Optimization of HS-SPME Using Artificial Neural Network and Response Surface Methodology in Combination with Experimental Design for Determination of Volatile Components by Gas Chromatography-Mass Spectrometry in Korla Pear Juice

摘要: In this study, the effects of five fibers on volatile compounds Korla pear (Pyrus bretschneideri rehd) juice were studied. Four main extraction parameters headspace solid-phase micro-extraction, namely, sample amount, temperature, time, and salt addition, optimized for first time using response surface methodology (RSM) artificial neural network-genetic algorithm (ANN-GA). The prediction generalization ability RSM ANN models same experimental design compared. absolute average deviations 0.458 0.313, correlation coefficients 0.927 0.978, respectively. These results indicated that 65-μm PDMS/DVB fiber is best SPME juice. model exhibited more accurate better capabilities than model, optimum conditions obtained by GA those RSM. addition 5.33 g, 45 °C, 25 min, 11.8% amount sample, Under conditions, content nine compounds, including propanoic acid ethyl ester, 1-hexanal, butanoic 1-hexanol, (E)-2-hexenal, hexanoic 2-hexenal, 1-nonanal, acetic was 3.37 ± 0.23 μg/g.