Sorry for a quick comment: Is there any evidence that computer vision systems can reliably do the QA step? what is the accuracy and precision like compared to traditional testing?
Thank you for the question — we recently presented our validation results at the Nutrition Society of India Conference, and you can view the full slide deck here: https://​​docs.google.com/​​presentation/​​d/​​1ReSC7R1HmV9_i61nu12YX5rOV56S6hGY_B3etFP5p3E/​​edit?usp=sharing. Our computer vision model, trained on 837 iron spot test images, currently achieves ~84% accuracy on test data and substantially reduces the subjectivity seen in manual qualitative testing. While it doesn’t replace quantitative lab methods like ICP-MS, it provides a low-cost, reliable, mill-level QA tool to flag potential under- or over-fortification more consistently.
Sorry for a quick comment: Is there any evidence that computer vision systems can reliably do the QA step? what is the accuracy and precision like compared to traditional testing?
Thank you for the question — we recently presented our validation results at the Nutrition Society of India Conference, and you can view the full slide deck here: https://​​docs.google.com/​​presentation/​​d/​​1ReSC7R1HmV9_i61nu12YX5rOV56S6hGY_B3etFP5p3E/​​edit?usp=sharing. Our computer vision model, trained on 837 iron spot test images, currently achieves ~84% accuracy on test data and substantially reduces the subjectivity seen in manual qualitative testing. While it doesn’t replace quantitative lab methods like ICP-MS, it provides a low-cost, reliable, mill-level QA tool to flag potential under- or over-fortification more consistently.