@article{SteinbachGolovkoKandelbaueretal.2023,
  author    = {Steinbach, Julia and Golovko, Dmytro and Kandelbauer, Andreas and Rebner, Karsten},
  title     = {Real-time quantification of meat paste constituents displaying nonlinear blending behavior including salt using an in-line NIR MEMS sensor},
  journal   = {ACS food science \& technology},
  volume    = {3},
  number    = {7},
  issn      = {2692-1944},
  doi       = {10.1021/acsfoodscitech.3c00163},
  institution = {Life Sciences},
  pages     = {1288 -- 1299},
  year      = {2023},
  abstract  = {Rapid and robust quality monitoring of the composition of meat pastes is of fundamental importance in processing meat and sausage products. Here, an in-line near-infrared spectroscopy/micro-electro-mechanical-system-(MEMS)-based approach, combined with multivariate data analysis, was used for measuring the constituents fat, protein, water, and salt in meat pastes within a typical range of meat paste recipes. The meat pastes were spectroscopically characterized in-line with a novel process analyzer prototype. By integrating salt content in the calibration set, robust predictive PLSR models of high accuracy (R2 > 0.81) were obtained that take interfering matrix effects of the minor and NIR-inactive meat paste recipe component "salt" into account as well. The nonlinear blending behavior of salt concentration on the spectral features of meat pastes is discussed based on a designed mixture experiment with four systematically varied components.},
  language  = {en}
}