Background: Redness can greatly influence the freshness of sausages. A precise, rapid and noncontact analytical method or tool is needed to quantify the color. Hyperspectral imaging (HSI) is an emerging technique that integrates spectroscopy and imaging to obtain the spectral and spatial information simultaneously. In the present study, the redness of cooked sausages stored up to 57 days was predicted using HSI in tandem with multivariate data analysis. The mean spectra of the sausages were extracted from the hyperspectral images. Partial least squares regression (PLSR) and forward stepwise multiple regression (FSMR) models were used to develop the relavent spectral profiles with the redness of the cooked sausages.
Results: Ten important wavelengths were selected based on the regression coefficient values from the PLSR model. The PLSR model established using the full wavelengths presented a good performance, with Rc of 0.934 and a root mean square error of calibration of 0.642 (redness ranged between 14.99 and 21.48). The prediction maps for demonstrating evolution of redness in sausages were developed for the first time using R statistics (R Foundation for Statistical Computing) and Matlab (MathWorks Inc., Natick, MA, USA).
Conclusion: HSI combined with PLSR and FSMR can be used to quantify and visualize evolution of sausage redness under different storage days. © 2017 Society of Chemical Industry.
Keywords: FSMR; PLSR; cooked sausages; hyperspectral imaging; redness.
© 2017 Society of Chemical Industry.