Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products

Doulgeraki³

et al. 2021

Foods

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Chicken is one of the most widely consumed meats worldwide. The exploration of the bacterial diversity of chicken meat may provide new insights into the chicken-associated microbiome that will lead to moderation of food spoilage or safety. This study was undertaken to explore the bacterial communities of chicken breast and thigh fillets stored at refrigeration (0 °C and 5 °C) and slightly abuse (10 °C) temperatures for 5 days through conventional cultural methods along with next-generation sequencing (NGS) analysis. Total viable counts (TVC), Brochothrix thermosphacta, Pseudomonas spp., and lactic acid bacteria (LAB) were enumerated, while the bacterial communities were mapped through 16S rRNA gene amplicon sequencing. Chicken breast and thigh fillets possessed a complex bacterial structure that incorporated a total of >200 Operational Taxonomic Units (OTUs) at the genus level. The core microbiota of fresh samples consisted of Acinetobacter, Brochothrix, Flavobacterium, Pseudomonas, Psychrobacter, and Vibrionaceae (family). These genera persisted until the end of storage in >80% of samples, except Psychrobacter and Flavobacterium, while Photobacterium was also identified. Hierarchical clustering showed a distinction of samples based on storage time and chicken part. Conventional plate counting with growth media commonly used in spoilage studies did not always correspond to the microbial community profiles derived from NGS analysis, especially in Pseudomonas, Acinetobacter, Photobacterium, and Vibrionaceae. Results of the present study highlight Photobacterium and Vibrionaceae, in general, as potent chicken meat spoilers and suggest the necessity to combine classical microbiological methods along with NGS technologies to characterize chicken meat spoilage microbiota.

Section: Discussionsupporting

confidence: 73%

Microbiota of Chicken Breast and Thigh Fillets Stored under Different Refrigeration Temperatures Assessed by Next-Generation Sequencing

Dourou¹,

Doulgeraki³

et al. 2021

Foods

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“…Moreover, MSI results on intra-batch performance and its low RMSE suggested that this analysis could be applicable for internal validation or quality control in the production line. The latter option has been confirmed via experiments performed in the production line of chicken products at industrial level ( Spyrelli et al., 2020 ). Furthermore, the fundamental role of training and testing data set definition is demonstrated by FT-IR lars model during B1 on B2 validation, which significantly outperformed batch-on-batch performance of MSI (RMSE: 0.851 vs 1.251 log CFU/cm 2 ).…”

Section: Discussionmentioning

confidence: 79%

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

Current Research in Food Science

Özcan

Mohareb

et al. 2021

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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/cm 2 for MSI and FT-IR analysis, respectively. Moreover, RMSE values for Pseudomonas spp. model were 1.574 log CFU/cm 2 for MSI data and 1.078 log CFU/cm 2 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/cm 2 ) for intra-batch testing, while lars outperformed nnet on independent batch testing with RMSE of 1.252 log CFU/cm 2 . Furthermore, lars models excelled with the FT-IR data with RMSE of 0.904 and 0.851 log CFU/cm 2 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.

“…The wavelengths 630, 645, 660, 700, and 850 nm were identified as significant (b coefficient greater than 0.2) for determining TVCs counts on the surface of chicken thigh. The significant contribution of the wavelength range 630-700 nm for the determination of meat and poultry spoilage has been reported in previous studies, and could be linked to myoglobin, metmyoglobin, deoxymyoglobin or oxymyoglobin [11,20]. According to the B regression coefficients of the PLS-R models, the quantitative equations for the estimation of TVCs and Pseudomonas spp.…”

Section: Correlation Of Microbiological Data To Spectral Informationmentioning

confidence: 74%

“…Moreover, fecal contaminants in poultry line [18] and the presence of tumors on the surface of chicken breasts [19] have been accurately detected via MSI analysis. This innovative method was successfully employed in the at-line estimation of the time from slaughter in four different poultry products [20].…”

Section: Introductionmentioning

confidence: 99%

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

Papachristou

Nychas

et al. 2021

Foods

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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.