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### 基于多重修饰酶电极技术检测大豆油中磷脂含量

1. （1.哈尔滨商业大学计算机与信息工程学院，黑龙江?哈尔滨 150028；2.黑龙江省电子商务与信息处理重点实验室，黑龙江?哈尔滨 150028；3.哈尔滨商业大学食品工程学院，黑龙江?哈尔滨 150076；4.东北农业大学食品学院，黑龙江?哈尔滨 150030）
• 出版日期:2019-01-25 发布日期:2019-01-22
• 基金资助:
黑龙江省教育厅研发项目（TSTAU-R2018010；TSTAU-C2018011）

### Determination of Phospholipid Content in Soybean Oil Using an Electrochemical Sensor Based on Multiple Modified Enzyme Electrode

WANG Liqi1,2, LIU Yuqi1, CHEN Yingshu1, WANG Wen3, WANG Ruizhi3, LIU Yanan1, ZHANG Xin4, SUI Yulin1,*, YU Dianyu4,*

1. (1. School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China; 2. Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin 150028, China;3. College of Food Engineering, Harbin University of Commerce, Harbin 150076, China;4. School of Food Science, Northeast Agricultural University, Harbin 150030, China)
• Online:2019-01-25 Published:2019-01-22

Abstract: The paper presents a new method for detecting phospholipid content in soybean oil based on electrochemical analysis and to establish accurate and reliable calibration models. A total of 40 mixed soybean oil samples with different contents of phospholipids were prepared, and the standard values of phospholipid contents were precisely detected by molybdenum blue colorimetry. Considering no electron transfer during the phospholipase hydrolysis process, we developed an electrochemical sensor based on a multiple modified enzyme electrode to obtain electrochemical signals by cyclic voltammetry measurement through an electrochemical workstation. The original electrochemical data were denoised by either Savitzky-Golay smoothing filter or Daubechies (dbN) wavelet series. Through the comparative analysis, it was found that the best denoise was achieved based on db6 wavelet three-layer decomposition. Finally, four methods were used respectively to set up regression models between the electrochemical data and phospholipid concentration, i.e., linear fitting between reductive peak current and phospholipid concentration, principal component regression (PCR) model, partial least squares regression (PLSR) model and support vector machine regression (SVMR) model. Comparison of these methods showed that the prediction accuracy of the SVRR model based on radial basis kernel function was the highest. A good linear relationship was observed in the phospholipid concentration range of 5.87–304.89 mg/L, and the detection limit (LOD) was 1.68 mg/L (RSN = 3). The coefficient of determination (R2), root-mean-square error of prediction (RMSEP) and relative standard deviation (RSD) were 0.998 7, 0.288 9 and 2.55%, respectively, all of which met the requirements of practical detection.