Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis

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 (<i>n</i> = 402) were subjected to spoilage experiments at eigh...

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Autores principales:	Evgenia D. Spyrelli, Christina K. Papachristou, George-John E. Nychas, Efstathios Z. Panagou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	poultry meat spoilage multispectral imaging Fourier-Transform Infrared spectroscopy (FT-IR) regression models classification models Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/8bc7f8dea7bb436b8ea136fc60bb475a
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spelling	oai:doaj.org-article:8bc7f8dea7bb436b8ea136fc60bb475a2021-11-25T17:34:48ZMicrobiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis10.3390/foods101127232304-8158https://doaj.org/article/8bc7f8dea7bb436b8ea136fc60bb475a2021-11-01T00:00:00Zhttps://www.mdpi.com/2304-8158/10/11/2723https://doaj.org/toc/2304-8158Fourier 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 (<i>n</i> = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and <i>Pseudomonas</i> 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<sup>2</sup>. Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and <i>Pseudomonas</i> 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 <i>Pseudomonas</i> spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm<sup>2</sup>, 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.Evgenia D. SpyrelliChristina K. PapachristouGeorge-John E. NychasEfstathios Z. PanagouMDPI AGarticlepoultry meatspoilagemultispectral imagingFourier-Transform Infrared spectroscopy (FT-IR)regression modelsclassification modelsChemical technologyTP1-1185ENFoods, Vol 10, Iss 2723, p 2723 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	poultry meat spoilage multispectral imaging Fourier-Transform Infrared spectroscopy (FT-IR) regression models classification models Chemical technology TP1-1185
spellingShingle	poultry meat spoilage multispectral imaging Fourier-Transform Infrared spectroscopy (FT-IR) regression models classification models Chemical technology TP1-1185 Evgenia D. Spyrelli Christina K. Papachristou George-John E. Nychas Efstathios Z. Panagou Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis
description	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 (<i>n</i> = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and <i>Pseudomonas</i> 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<sup>2</sup>. Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and <i>Pseudomonas</i> 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 <i>Pseudomonas</i> spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm<sup>2</sup>, 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.
format	article
author	Evgenia D. Spyrelli Christina K. Papachristou George-John E. Nychas Efstathios Z. Panagou
author_facet	Evgenia D. Spyrelli Christina K. Papachristou George-John E. Nychas Efstathios Z. Panagou
author_sort	Evgenia D. Spyrelli
title	Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis
title_short	Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis
title_full	Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis
title_fullStr	Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis
title_full_unstemmed	Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis
title_sort	microbiological quality assessment of chicken thigh fillets using spectroscopic sensors and multivariate data analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8bc7f8dea7bb436b8ea136fc60bb475a
work_keys_str_mv	AT evgeniadspyrelli microbiologicalqualityassessmentofchickenthighfilletsusingspectroscopicsensorsandmultivariatedataanalysis AT christinakpapachristou microbiologicalqualityassessmentofchickenthighfilletsusingspectroscopicsensorsandmultivariatedataanalysis AT georgejohnenychas microbiologicalqualityassessmentofchickenthighfilletsusingspectroscopicsensorsandmultivariatedataanalysis AT efstathioszpanagou microbiologicalqualityassessmentofchickenthighfilletsusingspectroscopicsensorsandmultivariatedataanalysis
_version_	1718412177727029248

Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis

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