Sparse regression for selecting fluorescence wavelengths for accurate prediction of food properties

This paper tested various regression models (PIS, Ridge, Lasso, and sparse group Lasso) to select the appropriate fluorescence wavelengths/variables in excitation-emission matrices (EEMs) to improve the prediction of food identities. A framework using sparse models (the Lasso and sparse group Lasso) was proposed and compared with the conventional models. These sparse regression techniques can simultaneously achieve the ideal design of the estimator and select the most effective feature-related wavelengths. The experimental results showed that the proposed framework provided high prediction accuracy in selecting variables for accurate prediction of fish freshness and meat safety. Specifically, in case of predicting fish freshness, the sparse group Lasso regression had a determination coefficient R-2 of 0.790 with 493 EEM variables while the standard PLS regression had R-2 of 0.748 using all 1054 EEM variables. (C) 2016 Elsevier B.V. All rights reserved.