# NH PHL Discipline C Rad Chem Analytical Track 1 - Food Defense

> **NIH FDA U19** · NH STATE DEPT/HLTH STATISTICS/DATA MGMT · 2020 · $250,000

## Abstract

Discipline C: Radiochemistry 
Analytical Track 1: Food Defense 
The NH PHL plans to prove presence or absence of radioactive contamination and determine the 
identities of radionuclides present in human or animal food samples through screening. The data 
generated by the NH PHL will be used to characterize the extent of food contamination, for 
following trends, and for calculating intake and to triage data to determine whether further analysis 
is needed. Semi-quantitative results will be used to determine if contamination is within regulatory 
limits. 
NH PHL is ready and able to perform analyses of human or animal food for the detection of 
gamma emitters, for example: Cs-137 and I-131. The NH PHL is also prepared to detect alpha 
emitters (Am-241 and Pu-239) and beta emitters (Sr-90) for food defense. 
All equipment is verified to be in working order and reagents and supplies are available. All 
analysts are trained annually and demonstrate competency in all testing activates performed. The 
NH PHL will participate in FDA-requested triage exercises, and/or surveillance activities to 
support and maintain readiness as well as participate in national security event exercises, as 
requested.

## Key facts

- **NIH application ID:** 10173079
- **Project number:** 1U19FD007070-01
- **Recipient organization:** NH STATE DEPT/HLTH STATISTICS/DATA MGMT
- **Principal Investigator:** Christine Louise Bean
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** FDA
- **Fiscal year:** 2020
- **Award amount:** $250,000
- **Award type:** 1
- **Project period:** 2020-09-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10173079

## Citation

> US National Institutes of Health, RePORTER application 10173079, NH PHL Discipline C Rad Chem Analytical Track 1 - Food Defense (1U19FD007070-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10173079. Licensed CC0.

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