Exploring the purity of chitin from crustacean sources using deep eutectic solvents: A machine learning approach.

IF 3.1 4区 医学 Q2 BIOPHYSICS Journal of Applied Biomaterials & Functional Materials Pub Date : 2024-01-01 DOI:10.1177/22808000241248887
Sasireka Rajendran, Madheswaran Muthusamy
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引用次数: 0

Abstract

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.

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利用深共晶溶剂探索甲壳素的纯度:机器学习方法。
目的甲壳素是一种天然聚合物,在甲壳类动物、软体动物、昆虫和真菌的外壳等多种来源中含量丰富。由于甲壳素在生物医学应用中的重要性,人们已尝试以各种形式(如水凝胶、纳米颗粒、纳米片、纳米线等)回收甲壳素。其中,深共晶溶剂因其环保和可回收的特性而备受关注。然而,要获得高产率的纯甲壳素需要解决几个因素。开发一种生产优质甲壳素的创新系统至关重要,但目前仍面临挑战:本研究旨在开发一种新颖、稳健的方法,利用深共晶溶剂从各种甲壳类贝壳废料中研究甲壳素的纯度。这项研究将有助于通过机器学习方法设想获得纯甲壳素的重要影响参数。考虑到从以前进行的实验中获取的大量实验数据集,我们提出了不同的机器学习算法,以建立甲壳素纯度模型。我们选择了几个输入变量来评估作为输出变量的甲壳素纯度:对所提模型的统计标准进行了严格研究,结果表明,与其他选定模型相比,XGBoost 的预测准确率最高,达到 0.95。XGBoost 模型的 RMSE 和 MAE 值也最小。此外,它还揭示了更好的输入变量,从而以最短的加工时间获得纯甲壳素:这项研究证明,机器学习为解决复杂问题和大量数据集铺平了道路,并且可以成为分析甲壳类动物甲壳素纯度的一种廉价省时的模型。
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来源期刊
Journal of Applied Biomaterials & Functional Materials
Journal of Applied Biomaterials & Functional Materials BIOPHYSICS-ENGINEERING, BIOMEDICAL
CiteScore
4.40
自引率
4.00%
发文量
36
审稿时长
>12 weeks
期刊介绍: The Journal of Applied Biomaterials & Functional Materials (JABFM) is an open access, peer-reviewed, international journal considering the publication of original contributions, reviews and editorials dealing with clinical and laboratory investigations in the fast growing field of biomaterial sciences and functional materials. The areas covered by the journal will include: • Biomaterials / Materials for biomedical applications • Functional materials • Hybrid and composite materials • Soft materials • Hydrogels • Nanomaterials • Gene delivery • Nonodevices • Metamaterials • Active coatings • Surface functionalization • Tissue engineering • Cell delivery/cell encapsulation systems • 3D printing materials • Material characterization • Biomechanics
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