Prediction of primal cuts by using an automatic ultrasonic device as a new method for estimating a pig-carcass slaughter and commercial value
P. Janiszewski A , K. Borzuta A , D. Lisiak A C , E. Grześkowiak A and D. Stanisławski B
+ Author Affiliations
- Author Affiliations
A Department of Meat and Fat Technology, Prof. Wacław Dąbrowski Institute of Agricultural and Food Biotechnology, Poznań,Głogowska Street 239, 60-111 Poznań, Poland.
B University of Life Sciences, Department of Informatics, Wojska Polskiego Str. 33, 60-322 Poznań, Poland.
C Corresponding author. Email: Dariusz.lisiak@ibprs.pl
Animal Production Science 59(6) 1183-1189 https://doi.org/10.1071/AN15625
Submitted: 16 September 2015 Accepted: 4 May 2018 Published: 17 September 2018
Abstract
The objective of the present work was to develop regression equations to estimate the percentage, weight (in g) and lean meat content (in %) of the primal cuts of a pig carcass by using Auto-Fom and to estimate the commercial value of the carcass on the slaughter line in a meat-processing plant. The research was conducted on 168 pig carcasses. From the whole pork carcass, only the most valuable cuts (i.e. belly, ham, loin, neck and shoulder) and also meat content in ham and shoulder were weighed at a 100 g accuracy and the percentage of each cut in carcass was calculated. Loin ‘eye’ height and belly-muscle thickness were also measured. The regression equations for the prediction of the primal-cut weights and their percentages in the pig carcasses were derived using the partial least-square procedure. The developed equations include 70 variables that are standard measurements taken with an Auto-Fom device. These equations have a satisfactory accuracy rate and are useful in estimating the yield of the elements, especially for loin, ham and belly content. Belly-muscle thickness (R2 = 0.98) and the percentage of meat in the ham (R2 = 0.93) can be estimated with a high precision. It was confirmed that the developed equations may be used in the current Auto-Fom software.
Additional keywords: Auto-Fom device, pig carcass value, regression equations, PLS procedure.
References
Andersen HJ, Oksbjerg N, Young JF, Therkildsen M (2005) Feeding and meat quality: a future approach. Meat Science 70, 543–554.| Feeding and meat quality: a future approach.Crossref | GoogleScholarGoogle Scholar |
Baulain U (1997) Magnetic resonance imaging for the in vivo determination of body composition in animal science. Computers and Electronics in Agriculture 17, 189–203.
| Magnetic resonance imaging for the in vivo determination of body composition in animal science.Crossref | GoogleScholarGoogle Scholar |
Borzuta K, Lisiak D, Borys A, Strzelecki J, Magda F, Grześkowiak E, Lisiak B (2010) Study on the effect of lean meat content on commercial value of porcine carcass. Science, Nature, Technology 4, 1–14.
Branscheid W, Dobrowolski A, Höreth R (1997) Bestimmung der Handelsklassen und des Handelswertes von Schweinehälften mit dem Gerät Autofom. Die Fleischwirtschaft 77, 619–622.
Busk HE, Olsen V, Brondum J (1999) Determination of lean meat in pig carcasses with the Autofom classification system. Meat Science 52, 307–314.
| Determination of lean meat in pig carcasses with the Autofom classification system.Crossref | GoogleScholarGoogle Scholar |
Causeur D, Daumas G, Dhorne T, Engel B, Font i Furnols M, Hojsgaard S (2003) ‘Statistical handbook for assessing pig classification methods: recommendations from the ‘EUPIGCLASS’ project group.’ EC working document. (Brussels)
Chmiel M, Słowiński M, Jasiewicz K, Florkowski T (2016) Use of computer vision system (CVS) for detection of PSE pork meat obtained from m. semimembranosus. Lebensmittel-Wissenschaft + Technologie 65, 532–536.
