Monitoring Pesticide Exposure and Accumulation: An Improved Rapid Identifier for all Pesticide Classes

Period of Performance: 04/01/2017 - 03/31/2018

$489K

Phase 2 SBIR

Recipient Firm

EIC Laboratories, Inc.
NORWOOD, MA 02062
Principal Investigator

Abstract

Project Summary The proposed program aims to quantify farmworker exposure to pesticides using Surface Enhanced Raman Spectroscopy (SERS) and an inexpensive sensor which can also be used for urinalysis. An earlier program, supported by NIEHS, provided reliable results for organophosphate (OP) and organochlorine (OC) pesticides. Fifty OP and OC pesticides and metabolites were evaluated in the laboratory in the low ppb range, and acephate and methyl parathion were detected at farmworker camps. In this program, we will utilize an innovative SERS sensing element which can also detect triazine pesticides, such as atrazine, simazine and cyromazine. In Phase I we developed a glycine-coated sensor that stabilized the adsorbed triazine ring leading to significant signal enhancement. This novel sensor detects triazines down to 1 ppb, is stable at temperatures and humidities encountered by farmworkers, and can be used for urinalyses. A new, combined sensor, to be developed in Phase II, will measure over 75 pesticides and/or metabolites at a small fraction of the cost of current analytical laboratory methods. It will provide the NIH (particularly the NIEHS Exposure Biology program) the means to develop a large data set on daily exposure/ingestion of pesticides. This approach should allow epidemiological studies and predictions of long-term health outcomes. Core technology has been demonstrated. The Phase II program is designed to demonstrate the capabilities of a combined sensor and provide full analytical specifications, finalize protocols for dosimetry, urine, cotton wipe and house dust collection measurements. Precision and detection limits for pesticides/metabolites of relevance and a thorough interference analysis will be quantitatively defined. System blind tests, statistical software optimization using training sets and unknown verifications and field testing with volunteer North Carolina farmworkers will be performed. Spectral dosimetry and urinalysis results will be correlated to worker activity through worker surveys. An automated system for third party evaluation will be completed in Phase II.