Interpretable machine-learning for predicting molecular weight of PLA based on artificial bee colony optimization algorithm and adaptive neurofuzzy inference system
Journal:
arXiv
Published Date:
Jan 13, 2025
Abstract
This article discusses the integration of the Artificial Bee Colony (ABC)
algorithm with two supervised learning methods, namely Artificial Neural
Networks (ANNs) and Adaptive Network-based Fuzzy Inference System (ANFIS), for
feature selection from Near-Infrared (NIR) spectra for predicting the molecular
weight of medical-grade Polylactic Acid (PLA). During extrusion processing of
PLA, in-line NIR spectra were captured along with extrusion process and machine
setting data. With a dataset comprising 63 observations and 512 input features,
appropriate machine learning tools are essential for interpreting data and
selecting features to improve prediction accuracy. Initially, the ABC
optimization algorithm is coupled with ANN/ANFIS to forecast PLA molecular
weight. The objective functions of the ABC algorithm are to minimize the root
mean square error (RMSE) between experimental and predicted PLA molecular
weights while also minimizing the number of input features. Results indicate
that employing ABC-ANFIS yields the lowest RMSE of 282 Da and identifies four
significant parameters (NIR wavenumbers 6158 cm-1, 6310 cm-1, 6349 cm-1, and
melt temperature) for prediction. These findings demonstrate the effectiveness
of using the ABC algorithm with ANFIS for selecting a minimal set of features
to predict PLA molecular weight with high accuracy during processing
