Artificial neural network in optimization of bioactive compound extraction: recent trends and performance comparison with response surface methodology.

IF 1.8 4区 化学 Q3 CHEMISTRY, ANALYTICAL Analytical Sciences Pub Date : 2024-11-06 DOI:10.1007/s44211-024-00681-w
Vigneshwaran Subramani, Vidisha Tomer, Gunji Balamurali, Paul Mansingh
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引用次数: 0

Abstract

Plant products and its by-products are rich source of bioactive compounds like antioxidants, flavonoids, phenolics, pigments and phytochemicals. Bioactive compound's health-promoting properties are well studied. However, optimal extraction of bioactive compounds is a complex, labour- and time-intensive process. It is also highly sensitive to experimental variables. Predicting output variables can reduce the experimental work and has positive environmental impact. Various tools such as Response Surface Methodology (RSM), Mathematical modelling have been commonly used for optimization and predictive modelling of the extraction process. Although mathematical modelling and RSM are efficient, recent studies have used Artificial Neural Network (ANN) which is more efficient and accurate and can perform extensive predictions with high accuracy. The manuscript focuses on current trends of ANN application in optimizing the extraction of bioactive compounds. In this study, ANN and RSM have been compared in terms of their performances in optimizing and modelling the extraction of bioactive compounds from herbs, medicinal plants, fruit, vegetables, and their by-products. The findings from the literature indicate that efficiency of ANN was superior to RSM. Future researches can focus on use of ANN in industrial optimization experiments.

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人工神经网络在生物活性化合物提取优化中的应用:最新趋势及与响应面方法的性能比较。
植物产品及其副产品是抗氧化剂、类黄酮、酚类、色素和植物化学物质等生物活性化合物的丰富来源。生物活性化合物对健康的促进作用已得到深入研究。然而,生物活性化合物的最佳提取是一个复杂、耗费人力和时间的过程。它对实验变量也非常敏感。预测输出变量可以减少实验工作量,并对环境产生积极影响。响应面方法(RSM)、数学建模等各种工具已被普遍用于萃取过程的优化和预测建模。虽然数学建模和 RSM 很有效,但最近的研究使用了人工神经网络(ANN),它更有效、更准确,可以进行广泛的高精度预测。本手稿重点介绍了当前在优化生物活性化合物提取过程中应用 ANN 的趋势。本研究比较了 ANN 和 RSM 在优化和模拟从草药、药用植物、水果、蔬菜及其副产品中提取生物活性化合物方面的性能。文献研究结果表明,ANN 的效率优于 RSM。今后的研究可侧重于在工业优化实验中使用方差网络。
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来源期刊
Analytical Sciences
Analytical Sciences 化学-分析化学
CiteScore
2.90
自引率
18.80%
发文量
232
审稿时长
1 months
期刊介绍: Analytical Sciences is an international journal published monthly by The Japan Society for Analytical Chemistry. The journal publishes papers on all aspects of the theory and practice of analytical sciences, including fundamental and applied, inorganic and organic, wet chemical and instrumental methods. This publication is supported in part by the Grant-in-Aid for Publication of Scientific Research Result of the Japanese Ministry of Education, Culture, Sports, Science and Technology.
期刊最新文献
Artificial neural network in optimization of bioactive compound extraction: recent trends and performance comparison with response surface methodology. pH monitoring in high ionic concentration environments: performance study of graphene-based sensors. Ionic liquid applications in analytical chemistry Correction: Effect of the alkyl chain length of α,ω‑dichloroalkane on the Gibbs energy of transfer for functional groups. Construction of all-solid-state ion-selective sensors using electrolyte-containing polymers.
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