Identification of fake Anoectochilus roxburghii based on Bayesian optimized convolutional neural network

doi:10.3969/j.issn.1004-1524.2022.02.21

Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (2): 391-396.DOI: 10.3969/j.issn.1004-1524.2022.02.21

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Identification of fake Anoectochilus roxburghii based on Bayesian optimized convolutional neural network

CHAI Qinqin¹^,²^,³(), ZENG Jian¹^,²^,³, ZHANG Xun⁴

1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
2. Ministry of Education Key Laboratory of Medical Instrument and Pharmaceutical Technology, Fuzhou University, Fuzhou 350108, China
3. Jinjiang Science and Education Park of Fuzhou University, Jinjiang 362251, Fujian, China
4. School of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China

Received:2020-09-18 Online:2022-02-25 Published:2022-03-02

Abstract

Abstract:

The phenomenon of mixing the same family of Anoectochilus roxburghii(A. roxburghii ) powder with the genus Anoectochilus formosanus or Goodyera schlechtendaliana, Ludisia discolor has seriously affected the drug efficacy and market order of A. roxburghii. Therefore, finding a fast and effective method to identify adulterated A. roxburghii is an urgent problem to be solved. In view of the shortcomings of the adaptive feature extraction of traditional identification methods and the difficulties of complex structure and difficult adjustment of super parameters of convolution neural network model, an adulterated A. roxburghii identification model based on 1D convolutional neural network (1D-CNN) was proposed in this paper, in which the Bayesian optimization algorithm was proposed to optimize its hyperparameters to realize automatic optimization and adjustment of the hyperparameters. Experimental results showed that the 1D-CNN model with hyperparameter optimization was more competitive than other traditional machine learning models. The proposed 1D-CNN model based on Bayesian optimization can quickly and effectively identify A. roxburghii and its counterfeits.

Key words: convolutional neural network, Anoectochilus roxburghii, qualitative analysis, chemometrics, Bayesian optimization

CLC Number:

TP183

CHAI Qinqin, ZENG Jian, ZHANG Xun. Identification of fake Anoectochilus roxburghii based on Bayesian optimized convolutional neural network[J]. Acta Agriculturae Zhejiangensis, 2022, 34(2): 391-396.

Figures/Tables 6

References 17

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模型 Model	超参数 Hyperparameter	搜索空间 Search space
1D-CNN	Filters	{12,16,32,64}
	Kernel-size	{3,5,7,15}
	Max pooling-size	{2,3,5}
	Units	{100,200,500}
	Batch-size	{12,16,32,64}
	Epoch	{100,150,200}
SVM	C	[0.001,1 000]
	Kernel	{rbf, linear, sigmoid, poly}
KNN	Neighbors	[1,50]
	Metric	{minkowski, euclidean,Chebyshev, manhattan}

模型 Model	超参数 Hyperparameter	搜索空间 Search space
1D-CNN	Filters	{12,16,32,64}
	Kernel-size	{3,5,7,15}
	Max pooling-size	{2,3,5}
	Units	{100,200,500}
	Batch-size	{12,16,32,64}
	Epoch	{100,150,200}
SVM	C	[0.001,1 000]
	Kernel	{rbf, linear, sigmoid, poly}
KNN	Neighbors	[1,50]
	Metric	{minkowski, euclidean,Chebyshev, manhattan}

模型Model	CIRT	CIRV	CIRP
KNN-B	95.65 d	95.16 c	90.24 b
SVM-B	91.30 c	93.54 d	84.15 d
1D-CNN	96.74 b	96.77 b	90.24 b
1D-CNN-B	98.37 a	100 a	98.78 a

模型Model	CIRT	CIRV	CIRP
KNN-B	95.65 d	95.16 c	90.24 b
SVM-B	91.30 c	93.54 d	84.15 d
1D-CNN	96.74 b	96.77 b	90.24 b
1D-CNN-B	98.37 a	100 a	98.78 a

Identification of fake Anoectochilus roxburghii based on Bayesian optimized convolutional neural network

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Figures/Tables 6

References 17

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样本类别 Category	样本数量 Number of samples	训练集 Training set	验证集 Validation set	预测集 Prediction set
A1	80	45	15	20
A2	84	47	16	21
A3	84	47	16	21
A4	80	45	15	20
总计 Total	328	184	62	82

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