Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms. (April 2016)

• Record Type: Journal Article
• Authors: Sanz, Jose Antonio; Fernandes, Armando M.; Barrenechea, Edurne; Silva, Severiano; Santos, Virginia; Gonçalves, Norberto; Paternain, Daniel; Jurio, Aranzazu; Melo-Pinto, Pedro
• Is Part Of: Journal of food engineering. Volume 174(2016:Apr.)
• Page Start: 92
• Page End: 100
• Publication Date: 2016-04
• Subjects: Lamb muscle -- Hyperspectral imaging -- Classification -- Machine learning; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2015.11.024
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: Lamb muscle discrimination is important for the meat industry due to the different pricing of each type of muscle. In this paper, we combine hyperspectral imaging, operating in the wavelength range 380–1028 nm, with several machine learning algorithms to deal automatically with the classification of lamb muscles. More specifically, we study the discrimination of four different lamb muscles, namely, Longissimus dorsi, Psoas major, Semimembranosus and Semitendinosus from thirty lambs of Churra Galega Mirandesa breed. The objective of the paper is to determine the best method for muscle classification. In the experimental study we report an analysis of the performance of seven classifiers. We study their behavior when they are applied over the original data as well as over the data pre-processed using Principal Component Analysis (PCA) to reduce the dimensionality of the problem. The seven classifiers used to differentiate the muscle types are two Artificial Neural Networks, namely the linear Least Mean Squares (LMS) classifier and the Multilayer Perceptron with Scaled Conjugate Gradient (MLP-SCG), two Support Vector Machines (SVM), namely the ν SVM and the SVM trained with Sequential Minimal Optimization (SMO), the Logistic Regression (LR), the Center Based Nearest Neighbor classifier and the Linear Discriminant Analysis. The best result, determined using a leave-one-animal-out scheme, is provided by the linear LMS classifier using the original data, since it correctly classifies 96.67% of the samples. The LR, the MLP-SCG using original data and the SVM trained with SMO on data preprocessed with PCA are also suitable techniques to tackle the lamb muscle classification problem. Highlights: Lamb muscle discrimination is faced with hyperspectral imaging and machine learning. The hyperspectral imaging technique uses the wavelength range 380–1028 nm. Several machine learning methods and PCA are used to perform the learning stage. Four different muscles of 30 Churra Galega Mirandesa breed lambs are differentiated. 96.67% of the samples are correctly classifier by the linear mean square classifier.