Journal
FOOD CONTROL
Volume 68, Issue -, Pages 260-270Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodcont.2016.03.031
Keywords
Microwave dielectric spectroscopy; Near infrared spectroscopy; Multivariate data analysis; Principal component analysis (PCA); Partial least squares (PLS) regression modelling
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Funding
- 'Meat Sense' project from the Irish Department of Agriculture, Food and Marine under Food Institutional Research Measure (FIRM) programme
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This study investigated using microwave dielectric and near infrared (NIR) spectroscopy for the determination of fat content in ground beef samples (n = 69) in a designed experiment Multivariate data analysis (principal component analysis (PCA) and partial least squares (PLS) regression modelling) was used to explore the potential of these spectroscopic techniques over selected multiple frequency or wavelength ranges. Chemical reference data for fat and water content in ground beef were obtained using a nuclear magnetic resonance-based SMART Trac analyser. Best performance of PLS prediction models for fat content revealed a coefficient of determination in prediction ((RP)-P-2) of 0.87 and a root mean square error of prediction (RMSEP) of 2.71% w/w for microwave spectroscopy; in a similar manner, (RP)-P-2 of 0.99 and RMSEP of 0.71% w/w were obtained for NIR spectroscopy. The influence of water content on fat content prediction by microwave spectroscopy was investigated by comparing the prediction performance of PLS regression models developed using a single Y-variable (PLS1; fat or water content) and using both Y-variables (PLS2; fat and water contents). (c) 2016 Elsevier Ltd. All rights reserved.
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