# A novel approach of age-grading of mosquitoes using SERS and machine learning models

> **NIH NIH R01** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2024 · $407,940

## Abstract

Project Summary
Mosquito-borne pathogens, including malaria, Zika, dengue, and chikungunya continue to be a major public
health concern globally. As only older mosquitoes are infectious and represent a risk to human health, scientists
have sought to age-grade mosquitoes based on this understanding; however, no reliable, cost effective and
practical methods exist to age mosquitoes despite the tremendous epidemiological value of this approach. The
overall objective of this R01 is to establish a novel approach to age-grade mosquitoes Aedes aegypti in the field.
The approach we took is based on surface-enhanced Raman spectroscopy (SERS) to analyze the biomolecules
from mosquito water extract that are bound with silver nanoparticles (AgNPs) and then the SERS spectra are
used in modern machine learning models to age-grade the mosquitoes. Our central hypothesis is that AgNPs
interact with specific biomolecules enabling SERS to generate unique and predictable spectral information for
establishing modern machine learning models to determine the age of mosquitoes. Our prior work demonstrates
the feasibility of SERS and Artificial Neuron Networks (ANNs) to determine the age of both lab (error <1 day)
and field-collected (error < 2 days) mosquitoes Ae. aegypti. In the proposed work, we will establish robust lab
and field-deployable protocols to produce reliable and repeatable SERS data of mosquito water extract. Then,
we will manipulate the lab and field conditions to determine the impact of biotic (food and infection status) and
abiotic (temperature) to SERS characteristics. Robust and accurate machine learning model based on modern
ANNs and Domain Adoption (DA) strategies will be established and validated for age-grading mosquitoes in the
field. In addition, we will explore the Multi-task Learning (MTL) strategies to simultaneously determine the age
and infection status. Our long-term goal is to establish a rapid, cost-effective, and field-deployable system that
enables real-time analysis and data sharing to facilitate epidemiological studies, risk assessment, vector control
intervention monitoring and evaluation.

## Key facts

- **NIH application ID:** 10780363
- **Project number:** 1R01AI180243-01
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST
- **Principal Investigator:** Lili He
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $407,940
- **Award type:** 1
- **Project period:** 2023-12-15 → 2028-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10780363

## Citation

> US National Institutes of Health, RePORTER application 10780363, A novel approach of age-grading of mosquitoes using SERS and machine learning models (1R01AI180243-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10780363. Licensed CC0.

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