Evaluation of pork meat quality and freshness using colorimetric and spectral methods

S. Atanassova, T. Stoyanchev, S. Ribarski
Abstract. The aim of the study was investigation of the feasibility of colour measurements and near infrared (NIR) spectroscopy as a tool for the prediction of pork meat freshness. Chilled pork loin samples (12 different batches) were collected from different retail meat markets. The meat was cut in slices 1-1,5 cm thick and placed in sterilized glass Petri dishes, in aseptic laboratory conditions. The samples were placed in cooling incubator for storage at 6°С for 10 days. On the day of samples preparing, as well as on the 3, 7 and 10 day during storage meat samples from each batch were taken for measurement. Biochemical and microbiological parameters – pH, amino acid nitrogen and total bacterial count were determined. Colour measurements were made by portable colorimeter Lovibond RT and data were presented as three-dimensional coordinates L*, a* and b* in the colorimetric system CIELab. NIR measurements were performed by NIRQuest 512 spectrometer in the region 900-1700 nm using reflection fibre-optic probe. Partial least square regression with internal cross-validation was used for calibration models development for determination of tested parameters on the base of spectral data.Differences in both colour coordinates and nearinfrared spectral data of fresh and spoiled meat samples were found. Colour measurements of meat samples in our experiment did not allow accurate determination of parameters, characterizing meat spoilage. The most significant spectral differences were observed in the region from 1360 to1470 nm and at 1642nm. Determination of pH, amino acid nitrogen and total bacterial count by PLS regression on the basis of near-infrared spectra showed good accuracy of determination for pH and amino acid nitrogen content and very good accuracy of determination of total bacterial count. The results demonstrated that the NIR spectral measurement is superior to colour measurement for predicting microbial contamination and meat spoilage.