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A robust method for multiplicative scatter correction (MSC) in infrared spectroscopy is presented. Using quantile regression, the outlier wavelengths (concentration‐dependent wavelengths) that are irrelevant to the regression are identified and therefore excluded from the regression model. This new MCS method, which could be implemented in its simple or extended form, is much simpler than the recently proposed methods and has only one hyperparameter (the quantile value) to be adjusted. To achieve this, a scoring function based on residual analysis can automatically determine the correct quantile value. The method is first explained using simulation data sets and then its validation is explained by analysing some experimental data sets. It was found that our new method can perform well in the presence of strong outlying variables. On the other hand, when the data sets are not associated outlying wavelengths, this method behaves similarly to the conventional MSC method. [ABSTRACT FROM AUTHOR]

1, 2-Ethandiol (ethylene glycol, EG) and its mixtures with water (W) have widespread industrial and biological applications due to their special physiochemical properties. In this study, two-dimensional correlation spectroscopy and chemometrics analysis of infrared spectra are used to characterize structural features of EG-W binary solutions. Three distinctive regions are observed with diverse contributions of hydrophobic backbone (CH2 groups) and hydrophilic (OH functional) groups. Both groups are simultaneously considered to characterize local structure in each triple region. The aqueous solutions of EG are changed from EG-like (xw ≤ 0.27) to W-like solution (xw > 0.83), where xw is W mole fraction. The wide range of EG-W mixtures (0.27 < xw ≤ 0.83) are stabilized by -OH ... O (proper hydrogen bonded) and - CH ... O (improper hydrogen bonded). Also, trend and relative equilibrium concentration variation of W, EG, and identified cluster are used to propose chemical model of EG-W interaction. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) is a versatile and well‐established approach to explore the relationship between observations represented by their descriptor variables. The latter relationship can be visualized by score plots, ie, the representation of the observations in low dimensional space. The important hyperparameter of PCA is the choice of the correct dimensionality for representing the observations since it bears information about the number of varying analytes contributing to the studied signals. Therefore, the latter dimension is also called the chemical rank of the system. In this paper, a new method for exploring the dependency between principal components of an evolutionary process is proposed. The dependency was quantitatively characterized by two measures: maximum information coefficient (MIC) and distance correlation (DC). Since the dependency measures could discriminate between chemical and nonchemical factors, this concept was used for rank determination. The performance of MIC‐derived indices in terms of detecting meaningful chemical information was better than the one based on indices derived from DC. The performance of each dependency measure in rank estimation was investigated in several simulations and with various real data sets. From the accuracy results and computation time, the proposed methods were compared with previously published rank determination methods. The results showed that MIC could provide accurate estimation of chemical rank in the reasonable timescale rather than DC and also the published rank estimation methods, in the most situations. ‐ Maximum information coefficient (MIC) and distance correlation (DC) were used to detect mutual dependencies between PCs in data matrices obtained from evolutionary chemical processes.‐ MIC and DC are good indicators to discriminate between primary and secondary PCs, which successfully used to rank determination.‐ Two new indices based on MIC and DC performed best in the benchmark to predict the correct chemical rank in case of ill‐conditioned and real‐world data sets regardless of the structure of the given data sets. [ABSTRACT FROM AUTHOR]