Machine Learning Approaches to Surpass the Limitations of the Beer–Lambert Law

Many scientific and industrial applications depend on
the precise
measurement of chemical concentrations. The current study demonstrates
how an inventive method of combining photographic images with a machine
learning (ML) model successfully estimates the concentration of a
chemical compound in solution. A machine learning model using linear
regression with L2 regularization (ridge regression model) was developed
as a part of a predictive model. The model was trained on captured
images of K2Cr2O7 solutions following
the standard setup. After completing the training, the model was evaluated
using a data set of test samples. The prediction precision of the
model had been evaluated using 210 images and a high correlation between
actual and predicted K2Cr2O7 concentrations
was obtained with MAE, MSE, and RMSE of 1.4 × 10–5, 3.4 × 10–10, and 1.0 × 10–5, respectively. The ridge regression model is also extended to predict
the concentration of potassium permanganate (KMnO4) and
highlights the potential of integrating machine learning techniques
with image analysis to accurately quantify the concentration of any
chemical species in the solution state. As this model depends solely
on the color intensity of the sample without any molecular interactions,
it exceeds the limitations of the Beer–Lambert law. The created
machine learning model also minimizes the requirement of substantial
expertise and training and hence bridges the gap between experienced
and novice analysts.