Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy

Barlocco, N.; Vadell, A.; Ballesteros, Fernando; Galietta, Giovanni; Cozzolino, Daniel

doi:10.1079/asc20055

Cited by 169 publications

(92 citation statements)

References 28 publications

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“…As expected, while exhibiting effects of similar magnitude, IMF% and H 2 O% had contrasting relationships with eating quality because of the inverse relationship between moisture content and IMF% (Barlocco et al, 2006;Pflanzer and de Felício, 2011). However, when the two covariates were run in the model simultaneously, their F-values reduced, though they both retained significance.…”

supporting

confidence: 62%

“…Furthermore Thompson (2004) in cattle and in lamb found a positive relationship between IMF% and the flavour liking and juiciness scores of untrained Australian consumers. IMF% is also positively associated with the oxidative capacity (Jurie et al, 2007;Kelman et al, 2014) and therefore the haem iron content of muscles (Turkki and Campbell, 1967;Lengyel et al, 2003;Kelman et al, 2014) and has a strong negative association with the moisture content of beef and pork (Barlocco et al, 2006;Pflanzer and de Felício, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Biochemical measurements of beef are a good predictor of untrained consumer sensory scores across muscles

Bonny

Gardner

Pethick

et al. 2015

Animal

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The ability of the biochemical measurements, haem iron, intramuscular fat (IMF%), moisture content, and total, soluble and insoluble collagen contents, to predict untrained consumer sensory scores both across different muscles and within the same muscle from different carcasses were investigated. Sensory scores from 540 untrained French consumers (tenderness, flavour liking, juiciness and overall liking) were obtained for six muscles; outside (m. biceps femoris), topside (m. semimembranosus), striploin (m. longissimus thoracis), rump (m. gluteus medius), oyster blade (m. infraspinatus) and tenderloin (m. psoas major) from each of 18 French and 18 Australian cattle. The four sensory scores were weighted and combined into a single score termed MQ4, which was also analysed. All sensory scores were highly correlated with each other and with MQ4. This in part reflects the fact that MQ4 is derived from the consumer scores for tenderness, juiciness, flavour and overall liking and also reflects an interrelationship between the sensory scores themselves and in turn validates the use of the MQ4 term to reflect the scope of the consumer eating experience. When evaluated across the six different muscles, all biochemical measurements, except soluble collagen, had a significant effect on all of the sensory scores and MQ4. The average magnitude of impact of IMF%, haem iron, moisture content, total and insoluble collagen contents across the four different sensory scores are 34.9, 5.1, 7.2, 36.3 and 41.3, respectively. When evaluated within the same muscle, only IMF% and moisture content had a significant effect on overall liking (5.9 and 6.2, respectively) and flavour liking (6.1 and 6.4, respectively). These results indicate that in a commercial eating quality prediction model including muscle type, only IMF% or moisture content has the capacity to add any precision. However, all tested biochemical measurements, particularly IMF% and insoluble collagen contents, are strong predictors of eating quality when muscle type is not known. This demonstrates their potential usefulness in extrapolating the sensory data derived from these six muscles to other muscles with no sensory data, but with similar biochemical parameters, and therefore reducing the amount of future sensory testing required.

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supporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Biochemical measurements of beef are a good predictor of untrained consumer sensory scores across muscles

Bonny

Gardner

Pethick

et al. 2015

Animal

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“…A number of studies on the chemical composition of pork meat used NORS, in the reflection and absorption modes (Barlocco et al, 2006;Chan et al, 2002;Horiuchi et al, 1999;Lanza, 1983;Savenije et al, 2006;Tøgersen et al, 1999). The differences observed across studies may be related to differences in instrument calibration, sample size, sampling location, statistical methodology, and physical conditions at the sampling locations (industry or laboratory) (Kapper et al, 2012a).…”

Section: Introductionmentioning

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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“…For IMF content, the statistical values obtained indicate that the model could be able to discriminate between high, medium, and low IMF meat content in carcasses for classifications slaughter proposes. In the same way, moderate correlation was found for moisture and IMF parameters by Barlocco et al (2006) on intact samples of pork muscles using an at-line scanning monochromator (400-2,500 nm).…”

Section: Resultsmentioning

confidence: 57%

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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Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy

Cited by 169 publications

References 28 publications

Biochemical measurements of beef are a good predictor of untrained consumer sensory scores across muscles

Biochemical measurements of beef are a good predictor of untrained consumer sensory scores across muscles

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

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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