PATTERN RECOGNITION FOR FOODS AND SUPPLEMENTS
Food Composition and Methods Development Lab
2011 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.
During FY2011 we examined SIMCA (soft independent modeling of class analogy) as a multivariate method for judging whether an unknown material is an authentic or adulterated material. This approach uses collections of authentic materials that reflect the natural variance arising from different growing years, growing sites, and processing conditions to establish a model. The unidentified material is then compared to the model and judged to be authentic or a different material. We have found an error measurement parameter that is sometimes used in identifying outliers that is particularly useful for comparing the unknown and the authentic materials.
This agreement was monitored through routine conversations and e-mails with the Cooperator at Ohio University.