PATTERN RECOGNITION FOR FOODS AND SUPPLEMENTS
Food Composition and Methods Development Lab
2012 Annual Report
1a.Objectives (from AD-416):
To assist in the application of analysis of variance-principal components analysis (ANOVA-PCA) to food and botanical data acquired at ARS and to develop an algorithm for aligning chromatograms obtained with ultra-violet (UV) and mass spectrometric (MS) detection. The purpose of this cooperative agreement is to develop spectral fingerprinting and chromatographic profiling methods for the rapid detection and categorization of foods and botanical materials.
1b.Approach (from AD-416):
The Food Composition and Methods Development Laboratory (FCMDL) is responsible for developing analytical methods for chemical components in foods and dietary supplements. New methods are being developed for spectral fingerprinting and chromatographic profiling of plant materials using UV and MS detection. Fingerprints are acquired by direct analysis with no chromatographic separation. Both types of data are analyzed using pattern recognition programs like ANOVA-PCA. Proper interpretation of the data requires considerable skill in the area of chemometrics, the area of expertise of the cooperator. A particularly troublesome problem is that the application of ANOVA-PCA to chromatographic profiles requires the alignment of the chromatograms prior to processing. The cooperator will assist in the application of ANOVA-PCA to data obtained at USDA and in the analysis of variable interaction. In addition, the cooperator will develop software for aligning chromatograms so that ANOVA-PCA can be applied to chromatographic profiles.
In collaboration with a Professor at Ohio University, FCMDL has developed methods for the analysis of complex data patterns that allow the determination of the authenticity of unknown botanical samples. This has been done using principal component analysis (PCA), one class classification with soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis ( PLS-DA), fuzzy rule-building expert system (FuRES), and fuzzy optimal associate memory (FOAM). These methods make it possible to determine if the fingerprint of the unknown material matches that of the authentic material. These methods were used to discriminate between American and Asian ginseng, American ginseng grown in Wisconsin, Canada, and China, and adulterated and authentic Ginkgo biloba.