Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review

Prieto, Nuria; Roehe, R.; Lavín, Paz; Batten, Graeme; Andrés, Sonia

doi:10.1016/j.meatsci.2009.04.016

Cited by 427 publications

(298 citation statements)

References 71 publications

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“…Szabo et al (1999) showed R 2 values for the fat (0.89), protein (0.83) and water (0.82) content in live animals, although they did not present prediction error values. The lower accuracy for protein can be explained by the narrow Pig chemical composition with computed tomography range of variation of the protein content in the present study in accordance with Prieto et al (2009) in NIRS, and also by analytical differences between the Kjeldahl methodology and CT. The available studies in the literature have focused on predicting the main carcass chemical components, but little is known about the prediction of the mineral content.…”

Section: Discussionsupporting

confidence: 67%

Predicting the carcass chemical composition and describing its growth in live pigs of different sexes using computed tomographys

Zomeño

Gispert

Carabús

et al. 2016

Animal

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The aims of this study were (1) to evaluate the ability of computed tomography (CT) to predict the chemical composition of live pigs and carcasses, (2) to compare the chemical composition of four different sex types at a commercial slaughter weight and (3) to model and evaluate the chemical component growth of these sex types. A total of 92 pigs (24 entire males (EM), 24 surgically castrated males (CM), 20 immunocastrated males (IM) and 24 females (FE)) was used. A total of 48 pigs (12 per sex type) were scanned repeatedly in vivo using CT at 30, 70, 100 and 120 kg and slaughtered at the end of the experiment. The remaining 44 were CT scanned in vivo and slaughtered immediately: 12 pigs (4 EM, 4 CM and 4 FE) at 30 kg and 16 pigs each at 70 kg and 100 kg (4 per sex type). The left carcasses were CT scanned, and the right carcasses were minced and analysed for protein, fat, moisture, ash, Ca and P content. Prediction equations for the chemical composition were developed using Partial Least Square regression. Allometric growth equations for the chemical components were modelled. By using live animal and carcass CT images, accurate prediction equations were obtained for the fat (with a root mean square error of prediction (RMSEP CV ) of 1.31 and 1.34, respectively, and R 2 = 0.91 for both cases) and moisture relative content (g/100 g) (RMSEP CV = 1.19 and 1.38 and R 2 = 0.94 and 0.93, respectively) and were less accurate for the protein (RMSEP CV = 0.65 and 0.67 and R 2 = 0.54 and 0.63, respectively) and mineral content (RMSEP CV from 0.28 to 1.83 and R 2 from 0.09 to 0.62). Better equations were developed for the absolute amounts of protein, fat, moisture and ash (kg) (RMSEP CV from 0.26 to 1.14 and R 2 from 0.91 to 0.99) as well as Ca and P (g) (RMSEP CV = 144 and 71, and R 2 = 0.76 to 0.66, respectively). At 120 kg, CM had a higher fat and lower moisture content than EM. For protein, CM and IM had lower values than FE and EM. The ash content was higher in EM and IM than in FE and CM, while IM had a higher Ca and P content than the others. The castrated animals showed a higher allometric coefficient for fat and a lower one for moisture, with IM having intermediate values. However, for the Ca and P models, IM presented higher coefficients than EM and FE, and CM were intermediate.

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Section: Discussionsupporting

confidence: 67%

Predicting the carcass chemical composition and describing its growth in live pigs of different sexes using computed tomographys

Zomeño

Gispert

Carabús

et al. 2016

Animal

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“…The control of the basic chemical component content of meat is important for technological, economical as well as legal aspect of process manufacturing. Raw material that is standardized for the basic chemical composition allows for obtaining the production repeatability (Prieto et al 2009a;Campagnol et al 2012;Ham et al 2016). The role of pork meat standardization in the technological aspect will be the determination of basic chemical component content in each production lot.…”

Section: Introductionmentioning

confidence: 99%

Using Density Measurement on Semispinalis capitis as a Tool to Determinate the Composition of Pork Meat

et al. 2018

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The aim of the present study was an attempt to utilize density measurements using different methods as 3D scanning, hydrostatic, and pycnometric for the estimation of chemical composition of 20 pork semispinalis capitis. The density determined with direct (hydrostatic, pycnometric) and indirect (3D scanning) methods was correlated with water, protein, fat, and ash content. The highest correlation coefficients (absolute value) between density and water (r = 0.86), protein (r = 0.79), and fat (r = − 0.87) content of pork necks were found for hydrostatic method. Despite the standard error of prediction (SEP) obtained for density determination using 3D scanning method was close to acceptable tolerance range recommended by the EU law, which amounts to 20% mean value on the declaration of calculated fat and protein content in a product, further studies are needed on a larger population of samples.

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“…These ratios are very acceptable with regard to the accuracy of the calibration (Savenije et al 2006). Regarding the literature, results of studies evaluating NIR spectroscopy for predictions of L*, a*, and b* are contradictory (Prieto et al 2009). In this research work, the results after applying SNV and second derivative as mathematical treatments, for all color-related parameters, L*, a*, and b*, displayed R 2 and r 2 values that ranged between 0.89-0.75 and 0.74-0.59, respectively, using a MEMS device with a small scan window which does not work in the optimum region for predicting color in the visible region of the spectrum (VIS), and applied on intact muscle.…”

Section: Resultsmentioning

confidence: 95%

“…Prieto et al (2009) established that the ability of NIR spectroscopy to predict the content of main chemical components such as moisture, crude protein, and fat is more accurate in minced meat samples than in intact tissues. In intact samples, the muscle fibers may conduct light along their length and they could cause internal reflections.…”

Section: Resultsmentioning

confidence: 99%

“…In summary, the ability of NIR spectroscopy to predict chemical characteristics using intact samples had been widely demonstrated (Prieto et al 2009), but nowadays, the implementation of quality controls (in situ or on-line) such as in the slaughterhouse line and at industrial level by using a handheld MEMS instrument is of great importance. In this context, it is significant that improvements and progress into NIR instruments more adapted for on-site analysis allow to develop new applications with real time response.…”

Section: Resultsmentioning

confidence: 99%

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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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Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review

Cited by 427 publications

References 71 publications

Predicting the carcass chemical composition and describing its growth in live pigs of different sexes using computed tomographys

Predicting the carcass chemical composition and describing its growth in live pigs of different sexes using computed tomographys

Using Density Measurement on Semispinalis capitis as a Tool to Determinate the Composition of Pork Meat

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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