Investigation of Menopause-Induced Changes on Hair by Raman Spectroscopy and Chemometrics

Abstract

The ending of estrogen production in the ovaries after menopause results in a series of important physiologic changes, including hair texture and growth. In this study we demonstrate that Raman spectroscopy can be used successfully as a tool to probe menopause-induced changes on hair, in particular when coupled with suitable chemometrics approaches.

The detailed analysis of the average Raman spectra (in particular of the Amide I and vS-S stretching spectral regions) of the hair samples of women pre- and post-menopause allowed to estimate that absence of estrogen in post-menopause women leads to an average reduction of similar to 12% in the thickness of the hair cuticle, compared to that of pre-menopause women, and revealed the strong prevalence of disulphide bonds in the most stable gauche-gauche-gauche conformation in the hair cuticle. From the analysis of the vS-S stretching spectral region it could also be concluded that the amount of alpha-helix keratin is slightly higher for post-menopause than for pre-menopause women.

A series of statistical models were developed in order to classify the hair samples. Outperforming the traditional PCA-LDA (principal component analysis - linear discriminant analysis) approach, in the present study a GA-LDA (genetic algorithm - linear discriminant analysis) strategy was used for variable reduction/selection and samples' classification. This strategy allowed to develop of a statistical model (L16), which has exceptional prediction capability (total accuracy of 96.6%, with excellent sensitivity and selectivity) and can be used as an efficient instrument for the hair samples' classification.

In addition, a new chemometrics approach is here presented, which allows to overcome the intrinsic limitations of the GA algorithm and that can be used to develop statistical models that use GA as the variable reduction/selection method, but superseding its stochastic nature. Three suitable models for classification of the hair samples according to the menopause status of the women were developed using this novel approach (LV17, BLV20 and PLS7 models), which are based on the Fisher's and Bayers' LDA approaches and the PLS-DA method. The followed new chemometrics approach uses the results of a large set of GA-LDA runs over the full data matrix for the selection of the reduced data matrices. The criterion for the selection of the variables is their statistical significance in terms of number of occurrences as solutions of the whole set of GA-LDA runs. (C) 2022 Elsevier B.V. All rights reserved.