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      Assessment of Visible Near-Infrared Hyperspectral Imaging as a Tool for Detection of Horsemeat Adulteration in Minced Beef

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          Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review.

          The requirements of reliability, expeditiousness, accuracy, consistency, and simplicity for quality assessment of food products encouraged the development of non-destructive technologies to meet the demands of consumers to obtain superior food qualities. Hyperspectral imaging is one of the most promising techniques currently investigated for quality evaluation purposes in numerous sorts of applications. The main advantage of the hyperspectral imaging system is its aptitude to incorporate both spectroscopy and imaging techniques not only to make a direct assessment of different components simultaneously but also to locate the spatial distribution of such components in the tested products. Associated with multivariate analysis protocols, hyperspectral imaging shows a convinced attitude to be dominated in food authentication and analysis in future. The marvellous potential of the hyperspectral imaging technique as a non-destructive tool has driven the development of more sophisticated hyperspectral imaging systems in food applications. The aim of this review is to give detailed outlines about the theory and principles of hyperspectral imaging and to focus primarily on its applications in the field of quality evaluation of agro-food products as well as its future applicability in modern food industries and research.
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            Fingerprinting food: current technologies for the detection of food adulteration and contamination.

            Major food adulteration and contamination events seem to occur with some regularity, such as the widely publicised adulteration of milk products with melamine and the recent microbial contamination of vegetables across Europe for example. With globalisation and rapid distribution systems, these can have international impacts with far-reaching and sometimes lethal consequences. These events, though potentially global in the modern era, are in fact far from contemporary, and deliberate adulteration of food products is probably as old as the food processing and production systems themselves. This review first introduces some background into these practices, both historically and contemporary, before introducing a range of the technologies currently available for the detection of food adulteration and contamination. These methods include the vibrational spectroscopies: near-infrared, mid-infrared, Raman; NMR spectroscopy, as well as a range of mass spectrometry (MS) techniques, amongst others. This subject area is particularly relevant at this time, as it not only concerns the continuous engagement with food adulterers, but also more recent issues such as food security, bioterrorism and climate change. It is hoped that this introductory overview acts as a springboard for researchers in science, technology, engineering, and industry, in this era of systems-level thinking and interdisciplinary approaches to new and contemporary problems.
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              Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis.

              The goal of this study was to explore the potential of near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. In this study, samples from three different muscles (semitendinosus (ST), semimembranosus (SM), longissimus dorsi (LD)) originated from Texel, Suffolk, Scottish Blackface and Charollais breeds were collected and used for image acquisition and quality measurements. Hyperspectral images were acquired using a pushbroom NIR hyperspectral imaging system in the spectral range of 900-1700 nm. A partial least-squares (PLS) regression, as a multivariate calibration method, was used to correlate the NIR reflectance spectra with quality values of the tested muscles. The models performed well for predicting pH, colour and drip loss with the coefficient of determination (R(2)) of 0.65, 0.91 and 0.77, respectively. Image processing algorithm was also developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of quality parameter in the imaged lamb samples. In addition, textural analysis based on gray level co-occurrence matrix (GLCM) was also conducted to determine the correlation between textural features and drip loss. The results clearly indicated that NIR hyperspectral imaging technique has the potential as a fast and non-invasive method for predicting quality attributes of lamb meat. Copyright © 2011 Elsevier B.V. All rights reserved.
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                Author and article information

                Journal
                Food and Bioprocess Technology
                Food Bioprocess Technol
                Springer Science and Business Media LLC
                1935-5130
                1935-5149
                May 2015
                January 22 2015
                May 2015
                : 8
                : 5
                : 1054-1062
                Article
                10.1007/s11947-015-1470-7
                83dbf7cd-22e5-48bc-b8a2-315947dbe1f2
                © 2015

                http://www.springer.com/tdm

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