Alaiz-Rodriguez, Rocio and Parnell, Andrew (2020) A Machine Learning Approach for Lamb Meat Quality Assessment Using FTIR Spectra. IEEE Access, 8. pp. 52385-52394. ISSN 2169-3536

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Abstract

The food industry requires automatic methods to establish authenticity of food products. In this work, we address the problem of the certification of suckling lamb meat with respect to the rearing system. We evaluate the performance of neural network classifiers as well as different dimensionality reduction techniques, with the aim of categorizing lamb fat by means of spectroscopy and analysing the features with more discrimination power. Assessing the stability of feature ranking algorithms also becomes particularly important. We assess six feature selection techniques (χ 2 , Information Gain, Gain Ratio, Relief and two embedded techniques based on the decision rule 1R and SVM (Support Vector Machine). Additionally, we compare them with common approaches in the chemometrics field like the Partial Least Square (PLS) model and Principal Component Analysis (PCA) regression. Experimental results with a fat sample dataset collected from carcasses of suckling lambs show that performing feature selection contributes to classification performance increasing accuracy from 89.70% with the full feature set to 91.80% and 93.89% with the SVM approach and PCA, respectively. Moreover, the neural classifiers yield a significant increase in the accuracy with respect to the PLS model (85.60% accuracy). It is noteworthy that unlike PCA or PLS, the feature selection techniques that select relevant wavelengths allow the user to identify the regions in the spectrum with the most discriminant power, which makes the understanding of this process easier for veterinary experts. The robustness of the feature selection methods is assessed via a visual approach.

Item Type:	Article
Additional Information:	Cite as: R. Alaiz-Rodríguez and A. C. Parnell, "A Machine Learning Approach for Lamb Meat Quality Assessment Using FTIR Spectra," in IEEE Access, vol. 8, pp. 52385-52394, 2020, doi: 10.1109/ACCESS.2020.2974623. Copyright: If Required by Funder OA Publishing This pathway includes Open Access publishing Embargo No Embargo Licence Copyright OwnerAuthors Location: Author's Homepage,Funder Designated Location Institutional Repository Institutional Website Journal Website
Keywords:	Meat quality assessment; feature selection; machine learning; neural networks; feature selection robustness;
Academic Unit:	Faculty of Science and Engineering > Mathematics and Statistics Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
Item ID:	16234
Identification Number:	https://doi.org/10.1109/ACCESS.2020.2974623
Depositing User:	Andrew Parnell
Date Deposited:	05 Jul 2022 14:56
Journal or Publication Title:	IEEE Access
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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