Detection of Corn Quality Based on Surface-Enhanced Raman Spectroscopy and Electronic Nose Technology - HuiHe Yang; XiaoYan Wei; Guifang Wu; PengCheng Qiu; JiaNing Di; XiangPeng Zhao; WenDong Zhong; He Ren (BioResources)
This study explored a corn quality detection method based on surface-enhanced Raman spectroscopy (SERS) and electronic nose technology. The content of aflatoxin (AFB1) and ochratoxin (OTA) in corn samples was detected by fluorescence immunoassay as the basic data for the experiment. Subsequently, the SERS curve of the corn samples was measured, and the electronic nose was used to analyze the odor of the samples. Combining the relationship between SERS curves, electronic nose data, and the toxin content in corn, a prediction model was established by using the random forest (RF) method. The results showed that the model’s coefficient of determination of the test set for predicting AFB1 reached 0.70, and the model’s coefficient of determination of the test set for predicting OTA reached 0.74. This experiment showed that SERS and electronic nose technology can effectively detect the mycotoxin content in corn samples, which provides a new method to predict the toxin content in corn.