After feature extraction, common machine learning classifiers were used to classify SERS spectra. Support vector machine (SVM) was selected due to its superior performance in the prior Raman study . A 5-fold cross-validation was conducted to assess the suitability of the classification algorithm . In brief, the training and the validation sets were established by randomly selecting from the Raman spectral data. The training dataset was used to generate a classification model, and the model predicted the validation dataset to evaluate the performance. The cross-validation approach was repeated five times, wherein the validation set consists of and 800 and 480 randomly selected SERS spectra in repetition for the whole milk glycan study and sialic acid linkage study, respectively. The model's performance was evaluated by using classification accuracies, sensitivity, and selectivity.