The main goal of this study was to investigate the potential of hyperspectral imaging in the near-infrared (NIR) range of 900–1700 nm for non-destructive prediction of chemical composition in lamb meat. Hyperspectral images were acquired for lamb samples originated from different breeds and different muscles. The mean spectra of the samples were extracted from the hyperspectral images and multivariate calibration models were built by using partial least squares (PLS) regression for predicting water, fat and protein contents. The models had good prediction abilities for these chemical constituents with determination coefficient (R 2 p ) of 0.88, 0.88 and 0.63 with standard error of prediction (SEP) of 0.51%, 0.40% and 0.34%, respectively. The feature wavelengths were identified using regression coefficients resulting from the PLSR analyses. New PLSR models were again created using the feature wavelengths and finally chemical images were derived by applying the respective regression equations on the spectral image in a pixel-wise manner. The resulting prediction maps provided detailed information on compositional gradient in the tested muscles. The results obtained from this study clearly revealed that NIR hyperspectral imaging in tandem with PLSR modeling can be used for the non-destructive prediction of chemical compositions in lamb meat. Industrial relevance The results obtained from this study clearly revealed that NIR hyperspectral imaging in tandem with PLSR modeling can be used for the non-destructive prediction of chemical compositions in lamb meat for the meat industry.

Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression

Diverse food-based applications of nuclear magnetic resonance (NMR) technology

Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging

Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging

Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beef

A peer-verified method is presented for the determination of percent moisture and fat in meat products by microwave drying and nuclear magnetic resonance (NMR) analysis. The method involves determining the moisture content of meat samples by microwave drying and using the dried sample to determine the fat content by NMR analysis. Both the submitting and peer laboratories analyzed 5 meat products by using the CEM SMART system (moisture) and the SMART Trac (fat). The samples, which represented a range of products that meat processors deal with daily in plant operations, included the following: (1) fresh ground beef, high-fat; (2) deboned chicken with skin; (3) fresh pork, low-fat; (4) all-beef hot dogs; and (5) National Institute of Standards and Technology Standard Reference Material. The results were compared with moisture and fat values derived from AOAC-approved methods, 950.46 (Forced Air Oven Drying) and 960.39 (Soxhlet Ether Extraction).

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Sarah M. Eddy

Papers

Rapid determination of moisture and fat in meats by microwave and nuclear magnetic resonance analysis--PVM 1:2003.

Chemical Methods for Decontamination of Meat and Poultry