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| Fast identification of edible vegetable oil kinds by infrared spectroscopy |
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| DOI： |
| KeyWord:vegetable oil infrared spectroscopy feature extraction CARS-CS-ELM random forest classification and recognition |
| FundProject:中央高校基本科研业务费专项资金资助（2021JKF208) |
| Author Name | Affiliation | | JIE Zhaowei1, LI Shen2, WANG Ruixuan3, WANG Jifen1, ZHANG Zhen1,
XU Xiaojie4, ZHOU Di4, SHI Xuejun4 | 1.School of Investigation, People′s Public Security University of China, Beijing 100038, China
2.Sports Training College, Wuhan Sports University, Wuhan 430079, China； 3.School of National
Security, People′s Public Security University of China, Beijing 100038, China；
4.Forensic Expertise Center of Beijing Customs Anti-smuggling
Bureau, Beijing 100000, China） |
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| Abstract: |
| In order to achieve rapid non-destructive identification of edible vegetable oil kinds and provide reference for the fight against food, drug and environment crimes in public security operations, the attenuated total reflection-Fourier transform infrared spectroscopy analysis technology was used to carry out multi-level classification and recognition of different kinds and brands of edible vegetable oils. The experiment used standard normal variation (SNV) and first-order derivative preprocessing to eliminate baseline and other background interference, resulting in the separation of overlapping peaks and improving detection resolution and sensitivity. The competitive adaptive reweighted sampling(CARS) algorithm was used to extract feature wavelengths, combining the extreme learning machine (CS-ELM) model optimized based on cuckoo search algorithm to classify and identify different kinds and brands of edible vegetable oils. The accuracy of random forest model and CARS-CS-ELM model were compared in the rapid classification and detection of edible vegetable oils. The results showed that the overall classification accuracy of three types of vegetable oil samples based on CARS-CS-ELM model reached 85.19%, among which the brand classification accuracy of the training set of sesame oil, peanut oil and corn oil was 92.5%, 100% and 96.7% respectively, and that of the test set was 100%, while the classification accuracy of nine brands of edible vegetable oil in random forest model was only 80%. In summary, the CARS-CS-ELM model has good performance in rapid classification and recognition of edible vegetable oils, and can provide certain reference for non-destructive and rapid testing of edible vegetable oils. |
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