A novel approach for the optimization of Shatavari (Asparagus racemosus Willd.) plant-based low alcohol nutra beverage production using Saccharomyces cerevisiae (NCIM 2428) in conjunction with artificial neural network and genetic algorithm (ANN-GA).

IF 2.6 3区农林科学 Q2 FOOD SCIENCE & TECHNOLOGY

Journal of Food Science and Technology-mysore Pub Date : 2025-08-01 Epub Date: 2025-04-29 DOI:10.1007/s13197-025-06275-2

Divya Chaudhary, S N Naik, P Hariprasad

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引用次数: 0

Abstract

Introduction: This study focused on optimizing the fermentation conditions of Shatavari plant-based roots using an artificial neural network and response surface methodology. The aim was to identify the optimal independent variables and corresponding responses by comparing experimental and predicted responses. The experimentation was validated using a genetic algorithm, determining the best temperature, pH, and inoculum parameters.

Material and methods: In this study, we used the Shatavari (Asparagus racemosus Willd.) plant's root as their primary raw material and subjected it to treatment with α amylase and gluco-amylase enzyme (EC 232-885-6) which exhibited a remarkable activity level ranging from 8000 to 12,000 U/mg The resulting hydrolysate was fermented using Saccharomyces cerevisiae (NCIM 2428) culture. To determine the optimal combination of input variables a Central Composite Rotatable Design was implemented, facilitated by the Design Expert software (Version 11.0.3.0 by Stat-Ease Inc.),.

Result and conclusion: The optimal conditions for the experiment were found to be a temperature of 32 °C, pH of 4.0, and inoculum concentration of 10% (v/v). The Artificial Neural Network (ANN) model was able to successfully predict the response variables with a marginal relative error rate of 8.722% and 24.312% for ethanol production and antioxidant activity, respectively. The fermented Shatavari-based low-alcohol Nutra beverage contained only fructose. The validation of Shatavari juice using the ANN model showed an enhanced ethanol yield of 3.21% and 421.47 μg/L antioxidant activity during fermentation. The experimental and predicted outcomes from the Artificial Neural Network-Genetic Algorithm (ANN-GA) model matched, proving its predictive precision.

Supplementary information: The online version contains supplementary material available at 10.1007/s13197-025-06275-2.

查看原文本刊更多论文

基于人工神经网络和遗传算法（ANN-GA）优化酿酒酵母（Saccharomyces cerevisiae, NCIM 2428）植物基低醇营养饮料生产的新方法

摘要：本研究采用人工神经网络和响应面法对沙棘根发酵条件进行优化。目的是通过比较实验反应和预测反应，确定最优的自变量和相应的反应。实验采用遗传算法进行验证，确定最佳温度、pH和接种量参数。材料与方法：本研究以总状芦笋（Shatavari）根为主要原料，经α淀粉酶和葡萄糖淀粉酶（EC 232- 886 -6）处理，其酶活性在8000 ~ 12000 U/mg之间，水解产物采用酿酒酵母（Saccharomyces cerevisiae, NCIM 2428）培养基发酵。为了确定输入变量的最佳组合，在Design Expert软件（Stat-Ease Inc.的11.0.3.0版本）的帮助下，实现了中央复合可旋转设计。结果与结论：实验的最佳条件为温度32℃，pH 4.0，接种量10% （v/v）。人工神经网络（ANN）模型能够成功预测乙醇产量和抗氧化活性的响应变量，边际相对错误率分别为8.722%和24.312%。以沙塔瓦里为基础的低酒精发酵Nutra饮料只含有果糖。经人工神经网络模型验证，发酵过程中乙醇产量提高3.21%，抗氧化活性提高421.47 μg/L。人工神经网络遗传算法（ANN-GA）模型的实验结果与预测结果吻合，证明了其预测精度。补充信息：在线版本包含补充资料，下载地址为10.1007/s13197-025-06275-2。

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来源期刊

Journal of Food Science and Technology-mysore 工程技术-食品科技

CiteScore

7.70

自引率

0.00%

发文量

274

审稿时长

11 months

期刊介绍： The Journal of Food Science and Technology (JFST) is the official publication of the Association of Food Scientists and Technologists of India (AFSTI). This monthly publishes peer-reviewed research papers and reviews in all branches of science, technology, packaging and engineering of foods and food products. Special emphasis is given to fundamental and applied research findings that have potential for enhancing product quality, extend shelf life of fresh and processed food products and improve process efficiency. Critical reviews on new perspectives in food handling and processing, innovative and emerging technologies and trends and future research in food products and food industry byproducts are also welcome. The journal also publishes book reviews relevant to all aspects of food science, technology and engineering.