Perspective of NIRS measurements early post mortem for prediction of pork quality

Hoving-Bolink, A.H.; Vedder, H.W; Merks, J.W.M.; Klein, Wilfried; Reimert, H.G.M.; Frankhuizen, R.; Broek, W.H.A.M. van den; Lambooij, E.

doi:10.1016/j.meatsci.2004.08.012

Cited by 112 publications

(50 citation statements)

References 12 publications

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“…Similar weak correlation equation values were reported by Čandek-Potokar et al (2006), using NORS to predict pork quality traits (R 2 = 0.76 and RPD = 1.6). Finally, the poor performance of the calibration parameter for final pH (Table 2) may have been due to the small variability of final pH in the samples, as Hoving-Bolink et al (2005) argued that a large number of samples are needed to minimize the effects of pork meat variation and to improve the quality of prediction equations. Andersen et al (1999) found similar values in the prediction model for pH (R 2 = 0.62 and RPD = 1.66) of processed pork meat.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2019

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“…Near-infrared spectroscopy (NIRS) is a sensitive, fast and non-destructive analytical technique with simplicity in sample preparation and has received attention for use in the quantitative and qualitative analysis of quality and adulteration in meat and meat products, such as beef [6], pork [7], poultry [8] and seafood [9]. As for the near-infrared spectroscopy technique, both the dispersive system and Fourier transform (FT) system showed potential for rapid analysis of meat quality [10,11].…”

Section: Introductionmentioning

confidence: 99%

Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy

et al. 2017

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Models for determining contents of soy products in ground beef were developed using near-infrared (NIR) spectroscopy. Samples were prepared by mixing four kinds of soybean protein products (Arconet, toasted soy grits, Profam and textured vegetable protein (TVP)) with ground beef (content from 0%-100%). NIR spectra of meat mixtures were measured with dispersive (400-2500 nm) and Fourier transform NIR (FT-NIR) spectrometers (1000-2500 nm). Partial least squares (PLS) regression with full leave-one-out cross-validation was used to build prediction models. The results based on dispersive NIR spectra revealed that the coefficient of determination for cross-validation (R cv 2 ) ranged from 0.91 for toasted soy grits to 0.99 for Arconet. The results based on FT-NIR spectra exhibited the best prediction for toasted soy grits (R cv 2 = 0.99) and R cv 2 > 0.98 for the other three soy types. For identification of different types of soy products, support vector machine (SVM) classification was used and the total accuracy for dispersive NIR and FT-NIR was 95% and 83.33%, respectively. These results suggest that either dispersive NIR or FT-NIR spectroscopy could be used to predict the content and the discrimination of different soy products added in ground beef products. In application, FT-NIR spectroscopy methods would be recommended if time is a consideration in practice.

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“…From economic point of view, high WHC is extremely desirable because meat is sold by weight and any water loss leads to a reduction in yield due to loss in the total weight of the meat (Hoving-Bolink et al, 2005;Micklander et al, 2005). Moisture is lost after slaughter in the form of drip while the carcass is still in the chilling room and when fresh cuts are in the retail display counter.…”

Section: Introductionmentioning

confidence: 99%

“…The rapid screening techniques to determine quality characteristics of meat at the level of slaughtering, meat cutting, and distribution are of great interest for both industry and consumers (Hoving-Bolink et al, 2005;Prieto et al, 2009). For large-scale meat production, online quality prediction for steaks, chops, and other intact pieces of meat is desired.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging

ElMasry

Sun

Allen

2011

Food Research International

252

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Perspective of NIRS measurements early post mortem for prediction of pork quality

Cited by 112 publications

References 12 publications

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

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

Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy

Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging

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