Real-time quantification of meat paste constituents displaying nonlinear blending behavior including salt using an in-line NIR MEMS sensor
- 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.
| Author of HS Reutlingen | Steinbach, Julia; Golovko, Dmytro; Kandelbauer, Andreas; Rebner, Karsten |
|---|---|
| DOI: | https://doi.org/10.1021/acsfoodscitech.3c00163 |
| ISSN: | 2692-1944 |
| Erschienen in: | ACS food science & technology |
| Publisher: | American Chemical Society |
| Place of publication: | Washington |
| Document Type: | Journal article |
| Language: | English |
| Publication year: | 2023 |
| Tag: | constituents DoE; MVA; NIR; design of experiments; in-line monitoring; meat pastes |
| Volume: | 3 |
| Issue: | 7 |
| Page Number: | 12 |
| First Page: | 1288 |
| Last Page: | 1299 |
| PPN: | Im Katalog der Hochschule Reutlingen ansehen |
| DDC classes: | 540 Chemie |
| Open access?: | Nein |
| Licence (German): |  In Copyright - Urheberrechtlich geschützt |
