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. 2024 May 21:15:1366264.
doi: 10.3389/fmicb.2024.1366264. eCollection 2024.

Lentinula edodes substrate formulation using multilayer perceptron-genetic algorithm: a critical production checkpoint

Naser Safaie  1 , Mina Salehi  2 , Siamak Farhadi  3 , Ali Aligholizadeh  1 , Valiollah Mahdizadeh  1
Affiliations

Lentinula edodes substrate formulation using multilayer perceptron-genetic algorithm: a critical production checkpoint

Naser Safaie et al. Front Microbiol. .

Abstract

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.

Keywords: Shiitake; artificial neural network; medicinal and edible mushroom; optimization; running.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Schematic of multilayer perceptron (MLP) architecture.
Figure 2
Figure 2
Schematic representation of genetic algorithm (GA) as an evolutionary optimization algorithm.
Figure 3
Figure 3
Flowchart of integrating multilayer perceptron (MLP) with genetic algorithm (GA) to optimize of the MLP architecture and input values to achieve the maximum level of each output.
Figure 4
Figure 4
Scatter plot of actual data vs. predicted values of the running length of Shiitake (Lentinula edodes) using multiple linear regression (MLR), stepwise regression (SR), principal component regression (PCR), ordinary least squares regression (OLSR), partial least squares regression (PLSR), and multilayer perceptron-genetic algorithm (MLP-GA) models in the training subset. The solid line shows a fitted simple regression line for scatter points.
Figure 5
Figure 5
Scatter plot of actual data vs. predicted values of shiitake (Lentinula edodes) running rate using multiple linear regression (MLR), stepwise regression (SR), principal component regression (PCR), ordinary least squares regression (OLSR), partial least squares regression (PLSR), and multilayer perceptron-genetic algorithm (MLP-GA) models in the training subset. The solid line shows a fitted simple regression line for scatter points.
See this image and copyright information in PMC

References

    1. Abramson M. (2007). Genetic algorithm and direct search toolbox user’s guide. Natick, MA: The MathWorks Inc.
    1. Agatonovic-Kustrin S., Beresford R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharm. Biomed. Anal. 22, 717–727. doi: 10.1016/S0731-7085(99)00272-1, PMID: - DOI - PubMed
    1. Ahmad I., Arif M., Xu M., Zhang J., Ding Y., Lyu F. (2023). Therapeutic values and nutraceutical properties of shiitake mushroom (Lentinula edodes): A review. Trends Food Sci. Technol. 134, 123–135. doi: 10.1016/j.tifs.2023.03.007 - DOI
    1. Akin M., Eyduran S. P., Eyduran E., Reed B. M. (2020). Analysis of macronutrient related growth responses using multivariate adaptive regression splines. Plant Cell Tiss. Org. Cult. 140, 661–670. doi: 10.1007/s11240-019-01763-8 - DOI
    1. Ashrafuzzaman M., Kamruzzaman A., Ismail M. R., Shahidullah S., Fakir S. (2009). Substrate affects growth and yield of shiitake mushroom. Afr. J. Biotechnol. 8, 2999–3006.

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