Detection of Non-structural Protein 1 (NS1) in saliva has become appealing as it may lead to a non-invasive detection method for NS1-related diseases at the febrile phase, before complication developed. NS1 is found to have its unique molecular fingerprint from Surface Enhanced Raman Spectroscopy (SERS) technique. Our work here intends to investigate the effect of termination criterion of Principal Component Analysis (PCA) on the classification performance by the different Artificial Neural Network (ANN) learning algorithms. This will help in optimizing the automated classification of NS1 adulterated saliva, and hence detection of NS1-related diseases. Raman spectra of saliva (n=64) and saliva mixed with NS1 (n=64) are acquired using SERS obtained from the UiTM-NMRR 12868-NS1-DENV database. Large input data dimension of the raw [128 x 1801] are reduced according to the respective PCA termination criteria: Scree test [128 x 5], Cumulative Percent of Total Variance (CPV) [128 x 70] and Eigenvalues One Criterion (EOC) [128 x 115]. The reduced data dimensions are used as inputs to ANN algorithms. Performance of these algorithms, in term of [accuracy, sensitivity, specificity, and precision] from Levenbergh Marquardt (LM), Scale Conjugate Gradient (SCG), Resilient Backpropagation (RPROP) and One Step Secant (OSS) are investigated. The best performance [100%, 100%, 100%, 100%] are achieved from the integration of Scree test criterion and SCG learning algorithm, the highest expected of adulterated data.