Estimation of the Microbiological Quality of Meat Using Rapid and Non-Invasive Spectroscopic Sensors

Abstract

Spectroscopic methods in tandem with machine learning methodologies have attracted considerable research interest for the estimation of food quality. The objective of this study was the evaluation of Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) coupled with appropriate machine learning regression algorithms for assessing meat microbiological quality. For this purpose, minced pork patties were stored aerobically and under modified atmosphere packaging (MAP) conditions, at isothermal and dynamic temperature conditions. At regular time intervals during storage, samples were subjected to (i) microbiological analysis, (ii) FTIR measurements and (iii) MSI acquisition. The collected FTIR data were processed by feature extraction methods to reduce dimensionality, and subsequently Support Vector Machines (SVM) regression models were trained using spectral features (FTIR and MSI) to estimate microbiological quality of meat (microbial population). The regression models were evaluated with different experimental replicates using distinct meat batches. The performance of the models was evaluated in terms of correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and residual prediction deviation (RPD). The RMSE values for the microbial population estimation models using FTIR were 1.268 and 1.024 for aerobic and MAP storage, respectively. The performance in terms of RMSE for the MSI-based models was 1.144 for aerobic and 0.923 for MAP storage, while the combination of FTIR and MSI spectra resulted in models with RMSE equal to 1.146 for aerobic and 0.886 for MAP storage. The experimental results demonstrated the potential of estimating the microbiological quality of minced pork meat from spectroscopic data. INDEX TERMS Food technology, microbiological quality, Fourier transform infrared spectroscopy, multispectral imaging, machine learning, support vector regression.

Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning

“…Following the feature selection, SVR ( Smola and Scholkopf, 2004 ) was applied to the reduced dataset for the estimation/prediction of the microbial populations from the corresponding spectroscopic data. SVM/R is a robust supervised tool for both classification and regression ( Vapnik et al, 1997 ) and has been used in various food quality applications ( Du et al, 2007 ; Argyri et al, 2013 , 2014 ; Schmutzler et al, 2015 ; Estelles-Lopez et al, 2017 ; Ropodi et al, 2018 ; Yu et al, 2019 ; Fengou et al, 2020 ; Tsakanikas et al, 2020 ). Briefly, in SVMs, the original data x are mapped from the input space onto a high-dimensional feature space via a non-linear mapping function (kernel function) in order to construct an optimal hyperplane that minimizes the total square distance to all data points.…”

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

“…Multispectral imaging systems have been used in applications from various fields such as agriculture [47], microbiology [16], entomology [48], etc. In specific, adulteration and defects of agricultural produce have been analyzed using MIS for turmeric [49], fruits [50]- [52], beef [53], and rice [54], as well as in packed foods [55], and tomato paste [56].…”

“…In particular, spectroscopy methods are useful in deriving elaborative quality parameters [14]- [16] using spectral characteristics. Especially, spectroscopy techniques such as Fourier Transform Infra-Red (FTIR) [17], and Raman spectroscopy [18] are regularly used in the compositional analysis of oil and agricultural produce [19].…”

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

“…FT-IR spectral data were modified by Savitzky-Golay first derivative (second polynomial order, 11-point window) for the development of PLS-R models, while for classification models' spectral data pre-treatment was based on the same model with a 9-point window in order to reduce baseline shift and noise [9]. Spectral data in the range of 1000 to 2000 cm −1 were included in the analysis, since these regions are documented as relevant to meat spoilage [37]. FT-IR models were also validated with data sets from dynamic temperature profiles (n = 63), including 30 (47.6 %) fresh and 33 (52.4 %) spoiled samples.…”

“…Partial least squares regression (PLS-R), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) have been reported as reliable tools for the development of predictive models for spoilage or adulteration assessment in meat [9,[29][30][31]. Moreover, deep learning methodologies such as artificial neural networks (ANNs) and support vector machines (SVMs) [32] have been employed, validated, and compared through available websites (e.g., sorfML, Metaboanalyst) or softwares (R, MatLab, Python), in an attempt to provide accurate quantitative and qualitative models for food spoilage assessment [28,[33][34][35][36][37].…”