TY - JOUR
T1 - Improved prediction of minced pork meat chemical properties with near-infrared spectroscopy by a fusion of scatter-correction techniques
AU - Mishra, P.
AU - Verkleij, T.J.
AU - Klont, Ronald
PY - 2021/3
Y1 - 2021/3
N2 - The modelling near-infrared (NIR) spectroscopy data requires removal of scattering effects from the data before applying advanced chemometrics methods. Often different scatter-correction techniques are explored, and the scatter-correction technique with the best performance is selected. However, the information highlighted by different scatter-correction techniques may be complementary and their fusion may result in better models for predicting characteristics, such as meat quality. To test this, sequential and parallel preprocessing fusion approaches will be used in this work to fuse information from different scatter-correction techniques to try to improve the predictive performance of NIR models. Three different chemical properties, i.e., moisture, fat and protein content, were predicted. For comparison, partial least-squares regression (PLSR) was performed on standard normal variate (SNV) corrected data, as this is a widely used scatter-correction technique. Compared to this commonly used procedure, the scattering fusion approaches reduced the error and bias by up to 52% and 84%, respectively. The results suggest that fusion of scatter-correction techniques is essential to achieve optimal NIR prediction models for predicting meat characteristics such as moisture, fat and protein content.
AB - The modelling near-infrared (NIR) spectroscopy data requires removal of scattering effects from the data before applying advanced chemometrics methods. Often different scatter-correction techniques are explored, and the scatter-correction technique with the best performance is selected. However, the information highlighted by different scatter-correction techniques may be complementary and their fusion may result in better models for predicting characteristics, such as meat quality. To test this, sequential and parallel preprocessing fusion approaches will be used in this work to fuse information from different scatter-correction techniques to try to improve the predictive performance of NIR models. Three different chemical properties, i.e., moisture, fat and protein content, were predicted. For comparison, partial least-squares regression (PLSR) was performed on standard normal variate (SNV) corrected data, as this is a widely used scatter-correction technique. Compared to this commonly used procedure, the scattering fusion approaches reduced the error and bias by up to 52% and 84%, respectively. The results suggest that fusion of scatter-correction techniques is essential to achieve optimal NIR prediction models for predicting meat characteristics such as moisture, fat and protein content.
KW - Fusion
KW - Multiblock
KW - Multivariate
KW - Preprocessing
KW - Spectroscopy
U2 - 10.1016/j.infrared.2021.103643
DO - 10.1016/j.infrared.2021.103643
M3 - Article
AN - SCOPUS:85099717150
SN - 1350-4495
VL - 113
JO - Infrared Physics and Technology
JF - Infrared Physics and Technology
M1 - 103643
ER -