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

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.

Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef

“…The PLS-DA accuracy of Raman in the present study was comparable to recent findings scanning fat (85–96%), 22 although lean was less accurate for discrimination purposes, being 69.5% compared to 96.5%–98.5%. 13,21 Similarly, precision for fat using PLS-DA in both the present study and Logan et al 22 was 100%. For future work with Raman, care would need to be taken when scanning to reduce analytical variability because Raman spectral scans recorded in the OPUS software are averages of the total number of scans and therefore one being incorrect may compromise the entire sample.…”

“…For future work with Raman, care would need to be taken when scanning to reduce analytical variability because Raman spectral scans recorded in the OPUS software are averages of the total number of scans and therefore one being incorrect may compromise the entire sample. This was also reflected in the high number of outliers, which were not detected in recent studies by Logan et al 13,21 This finding shows that operator experience and usage may affect the results and training operators on using Raman spectroscopy correctly would be needed to avoid fluorescence reflection from raw samples, which in turn affects the amount of sub-samples. 63…”

“…The use of analytical methods such as Raman spectroscopy 9,11,13 and near infrared reflectance (NIR) spectroscopy have been used previously to differentiate beef based on feeding regime. 14,15 Cozzolino et al 14 found accuracies of 79–83% in classification of beef either from pasture or maize silage feeding.…”

“…16–20 However, no evidence of studies using NIR spectroscopy of intact beef for purposes of discriminating grass-fed and grain-fed feeding regime have been found. Recent studies have shown successful discrimination analysis of feeding regime (grass-fed and grain-fed) on intact beef brisket muscle (accuracy 97.1–98.5%) 13,21 and on subcutaneous fat (accuracy 85–96%) 22 using a handheld Raman spectrometer. This evidence presents an opportunity for future use of both NIR and Raman spectroscopy in meat processing or retail sectors for authentication.…”