The prediction of ham composition by bioelectrical impedance analysis
A. Mateos A , C. J. López-Bote B , I. Ovejero A , M. A. Latorre C D and A. Daza AA Departamento de Producción Animal, E.T.S. Ingenieros Agrónomos, Universidad Politécnica de Madrid, Spain.
B Departamento de Producción Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Spain.
C Departamento de Producción Animal y Ciencia de los Alimentos, Facultad de Veterinaria, Universidad de Zaragoza, Spain.
D Corresponding author. Email: malatorr@unizar.es
Animal Production Science 53(10) 1119-1125 https://doi.org/10.1071/AN12385
Submitted: 7 June 2012 Accepted: 18 December 2012 Published: 3 April 2013
Abstract
The objective of this preliminary experiment was to study whether bioelectrical impedance analysis (BIA) can accurately predict the components of fresh pig hams. The trimmed right hams from 20 Iberian barrows were used. Six measures of resistance and reactance were taken by a bioelectrical impedance analyser. Simple and multiple regression equations were calculated for estimating bone, lean, intermuscular fat (IF), subcutaneous fat (SF), total fat (TF) and skin weights and percentages with respect to ham weight (HW). The HW accounted for 22% (P < 0.05) and 35% (P < 0.01) in the variations in lean and skin percentages, respectively. The ham compactness index (HCI), calculated as HW (in g)/(ham length, in cm)2, accounted for 20% (P < 0.05) and 38% (P < 0.01) in the variations in SF and TF percentages, respectively. The HW and BIA variables accounted for 60% (P < 0.001) of the variation in skin percentage; the HCI and BIA measures accounted for 79% (P < 0.0001), 66% (P < 0.001) and 78% (P < 0.0001) of the variation in lean, IF and SF percentages; and BIA variables accounted for 72% (P < 0.0001) of the variation in bone percentage. To determine the accuracy of the calculated regression equations, five additional trimmed fresh hams from Iberian barrows were used. Actual and predicted values were compared. The HW accurately predicted lean weight and skin percentage in linear regression equations, and HCI adequately predicted SF and TF weights in simple linear regression equations, and also SF percentage in inverse function. The additional inclusion of HW, HCI or BIA variables in the regression models did not improve the accuracy of the equations. It is concluded that BIA might be applied to predict the components of fresh hams but more studies are needed to determine whether levels of accuracy and precision are sufficient for this method to be used in practice.
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