使用多层感知器-遗传算法的扁豆底物配方:关键的生产检查点

N. Safaie, M. Salehi, S. Farhadi, Ali Aligholizadeh, V. Mahdizadeh
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摘要

香菇(冬菇)是全世界种植和食用最广泛的蘑菇品种之一。香菇营养丰富,含有各种矿物质、维生素、必需的宏量和微量营养元素以及生物活性化合物,是一种潜在的食品和药物来源。从生态和经济角度来看,农业和工业残留物的再利用至关重要。在这项研究中,我们记录了在由甘蔗渣(B)、麦麸(WB)和榉木锯屑(BS)按不同比例混合而成的 64 种基质上培养出的益多虫在培养后 5 天至第 40 天的运行长度(RL)。采用多层感知器-遗传算法(MLP-GA)、多元线性回归、逐步回归、主成分回归、普通最小二乘法回归和偏最小二乘法回归来预测和优化 L. edodes 的 RL 和运行率(RR)。统计值显示,MLP-GA 模型对 RL 和 RR 的预测准确率(分别为 92% 和 97%)高于回归模型(分别为 52% 和 71%)。L. edodes 的 RL 和 RR 预测值与实际值之间的高度拟合证实了所开发的 MLP-GA 模型的卓越性能。对已建立的 MLP-GA 模型进行的优化分析表明,基质中含有 15.1% 的 B、45.1% 的 WB 和 10.16% 的 BS,以及 28 天和 10 小时的运行时间可使 L. edodes 的 RL 达到最大值(10.69 厘米)。此外,含有 30.7% B、90.4% WB 和 0.0% BS 的基质可获得最高的 L. edodes RL(0.44 cm d-1)。据观察,MLP-GA 是一种有效的方法,可用于预测并进而选择最佳基质组成,以获得 L. edodes 的最大 RL 和 RR。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lentinula edodes substrate formulation using multilayer perceptron-genetic algorithm: a critical production checkpoint
Shiitake (Lentinula edodes) is one of the most widely grown and consumed mushroom species worldwide. They are a potential source of food and medicine because they are rich in nutrients and contain various minerals, vitamins, essential macro- and micronutrients, and bioactive compounds. The reuse of agricultural and industrial residues is crucial from an ecological and economic perspective. In this study, the running length (RL) of L. edodes cultured on 64 substrate compositions obtained from different ratios of bagasse (B), wheat bran (WB), and beech sawdust (BS) was recorded at intervals of 5 days after cultivation until the 40th day. Multilayer perceptron-genetic algorithm (MLP-GA), multiple linear regression, stepwise regression, principal component regression, ordinary least squares regression, and partial least squares regression were used to predict and optimize the RL and running rate (RR) of L. edodes. The statistical values showed higher prediction accuracies of the MLP-GA models (92% and 97%, respectively) compared with those of the regression models (52% and 71%, respectively) for RL and RR. The high degree of fit between the forecasted and actual values of the RL and RR of L. edodes confirmed the superior performance of the developed MLP-GA models. An optimization analysis on the established MLP-GA models showed that a substrate containing 15.1% B, 45.1% WB, and 10.16% BS and a running time of 28 days and 10 h could result in the maximum L. edodes RL (10.69 cm). Moreover, the highest RR of L. edodes (0.44 cm d−1) could be obtained by a substrate containing 30.7% B, 90.4% WB, and 0.0% BS. MLP-GA was observed to be an effective method for predicting and consequently selecting the best substrate composition for the maximal RL and RR of L. edodes.
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