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• NIR spectroscopy has been used for sexual identification of many biological materials. • By using the papaya seeds a F-score of 0.81 was obtained employing PCA-QDA. • By using the papaya leaves a F-score of 0.79 was obtained applying PCA-LDA. • It is possible to use NIR spectroscopy to determine the sex types in papaya trees. Papaya trees (Carica papaya L.) can bear female, hermaphrodite, and male flowers. However, only the hermaphrodite type produces elongate fruit required and appreciated by the consumer market. Sex type determination is carried out based on the plant phenotype after planting the seedlings, increasing the production costs. In this regard, the objective of this study was to verify the feasibility of using near infrared spectroscopy (NIR) as a non-destructive method for sexing papaya trees. The NIR spectra were collected using seeds and leaves of the respective seedlings of the cultivars 'T2′, 'Formosa', and 'Calimosa' (Formosa group), and 'THB' and 'Ouro' (Solo group). By using the seeds, it was possible to obtain a F-score value of 0.81 for the external validation set applying the principal component analysis and quadratic discriminant analysis (PCA-QDA). By using the leaves, the F-score values was slightly lower (0.79) applying PCA and linear discriminant analysis (PCA-LDA). It was possible to use NIR spectroscopy associated with multivariate techniques as a non-destructive method to determine the sex types in papaya trees using both seeds and leaves of the seedlings. [ABSTRACT FROM AUTHOR]

Raman spectroscopy (RS) is widely used as a non-invasive technique in screening for the diagnosis of oral cancer. The potential of this optical technique for several biomedical applications has been proved. This work studies the efficacy of RS in detecting oral cancer using sub-site-wise differentiation. A total of 80 samples (44 tumor and 36 normal) were cryopreserved from three different sub-sites: The tongue, the buccal mucosa, and the gingiva of the oral mucosa during surgery. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The normal and tumor tissues were differentiated under the PCA-LDA model with an accuracy of 81.25% (sensitivity: 77.27%, specificity: 86.11%). The PCA-QDA classifier model differentiated these tissues with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The PCA-QDA classifier model outperformed the PCA-LDA-based classifier. The model studies revealed that protein, amino acid, and beta-carotene variations are the main biomolecular difference markers for detecting oral cancer. [ABSTRACT FROM AUTHOR]