Characterisation of meat quality and assessment with the application of Raman spectroscopy
Creators: name: Shoko, Patience
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 300602
subject: Food chemistry and food sensory science
classificationCode: 300605
subject: Food safety, traceability, certification and authenticity
classificationCode: 340107
subject: Quality assurance, chemometrics, traceability and metrological chemistry

description: Meat quality is an important aspect for both meat producers and consumers. There is a drive by the meat industry to deliver good quality products to the consumer. The perceived good quality of meat by consumers, is palatability, tenderness, juiciness, flavour, healthy fats, food safety and above all trust in the product labelling. To achieve this quality, meat processors require good quality assurance systems in place. However, the current methods used for quality assurance are time consuming, laborious, and expensive. Raman spectroscopy is a rapid, non-invasive, label free technique that has previously been applied to the assessment of meat in the following ways: tenderness prediction, species differentiation, fatty acid profiling, meat spoilage and post-mortem prediction of meat quality. Raman spectroscopy as a technique, radiates a laser on to a sample, stimulating vibration of molecules which changes the wavelength of scattered light. The shift in wavelength gives a spectroscopic fingerprint which is used to identify and quantify chemical compounds.

The aim of this research was to explore the characterisation of meat quality using Raman spectroscopy. Raman spectroscopy studies were performed in combination with chemometrics, which is a tool for analysing large spectral data. Raman spectra measurements are affected by light scattering and the resultant spectra contains noise, overlapped peaks which presents limitations in the interpretation of the data. Pre-processing methods are applied on the spectral data to produce distinct Raman peaks before other chemometric methods for classification or prediction are employed. In this research the best pre-processing methods were selected after several trials to provide meaningful information.

Discrimination studies were conducted by Raman spectroscopy, using chicken as a model. Chicken tissues and muscles were differentiated by Principal component analysis (PCA) and Linear discriminant analysis (LDA). Raman spectroscopy was able to differentiate the small muscles and tissues based on their biomolecular differences. The study was extended to discrimination of beef muscles, different species, and feeding regimes. Repeatedly, Raman spectroscopy proved to be robust and sensitive in discriminating samples of similar biomolecular composition. Another study was completed to identify dark cutting in beef, a common challenge for the meat industry. Dark cutting is a quality defect that occurs in beef during rigor mortis due to meat pH&gt;6. It is induced by low glycogen levels in the animal at slaughter arising from the animal being stressed. The resultant beef is of inferior quality with a dark colour, tough and easily prone to spoilage. The meat industry faces financial losses at retail and could benefit from a real time sorting technique. In our study, Raman spectroscopy successfully identified dark cutting amongst different striploin muscles.

Prediction studies were performed to use Raman in the prediction of meat sensory quality (tenderness, juiciness, chewiness, flavour and overall likeability) and organoleptic quality parameters (pH, intramuscular fat (IMF), Instrumental hardness, glycogen concentration, colour). The prediction values were good after improving the model by sample grouping.

A further study applied Raman spectroscopy in the prediction of fatty acids composition in different beef muscles. The model performance was improved by using fatty acid ratios, using the lipid spectral wavenumber range and grouping the samples. Good results were achieved for prediction of saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, omega-3 fatty acids and omega-6 fatty acids.