Untargeted Identification of Black Rice by Near-Infrared Spectroscopy and One-Class Models

Black rice is one of the famous rare rice varieties in China. It is common to sell inferior black rice intentionally declared as famous brands due to economical motivation. There is an urgent need to develop an analytical method for untargeted identification of black rice. The present work focuses on exploring the feasibility of the untargeted identification of black rice by the combination of near-infrared (NIR) spectroscopy and data driven-based class modeling and variable selection. A total of 142 samples of three brands were collected and used for measurements. The samples of a specific class were used as the target class. Principal component analysis was applied for the preliminary analysis. The model-independent variable selection method, i.e., joint mutual information, was used for spectral compression. Only the 10 most informative variables were picked from original variables based on which an optimal class-model for the target class was constructed and validated by means of an external test set. As a result, the model achieved 100% of sensitivity and specificity. It can be concluded that NIR spectroscopy combined with one-class modeling is a feasible tool for the untargeted identification of black rice.