Development and validation of a paper-based sensor and convolutional neural networks integrated in a single-board computer for the quantification of sodium iron-EDTA and zinc oxide in fortified corn flours
Toc Sagra, Marco Eduardo
This item is closed and only viewable by specific users.
Permalink
https://hdl.handle.net/2142/121201
Description
Title
Development and validation of a paper-based sensor and convolutional neural networks integrated in a single-board computer for the quantification of sodium iron-EDTA and zinc oxide in fortified corn flours
Author(s)
Toc Sagra, Marco Eduardo
Issue Date
2023-06-12
Director of Research (if dissertation) or Advisor (if thesis)
Andrade, Juan E.
Doctoral Committee Chair(s)
Stasiewicz, Matthew J.
Committee Member(s)
Engeseth, Nicki J.
Wang, Yi-Cheng
Department of Study
Food Science & Human Nutrition
Discipline
Food Science & Human Nutrition
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Paper-based Sensor
Computer Vision
Quality Control
Language
eng
Abstract
Micronutrient deficiencies have a high prevalence in low- and middle-income countries (LMICs), particularly among children under five years and women of reproductive age. To combat micronutrient deficiencies in LMICs, the addition of micronutrients to fortify staple foods, e.g., corn flour, through rural small-scale mills has proven to be a cost-effective strategy. The laboratory capacity to assess quality control and assurance (QC/QA) in these contexts, however, is either unavailable or prohibitively expensive. The overall objective of this dissertation is to design, develop, and validate two paper-based sensors which interface with a built-in-house computer vision-based analyzer to quantify iron and zinc in corn flour. The dissertation will accomplish the aforementioned objective using mixed-methods studies. The first study will develop a paper-based sensor to quantify iron concentration in fortified corn flour and will validate its performance using samples from Tanzania’s small-scale mill fortification programs to determine its applicability as a monitoring and evaluation tool. In addition, this study will optimize the mineral extraction method and sampling instrument design. The second study will develop a paper-based sensor to quantify zinc concentration in fortified corn flour and will validate its performance using samples from Tanzania’s small-scale mill fortification programs to determine its applicability as a means of monitoring and evaluating. The third study will design and optimize a convolutional neural network architecture as the backbone of the computer vision-based analyzer that predicts iron and zinc concentration using digital images from the paper-based sensor. In collaboration with SANKU Project Healthy Children, a non-governmental organization supporting flour fortification in Tanzania, this dissertation ultimately serves as a bottom-up solution to the monitoring and evaluation limitations of low-resource fortification programs.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
