Differentiation of mezcales from four agave species using FT-MIR and multivariate statistical analysis

Fourier Transform Mid-Infrared (FT-MIR) spectroscopy and multivariate statistical analysis were used to differentiate mezcales elaborated with four agave species. The FT-MIR data matrix was subjected to spectral transformations using first and second derivatives. The Partial Least Squares (PLS)-Disc...

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Detalles Bibliográficos
Autores: López Aguilar, Rosa, Hernández Núñez, Emanuel, Hernández Montes, Arturo, Zuleta Prada, Holber, Herbert Pucheta, José Enrique
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:México
Institución:UNIVERSIDAD DE SONORA
Repositorio:Biotecnia
Idioma:inglés
OAI Identifier:oai:oai.biotecnia.unison.mx:article/2210
Acceso en línea:https://biotecnia.unison.mx/index.php/biotecnia/article/view/2210
Access Level:acceso abierto
Palabra clave:Mezcal
agave
discrimination
spectroscopy
discriminación
espectroscopía
Descripción
Sumario:Fourier Transform Mid-Infrared (FT-MIR) spectroscopy and multivariate statistical analysis were used to differentiate mezcales elaborated with four agave species. The FT-MIR data matrix was subjected to spectral transformations using first and second derivatives. The Partial Least Squares (PLS)-Discriminant Analysis (DA) with the matrix transformed by the first and second derivative allowed the differentiation of mezcales. While Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was more robust when it was analyzed with second-derivative data. Pairwise comparisons by OPLS-DA allowed mezcales to be correctly discriminated, mainly between Agave karwinskii and Agave potatorum (Q2 = 0.654 and p – value < 0.01; R2Y = 0.985 and p-value < 0.01) and between Agave angustifolia and Agave karwinskii (Q2 = 0.563 and p-value = 0.01; R2Y = 0.989 and p-value = 0.01). FT-MIR spectrophotometry and the PLS-Regression (PLS-R) were applied to predict the ethanol percentage (% v/v) of mezcales collected in 2022 based on the PLS-R model previously run on samples evaluated in 2021.