ChatGPT based method for obtaining repeatable and quantitative colorimetric measurements

Area of Science:

• Environmental Science
• Analytical Chemistry
• Artificial Intelligence

Background:

• Artificial intelligence (AI) applications enhance productivity across various industries.
• ChatGPT, a generative AI model, produces human-like dialogue and responses.
• Accurate colorimetric analysis is crucial for assessing environmental pollutants.

Purpose of the Study:

• To evaluate ChatGPT's capability in determining colorimetric values for environmental pollutants.
• To assess the reproducibility of AI-driven colorimetric analysis.
• To compare AI-based color measurement with established spectrophotometric methods.

Main Methods:

• Utilized ChatGPT in conjunction with the Color Grab Android app to quantify RGB values of pollutant solutions.
• Investigated color difference (ΔE), hue, and chroma for pollutant color absorption.
• Validated results using a standard ultra-violet visible (UV-Vis) spectrophotometer.

Main Results:

• ChatGPT demonstrated consistent reproducibility in determining colorimetric values for Congo Red and Coomassie Brilliant Blue.
• AI-driven analysis provided quantitative RGB values and color difference metrics.
• Results obtained from ChatGPT and Color Grab were confirmed by UV-Vis spectrophotometry.

Conclusions:

• ChatGPT can be effectively employed for quantitative colorimetric assessment of environmental pollutants.
• This AI-powered approach offers a reproducible and validated alternative for pollutant analysis.
• The integration of AI tools like ChatGPT presents a novel avenue for environmental monitoring.