Classification before regression for improving the accuracy of glucose quantification using absorption spectroscopy.,Talanta

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Classification before regression for improving the accuracy of glucose quantification using absorption spectroscopy.
Talanta ( IF 5.6 ) Pub Date : 2020-01-13 , DOI: 10.1016/j.talanta.2020.120740
Heydar Khadem ₁ , Mohammad R Eissa ₁ , Hoda Nemat ₁ , Osamah Alrezj ₁ , Mohammed Benaissa ₁

Affiliation

This work contributes to the improvement of glucose quantification using near-infrared (NIR), mid-infrared (MIR), and combination of NIR and MIR absorbance spectroscopy by classifying the spectral data prior to the application of regression models. Both manual and automated classification are presented based on three homogeneous classes defined following the clinical definition of the glycaemic ranges (hypoglycaemia, euglycaemia, and hyperglycaemia). For the manual classification, partial least squares and principal component regressions are applied to each class separately and shown to lead to improved quantification results compared to when applying the same regression models for the whole dataset. For the automatic classification, linear discriminant analysis coupled with principal component analysis is deployed, and regressions are applied to each class separately. The results obtained are shown to outperform those of regressions for the entire dataset.

中文翻译：

回归前进行分类，以提高使用吸收光谱法进行葡萄糖定量的准确性。

这项工作通过在应用回归模型之前对光谱数据进行分类，有助于使用近红外（NIR），中红外（MIR）以及NIR和MIR吸收光谱的组合来改善葡萄糖定量。基于血糖范围的临床定义（低血糖，正常血糖和高血糖）定义的三个同类类别，提供了手动分类和自动分类。对于手动分类，将偏最小二乘和主成分回归分别应用于每个类别，并且与在整个数据集上应用相同的回归模型相比，这可以导致改进的量化结果。对于自动分类，部署了线性判别分析和主成分分析，并将回归分别应用于每个类别。对于整个数据集，显示出的结果优于回归分析的结果。

更新日期：2020-01-13

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