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Ultraviolet (UV) hyperspectral imaging shows significant promise for the classification and quality assessment of raw cotton, a key material in the textile industry. This study evaluates the efficacy of UV hyperspectral imaging (225–408 nm) using two different light sources: xenon arc (XBO) and deuterium lamps, in comparison to NIR hyperspectral imaging. The aim is to determine which light source provides better differentiation between cotton types in UV hyperspectral imaging, as each interacts differently with the materials, potentially affecting imaging quality and classification accuracy. Principal component analysis (PCA) and Quadratic Discriminant Analysis (QDA) were employed to differentiate between various cotton types and hemp plant. PCA for the XBO illumination revealed that the first three principal components (PCs) accounted for 94.8% of the total variance: PC1 (78.4%) and PC2 (11.6%) clustered the samples into four main groups—hemp (HP), recycled cotton (RcC), and organic cotton (OC) from the other cotton samples—while PC3 (6%) further separated RcC. When using the deuterium light source, the first three PCs explained 89.4% of the variance, effectively distinguishing sample types such as HP, RcC, and OC from the remaining samples, with PC3 clearly separating RcC. When combining the PCA scores with QDA, the classification accuracy reached 76.1% for the XBO light source and 85.1% for the deuterium light source. Furthermore, a deep learning technique called a fully connected neural network for classification was applied. The classification accuracy for the XBO and deuterium light sources reached 83.6% and 90.1%, respectively. The results highlight the ability of this method to differentiate conventional and organic cotton, as well as hemp, and to identify distinct types of recycled cotton, suggesting varying recycling processes and possible common origins with raw cotton. These findings underscore the potential of UV hyperspectral imaging, coupled with chemometric models, as a powerful tool for enhancing cotton classification accuracy in the textile industry. [ABSTRACT FROM AUTHOR]

• Aims to classify HCV infected and healthy sera based on ATR-FTIR spectroscopy. • Detection of HCV infection and assessment of non-cirrhotic/cirrhotic status of patient. • PCA-LDA, PCA-QDA and SVM based classification was employed for sample separation. • SVM showed better results based on confusion matrix for internal and external validation. • Study showed promising results for diagnosing patients prior to choose appropriate treatment regime. Conventional techniques to diagnose (HCV) and assess non-cirrhotic/cirrhotic status of the patient for appropriate treatment regime are expensive and invasive. Present available diagnostic tests are expensive as they include multiple screening steps. Therefore, there is a need of cost-effective, less time consuming and minimally invasive alternative diagnostic approaches can be used for effective screening. We propose that (ATR-FTIR) in conjunction with (PCA-LDA),(PCA-QDA) and (SVM) multivariate algorithms can be used as a sensitive tool for detection of HCV infection and to assess non-cirrhotic/cirrhotic status of patients. We used 105 sera samples, of which, 55 were from healthy and 50 were from HCV positive individuals. These 50 HCV positive patients were further classified into cirrhotic and non-cirrhotic categories using serum markers and imaging techniques. These samples were freeze dried prior to spectral acquisition then multivariate data classification algorithms were employed to classify these sample types. PCA-LDA and SVM model computed the diagnostic accuracy of 100% for detection of HCV infection. To further classify the non-cirrhotic/cirrhotic status of a patient, diagnostic accuracy of 90.91% for PCA-QDA and 100% for SVM was observed. Internal and external validation for SVM based classifications observed 100% sensitivity and specificity. The confusion matrix generated by PCA-LDA model computed the validation and calibration accuracy showed 100% sensitivity and specificity, by using 2 PCs for HCV infected and healthy individuals. However, when the PCA QDA analysis was done to classify the non-cirrhotic sera samples from cirrhotic sera samples the diagnostic accuracy achieved was 90.91% based on 7 PC's. SVM was also employed for classification and developed model showed the best results with 100% sensitivity and specificity when external validation was applied. This study provides an initial insight that ATR-FTIR spectroscopy in conjugation with multivariate data classification tools holds a potentialnot onlytoeffectively diagnosis HCV infection but also to assess non-cirrhotic/cirrhotic status of patients. [Display omitted] [ABSTRACT FROM AUTHOR]

• 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]