Classification of beef longissimus thoracis muscle tenderness using hyperspectral imaging and chemometrics
Fecha
2022Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Identificador del proyecto
Impacto
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10.3390/foods11193105
Resumen
Nowadays, the meat industry requires non-destructive, sustainable, and rapid methods
that can provide objective and accurate quality assessment with little human intervention. Therefore,
the present research aimed to create a model that can classify beef samples from longissimus thoracis
muscle according to their tenderness degree based on hyperspectral imaging (HSI). In order to
obtain diffe ...
[++]
Nowadays, the meat industry requires non-destructive, sustainable, and rapid methods
that can provide objective and accurate quality assessment with little human intervention. Therefore,
the present research aimed to create a model that can classify beef samples from longissimus thoracis
muscle according to their tenderness degree based on hyperspectral imaging (HSI). In order to
obtain different textures, two main strategies were used: (a) aging type (wet and dry aging with
or without starters) and (b) aging times (0, 7, 13, 21, and 27 days). Categorization into two groups
was carried out for further chemometric analysis, encompassing group 1 (ngroup1 = 30) with samples
with WBSF < 53 N whereas group 2 (ngroup2 = 28) comprised samples with WBSF values 53 N.
Then, classification models were created by applying the partial least squares discriminant analysis
(PLS-DA) method. The best results were achieved by combining the following pre-processing
algorithms: 1st derivative + mean center, reaching 70.83% of correctly classified (CC) samples and
67.14% for cross validation (CV) and prediction, respectively. In general, it can be concluded that HSI
technology combined with chemometrics has the potential to differentiate and classify meat samples
according to their textural characteristics. [--]
Materias
Meat quality,
Texture,
HSI,
PLS-DA,
Chemometrics
Editor
MDPI
Publicado en
Foods 2022, 11, 3105
Departamento
Universidad Pública de Navarra. Departamento de Agronomía, Biotecnología y Alimentación /
Nafarroako Unibertsitate Publikoa. Agronomia, Bioteknologia eta Elikadura Saila /
Universidad Pública de Navarra. Departamento de Ingeniería /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
Versión del editor
Entidades Financiadoras
This research was funded by Fondo Europeo de Desarrollo Regional, program FEDER 2014-2020 of Navarra, and by the Government of Navarra (project 0011-1365-2019-000091). Open Access funding was provided by Universidad Pública de Navarra. This work was also supported by the Spanish Ministry MCIN/AEI/10.13039/501100011033/FEDER, via Project No. RTI2018-094475-B-I00.