Journal
MEAT SCIENCE
Volume 119, Issue -, Pages 102-109Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.meatsci.2016.04.031
Keywords
Meat; Freshness; Nondestructively sensing; Light scattering; AdaBoost-OLDA
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Funding
- National Natural Science Foundation of China [31271875]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
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Rich nutrient matrix meat is the first-choice source of animal protein for many people all over the world, but it is also highly susceptible to spoilage due to chemical and microbiological activities. In this work, we attempted the feasibility study of rapidly and nondestructively sensing meat's freshness using a light scattering technique. First, we developed the light scattering system for image acquisition. Next, texture analysis was used for extracting characteristic variables from the region of interest (ROI) of a scattering image. Finally, a novel classification algorithm adaptive boosting orthogonal linear discriminant analysis (AdaBoost-OLDA) was proposed for modeling, and compared with two classical classification algorithms linear discriminant analysis (LDA) and support vector machine (SVM). Experimental results showed that classification results by AdaBoost-OLDA algorithm are superior to LDA and SVM algorithms, and eventually achieved 100% classification rate in the calibration and prediction sets. This work demonstrates that the developed light scattering technique has the potential in noninvasively sensing meat's freshness. (C) 2016 Elsevier Ltd. All rights reserved.
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