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Raman spectroscopy is a promising technique used for noninvasive analysis ofsamples in various fields of application due to its ability for fingerprintprobing of samples at the molecular level. Chemometrics methods are widely usednowadays for better understanding of the recorded spectral fingerprints ofsamples and differences in their chemical composition. This review considers anumber of manuscripts published in the Spectrochimica Acta, Part A: Molecularand Biomolecular Spectroscopy Journal that presented findings regarding theapplication of Raman spectroscopy in combination with chemometrics to studysamples and their changes caused by different factors. In 57 reviewedmanuscripts, we analyzed application of chemometrics algorithms, statisticalmodeling parameters, utilization of cross validation, sample sizes, as well asthe performance of the proposed classification and regression model. Wesummarized the best strategies for creating classification models andhighlighted some common drawbacks when it comes to the application ofchemometrics techniques. According to our estimations, about 70% of the papersare likely to contain unsupported or invalid data due to insufficientdescription of the utilized methods or drawbacks of the proposed classificationmodels. These drawbacks include: (1) insufficient experimental sample size forclassification/regression to achieve significant and reliable results, (2) lackof cross validation (or a test set) for verification of theclassifier/regression performance, (3) incorrect division of the spectral datainto the training and the test/validation sets; (4) improper selection of thePC number to reduce the analyzed spectral data dimension.

Combination of Raman spectroscopy and chemometrics: A review of recent  studies published in the Spectrochimica Acta, Part A: Molecular and  Biomolecular Spectroscopy Journal