Non-parametric partial least squares–discriminant analysis model based on sum of ranking difference algorithm for tea grade identification using electronic tongue data

Abstract Identifying tea grades is crucial to providing consumers with tea and ensuring consumer rights. Partial least squares–discriminant analysis (PLS-DA) is a simple and traditional classification algorithm in analyzing e-tongue data. However, the number of latent variables (LVs) in a PLS-DA model needs to be determined, and cross-validation is the most common way to identify the optimal latent variables. To overcome this obstacle, sum of ranking difference (SRD) algorithm was applied to create a non-parametric PLS-DA-SRD model. The performance of PLS-DA and PLS-DA-SRD models were then compared, and significant improvement in term of accuracy, sensitivity, and specificity was obtained when SRD was combined with PLS-DA algorithm. Moreover, no training phase was needed to identify the optimal LVs for PLS-DA, making the calculation of classification rapid and concise. The PLS-DA-SRD method demonstrated its efficiency and capability by successfully identifying the tea sample grade.