Identification of individual goat animals by means of near infrared spectroscopy and chemometrics analysis of commercial meat cuts.

Although the identification of animal species and muscles have been reported previously, no studies have been found on the use of NIR spectroscopy to identify individual animals from the analysis of commercial meat cuts. The aim of this study was to evaluate the use of a portable near infrared (NIR) instrument combined with classical chemometrics methods [principal component analysis (PCA) and partial least squares discriminant analysis PLS-DA)] to identify the origin of individual goat animals using the spectral signature of their commercial cut. Samples were collected from several carcasses (6 commercial cuts x 24 animals) sourced from a commercial abattoir in Queensland (Australia). The NIR spectra of the samples were collected using a portable NIR instrument in the wavelength range between 950 and 1600 nm. Overall, the PLS-DA models correctly classify 82% and 79% of the individual goat samples using either the goat rack or loin cut samples, respectively. The study demonstrated that NIR spectroscopy was able to identify individual goat animals based on the spectra properties of some of the commercial cut samples analysed (e.g. loin and rack). These results showed the potential of this technique to identify individual animals as an alternative to other laboratory methods and techniques commonly used in meat traceability.

The ability of a portable near infrared instrument to evaluate the shelf-life of fresh and thawed goat muscles.

The objective of this study was to evaluate the use of a portable near infrared (NIR) instrument to monitor the shelf-life of four goat muscles [longissimus thoracis et lumborum (LTL), semimembranosus (SM), semitendinosus (ST) and biceps femoris (BF)] stored for up to 8 days (4 °C). The NIR spectra of the muscle samples were collected at day 0, and after 1, 4 and 8 days of storage using a MicroNIR instrument (900-1600 nm). The coefficient of determination in cross-validation (R2) and the standard error in cross validation (SECV) obtained for the prediction of days of storage ranged between 0.76 and 0.86, where the SECV ranged from 0.32 to 0.41. The best statistics in cross-validation were obtained for the prediction of days of storage in the BF samples, followed by the ST and LTL muscles. Differences in the PLS loadings for the cross-validation models were observed due to the interactions between the different muscle samples and days of storage. Overall, these results showed the potential of NIR spectroscopy to identify the time of storage in four different goat muscles. Similar data and techniques could be used to predict the remaining shelf life of meat derived from different species under storage. This information can then be used as a tool to predict and guarantee the safety of meat samples to the consumer along the meat supply and value chains.