and partial least squares regression (PLS-R) were used to predict and classify the amount of honeydew at each pixel of a hyperspectral image of raw cotton samples. The results show that the PCA model was able to differentiate cotton samples based on their sugar concentrations. The first two principal components (PCs) explain nearly 91.0% of the total variance. A PLS-R model was built, showing a performance with a coefficient of determination for the validation (R2cv) = 0.91 and root mean square error of cross-validation (RMSECV) = 0.036 g. This PLS-R model was able to predict the honeydew content in grams on raw cotton samples for each pixel. In conclusion, UV hyperspectral imaging, in combination with multivariate data analysis, shows high potential for quality control in textiles.
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