Botanical authentication using one-class modeling.

Sample authentication using one-class modeling is simpler and more versatile than the approved AOAC Probability of Identification (POI) method. This approach develops a one-class model for authentic samples and does not identify or model non-authentic or adulterated samples. Unknown samples are classified as either authentic (in the same class as the authentic samples) or not authentic (outside the authentic model class). One-class modeling uses chemometric analysis based on soft independent modeling of class analogy and the specific pre-processing steps of sample vector normalization and autoscaling. Data from flow injection mass spectrometry are used to illustrate the method. Autoscaling reduces the impact of the relative ion intensities and provides much greater sensitivity to changes in the intensities of individual variables. Detection limits for each variable can be predicted based on an average spectrum and types of uncertainty (random or proportional noise). Method validation is readily achieved using cross validation. Detection of adulteration is spectrally oriented and requires no identification of adulterants. Thus, both the data acquisition and the selection of adulterants are untargeted.