Abstract: In anomaly detection of a substance, a first set of chemical fingerprints is obtained. Each fingerprint in the first set is indicative of a plurality of physiochemical properties for a normal sample of the substance. The first set is converted into a cluster of data points in a multi-dimensional principal component analysis (PCA) plot. Each dimension of the plot is based on a principal component (PC) corresponding to one of the physiochemical properties. A profile pattern of the cluster of data points is constructed as a prediction model to identify a new sample with a chemical fingerprint outside the profile pattern as an anomaly. The prediction model is optimized using a second set of chemical fingerprints. Each chemical fingerprint in the second set is indicative of the plurality of physiochemical properties for testing samples that include normal testing samples and abnormal ones of the substance.