TY - JOUR
T1 - Modelling and optimization of high-pressure homogenization of not-from-concentrate juice
T2 - Achieving better juice quality using sustainable production
AU - Liu, Jianing
AU - Bi, Jinfeng
AU - Liu, Xuan
AU - Liu, Dazhi
AU - Verkerk, Ruud
AU - Dekker, Matthijs
AU - Lyu, Jian
AU - Wu, Xinye
PY - 2022/2/15
Y1 - 2022/2/15
N2 - The present work optimized high-pressure homogenization (HPH) parameters for not-from-concentrate combined peach and carrot juices, based on a two-step comprehensive model using factor analysis and analytic hierarchy process methods. Treating combined juice with pressures over 200 MPa retained more amounts of the bioactive compounds (carotenoids and polyphenols) than non-homogenization. Nutrition-oriented optimization, with higher judgement weight on nutritional properties, and sense-oriented optimization, with higher weight on sensory properties, were set up. Combined juice (250 MPa, 1 pass and 25 °C) had the best quality, based on the nutrition- and sense-oriented models. Back propagation neural network (BPNN) models could predict antioxidant capacities of the combined juice with greater accuracy compared with stepwise linear regression. The relative errors of BPNN prediction model were ≤ 5%.
AB - The present work optimized high-pressure homogenization (HPH) parameters for not-from-concentrate combined peach and carrot juices, based on a two-step comprehensive model using factor analysis and analytic hierarchy process methods. Treating combined juice with pressures over 200 MPa retained more amounts of the bioactive compounds (carotenoids and polyphenols) than non-homogenization. Nutrition-oriented optimization, with higher judgement weight on nutritional properties, and sense-oriented optimization, with higher weight on sensory properties, were set up. Combined juice (250 MPa, 1 pass and 25 °C) had the best quality, based on the nutrition- and sense-oriented models. Back propagation neural network (BPNN) models could predict antioxidant capacities of the combined juice with greater accuracy compared with stepwise linear regression. The relative errors of BPNN prediction model were ≤ 5%.
KW - Back propagation neural network
KW - Comprehensive quality
KW - High-pressure homogenization
KW - Not-from-concentrate (NFC) combined peach and carrot juice
KW - Sustainable production
U2 - 10.1016/j.foodchem.2021.131058
DO - 10.1016/j.foodchem.2021.131058
M3 - Article
AN - SCOPUS:85115296451
SN - 0308-8146
VL - 370
JO - Food Chemistry
JF - Food Chemistry
M1 - 131058
ER -