Multivariate image analysis for assessment of textural attributes in transglutaminase-reconstituted meat
[EN] The control of sensorial textural attributes has high interest to the meat industry focused on the recovery of the value of meat by-products by developing reconstituted meat pieces with added sensory and nutritional values. Sensorial analysis of foods is still a quite subjective methodology, hi...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/231185 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/231185 |
| Access Level: | acceso embargado |
| Palabra clave: | Spectral imaging Multivariate image analysis Chemometrics Meat texture Contactless inspection Transglutaminase |
| Sumario: | [EN] The control of sensorial textural attributes has high interest to the meat industry focused on the recovery of the value of meat by-products by developing reconstituted meat pieces with added sensory and nutritional values. Sensorial analysis of foods is still a quite subjective methodology, highly dependent of a well-trained team of inspectors, which is simulated by textural analysis in order to measure objective physical properties. This work presents a non-destructive and contactless experimental methodology to predict the physical properties of a reconstituted meat product, based on integrating multispectral imaging and multivariate image analysis (MIA). The experiment was based on reconstituting grounded meat with different concentrations of transglutaminase (0.1, 1, 3, 6 and 10 %), from which textural properties and multispectral imaging data were measured. Multispectral images (UV, VIS and NIR wavelengths) were processed with chemometric procedures to obtain the distribution maps and score images, from which different blocks of features were extracted to generate feature vectors (basic statistics and co-occurrence matrix) for each image. The obtained regression models built with these features predicted all physical properties of the meat with Q2 > 0.90, after feature selection using VIPs. These results evidenced the capacity of multispectral imaging, combined with chemometric procedures, to capture the variability of physical properties induced by transglutaminase in a derivate meat product. It could represent the base of a potential contactless application for a meat industrial inspection, where work environments have strong hygienic requirements. |
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