Classification of Peruvian Flours via NIR Spectroscopy Combined with Chemometrics

• Autores: Martínez-Julca, Milton, Nazario-Naveda, Renny, Gallozzo-Cárdenas, Moises, Rojas-Flores, Segundo, Chinchay-Espino, Hector, Alvarez-Escobedo, Amilu, Murga-Torres, Emzon
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Autónoma del Perú
• Repositorio: AUTONOMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.autonoma.edu.pe:20.500.13067/3061
• Enlace del recurso: https://hdl.handle.net/20.500.13067/3061
• Nivel de acceso: acceso abierto
• Materia: PCA; NIR spectroscopy; Peruvian flours; Chemometrics; Maca; https://purl.org/pe-repo/ocde/ford#2.07.00

Nowadays, nutritional foods have a great impact on healthy diets. In particular, maca, oatmeal, broad bean, soybean, and algarrobo are widely used in different ways in the daily diets of many people due to their nutritional components. However, many of these foods share certain physical similarities with others of lower quality, making it difficult to identify them with certainty. Few studies have been conducted to find any differences using practical techniques with minimal preparation and in short durations. In this work, Principal Component Analysis (PCA) and Near Infrared Spectroscopy (NIR) were used to classify and distinguish samples based on their chemical properties. The spectral data were pretreated to further highlight the differences among the samples determined via PCA. The results indicate that the raw spectral data of all the samples had similar patterns, and their respective PCA analysis results could not be used to differentiate them. However, pretreated data differentiated the foods in separate clusters according to score plots. The main difference was a C-O band that corresponded to a vibration mode at 4644 cm−1 associated with protein content. PCA combined with spectral analysis can be used to differentiate and classify foods using small samples through the chemical properties on their surfaces. This study contributes new knowledge toward the more precise identification of foods, even if they are combined.