Predictions of multiple food quality parameters using near-infrared spectroscopy with a novel multi-task genetic programming approach

In order to meet the increasing demand for food safety and quality, new methods for simultaneous and rapid determination of multiple food quality parameters (FQPs) are urgently needed in the food industry. Incorporating near-infrared (NIR) spectroscopy and spectral prediction model for rapid, repeatable, non-destructive, and low running costs quantitative analysis of FQPs is enjoying increasing popularity in the food industry. However, most existing spectrum-based prediction models are trained under a single-task learning framework, that is, a prediction model for each quality parameter and spectrum is constructed separately. This paradigm ignores possible connections among prediction tasks of different FPQs, which may result in the performance degradation of a single FPQ prediction model. This study proposes a novel multi-task genetic programming-based approach named EM4GPO for building multiple FQPs predictions simultaneously. In EM4GPO, the multi-dimensional trees are used to encode the raw NIR spectrum to shared features of multiple FQPs; for each FQP, a least square support vector regression (LS-SVR) modeling is performed on the shared features to obtain private features and prediction model; during the optimization process, a new algorithm is developed to optimize the previously obtained shared and private features, and LS-SVR prediction models through population evolution by combining the multidimensional multiclass genetic programming with multidimensional populations optimization method with nondominated sorting method. The proposed EM4GPO model is evaluated and compared with nine popular NIR prediction models using 10 NIR spectral datasets. The experimental results showed that EM4GPO outperformed other commonly used methods in all datasets which indicates that EM4GPO is competitive and effective in solving the problem of multiple FQPs predictions using the NIR spectrum.