Estimation of lean meat content in pig carcasses using X-ray Computed Tomography and PLS regression

Font-i-Furnols, Maria; Teran, M. Fabiana; Gispert, M.

doi:10.1016/j.chemolab.2009.04.009

Cited by 54 publications

(21 citation statements)

References 16 publications

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“…CT has been demonstrated to be useful in the estimation of body composition in animals (Skjervold et al, 1981;Luiting et al, 1995;Toldi et al, 2007) and to measure lean percentage in pig carcasses (Romvári et al, 2006;Picouet et al, submitted for publication;Font i Furnols et al, 2009). Fat distribution in salmon (Rye, 1991) and quantification of dry matter and liquid leakage in Atlantic cod (Kolstad et al, 2008) have also been studied using CT.…”

Section: Introductionmentioning

confidence: 96%

Prediction of salt and water content in dry-cured hams by computed tomography

Fulladosa

Santos-Garcés

Picouet

et al. 2010

Journal of Food Engineering

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Section: Introductionmentioning

confidence: 96%

Prediction of salt and water content in dry-cured hams by computed tomography

Fulladosa

Santos-Garcés

Picouet

et al. 2010

Journal of Food Engineering

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“…The variables used in the models were volumes associated with attenuation HU values. Initially, the range was −149 to +140 HU for the live animals and −100 to +120 HU for the carcasses, according to previous studies carried out on live pigs and on carcasses (Font-i-Furnols et al, 2009). The ranges were extended to +411 HU for the prediction of ash, Ca and P to include part of the bone region.…”

Section: Discussionmentioning

confidence: 99%

“…The left side of each carcass was prepared and scanned with the CT equipment. The acquisition parameters were the same as those used for the live animals, except that the images were acquired helically (1 pitch) (Font-i-Furnols et al, 2009). The right carcasses were stored at −20°C until the mincing process.…”

Section: Animals and Experimental Designmentioning

confidence: 99%

“…Viscera were not removed from the images because the correlation of volumes from images with and without viscera is high (R 2 = 0.99). Then, the voxels were transformed into volume to homogenize the data using the DFOV value, matrix size and image thickness as described by Font i Furnols et al (2009).…”

Section: Image Analysismentioning

confidence: 99%

“…Computed tomography (CT) is a non-invasive technology that is based on the attenuation of X-rays as they pass through body tissues. Previous studies have proven that CT is a suitable method to estimate body composition in live pigs (Szabo et al, 1999;Font-i-Furnols et al, 2015) and carcasses (Font i Furnols et al, 2009). Szabo et al (1999) and Arthur et al (2011) showed the potential of this technology to measure the main chemical body components, but information on the relationship between CT and chemical composition is still limited.…”

Section: Introductionmentioning

confidence: 99%

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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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X‐Ray Computed Tomography Meets Robust Chemometric Latent Space Modeling for Lean Meat Percentage Prediction in Pig Carcasses

Mishra,

Font‐i‐Furnols

2024

Journal of Chemometrics

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This study presents a case of processing X‐ray computed tomography (CT) data for pork scans using chemometric latent space modeling. The distribution of voxel intensities is shown to exemplify a multivariate, multi‐collinear signal mixture. While this concept is not novel, it is revisited here from a chemometric perspective. To extract meaningful information from such multivariate signals, latent space modeling based on partial least squares (PLS) is an ideal solution. Furthermore, a robust PLS approach is even more effective for latent space modeling, as it can extract latent spaces unaffected by outliers, thereby enhancing predictive modeling. As an example, lean meat percentage is predicted using X‐ray CT data and robust PLS regression. This method is applicable to X‐ray CT quantification analysis, particularly in cases where unclear, erroneous, and outlying observations are suspected in the data.

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Estimation of lean meat content in pig carcasses using X-ray Computed Tomography and PLS regression

Cited by 54 publications

References 16 publications

Prediction of salt and water content in dry-cured hams by computed tomography

Prediction of salt and water content in dry-cured hams by computed tomography

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

X‐Ray Computed Tomography Meets Robust Chemometric Latent Space Modeling for Lean Meat Percentage Prediction in Pig Carcasses

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