Interpretable machine-learning for predicting molecular weight of PLA based on artificial bee colony optimization algorithm and adaptive neurofuzzy inference system

This article discusses the integration of the artificial bee colony (ABC) algorithm with two supervised learning methods, namely artificial neural network (ANN) 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 features, appropriate machine learning tools are essential for interpreting data and selecting features to improve prediction accuracy. Initially, the ABC optimization algorithm is combined with ANN/ANFIS to predict PLA molecular weight. The objective function of the ABC algorithm is to minimize the mean cross-validation root mean square error (RMSE) between experimental and predicted PLA molecular weights with a defined number of features. Results indicate that employing ABC-ANFIS yields the lowest mean RMSE of 631 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 optimization algorithm with ANFIS for selecting a minimal set of features to predict PLA molecular weight with high accuracy during processing.