Abstract
In this study a technique was developed to predict the meat freshness decay by employing a nondestructive visible imaging method and a computer assisted analysis. The technique uses Opto-magnetic imaging spectroscopy and machine learning algorithms for detecting of freshness during storage. The opto-magnetic spectra of meat samples were acquired at 0, 12 and 24 hours of refrigerated storage using specially developed imaging system and computer image processing algorithm. The obtained spectra were related to the storage time of the samples, and several machine learning classification algorithms were tested. The best prediction results of freshness for chicken and beef was achieved using lazy IB1 classifier with the accuracy of 97.47% for chicken, and 98.23% for beef. Since the method is concerned with detecting changes in the state of water in tissues, the freshness decay period was estimated based on changes in meat hydration properties.
Highlights
Meat is a high-value item in the human diet and greatly prized by consumers
Optical spectroscopy offers a variety of techniques for meat characterization because of its non-contacting characteristics and easy to use portable devices
The results show the feasibility of Opto-magnetic spectroscopy for estimating quality decay of fresh beef and chicken meat during refrigerated storage
Summary
Meat is a high-value item in the human diet and greatly prized by consumers. In most developed countries meat is very high on the list of food market demands. Various literature reports have covered extensively the application of spectroscopy based methods and techniques for meat quality assessment and evaluation of freshness [3, 6, 9, 10]. Imaging methods such as hyperspectral imaging [11] in combination with powerful techniques of data analysis can make significant improvement in meat science and quality control. The purpose of present research is to explore possibilities of using Opto-magnetic imaging spectro– scopy [12] with computer assisted data analysis as a non-destructive method for rapid evaluation of meat freshness. This paper presents results of application of this method in the characterization of meat and estimation of freshness using classification algorithms based on machine learning
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