Objective: Chitin a natural polymer is abundant in several sources such as shells of crustaceans, mollusks, insects, and fungi. Several possible attempts have been made to recover chitin because of its importance in biomedical applications in various forms such as hydrogel, nanoparticles, nanosheets, nanowires, etc. Among them, deep eutectic solvents have gained much consideration because of their eco-friendly and recyclable nature. However, several factors need to be addressed to obtain a pure form of chitin with a high yield. The development of an innovative system for the production of quality chitin is of prime importance and is still challenging.
Methods: The present study intended to develop a novel and robust approach to investigate chitin purity from various crustacean shell wastes using deep eutectic solvents. This investigation will assist in envisaging the important influencing parameters to obtain a pure form of chitin via a machine learning approach. Different machine learning algorithms have been proposed to model chitin purity by considering the enormous experimental dataset retrieved from previously conducted experiments. Several input variables have been selected to assess chitin purity as the output variable.
Results: The statistical criteria of the proposed model have been critically investigated and it was observed that the results indicate XGBoost has the maximum predictive accuracy of 0.95 compared with other selected models. The RMSE and MAE values were also minimal in the XGBoost model. In addition, it revealed better input variables to obtain pure chitin with minimal processing time.
Conclusion: This study validates that machine learning paves the way for complex problems with substantial datasets and can be an inexpensive and time-saving model for analyzing chitin purity from crustacean shells.
Keywords: Chitin; XGBoost; hydrogen bond acceptor; hydrogen bond donor; machine learning.