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| Rapid determination of peroxide value of soybean oil using 1H NMR spectroscopy combined with random forest algorithm |
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| DOI： |
| KeyWord:1H nuclear magnetic resonance (1H NMR) peroxide value random forest algorithm quantitative prediction soybean oil |
| FundProject:广东高校省级重点平台及重大科研项目（青年创新人才类）（2017KQNCX250）；北京市科技计划（Z1711000 01317004） |
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| Abstract: |
| A rapid detection method of peroxide value of soybean oil was established by 1H nuclear magnetic resonance (1H NMR) spectroscopy combined with random forest algorithm. By comparing the 1H NMR data of soybean oil, five kinds of oxidation products were identified, such as (Z,E)-conjugated form, (E,E)-conjugated form, E-alkenals, (E,E)-alkadienals and n-alkanals. According to the content of five kinds of oxidation product in soybean oil samples determined by the 1H NMR data and the peroxide value of soybean oil samples detected by colorimetry, the quantitative prediction model for the rapid detection of peroxide value was constructed by cross-validation. The results showed that the model could present the stronger learning ability and accuracy when the optimization parameters of ntree 700,max_depth 5 and min_samples_split 4, and the model’s coefficient(R2) and root mean square error (RMSE) were 0.943 and 2.851%, respectively. The method based on 1H NMR spectroscopy combined with random forest algorithm could determine peroxide value rapidly, which provided scientific support for monitoring and evaluating the oil quality online. |
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