Analysis of Raman spectra for the verification of Australian grass- and grain-fed beef using principal component analysis and partial least square models

Research output: Contribution to journalArticlepeer-review

Abstract

The diet of cattle has been shown to alter both subcutaneous fat and muscle fatty acid composition, and these differences cause a change in the Raman spectra of the fat due to the different chemical bonds associated with these biochemical changes. Because biochemical changes only lead to subtle changes in the Raman spectra, statistical methods based upon chemometric modelling are necessary to extract indicative information on the type of feed consumed. This investigation undertook a feasibility study using a limited sample set derived from two producers in one location to ascertain the potential for Raman spectroscopy to accurately discriminate grain-fed cattle from grass-fed cattle and investigated the usefulness of principal component analysis (PCA) and partial least squares discriminate analysis (PLS-DA) to best discriminate between feeding groups using the information from the Raman spectra. The first two principal components accounted for 83% of the variation in the spectra and demonstrated discrimination of samples by feed type. PLS-DA resulted in a model that was able to accurately predict grain- and grass-fed carcases with a misclassification rate of 3.5%. Changes in beef cattle diets lead to subtle changes in subcutaneous fat; using Raman spectroscopy with chemometric modelling, these changes can be identified and used to identify the production system of beef products. This study successfully uses Raman spectroscopy as an automated, nondestructive and rapid technique in the range of 600–2000 cm−1 in combination with pattern recognition of unsupervised (PCA) and supervised (PLS-DA) techniques to classify the production system of cattle between grain and grass fed.
Original languageEnglish
Pages (from-to)2338-2346
Number of pages9
JournalJournal of Raman Spectroscopy
Volume51
Issue number11
Early online date02 Sep 2020
DOIs
Publication statusPublished - Nov 2020

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