Prediction of pH and color in pork meat using VIS-NIR Near-infrared Spectroscopy (NIRS)

Abstract

The potential of near-infrared spectroscopy (NORS) to predict the physicochemical characteristics of the porcine longissimus dorsi (LD) muscle was evaluated in comparison to the standard methods of pH and color for meat quality analysis compared to the pH results with Colorimeter and pH meter. Spectral information from each sample (n = 77) was obtained as the average of 32 successive scans acquired over a spectral range from 400 -2498 nm with a 2 -nm gap for calibration and validation models. Partial least squares (PLS) regression was used for each individual model. An R 2 and a residual predictive deviation (RPD) of 0.67/1.7, 0.86/2, and 0.76/1.9 were estimated for color parameters L*, a*, and b*, respectively. Final pH had an R 2 of 0.67 and a RPD of 1.6. NORS showed great potential to predict color parameter a* of porcine LD muscle. Further studies with larger samples should help improve model quality.Keywords: carcasses; color analysis; longissimus dorsi; partial least squares regression; pH analysis.Practical Application: Development of prediction curves for evaluation of pork meat quality.

Evaluation of WBSF, Color, Cooking Loss of Longissimus Lumborum Muscle with Fiber Optic Near-Infrared Spectroscopy (FT-NIR), Depending on Aging Time

“…However, in the case of redness a*, yellowness b*, and CL muscle in each day of aging, CV ranges were 11.38–15.44, 12.44–14.76, and 13.02–16.41 respectively. The value of the CV indicates that the data can guarantee significant calibration [ 34 ]. According to Andrés, [ 35 ], the acceptable CV level should be no less than 20%, while De Marchi [ 25 ] claims that a range of CV 6%–19% for color index indicates the existence of exploitable variability in developing calibration models.…”

“…In the internal full cross-validation process, the prediction capability of the model was evaluated, using the coefficient of determination in calibration (R 2 c ), standard error of calibration (SEC), coefficient of determination in prediction (R 2 p ), and standard error of prediction (SEP). Moreover, the prediction models were evaluated using the residual prediction deviation (RPD = SD/SEP) [ 34 ]. An RPD value above 3 is considered adequate for routine analysis, between 2 to 3 represents a good prediction, and less than 1.5 indicates incorrect prediction and the model cannot be used for prediction [ 43 ].…”

Metabolomic and proteomic features of fresh pork loin that vary in ultimate pH and pork quality

“…Further, both NIRS and Raman spectroscopy are used to predict meat quality based on the chemical composition of the products. Furtado et al (2018) found that NIRS was able to predict color in pork when measured at 24 hr postmortem. However, spectroscopy methods still have inconsistencies in their ability to measure other meat quality characteristics such as tenderness.…”

Smartphone imaging spectrometer for egg/meat freshness monitoring

“…11 The PH value and color of pork change with the deterioration of pork. 12 It is worth mentioning that these changes can be identified from near-infrared spectroscopy. For example, Douglas F. Barbin et al evaluated microbial contamination in pork through hyperspectral technology.…”