Abstract
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/cm2. 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/cm2, 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.
Highlights
At pre-determined time intervals, packages were subjected to microbiological analyses for the enumeration of total viable counts (TVCs) and Pseudomonas spp., in parallel with multispectral imaging (MSI) and Fourier transform infrared spectroscopy (FT-IR) spectral data acquisition
Storage temperature significantly influenced microbial growth resulting in sample deterioration and spoilage
The findings of this research indicated that MSI spectral data combined with Partial least squares regression (PLS-R)
Summary
Consumer’s awareness and demand for high quality and safety meat and poultry has been continuously increased For this purpose, non-invasive spectroscopic sensors have been used in the evaluation of the quality and freshness of meat products [3] through the implementation of process analytical technology (PAT) [4,5]. The underlying principle of PAT is to combine spectral data acquired through real-time (in-, on-, at-line) non-destructive analytical techniques with multivariate data analysis for the development of models assessing food quality These models, along with their datasets, could be uploaded in the cloud, updated regularly with new data in order to be consultative to the food industry [6]
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