| Use of computer vision system (CVS) for detection of PSE pork meat obtained from m. semimembranosus.Crossref | GoogleScholarGoogle Scholar |
Duziński K, Knecht D, Lisiak D, Janiszewski P (2015) Factors affecting the tissues composition of pork belly. Animal 9, 1897–1903.
| Factors affecting the tissues composition of pork belly.Crossref | GoogleScholarGoogle Scholar |
Engel B, Lambooij E, Buist WG, Vereijken P (2012) Lean meat prediction with HGP, CGM and CSB-Image-Meater, with prediction accuracy evaluated for different proportions of gilts, boars and castrated boars in the pig population. Meat Science 90, 338–344.
| Lean meat prediction with HGP, CGM and CSB-Image-Meater, with prediction accuracy evaluated for different proportions of gilts, boars and castrated boars in the pig population.Crossref | GoogleScholarGoogle Scholar |
European Commission (EC) (2008) ‘Laying down detailed rules on the implementation of the community scales for the classification of beef, pig and sheep carcasses and the reporting of prices thereof, 10 December 2008, Brussels. EC regulation 1249/2008.’ (EC: Brussels)
European Commission (EC) (2011) ‘Implementing decision of 16 August 2011 amending decision 2005/240/EC authorising methods for grading pig carcasses in Poland (2011/506/EU).’ (EC: Brussels)
Font i Furnols M, Gispert M (2009) Comparison of different devices for predicting the lean meat percentage of pig carcasses. Meat Science 83, 443–446.
| Comparison of different devices for predicting the lean meat percentage of pig carcasses.Crossref | GoogleScholarGoogle Scholar |
Font i Furnols M, Teran M, Gispert M (2009) Estimation of lean meat content in pig carcasses using X-ray computed tomography and PLS regression. Chemometrics and Intelligent Laboratory Systems 98, 31–37.
| Estimation of lean meat content in pig carcasses using X-ray computed tomography and PLS regression.Crossref | GoogleScholarGoogle Scholar |
Fortin A, Tong AK, Robertson WM, Zawadski SM, Landry SJ, Robinson DJ, Liu T, Mockford RJ (2003) A novel approach to grading pork carcasses: computer vision and ultrasound. Meat Science 63, 451–462.
| A novel approach to grading pork carcasses: computer vision and ultrasound.Crossref | GoogleScholarGoogle Scholar |
Fortin A, Tong AK, Robertson WM (2004) Evaluation of three ultrasound instruments, CVT-2, UltraFom 300 and AutoFom for predicting salable meat yield and weight of lean in the primals of pork carcasses. Meat Science 68, 537–549.
| Evaluation of three ultrasound instruments, CVT-2, UltraFom 300 and AutoFom for predicting salable meat yield and weight of lean in the primals of pork carcasses.Crossref | GoogleScholarGoogle Scholar |
Grunert KG, Brendahl L, Brunso K (2004) Consumer perception of meat quality and implications for product development in the meat sector: a review. Meat Science 66, 259–272.
| Consumer perception of meat quality and implications for product development in the meat sector: a review.Crossref | GoogleScholarGoogle Scholar |
Grześkowiak E, Borzuta K (2004) Results of meat quality and technological stability assesment of fatteners obtained from mating Naima sows with P76 boars. Polish Journal of Food and Nutrition Sciences 13/54, 199–202.
Grześkowiak E, Strzelecki J, Borys A, Borzuta K, Lisiak D (2005) Comparison of selected meat quality traits of hybrid and the meat-fat type fatteners. Annals of Animal Science 145–152.
