Particle swarm optimization-based protocol for partial least-squares discriminant analysis: Application to 1H nuclear magnetic resonance analysis of lung cancer metabonomics

• Particle swarm optimization was invoked to meliorate PLS-DA forming PSO-PLSDA.
• PSO-PLSDA was applied to 1H NMR analysis of lung cancer metobonomics.
• Results revealed that PSO-PLSDA yielded superior generalization ability to PLS-DA.
• By PSO-PLSDA, the total RR for the test set was 85%.

The complexity of metabolic profiles makes multivariate chemometric techniques crucial for extracting mostly significant information and offering biological insight. Partial least-squares discriminant analysis (PLS-DA) was proven fruitful in metabonomic community, due to its promising properties. The issues of suboptimum and overfitting, however, often occur in PLS-DA modeling. In the current study, particle swarm optimization (PSO) was invoked to meliorate PLS-DA via simultaneously selecting the optimal variable subset as well as the associated weights and the best number of latent variables in PLS-DA, forming a new algorithm named PSO-PLSDA. Combined with 1H NMR-based metabonomics, PSO-PLSDA compared with PLS-DA was applied to recognize lung cancer patients from healthy controls. Relatively to the recognition rates of 86% and 65% for the training and test sets yielded by PLS-DA, 99% and 85% were obtained by PSO-PLSDA. Moreover, several most discriminative metabolites were identified by PSO-PLSDA to aid the diagnosis of lung cancer, including lactate, proline, glycoprotein, glutamate, alanine, threonine, taurine, glucose (α- and β-), trimethylamine, glutamine, glycine, and myo-inositol.