中国农业科学 ›› 2018, Vol. 51 ›› Issue (23): 4535-4547.doi: 10.3864/j.issn.0578-1752.2018.23.012
张芳2(),未志胜2,王鹏2,李凯旋2,詹萍1(
),田洪磊1(
)
收稿日期:
2018-05-25
接受日期:
2018-09-17
出版日期:
2018-12-01
发布日期:
2018-12-12
基金资助:
ZHANG Fang2(),WEI ZhiSheng2,WANG Peng2,LI KaiXuan2,ZHAN Ping1(
),TIAN HongLei1(
)
Received:
2018-05-25
Accepted:
2018-09-17
Online:
2018-12-01
Published:
2018-12-12
摘要:
【目的】通过BP神经网络结合遗传算法对固相微萃取条件(萃取头种类、样品量、萃取温度、萃取时间和盐离子添加量)的优化,建立高效的库尔勒香梨汁香气物质的萃取分析方法,为库尔勒香梨相关产品天然风味的拟合及调控奠定基础。【方法】通过单因素试验确定最佳萃取纤维头种类和样品量、萃取温度、萃取时间和盐离子添加量的最佳萃取参数。在单因素试验设计的基础上,以库尔勒香梨汁中关键香气物质(己醛、丁酸乙酯、壬醛、乙酸乙酯、(E)-2-己烯醛、己醇、(Z)-2己烯醛、丙酸乙酯和己酸乙酯)的含量为评价指标进行中央复合设计试验进一步考察样品量、萃取温度、萃取时间和盐离子添加量对库尔勒香梨汁固相微萃取效果的影响。最后以中央复合设计的试验结果为初始种群,以样品量、萃取温度、萃取时间和盐离子添加量为输入值,以库尔勒香梨汁中关键香气物质含量为其函数的输出值,通过BP神经网络模型来调试函数适应度。使用中央复合设计数据以外的数据集对BP神经网络外推能力进行验证。利用遗传算法(算法参数:最大进化代数100、初始种群数20、变异概率0.2、交叉概率0.4)在试验水平范围内预测全局最优的萃取条件。【结果】统计分析结果表明,65 μm PDMS/DVB萃取头对库尔勒香梨汁中关键香气物质的萃取效果最好,样品量、萃取温度、萃取时间和盐离子添加量对固相微萃取效果具有显著影响(P<0.05)。BP神经网络的拓扑结构为‘4-15-1’。验证数据和训练数据的MSE均大于0.017;训练、测试和验证数据的相关系数分别为0.990、0.951和0.973,表明BP神经网络预测模型有很好的准确性,可以用于库尔勒香梨汁香气物质固相微萃取结果的预测。BP神经网络外推能力验证试验的结果与BP神经网络模型预测值的拟合度(R 2)为0.992,表明试验建立的BP神经网络模型有很好的外推能力,能够准确地预测不在训练集内的数据集的输出值。遗传算法迭代100代后确定的最优个体为3.41 μg·g -1,最佳固相微萃取条件是样品量5.33 g、萃取温度44.70℃、萃取时间25.22 min和盐离子添加量0.63 g。在尽可能接近此条件下进行验证试验(试验条件:样品量5.33 g、萃取温度45℃、萃取时间25 min和盐离子添加量0.63 g),测得库尔勒香梨汁中9种关键香气物质的含量为(3.37±0.23)μg·g -1,与最优个体预测值相比,误差仅为-1.19%。【结论】上述结果表明BP神经网络模型结合遗传算法是一种准确度较好的优化固相微萃取参数的新方法,同时也为解决非线性模型工艺优化的问题提供了新思路。库尔勒香梨汁香气物质固相微萃取的最佳萃取条件为:样品量5.33 g,萃取温度45℃,萃取时间 25 min,盐离子添加量0.63 g。
张芳,未志胜,王鹏,李凯旋,詹萍,田洪磊. 基于BP神经网络和遗传算法的库尔勒香梨挥发性物质萃取条件的优化[J]. 中国农业科学, 2018, 51(23): 4535-4547.
