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Título
Predicting the physicochemical properties and geographical ORIGIN of lentils using near infrared spectroscopy
Autor(es)
Palabras clave
Lentil
Physicochemical properties
Calibration
Protected geographical indication
NIRS
Discriminant analysis
Food analysis
Food composition
Lenteja
Propiedades fisioquímicas
Calibración
Indicación geográfica protegida
Análisis discriminatorio
Análisis de los alimentos
Composición de los alimentos
Clasificación UNESCO
2302.90 Bioquímica de Alimentos
3309 Tecnología de Los Alimentos
Fecha de publicación
2019
Editor
Elsevier
Citación
Revilla, I., Lastras, C., González-Martín, M. I., Vivar-Quintana, A. M., Morales-Corts, R., Gómez-Sánchez, M. A., & Pérez-Sánchez, R. (2019). Predicting the physicochemical properties and geographical ORIGIN of lentils using near infrared spectroscopy. Journal of Food Composition and Analysis, 77, 84–90. https://doi.org/10.1016/j.jfca.2019.01.012
Resumen
[EN] Calibration statistical descriptors for both whole and ground lentils using Near Infrared Spectroscopy (NIRS), combined with fiber-optic probe, are presented and discussed. The models were developed for estimating the weight, size, total raw protein, moisture, total fat, total fiber, and ash. Standard methods were used to determine compositional parameters of 42 samples of different varieties of lentils. The calibration curves show a wide range of validity for all parameters. The results showed excellent predictability for the determination of weight, fiber, and ash in whole lentils. However, size, moisture, and total fat were predicted satisfactorily in ground lentils. The total protein content could be predicted for both whole and ground lentils. Moreover, NIRS and Direct Partial Least Squares (DPLS) were used to determine whether a sample of lentils belonged to the Protected Geographical Indication (PGI) “Lenteja de La Armuña” or not. The results showed that 95% of the samples were correctly classified as belonging to a PGI. This result demonstrates that this technique allows the differentiation of samples from nearby regions.
URI
ISSN
0889-1575
DOI
10.1016/j.jfca.2019.01.012
Versión del editor
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- GAPEC. Artículos [71]












