TY - JOUR
T1 - Analysis of the production of salmon fillet - Prediction of production yield
AU - Johansson, Gine Ørnholt
AU - Guðjónsdóttir, María
AU - Nielsen, Michael Engelbrecht
AU - Skytte, Jacob Lercke
AU - Frosch, Stina
PY - 2017
Y1 - 2017
N2 - The aim was to investigate the influence of raw material variation in Atlantic salmon from aquaculture on filleting yield, and to develop a decision tool for choosing the appropriate raw material for optimized yield. This was achieved by tracking salmon on an individual level (n = 60) through a primary production site. The majority of the salmon exhibited a heavier right fillet compared to the left fillet after filleting. No explicit explanation was found for this observation although the heading procedure was shown to have a large impact. A Partial Least Square model was built to predict the yield after filleting. The model was based on six pre-processing variables and allowed an acceptable prediction of the filleting yield with a root mean square error cross validation of 0.68. The presented model can estimate the slaughter yield for a certain batch before ordering from the slaughterhouse. This may facilitate optimal planning of the production of salmon fillets by ordering and assigning the right batch to the right product category to obtain an optimal yield and quality.
AB - The aim was to investigate the influence of raw material variation in Atlantic salmon from aquaculture on filleting yield, and to develop a decision tool for choosing the appropriate raw material for optimized yield. This was achieved by tracking salmon on an individual level (n = 60) through a primary production site. The majority of the salmon exhibited a heavier right fillet compared to the left fillet after filleting. No explicit explanation was found for this observation although the heading procedure was shown to have a large impact. A Partial Least Square model was built to predict the yield after filleting. The model was based on six pre-processing variables and allowed an acceptable prediction of the filleting yield with a root mean square error cross validation of 0.68. The presented model can estimate the slaughter yield for a certain batch before ordering from the slaughterhouse. This may facilitate optimal planning of the production of salmon fillets by ordering and assigning the right batch to the right product category to obtain an optimal yield and quality.
KW - Production analysis
KW - Prediction
KW - Atlantic salmon
KW - Yield
KW - Multivariate data analysis
KW - PLS
U2 - 10.1016/j.jfoodeng.2017.02.022
DO - 10.1016/j.jfoodeng.2017.02.022
M3 - Journal article
SN - 0260-8774
VL - 204
SP - 80
EP - 87
JO - Journal of Food Engineering
JF - Journal of Food Engineering
ER -