Journal
FOODS
Volume 10, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/foods10112723
Keywords
poultry meat; spoilage; multispectral imaging; Fourier-Transform Infrared spectroscopy (FT-IR); regression models; classification models; multivariate data analysis
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Funding
- European Regional Development Fund of the European Union
- Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation [T1EDK-04344]
- HORIZON EU project [861915]
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FT-IR and MSI were evaluated for predicting the microbiological quality of poultry meat, with SVM model coupled to MSI data exhibiting the highest performance in predicting TVCs and Pseudomonas spp. counts. In the case of FT-IR, improved classification was obtained with the QDA model. These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.
Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and Pseudomonas spp.), while simultaneously MSI and FT-IR spectra were acquired. The organoleptic quality of the samples was also evaluated by a sensory panel, establishing a TVC spoilage threshold at 6.99 log CFU/cm(2). Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and Pseudomonas spp. counts on chicken's surface. Furthermore, classification models (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), and quadratic support vector machines (QSVMs)) were developed to discriminate the samples in two quality classes (fresh vs. spoiled). PLS-R models developed on MSI data predicted TVCs and Pseudomonas spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm(2), respectively. SVM model coupled to MSI data exhibited the highest performance with an overall accuracy of 94.4%, while in the case of FT-IR, improved classification was obtained with the QDA model (overall accuracy 71.4%). These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.
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