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ARS Home » Northeast Area » Beltsville, Maryland (BHNRC) » Beltsville Human Nutrition Research Center » Methods and Application of Food Composition Laboratory » Research » Research Project #436639

Research Project: Development of Analytical and Chemometric Methods for Identification and Authentication of Foods and Botanical Supplements

Location: Methods and Application of Food Composition Laboratory

Project Number: 8040-52000-066-10-A
Project Type: Cooperative Agreement

Start Date: Jul 1, 2019
End Date: Dec 31, 2020

Objective:
To develop strategies for the identification and authentication of foods and botanical supplements using non-targeted analytical methods and chemometric analysis. Non-targeted methods will use the full spectra from simple near infrared (NIR) and ultraviolet spectrophotometry (UV) and sophisticated mass spectrometry (MS) and nuclear magnetic resonance (NMR) instruments to acquire as complete data as possible for botanical materials. The complex spectral data will be analyzed using conventional and new chemometric methods to detect patterns associated with experimental parameters such as genetics, environment, management, and processing. The analytical and data processing strategies will be used to develop guidelines for validation of botanical identification methods and will serve as examples for researchers in the field.

Approach:
Post-doctoral research associates will be hired by the university to work with the University and ARS PI's to evaluate the use of non-targeted analytical methods employing full spectra from NIR, MS, Ultra performance Liquid Chromatography Mass Spectrometer (UHPLC-MS), and NMR instruments. The spectral data will be analyzed using chemometric analysis employing principal component analysis (PCA), one-class modeling, and soft independent modeling of class analogy (SIMCA). These techniques will be applied to authenticate selected botanical materials (e.g., cranberries, apples, potatoes, rice), to differentiate between species, cultivars, growing locations, and processing methods. Simultaneously, data sets will be modeled and the limits of the methods will be determined using mathematical techniques. Analytical results, metadata, and spectral profiles will be entered into the USDA database, Food DataCentral. Generalized results will be used to develop guidelines for the development of botanical identification methods for Association of Official Analytical Chemists (AOAC) International. These guidelines will specify the number of samples needed to construct accurate models and allow researchers to determine the biological variability of the plant foods consumed in the U.S.