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Changes on an organism by the exposure to environmental stressors may be characterized by hyperspectral images (HSI), which preserve the morphology of biological samples, and suitable chemometric tools. The approach proposed allows assessing and interpreting the effect of contaminant exposure on heterogeneous biological samples monitored by HSI at specific tissue levels. In this work, the model example used consists of the study of the effect of the exposure of chlorpyrifos-oxon on zebrafish tissues. To assess this effect, unmixing of the biological sample images followed by tissue-specific classification models based on the unmixed spectral signatures is proposed. Unmixing and classification are performed by multivariate curve resolution-alternating least squares (MCR-ALS) and partial least squares-discriminant analysis (PLS-DA), respectively. Crucial aspects of the approach are: (1) the simultaneous MCR-ALS analysis of all images from 1 population to take into account biological variability and provide reliable tissue spectral signatures, and (2) the use of resolved spectral signatures from control and exposed populations obtained from resampling of pixel subsets analyzed by MCR-ALS multiset analysis as information for the tissue-specific PLS-DA classification models. Classification results diagnose the presence of a significant effect and identify the spectral regions at a tissue level responsible for the biological change.

Raman images were used to study the effect of the contaminant chlorpyriphos‐oxon on zebrafish eye samples. Multivariate Curve Resolution‐Alternating Least Squares (MCR‐ALS) was used to obtain the distribution maps and spectral signatures of biological components present in the images analyzed. The use of MCRALS spectral signatures as starting information for Partial Least Squares‐Discriminant Analysis allowed statistical assessment of the effect of the contaminant at a specific tissue level. Further details can be found in the article by Víctor Olmos et al. (https://doi.org/10.1002/jbio.201700089).

The study of pollutant effects on living organisms provides information about the possible biological and environmental response to a contaminant. Progression of prostate cancer may be related to exposure to pesticides or other chemical substances. In this work, the effect of the pesticide aldrin on human prostate cancer cells (DU145) is studied using Raman spectroscopy and chemometric techniques. Prostate cancer cell line DU145 has been exposed acutely the pesticide aldrin. Individual Raman spectra coming from control and treated cell populations have been acquired. Partial least squares discriminant analysis (PLSDA) has been used to assess differences among treated and control samples and to identify spectral biomarkers associated with pollutant stress. Some preprocessing methodologies have been tested in order to improve the capability of discrimination between fingerprints. Partial least squares discriminant analysis results suggest that the best normalization-scaling preprocessing combination is provided by Euclidean normalization (EN)-SIMPLISMA-based scaling (SBS). SIMPLISMA-based scaling has been proposed as a scaling method focused on the classification objective, which enhances variables with high relative variation among samples. The most relevant spectral variables related to aldrin effect on DU145 seem to be mainly related to lipids, proteins, and variations in nucleic acids.

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