Optimization of extraction technology of alkaloids in lotus leaf based on BP neural network

DONG, Zhigui; SONG, Qingfeng; YANG, Jinghong

doi:10.1590/fst.106522

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Original Article • Food Sci. Technol 43 • 2023 • https://doi.org/10.1590/fst.106522 copy

Optimization of extraction technology of alkaloids in lotus leaf based on BP neural network

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

In order to overcome the bad precision of fitted error, lower accuracy optimization results and other flaws, when extraction technology of the lotus leaf alkaloids was optimized by response surface method or regression analysis method, a linear constraint optimization method based on BP neural network is proposed. The testing program of three factors, three level was designed, which selected the hydrochloric acid mass fraction, ultrasound time, liquid-solid ratio as experimental factors. Taking the experiment data as training sample, the BP neural network model of the lotus leaf alkaloid yield and the influencing factors was obtained, and it was optimized by the proposed optimization method. The optimal parameter combination of extraction technology for lotus leaf alkaloid was obtained as follows: extraction temperature 60 °C, ultrasonic power 500W, hydrochloric acid mass fraction 0.3%, ultrasonic time 43 min, liquid-solid ratio 27, the yield of lotus leaf alkaloids under this process condition is 4.26 mg/g. It better than the best extraction technology obtained by response surface method. The obtained results is used for verification experiment, the verification results shown that the method has high fitting accuracy and stable optimization results, which optimize the extraction technology for lotus leaf alkaloid.

Keywords:
lotus leaf; alkaloids; BP neural network; extraction Technology; optimization

Factors	Step	Level
Factors	Step	-1	0	1
Hydrochloric acid mass fraction /(%)	0.1	0.2	0.3	0.4
Ultrasound time /(min)	10	30	40	50
Liquid-solid ratio	10	20	30	40

No.		Hydrochloric acid mass fraction / (%)	Ultrasound time / (min)		Liquid-solid ratio	Alkaloid yield / (mg/g)
1		0(0.3)	0(40)		0(30)	4.21
2		0(0.3)	1(50)		-1(20)	3.61
3		-1(0.4)	-1(30)		0(30)	3.17
4		1(0.2)	-1(30)		0(30)	3.29
5		-1(0.4)	1(50)		0(30)	3.20
6		0(0.3)	0(40)		0(30)	4.18
7		-1(0.4)	0(40)		1(40)	3.69
8		-1(0.4)	0(40)		-1(20)	3.25
9		0(0.3)	0(40)		0(30)	4.05
10	0(0.3)		0(40)	0(30)		4.07
11	1(0.2)		1(50)	0(30)		3.44
12	1(0.2)		0(40)	1(40)		3.60
13	0(0.3)		1(50)	1(40)		3.64
14	1(0.2)		0(40)	-1(20)		3.40
15	0(0.3)		-1(30)	-1(20)		3.22
16	0(0.3)		-1(30)	1(40)		3.63
17	0(0.3)		0(40)	0(30)		4.10

Source	Sum of squares	DF	Mean square	F value	P value
Model	2.110	9	0.230	39.22	<0.0001
x ₁	0.022	1	0.022	3.69	0.0960
x ₂	0.042	1	0.042	7.05	0.0327
x ₃	0.150	1	0.150	24.43	0.0017
x ₁ x ₂	0.004	1	0.004	0.60	0.4628
x ₁ x ₃	0.014	1	0.014	2.41	0.1643
x ₂ x ₃	0.036	1	0.036	6.05	0.0435
x ₁ ²	0.830	1	0.830	138.76	<0.0001
x ₂ ²	0.690	1	0.690	114.86	<0.0001
x ₃ ²	0.160	1	0.160	26.41	0.0013
bias	0.042	7	0.006
Lock of fit	0.022	3	0.007	1.53	0.3373
Error	0.019	4	0.005
Total	2.150	16

No.	Initial Point			Optimal solution			Optimal value alkaloid yield / (mg/g)
No.	Hydrochloric acid mass fraction / (%)	Ultrasound time /(min)	Liquid-solid ratio	Hydrochloric acid mass fraction / (%)	Ultrasound time /(min)	Liquid-solid ratio	Optimal value alkaloid yield / (mg/g)
1	0.25	40	25	0.30	42.85	27.18	4.27
2	0.34	35	30	0.30	42.85	27.18	4.27
3	0.32	45	28	0.30	42.85	27.18	4.27
4	0.28	48	33	0.30	42.85	27.18	4.27
5	0.37	40	29	0.30	42.85	27.18	4.27
6	0.35	38	34	0.30	42.85	27.18	4.27
7	0.22	48	35	0.30	42.85	27.18	4.27
8	0.36	32	39	0.30	42.85	27.18	4.27
9	0.30	42	28	0.30	42.85	27.18	4.27
10	0.38	44	3	0.30	42.85	27.18	4.27

Method	Index	Alkaloids yield /(mg/g)
Test	Minimum value	4.24
	Maximum value	4.36
	Mean value	4.28
BP neural network	Optimal value	4.26
Relative errors /(/%)		0.46

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[1] *Corresponding author: 201513424@sdtbu.edu.cn