Scientia Agricultura Sinica ›› 2018, Vol. 51 ›› Issue (23): 4535-4547.doi: 10.3864/j.issn.0578-1752.2018.23.012

• FOOD SCIENCE AND ENGINEERING • Previous Articles     Next Articles

Using Neural Network Coupled Genetic Algorithm to Optimize the SPME Conditions of Volatile Compounds in Korla Pear

ZHANG Fang2(),WEI ZhiSheng2,WANG Peng2,LI KaiXuan2,ZHAN Ping1(),TIAN HongLei1()   

  1. 1College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi’an 710119;
    2Food College of Shihezi University, Shihezi 832000, Xinjiang
  • Received:2018-05-25 Accepted:2018-09-17 Online:2018-12-01 Published:2018-12-12


【Objective】 In order to lay the foundation for fitting and regulation of natural flavors of Korla fragrant pear-related products, the extraction and analytical method for Korla pear aroma compounds was established by the optimization of solid phase microextraction (SPME) conditions (extraction fiber type, sample amount, extraction temperature, extraction time, and salt addition amount) with the back propagation neural network coupled genetic algorithm (BPNN-GA). 【Method】The type of extraction fiber and the best extraction parameters of sample amount, extraction temperature, extraction time, and salt addition amount were determined by single factor test. On the basis of the single factor test, the central composite design was applied to further investigate the effects of sample amount, extraction temperature, extraction time, and salt addition amount on the SPME of Korla pear juice by using the content of the key aroma substances in Korla pear juice (1-Hexanal, butanoic acid ethyl ester, 1-nonanal, hexanoic acid ethyl ester, acetic acid ethyl ester, (E)-2-hexenal, 1-hexanol, (Z)-2-hexenal, and propanoic acid ethyl ester) as evaluation indicators. The model of BPNN was used to debug fitness of the function based on the results of central composite design, and then the sample amount, extraction temperature, extraction time, salt addition amount and the content of the key aroma substances in Korla pear juice were used as initial population, input values, and output value, respectively. The generalization ability of BPNN was validated using the data set outside the central composite design. The global optimal extraction conditions within the experimental level were predicted using the genetic algorithm (algorithm parameter setting: maximum evolution algebra 100, population size 20, mutation probability 0.2, and crossover probability 0.4).【Result】The result of statistical analysis showed that the extraction fibers had a significant (P<0.05) effect on the extraction efficiency of SPME, and the 65 μm PDMS/DVB fiber was the best extraction fiber of SPME for volatile compounds of Korla pear juice. The sample amount, extraction temperature, extraction time, and salt addition had significant (P<0.05) effect on the SPME efficiency. The topology of the BPNN was ‘4-15-1’. The Root Mean Square Error (MSE) of the verification data and the training data were both greater than 0.017, and the correlation coefficients of training, test and verification data were 0.990, 0.951 and 0.973, respectively, indicating that the BPNN prediction model had good accuracy and could be used for the prediction of the result of SPME of the aroma substances in Korla pear juice. The fitting degree of the prediction value of BPNN and the verification experiment result validating the generalization ability of the BPNN was 0.992, which indicated the established BPNN model had good generalization ability and could accurately predict the output value of datasets outside the training datasets. Using genetic algorithm after evaluation of data for 100 generations determined the best individual (3.41 μg?g -1) and optimum condition: sample amount 5.33 g, extraction temperature 44.7℃, extraction time 25.22 min and salt addition 0.63 g. The verification test were conducted under these conditions as closely as possible (sample amount 5.33 g, extraction temperature 45℃, extraction time 25 min and salt addition 0.63 g), and the content of 9 key volatile substances in Korla pear juice was (3.37±0.23) μg?g -1, which compared with the predicted value, and the error was -1.19%.【Conclusion】 The above results indicated that BPNN-GA was a new method with better accuracy to optimize the solid-phase microextraction parameters, and the method of BPNN-GA provided a new way to solve the nonlinear problem. And the optimum extraction conditions for SPME of Koral pear juice were as follows: sample amount 5.33 g, extraction temperature 45℃, extraction time 25 min and salt addition 0.63 g.

Key words: Korla pear, volatile compounds, solid phase microextraction (SPME), back propagation neural network, genetic algorithm (GA)

Table 1

Central composite experimental design and its results"

Sample amount (g)
Extraction temperature (℃)
Extraction time (min)
Salt addition (g)
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

Fig. 1

The flow chart of the neural network coupled genetic algorithm optimization"

Fig. 2

Effects of different extraction fibers on major aroma substance of Koral pear juice Different lowercase values represent there are significant differences in the same aroma substances among treatments (P<0.05). The same as below"

Fig. 3

Effects of different sample amount on volatile substance in Koral pear juice"

Fig. 4

Effects of different extraction temperature on volatile substance in Koral pear juice"

Fig. 5

Effects of different extraction time on volatile substance in Koral pear juice"

Fig. 6

Effects of different salt addition on volatile substance in Koral pear juice"

Fig. 7

MSE curve of BP neural network (a), the correlation between experimental and predicted value (b)"

Table 2

Verification of experimental data set"

Sample amount
Extraction temperature (℃)
Extraction time (min)
Salt addition
Observed value (μg?g-1)
Predictive value (μg?g-1)
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

Fig. 8

The fitting degree between experimental value and predicted value"

Fig. 9

Optimized fitness curve of genetic algorithm"

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