TY - JOUR
T1 - Near Infrared Reflectance spectroscopy to analyse texture related characteristics of sous vide pork loin
AU - Perez-Palacios, Trinidad
AU - Caballero, Daniel
AU - González-Mohíno, Alberto
AU - Mir-Bel, Jorge
AU - Antequera, Teresa
PY - 2019/12
Y1 - 2019/12
N2 - This study aims to evaluate the ability of the Near Infrared Reflectance spectroscopy (NIRs) technique to analyse texture-related characteristics of sous-vide pork loins at different times of cooking. For that, pork loins were sous-vide at 70 °C for 1, 2, 4, 6 and 8 h. Cooked samples were analysed by means of NIRs, instrumental (cooking loss, pH, moisture, hydrolysed collagen and texture profile analysis) and sensory analysis. Classification and predictive techniques of data mining were applied on the obtained data. Sous-vide loins were correctly classified as a function of time of cooking and their texture-related characteristics were predicted accurately, achieving correlation coefficients (R) higher than 0.5 and Mean Absolute Scaled Errors lower than 1 for most parameters. Thus, it is demonstrated the capability of NIRs to analyse most texture-related parameters of warm loin samples, and it may be recommended as a rapid and automatic techniques to stablish optimal cooking conditions of food.
AB - This study aims to evaluate the ability of the Near Infrared Reflectance spectroscopy (NIRs) technique to analyse texture-related characteristics of sous-vide pork loins at different times of cooking. For that, pork loins were sous-vide at 70 °C for 1, 2, 4, 6 and 8 h. Cooked samples were analysed by means of NIRs, instrumental (cooking loss, pH, moisture, hydrolysed collagen and texture profile analysis) and sensory analysis. Classification and predictive techniques of data mining were applied on the obtained data. Sous-vide loins were correctly classified as a function of time of cooking and their texture-related characteristics were predicted accurately, achieving correlation coefficients (R) higher than 0.5 and Mean Absolute Scaled Errors lower than 1 for most parameters. Thus, it is demonstrated the capability of NIRs to analyse most texture-related parameters of warm loin samples, and it may be recommended as a rapid and automatic techniques to stablish optimal cooking conditions of food.
KW - Near Infrared Reflectance spectroscopy
KW - Texture-related characteristics
KW - Loin
KW - Sous-vide
U2 - 10.1016/j.jfoodeng.2019.07.028
DO - 10.1016/j.jfoodeng.2019.07.028
M3 - Journal article
SN - 0260-8774
VL - 263
SP - 417
EP - 423
JO - Journal of Food Engineering
JF - Journal of Food Engineering
ER -