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.
Highlights
According to the Food and Agriculture Organization (FAO, 2019) around 14 % of the world’s food is lost after harvest and before reaching the retail level, including on-farm activities, storage and transportation
These findings suggested that Fourier transforms infrared spectroscopy (FT-IR) analysis is more efficient than multispectral image analysis (MSI) to predict the microbiological quality on the surface of chicken breast fillets
The highest performance was achieved with nnet with root mean square error (RMSE) value of 0.717 log CFU/cm2 on B1 and 0.752 log CFU/cm2 on B2
Summary
According to the Food and Agriculture Organization (FAO, 2019) around 14 % of the world’s food is lost after harvest and before reaching the retail level, including on-farm activities, storage and transportation. An alternative approach for rapid quality assessment, feasible by technology and science evolution, is the implementation of spectroscopic methods such as vibrational spectroscopy (FT-IR, NIR, Raman) (Argyri et al, 2013; Alamprese et al, 2016; Grassi and Alamprese, 2018), hyperspectral and multispectral imaging (Liu et al, 2014; Qin et al, 2013) and biomimetic sensors (e-nose, e-tongue) (Loutfi et al, 2015; Wojnowski et al, 2017) These nondestructive methods can be combined with microbiological, sensory and multivariate data analysis for the development of models evaluating meat quality. The developed models accompanied by their datasets could be uploaded and maintained in the cloud, updated constantly with new data in order to be consultative to food industries (Nychas et al, 2016; Tsakanikas et al, 2020)
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