Jankowska-Mąkosa A, Knecht D (2015) The influence of endoparasites on selected production parameters in pigs in various housing systems. Research in Veterinary Science 100, 153–160.
| The influence of endoparasites on selected production parameters in pigs in various housing systems.Crossref | GoogleScholarGoogle Scholar |
Knecht D, Popiołek M, Zaleśny G (2011) Does meatiness of pigs depend on the level of gastro-intestinal parasites infection? Preventive Veterinary Medicine 99, 234–239.
| Does meatiness of pigs depend on the level of gastro-intestinal parasites infection?Crossref | GoogleScholarGoogle Scholar |
Knecht D, Jankowska-Mąkosa A, Duziński K (2015) Does the activity of producer group organizations improve the production of pigs? Annals of Animal Science 15, 759–774.
| Does the activity of producer group organizations improve the production of pigs?Crossref | GoogleScholarGoogle Scholar |
Knecht D, Duziński K, Lisiak D (2016) Accuracy of estimating the technological and economic value of pig carcass primal cuts with an optical-needle device. Canadian Journal of Animal Science 96, 37–44.
| Accuracy of estimating the technological and economic value of pig carcass primal cuts with an optical-needle device.Crossref | GoogleScholarGoogle Scholar |
Kristensen L, Støier S, Würtz J, Hinrichsen L (2014) Trends in meat science and technology: the future looks bright, but the journey will be long. Meat Science 98, 322–329.
| Trends in meat science and technology: the future looks bright, but the journey will be long.Crossref | GoogleScholarGoogle Scholar |
Lisiak D (2014) Problems of modern slaughter animals classification system. In ‘XLII dni przemysłu mięsnego conference, monography, Warszawa’. 19–26. [In Polish]
Lisiak D, Borzuta K, Janiszewski P, Magda F, Grześkowiak E, Strzelecki J, Powałowski K, Lisiak B (2012) Verification of regression equations for estimating pork carcass meatiness using CGM, IM-03, Fat-O-Meat’er II and UltraFom 300 devices. Annals of Animal Science 12, 585–596.
| Verification of regression equations for estimating pork carcass meatiness using CGM, IM-03, Fat-O-Meat’er II and UltraFom 300 devices.Crossref | GoogleScholarGoogle Scholar |
Lisiak D, Duzinski K, Janiszewski P, Borzuta K, Knecht D (2015) A new simple method for estimating the pork carcass mass of primal cuts and lean meat content of the carcass. Animal Production Science 55, 1044–1050.
| A new simple method for estimating the pork carcass mass of primal cuts and lean meat content of the carcass.Crossref | GoogleScholarGoogle Scholar |
Marcoux M, Pomar C, Faucitano L, Brodeur C (2007) The relationship between different pork carcass lean yield definitions and the market carcass value. Meat Science 75, 94–102.
| The relationship between different pork carcass lean yield definitions and the market carcass value.Crossref | GoogleScholarGoogle Scholar |
Micklander E, Bertram H, Marnø H, Søvad , Bak L, Andersen HJ, Engelsen SE, Nørgaard L (2005) Early post-mortem discrimination of water-holding capacity in pig longissimus muscle using new ultrasound method. Lebensmittel-Wissenschaft + Technologie 38, 437–445.
| Early post-mortem discrimination of water-holding capacity in pig longissimus muscle using new ultrasound method.Crossref | GoogleScholarGoogle Scholar |
Ngapo TM (2017) Consumer preferences for pork chops in five Canadian provinces. Meat Science 129, 102–110.
| Consumer preferences for pork chops in five Canadian provinces.Crossref | GoogleScholarGoogle Scholar |
Olsen E, Candek-Potokar M, Oksama M, Kien S, Lisiak D, Busk H (2007) On-line measurements in pig carcass classification: repeatability and variation caused by the operator and the copy of instrument. Meat Science 75, 29–38.
| On-line measurements in pig carcass classification: repeatability and variation caused by the operator and the copy of instrument.Crossref | GoogleScholarGoogle Scholar |
Picouet PA, Teran F, Gispert M, Font i Furnols M (2010) Lean content prediction in pig carcasses, loin and ham by computed tomography (CT) using a density model. Meat Science 86, 616–622.
