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Use of SERS Methods to Improve Food Safety

Lin, Mengshi
University of Missouri - Columbia
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End date
Establish surface enhanced Raman spectroscopy (SERS) methods coupled with SERS-active substrates for detecting pesticides in foods.

The objective of this study is to investigate the feasibility of using SERS coupled with gold substrates for rapid detection of pesticides in strawberries. Multivariate statistical analysis techniques such as partial least squares (PLS) and principal component analysis (PCA) will be used to analyze the SERS spectral data. Objective 2: Develop novel nanosubstrates, nanostructures, nanoparticles using inorganic materials (i.e. gold, silver, zinc oxide, titanium dioxide, etc) for food safety applications; Objective 3. Characterization and quantification of engineered nanoparticles in foods and investigate the fate, translocation, and behavior of nanomaterials and their toxicity.

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Non-Technical Summary:
In summary, this project will focus on developing new detection methods in combination with the latest nanotechnology to improve food safety. Our results show that SERS and other nanotechnology-based methods are promising approaches to detect food contaminants. We expect that the outcomes this project will include new methodologies and novel, simple, rapid, and sensitive novel sensing techniques that can be used to greatly assist food safety regulators and the food industry to safeguard our food supply. These new methods will be used to monitor the contamination and adulteration of foods.

Pesticides (captan, pyraclostrobin, and myclobutanil) were selected for this study. Organic strawberries were purchased from a local supermarket. Organic fruits were selected and cleaned to ensure that no pesticide residues existed on the samples. 100 ppm (w/v) of captan, pyraclostrobin, and myclobutanil stock solutions were prepared using a mixed solvent system (acetonitrile : H2O = 1 : 1, v/v). Solutions of 50, 10, 5, 1, 0.5, and 0.1 ppm pesticides were prepared by serial dilutions from the 100 ppm solution. The solvent without pesticides was used as the control.The strawberries were spiked with the corresponding volume of the working solution and extracted by 10 mL acetonitrile. A Renishaw RM1000 Raman Spectrometer System was used in SERS analysis. Klarite SERS-active substrates were used. SERS spectral data were analyzed by Delight software. Two multivariate statistical regression models, PLS and PCA, were constructed to predict analyte concentrations in tested samples. Please see the outline for more details.

Funding Source
Nat'l. Inst. of Food and Agriculture
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Bacterial Pathogens
Food Defense and Integrity
Natural Toxins