Image-Based Machine Learning Using Inkjet-Printed Chemicals: Mixing Ratio Prediction and Metal Ion Detection

Inkjet printing of π-conjugated organic compounds
enabled
rapid, low-cost generation of training images for the image-based
machine learning (ML) prediction of mixing ratios. ML models with
mean absolute errors below 4% were achieved within hours, even for
dyes with subtle color differences. Changing the printing surface
from filter paper to a polypropylene film extended the method to colorless
compounds, including isomeric and macrocyclic systems. This approach
also enabled spatial mapping of sub-microgram levels of Zn2+ ions using a weakly responsive colorimetric sensor, without the
need for a spectrometer. This work demonstrates a simple, versatile
strategy for integrating π-conjugated materials with ML in colorimetric
sensing and mixture analysis.