| Lean content prediction in pig carcasses, loin and ham by computed tomography (CT) using a density model.Crossref | GoogleScholarGoogle Scholar |
Picouet PA, Muñoz I, Fulladosa E, Daumas G, Gou P (2014) Partial scanning using computed tomography for fat weight prediction in green hams: scanning protocols and modelling. Journal of Food Engineering 142, 146–152.
| Partial scanning using computed tomography for fat weight prediction in green hams: scanning protocols and modelling.Crossref | GoogleScholarGoogle Scholar |
PN-86/A-82002 (2002) ‘Polish Standard. Pork meat. Primal cuts.’ (Polish Committee for Standardization: Warszawa)
Pomar C, Marcoux M (2003) Comparing the Canadian pork lean yields and grading indexes predicted from grading methods based on Destron and Hennessy probe measurements. Canadian Journal of Animal Science 83, 451–458.
| Comparing the Canadian pork lean yields and grading indexes predicted from grading methods based on Destron and Hennessy probe measurements.Crossref | GoogleScholarGoogle Scholar |
Przybylak A, Boniecki P, Koszela K, Ludwiczak A, Zaborowicz M, Lisiak D, Stanisz M, Ślósarz P (2016) Estimation of intramuscular level of marbling among Whiteheaded mutton sheep lambs. Journal of Food Engineering 168, 199–204.
| Estimation of intramuscular level of marbling among Whiteheaded mutton sheep lambs.Crossref | GoogleScholarGoogle Scholar |
Różycki M (2003) Selected traits of Polish pedigree pigs: progress in carcass meat deposition and meat quality. Animal Science Papers and Reports 21, 163–171.
Strzelecki J, Borzuta K, Lisiak D (1998) Development of the regression equation to estimate the meat content of pig carcases with the Auto-Fom device. Roczniki Instytutu Przemysłu Mięsnego i Tłuszczowego 35, 35–42. [in Polish]
Szulc K, Skrzypczak E, Buczyński JT, Stanisławski D, Jankowska-Mąkosa A, Knecht D (2012) Evaluation of fattening and slaughter performance and determination of meat quality in Złotnicka Spotted pigs and their crosses with the Duroc breed. Czech Journal of Animal Science 57, 95–107.
| Evaluation of fattening and slaughter performance and determination of meat quality in Złotnicka Spotted pigs and their crosses with the Duroc breed.Crossref | GoogleScholarGoogle Scholar |
Troy DJ, Kerry JP (2010) Consumer perception and the role of science in the meat industry. Meat Science 86, 214–226.
| Consumer perception and the role of science in the meat industry.Crossref | GoogleScholarGoogle Scholar |
Vangen O (1984) Evaluation of carcass composition of live pigs based on computed tomography. In ‘35th annual meeting of the EAAP’, 6–9 August, The Hague, The Netherlands. pp. 159–162.
Wajda S, Daszkiewicz T, Wajda M (2004) Quality of pig carcasses belonging to different classes in the EUROP grading system. Animal Science Papers and Reports 22, 569–576.
Zelenák L, Körmendy L, Vada-Kovács M (2004) The effect of different animal types on the prediction (calibration) equations used for pig carcass classification. Journal of Food Engineering 61, 431–437.
| The effect of different animal types on the prediction (calibration) equations used for pig carcass classification.Crossref | GoogleScholarGoogle Scholar |
Zelenák L, Körmendy L, Vada-Kovács M (2005) The effect of animal types on a simple control method used in the calibration procedure for assessing lean content in pig carcasses. Journal of Food Engineering 69, 351–358.
| The effect of animal types on a simple control method used in the calibration procedure for assessing lean content in pig carcasses.Crossref | GoogleScholarGoogle Scholar |
Zybert A, Koćwin-Podsiadła M, Krzęcio E, Sieczkowska H, Antosik K (2005) The gain and per cent content of the meat weight and fat in total, in half-carcasses produced by cutting and trimming pork carcasses differentiated by hot carcass weight and class of leanness under the EUROP carcass grading system. Żywność. Nauka – Technologia. Jakość 3, 255–264.
