Prediction of pork quality attributes from near infrared reflectance spectra

Geesink, G.H.; Schreutelkamp, F.H; Frankhuizen, R.; Vedder, H.W; Faber, Nicolaas M.; Kranen, R.W.; Gerritzen, M.A.

doi:10.1016/s0309-1740(02)00269-3

Cited by 124 publications

(60 citation statements)

References 18 publications

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“…The data quality of the statistical set was analyzed for the analysis of the validation set for each model, based on the proportion coefficient of determination (R 2 ), the mean square error of calibration (RMSEC), mean square error of cross-validation (RMSECV) (Geesink et al, 2003). The equations of evaluation of the prediction model were also determinants for assessing the quality of the model in cross-validation (Balage et al, 2015).…”

Section: Statistical Analysis and Partial Least Squares (Pls) Calibramentioning

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: Statistical Analysis and Partial Least Squares (Pls) Calibramentioning

confidence: 99%

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

et al. 2019

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“…Drip loss and shear force for pork were studied by Geesink et al [160]. The data (reflectance spectra (1000-2500 nm), water-holding capacity, shear force, ultimate pH, and colour (L * , a * , b * -value) of 96 pork longissimus) were obtained two days post mortem and subjected to stepwise multiple linear regression (SMLR) and partial least-square regression (PLSR).…”

Section: B113 Sensory Characteristics Other Than Appearancementioning

confidence: 99%

“…Study of Geesink et al [160] indicates poor predictability. Liao et al [133] obtained relatively good prediction for the calibration set but over 2.5 times poorer for the validation set.…”

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confidence: 97%

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

Alander

Bochko

Martinkauppi

et al. 2013

International Journal of Spectroscopy

129

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This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.

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“…The use of rapid spectroscopic techniques for quality inspection of meat has been successfully demonstrated for a number of quality aspects of pork. Amongst these are the fatty acid composition of pork fat (García Olmo et al 2001), IMF (BrØrdum et al 2000), drip loss, tenderness (Geesink et al 2003), and pH (Savenije et al 2006). Most of these applications were from moderately successful to successful, depending on instrumentation, wavelength range, or scanning meat samples intact or minced.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Value of a Portable Near Infrared Spectroscopy Sensor for Predicting Pork Meat Quality Traits of “Asturcelta Autochthonous Swine Breed”

et al. 2013

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Sixty-one intact meat samples from Asturcelta autochthonous swine breed were scanned in the slaughterhouse in reflectance mode. A handheld microelectromechanical system digital transform (Phazir1624, Polychromix Inc.), with a window sampling area of 0.8×1 cm and wavelengths ranging from 1,600 to 2,400 nm, was used. With the spectra database recorded were developed different chemometrical models assaying first and second derivatives as math treatment and standard normal variate (SNV) and multiplicative scatter correction for minimizing scattering effect. The greatest predictive capacity was achieved after applying SNV and first derivative for moisture, intramuscular fat (IMF) content, and pH parameters and second derivative for CIE L*, a*, b* colorimetric values, and the Warner-Bratzler force (instrumental texture). The coefficients of determination for calibration ranged from 0.63 to 0.89. The ratio between the standard error of the laboratory and the standard error of calibration ranged from 0.8 to 2.5 for all parameters (1.7 on average) with the exception of b and pH with ratios of 3.5 and 4.1, respectively. The statistical values obtained for the models developed to estimate IMF, CIE L*, a*, b*, moisture, and pH, displayed acceptable predictive capacity. For instrumental texture, the model could be able to discriminate among tender, medium, and hard meat in carcasses for characterization slaughter purposes.

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Prediction of pork quality attributes from near infrared reflectance spectra

Cited by 124 publications

References 18 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)

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

Assessing the Value of a Portable Near Infrared Spectroscopy Sensor for Predicting Pork Meat Quality Traits of “Asturcelta Autochthonous Swine Breed”

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