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United States Department of Agriculture

Agricultural Research Service

Research Project: DEVELOP METHODS TO ASSESS AND IMPROVE POULTRY AND EGG QUALITY

Location: Quality and Safety Assessment Research Unit

Title: Principal component analysis of phenolic acid spectra

Author
item Holser, Ronald

Submitted to: Spectroscopy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 3, 2012
Publication Date: October 15, 2012
Citation: Holser, R.A. 2012. Principal component analysis of phenolic acid spectra. Spectroscopy. 2012:1-5.

Interpretive Summary: Plant materials are common sources of nutritional supplements for food and animal feed. The ability to identify these supplements in low-cost sources such as agricultural residues will increase their use and lead to improved health of the consumer. The phenolic compounds are one group of nutritional supplements that have health benefits and are commonly found in both commodity crops and low-cost biomass. A spectroscopic technique was developed that can detect phenolic compounds in agricultural materials without expensive laboratory instrumentation. This method is expected to support the use of phenolic compounds by rapidly identifying alternative sources of these compounds.

Technical Abstract: Phenolic acids are common plant metabolites that exhibit bioactive properties and have applications in functional food and animal feed formulations. The ultraviolet (UV) and infrared (IR) spectra of four closely related phenolic acid structures were evaluated by principal component analysis (PCA) to develop spectral models for their rapid detection. Results demonstrated that UV and IR spectra could discriminate between each of the phenolic acids in overall models. Calculation of model scores and loadings showed derivative UV spectra accounted for 99% variation with 2 principal components (PC) while derivative IR spectra required 3 PCs. Individual PCA models were developed for ferulic acid and p-coumaric acid using derivative UV spectra for detection and classification by soft independent modeling of class analogy (SIMCA). The application of this spectral technique as a screening tool is expected to promote the use of agricultural residues as a source of these phenolic compounds.

Last Modified: 8/29/2014
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