Milk is among the most popular nutrient source worldwide, which is of great interest due to its beneficial medicinal properties. The feasibility of the classification of milk powder samples with respect to their brands and the determination of protein concentration is investigated by NIR spectroscopy along with chemometrics. Two datasets were prepared for experiment. One contains 179 samples of four brands for classification and the other contains 30 samples for quantitative analysis. Principal component analysis (PCA) was used for exploratory analysis. Based on an effective model-independent variable selection method, i.e., minimal-redundancy maximal-relevance (MRMR), only 18 variables were selected to construct a partial least-square discriminant analysis (PLS-DA) model. On the test set, the PLS-DA model based on the selected variable set was compared with the full-spectrum PLS-DA model, both of which achieved 100% accuracy. In quantitative analysis, the partial least-square regression (PLSR) model constructed by the selected subset of 260 variables outperforms significantly the full-spectrum model. It seems that the combination of NIR spectroscopy, MRMR and PLS-DA or PLSR is a powerful tool for classifying different brands of milk and determining the protein content.
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http://dx.doi.org/10.1016/j.saa.2017.08.034 | DOI Listing |
RSC Adv
January 2025
Département de Chimie, Faculté des Sciences et de Génie, Université Laval Québec QC G1V 0A6 Canada.
Blood carries some of the most valuable biomarkers for disease screening as it interacts with various tissues and organs in the body. Human blood serum is a reservoir of high molecular weight fraction (HMWF) and low molecular weight fraction (LMWF) proteins. The LMWF proteins are considered disease marker proteins and are often suppressed by HMWF proteins during analysis.
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January 2025
Programa de Pós-Graduação em Ciências Farmacêuticas, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil.
Brazilian stingless bee species produce honey with distinct physicochemical and bioactive properties shaped by environmental factors. This study investigated the effects of the rainy and dry seasons on the physicochemical characteristics, chemical fingerprinting, mineral content, and antioxidant capacity of honey from and . The honey samples were analyzed for their phytochemical properties (official methods), total phenolics (Folin-Ciocalteu method), flavonoid content (aluminum complex formation method), antioxidant capacity (FRAP and ABTS assays), and antioxidant activity (erythrocyte model).
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January 2025
College of Biosystems Engineering and Food Science, Key Laboratory of Agro-Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou 310058, China.
Volatile organic compounds (VOCs) are closely associated with the maturity and variety of strawberries. However, the complexity of VOCs hinders their potential application in strawberry classification. This study developed a novel classification workflow using strawberry VOC profiles and machine learning (ML) models for precise fruit classification.
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January 2025
Laboratory of Viticulture, School of Agriculture, Faculty of Agriculture Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
A non-targeted analytical approach using gas chromatography-mass spectrometry (GC-MS) is proposed for the analysis of the free and bound volatile fractions of three emblematic indigenous Greek white winegrape varieties belonging to Vidiano, Malagousia, and Savvatiano and establish volatile varietal markers using multivariate chemometrics. A total of 89 free and 103 bound volatile compounds were identified, categorized into alcohols, aldehydes, esters, acids, terpenes, norisoprenoids, C6 compounds, phenols, and ketones. A robust Partial Least Squares Discriminant Analysis (PLS-DA) prediction model was developed and validated, and successfully classified the grape samples according to the variety with 100 % accuracy, demonstrating the potential of volatile profiling as a non-targeted fingerprinting approach for varietal discrimination.
View Article and Find Full Text PDFSpectrochim Acta A Mol Biomol Spectrosc
January 2025
Centre for Radiation and Environmental Science, FOCAS Research Institute, Technological University Dublin, City Campus, Dublin, Ireland; School of Physics, Clinical and Optometric Sciences, Technological University Dublin, City Campus, Dublin, Ireland.
The gold standard method of diagnosis of oral leukoplakia (OLK) is a tissue biopsy followed by histological examination. Raman spectroscopic studies of cytological brush biopsy and saliva samples have previously been shown to differentiate low (no and mild dysplasia) and high risk (moderate and severe dysplasia) OLKs, discriminant models of cellular samples achieving higher specificity, whereas those based on saliva samples achieved higher sensitivity. The current study combines the spectral data sets of cell and saliva samples in an attempt to improve the overall efficiency of the discriminating models.
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