Visible and Near-Infrared Multispectral Features in Conjunction with Artificial Neural Network and Partial Least Squares for Predicting Biochemical and Micro-Structural Features of Beef Muscles.

Jean-Michel Roger,Jean-François Hocquette,Donato Andueza,Anne Listrat,Annabelle Dubost,Abderrahmane Aït-Kaddour

doi:10.3390/foods9091254

Abstract

The objective of this study was to determine the potential of multispectral imaging (MSI) data recorded in the visible and near infrared electromagnetic regions to predict the structural features of intramuscular connective tissue, the proportion of intramuscular fat (IMF), and some characteristic parameters of muscle fibers involved in beef sensory quality. In order to do this, samples from three muscles (Longissimus thoracis, Semimembranosus and Biceps femoris) of animals belonging to three breeds (Aberdeen Angus, Limousine, and Blonde d’Aquitaine) were used (120 samples). After the acquisition of images by MSI and segmentation of their morphological parameters, a back propagation artificial neural network (ANN) model coupled with partial least squares was applied to predict the muscular parameters cited above. The results presented a high accuracy and are promising (R2 test > 0.90) for practical applications. For example, considering the prediction of IMF, the regression model giving the best ANN model exhibited R2P = 0.99 and RMSEP = 0.103 g × 100 g−1 DM.

Highlights

IntroductionDue to the different food crises (mad cow disease, adulterated milk in China, “horse gate” etc.)and consumer mistrust in the agro-food industry, the sector needs effective techniques to authenticate and determine food quality during processing
Due to the different food crisesand consumer mistrust in the agro-food industry, the sector needs effective techniques to authenticate and determine food quality during processing
This study highlighted that a spectral signature in meat samples does exist

Summary

Introduction

Due to the different food crises (mad cow disease, adulterated milk in China, “horse gate” etc.)and consumer mistrust in the agro-food industry, the sector needs effective techniques to authenticate and determine food quality during processing. Different techniques have been proposed and used in practice by the industry for this purpose Among these techniques, classical spectroscopic techniques (e.g., infrared) can be used to perform in-, on-, or at-line measurements, because they have the advantage of not being in physical contact with food products. Classical spectroscopic techniques (e.g., infrared) can be used to perform in-, on-, or at-line measurements, because they have the advantage of not being in physical contact with food products This decreases the risks of sample physical alteration or contamination during the measurement, for sensitive samples like meat. These techniques are known to be generally quick, cost-effective, and environmentally safe. Multispectral imaging (MSI) is able to depict both spectral and spatial features of a region

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