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Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review

Author:
Prieto, N., Roehe, R., Lavín, P., Batten, G., Andrés, S.
Source:
Meat science 2009 v.83 no.2 pp. 175-186
ISSN:
0309-1740
Subject:
chemical composition, juiciness, shear strength, texture, sensory evaluation, marbling, meat grades, meat, near-infrared spectroscopy, meat tenderness, pH, color, flavor, firmness, water holding capacity, odors, prediction
Abstract:
Over the past three decades, near infrared reflectance (NIR) spectroscopy has been proved to be one of the most efficient and advanced tools for the estimation of quality attributes in meat and meat products. This review focuses on the use of NIR spectroscopy to predict different meat properties, considering the literature published mainly in the last decade. Firstly, the potential of NIR to predict chemical composition (crude protein, intramuscular fat, moisture/dry matter, ash, gross energy, myoglobin and collagen), technological parameters (pH value; L*, a*, b* colour values; water holding capacity; Warner-Bratzler and slice shear force) and sensory attributes (colour, shape, marbling, odour, flavour, juiciness, tenderness or firmness) are reviewed. Secondly, the usefulness of NIR for classification into meat quality grades is presented and thirdly its potential application in the industry is shown. The review indicates that NIR showed high potential to predict chemical meat properties and to categorize meat into quality classes. In contrast, NIR showed limited ability for estimating technological and sensory attributes, which may be mainly due to the heterogeneity of the meat samples and their preparation, the low precision of the reference methods and the subjectivity of assessors in taste panels. Hence, future work to standardize sample preparation and increase the accuracy of reference methods is recommended to improve NIR ability to predict those technological and sensory characteristics. In conclusion, the review shows that NIR has a considerable potential to predict simultaneously numerous meat quality criteria.
Agid:
760369