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Standardization of near infrared spectroscopies via sample spectral correlation equalization
Analytica Chimica Acta ( IF 6.2 ) Pub Date : 2023-03-07 , DOI: 10.1016/j.aca.2023.341031
Bai Xue, Glenn Cloud, Sergey Vishnyakov, Zubin Mehta, Evan Ramer, Feng Jin, Meiping Song, Chein-I Chang

A novel method for near-infrared (NIR) spectroscopy spectra standardization is presented. NIR spectroscopies have been widely used in analytical chemistry, and many methods have been developed for NIR spectra standardization. To establish a robust standardization transformation, most existing methods require spectral data sets from both primal and secondary instruments for 1-1 correspondence validation. However, this limits the usage of standardization methods. This paper investigates an interesting issue, “Can spectra data in sets be arbitrarily order?” and further develops a completely different approach from existing methods in view of statistical signal processing. The key idea is to first compensate for the distortion along the wavelength and intensity of the spectra, and then transfer the second order statistic (2OS) from the primal spectra to the secondary spectra via data sphering and an inverse sphering transform so that the 2OS can be estimated regardless of the sample statistic order. To further demonstrate how the developed method can extend the usage of the NIR spectra standardization, several application-driven experiments on classification and regression are conducted for demonstration, and a comparison to the piecewise direct standardization (PDS) is also studied.



中文翻译:

通过样品光谱相关均衡化对近红外光谱进行标准化

提出了一种用于近红外 (NIR) 光谱标准化的新方法。NIR 光谱学已广泛应用于分析化学,并且已开发出许多用于 NIR 光谱标准化的方法。为了建立稳健的标准化转换,大多数现有方法都需要来自原始仪器和二次仪器的光谱数据集以进行 1-1 对应验证。然而,这限制了标准化方法的使用。本文研究了一个有趣的问题,“集合中的光谱数据可以任意排序吗?” 并在统计信号处理方面进一步开发了一种与现有方法完全不同的方法。关键思想是首先补偿沿光谱波长和强度的失真,然后通过数据球化和逆球化变换将二阶统计量 (2OS) 从原始光谱转移到二次光谱,这样无论样本统计顺序如何,都可以估计 2OS。为了进一步证明所开发的方法如何扩展 NIR 光谱标准化的使用,进行了几个应用驱动的分类和回归实验以进行证明,并且还研究了与分段直接标准化 (PDS) 的比较。

更新日期:2023-03-07
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