Spoilage assessment of chicken breast fillets by means of fourier transform infrared spectroscopy and multispectral image analysis

Evgenia D Spyrelli; Onur Ozcan; Fady Mohareb; Efstathios Z Panagou; George-John E Nychas

doi:10.1016/j.crfs.2021.02.007

Spoilage assessment of chicken breast fillets by means of fourier transform infrared spectroscopy and multispectral image analysis

Curr Res Food Sci. 2021 Feb 25:4:121-131. doi: 10.1016/j.crfs.2021.02.007. eCollection 2021.

Authors

Evgenia D Spyrelli¹, Onur Ozcan², Fady Mohareb², Efstathios Z Panagou¹, George-John E Nychas¹

Affiliations

¹ Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera odos 75, 11855, Athens, Greece.
² Bioinformatics Group, Department of Agrifood, School of Water, Energy and Environment Cranfield University, College Road, Cranfield, Bedfordshire, MK43 0AL, UK.

Abstract

The objective of this research was the evaluation of Fourier transforms infrared spectroscopy (FT-IR) and multispectral image analysis (MSI) as efficient spectroscopic methods in tandem with multivariate data analysis and machine learning for the assessment of spoilage on the surface of chicken breast fillets. For this purpose, two independent storage experiments of chicken breast fillets (n = 215) were conducted at 0, 5, 10, and 15 °C for up to 480 h. During storage, samples were analyzed microbiologically for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. In addition, FT-IR and MSI spectral data were collected at the same time intervals as for microbiological analyses. Multivariate data analysis was performed using two software platforms (a commercial and a publicly available developed platform) comprising several machine learning algorithms for the estimation of the TVC and Pseudomonas spp. population of the surface of the samples. The performance of the developed models was evaluated by intra batch and independent batch testing. Partial Least Squares- Regression (PLS-R) models from the commercial software predicted TVC with root mean square error (RMSE) values of 1.359 and 1.029 log CFU/cm² for MSI and FT-IR analysis, respectively. Moreover, RMSE values for Pseudomonas spp. model were 1.574 log CFU/cm² for MSI data and 1.078 log CFU/cm² for FT-IR data. From the implementation of the in-house sorfML platform, artificial neural networks (nnet) and least-angle regression (lars) were the most accurate models with the best performance in terms of RMSE values. Nnet models developed on MSI data demonstrated the lowest RMSE values (0.717 log CFU/cm²) for intra-batch testing, while lars outperformed nnet on independent batch testing with RMSE of 1.252 log CFU/cm². Furthermore, lars models excelled with the FT-IR data with RMSE of 0.904 and 0.851 log CFU/cm² in intra-batch and independent batch testing, respectively. These findings suggested that FT-IR analysis is more efficient than MSI to predict the microbiological quality on the surface of chicken breast fillets.

Keywords: Chicken breast fillets; Fourier transform infrared spectroscopy; Machine learning; Multispectral imaging; Multivariate data analysis; SorfML platform.