Structured-illumination reflectance imaging for the evaluation of microorganism contamination in pork: effects of spectral and imaging features on its prediction performance
Structured-illumination reflectance imaging (SIRI) provides a new means for food quality detection. This original work investigated the capability of (SIRI) technique coupled with multivariate chemometrics to evaluate the microbial contamination in pork inoculated with Pseudomonas fluorescens and Br...
| Autores: | , , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/172697 |
| Acesso em linha: | https://hdl.handle.net/11441/172697 https://doi.org/10.26599/FSHW.2024.9250104 |
| Access Level: | acceso abierto |
| Palavra-chave: | Pseudomonas fl uorescens Brochothrix thermosphacta Pork Structured-illumination reflectance imaging Data fusion |
| Resumo: | Structured-illumination reflectance imaging (SIRI) provides a new means for food quality detection. This original work investigated the capability of (SIRI) technique coupled with multivariate chemometrics to evaluate the microbial contamination in pork inoculated with Pseudomonas fluorescens and Brochothrix thermosphacta during storage at different temperatures. The prediction performances based on different spectrum and the textural features of direct component and amplitude component images demodulated from the SIRI pattern, as well as their data fusion were comprehensively compared. Based on the full wavelength spectrum (420–700 nm) of amplitude component images, the orthogonal signal correction coupled with support vector machine regression provided the best predictions of the number of P. fl uorescens and B. thermosphacta in pork, with the determination coefficients of prediction (Rp 2 ) values of 0.870 and 0.906, respectively. Besides, the prediction models based on the amplitude component or direct component image textural features and the data fusion models using spectrum and textural features from direct component and amplitude component images cannot significantly improve their prediction accuracy. Consequently, SIRI can be further considered as a potential technique for the rapid evaluation of microbial contaminations in pork meat. |
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