ZHANG Fang,WEI ZhiSheng,WANG Peng,LI KaiXuan,ZHAN Ping,TIAN HongLei. Using Neural Network Coupled Genetic Algorithm to Optimize the SPME Conditions of Volatile Compounds in Korla Pear[J]. Scientia Agricultura Sinica, 2018, 51(23): 4535-4547.
表1
中央复合试验设计及结果"
序列 Sequence | 样品量 Sample amount (g) | 萃取温度 Extraction temperature (℃) | 萃取时间 Extraction time (min) | 盐添加量 Salt addition (g) | 试验值 Experimental value (μg?g-1) |
---|---|---|---|---|---|
1 | 4 | 40 | 20 | 0.4 | 2.24 |
2 | 8 | 40 | 20 | 0.4 | 2.02 |
3 | 4 | 50 | 20 | 0.4 | 2.25 |
4 | 8 | 50 | 20 | 0.4 | 1.20 |
5 | 4 | 40 | 40 | 0.4 | 2.75 |
6 | 8 | 40 | 40 | 0.4 | 2.54 |
7 | 4 | 50 | 40 | 0.4 | 2.52 |
8 | 8 | 50 | 40 | 0.4 | 2.21 |
9 | 4 | 40 | 20 | 1.2 | 1.87 |
10 | 8 | 40 | 20 | 1.2 | 2.18 |
11 | 4 | 50 | 20 | 1.2 | 2.52 |
12 | 8 | 50 | 20 | 1.2 | 1.10 |
13 | 4 | 40 | 40 | 1.2 | 1.45 |
14 | 8 | 40 | 40 | 1.2 | 2.46 |
15 | 4 | 50 | 40 | 1.2 | 2.15 |
16 | 8 | 50 | 40 | 1.2 | 1.41 |
17 | 6 | 45 | 30 | 0.8 | 2.30 |
18 | 6 | 45 | 30 | 0.8 | 2.26 |
19 | 6 | 45 | 30 | 0.8 | 2.12 |
20 | 6 | 45 | 30 | 0.8 | 1.60 |
21 | 2 | 45 | 30 | 0.8 | 1.16 |
22 | 10 | 45 | 30 | 0.8 | 2.05 |
23 | 6 | 35 | 30 | 0.8 | 2.49 |
24 | 6 | 55 | 30 | 0.8 | 1.69 |
25 | 6 | 45 | 10 | 0.8 | 3.00 |
26 | 6 | 45 | 50 | 0.8 | 3.19 |
27 | 6 | 45 | 30 | 0 | 3.34 |
28 | 6 | 45 | 30 | 1.6 | 3.22 |
29 | 6 | 45 | 30 | 0.8 | 3.57 |
30 | 6 | 45 | 30 | 0.8 | 3.27 |
表2
验证试验数据集"
序列 Sequence | 样品量 Sample amount (g) | 萃取温度 Extraction temperature (℃) | 萃取时间 Extraction time (min) | 盐添加量 Salt addition (g) | 实验值 Observed value (μg?g-1) | 预测值 Predictive value (μg?g-1) | 相对误差 Relative error (%) |
---|---|---|---|---|---|---|---|
1 | 7 | 50 | 30 | 0.8 | 1.52 | 1.54 | -1.60 |
2 | 4 | 45 | 15 | 0.5 | 2.88 | 2.81 | 2.43 |
3 | 6 | 40 | 15 | 0.7 | 2.24 | 2.27 | -1.34 |
4 | 5 | 45 | 25 | 0.9 | 3.10 | 3.00 | 3.23 |
5 | 6 | 35 | 20 | 1.0 | 2.05 | 2.11 | -2.93 |
6 | 5 | 50 | 25 | 0.5 | 1.96 | 2.02 | -3.06 |
7 | 3 | 35 | 25 | 0.8 | 1.64 | 1.59 | 1.24 |
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