Title:Application of Virtual Sample Generation and Screening in Process
Parameter Optimization of Botanical Medicinal Materials
Volume: 23
Issue: 8
Author(s): Yalin Guan, Juan Chen*Cuiying Dong
Affiliation:
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China
Keywords:
Virtual samples, Support vector regression (SVR), Extreme learning machine, Optimization of process parameters, Botanical medicinal materials, Response surface methodology (RSM).
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.
