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Current Topics in Medicinal Chemistry

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ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

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Research Article

Application of Virtual Sample Generation and Screening in Process Parameter Optimization of Botanical Medicinal Materials

Author(s): Yalin Guan, Juan Chen* and Cuiying Dong

Volume 23, Issue 8, 2023

Published on: 02 February, 2023

Page: [618 - 626] Pages: 9

DOI: 10.2174/1568026623666230117121531

Price: $65

Abstract

Background: The small sample problem widely exists in the fields of the chemical industry, chemistry, biology, medicine, and food industry. It has been a problem in process modeling and system optimization. The aim of this study is to focus on the problems of small sample size in modeling, the process parameters in the ultrasonic extraction of botanical medicinal materials can be obtained by optimizing the extraction rate model. However, difficulty in data acquisition results in problem of small sample size in modeling, which eventually reduces the accuracy of modeling prediction.

Methods: A virtual sample generation method based on full factorial design (FFD) is proposed to solve the problem ofa small sample size. The experiments are first conducted according to the Box- Behnken Design (BBD) to obtain small-size samples, and the response surface function is established accordingly. Then, virtual sample inputs are obtained by the FFD, and the corresponding virtual sample outputs are calculated by the response surface function. Furthermore, a screening method of virtual samples is proposed based on an extreme learning machine (ELM). The connection weights of ELM are used for further optimization and screening of the generated virtual samples.

Result: The results show that virtual sample data can effectively expand the sample size. The precision of the model trained on semi-synthetic samples (small-size experimental simples and virtual samples) is higher than the model trained merely on small-size experimental samples.

Conclusion: The virtual sample generation and screening methods proposed in this paper can effectively solve the modeling problem of small samples. The reliable process parameters can be obtained by optimizing the model trained by the semi-synthetic samples.

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[1]
Zhao, K.L.; Jin, X.L.; Wang, Y.Z. Survey on few-shot learning. J. Softw., 2021, 32(2), 349-369.
[2]
He, P.; Sun, F.; Hu, X.F.; Lin, Y.P.; Duan, S.K. Optimization of prediction system for sample laser cutting process parameters. Las. J., 2021, 42(12), 170-175.
[3]
Sheng, H.; Liu, X.; Bai, L.; Dong, H.; Cheng, Y. Small sample state of health estimation based on weighted Gaussian process regression. J. Energy Storage, 2021, 41(1)102816
[http://dx.doi.org/10.1016/j.est.2021.102816]
[4]
Lv, Y.Q.; Min, W.Q.; Duan, H.; Jiang, S.Q. Few-shot food recognition combining triplet convolutional neural network with relation network. Comput. Sci., 2020, 47(1), 136-143.
[5]
Guo, X.P.; Song, Y.F.; Liu, S.H.; Gao, M.H.; Qi, Y.; Shang, X.Q. Linking genotype to phenotype in multi-omics data of small sample. BMC. Geno., 2021, 22(1), 1-11.
[http://dx.doi.org/10.1080/0305215X.2012.737786]
[6]
Yan, B.; Zhou, P.; Yan, L. Disease identification of small sample crop based on transfer learning. Mod. Agri. Sci. Tech., 2019, 6, 87-89.
[7]
Zhou, Z.H. Learning with unlabeled data and its application to image retrieval. Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence, Guilin, China2006, pp. 5-10.
[http://dx.doi.org/10.1007/978-3-540-36668-3_3]
[8]
Wang, Y.; Wang, Z.; Sun, J.; Zhang, J.; Zissimos, M. Gray bootstrap method for estimating frequency-varying random vibration signals with small samples. Chin. J. Aeronauti., 2014, 27(2), 383-389.
[http://dx.doi.org/10.1016/j.cja.2013.07.023]
[9]
Snell, J.; Swersky, K.; Zemel, R.S. Prototypical networks for few-shot learning. Adv. Neur. Inf. Proc. Syst., 2017, 2017, 30.
[10]
Vinyals, O.; Blundell, C.; Lillicrap, T.; Kavukcuoglu, K.; Wierstra, D. Matching networks for one shot learning. Adv. Neur. Inf. Proc. Syst., 2016, 2016, 29.
[11]
Lv, F.; Wang, Y.; Ruan, H.L.; Qin, Y.; Wang, P. Labeled sample augmentation based on deep embedding relation space for semi-supervised fault diagnosis of gearbox. Chinese. J. Sci. Instrum., 2021, 42(2), 55-65.
[12]
Zang, D.; Liu, J.; Qu, F. Pipeline small leak detection based on virtual sample generation and unified feature extraction. Measurement, 2021, 184109960
[http://dx.doi.org/10.1016/j.measurement.2021.109960]
[13]
Poggio, T.; Vetter, T. Recognition and structure from one 2D model view: Observations on prototypes, object classes and symmetries; Massachusetts Inst of Tech Cambridge Artificial Intelligence LAB 1992. Available from: https://apps.dtic.mil/sti/citations/ADA259735
[http://dx.doi.org/10.21236/ADA259735]
[14]
Li, D.C.; Fang, Y.H. A non-linearly virtual sample generation technique using group discovery and parametric equations of hypersphere. Expert Syst. Appl., 2009, 36(1), 844-851.
[http://dx.doi.org/10.1016/j.eswa.2007.10.029]
[15]
Wang, W.D.; Yang, J.Y. Quadratic discriminant analysis method based on virtual training samples. Acta. Auto. Sin., 2008, 34(34), 400-407.
[16]
Tang, J.; Wang, D.D.; Guo, Z.H.; Qiao, J.F. Prediction of dioxin emission concentration in municipal solid waste incineration process based on optimal selection of virtual Samples. J. Beijing Univ. Tech., 2021, 47(5), 431-443.
[17]
Liu, B.Y. Optimization of process parameters of single drug and compound drugs by dual-frequency ultrasound. 2019.
[18]
Sui, Y.K.; Yu, H.P. Improvement of response surface method and its application to engineering optimization; Sci. Pre, 2011, pp. 8-10.
[19]
Huang, G.B.; Zhu, Q.Y.; Siew, C.K. Extreme learning machine: a new learning scheme of feedforward neural networks. 2004IEEE International Joint Conference on Neural Networks, Budapest, Hungary2004, pp. 985-990.
[http://dx.doi.org/10.1109/IJCNN.2004.1380068]
[20]
Xu, S.H.; Mu, X.D.; Chai, D.; Luo, C. Domain adaptation algorithm with ELM parameter transfer. Acta. Auto. Sin., 2018, 44(2), 311-317.
[21]
Saremi, S.; Mirjalili, S.; Lewis, A. Grasshopper optimisation algorithm: Theory and application. Adv. Eng. Softw., 2017, 105, 30-47.
[http://dx.doi.org/10.1016/j.advengsoft.2017.01.004]

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