Integration of textural and spectral features of Raman hyperspectral imaging for quantitative determination of a single maize kernel mildew coupled with chemometrics. (15th March 2022) - Long, Yuan; Huang, Wenqian; Wang, Qingyan; Fan, Shuxiang; Tian, Xi
Highlights: Fungal spore quantity of a single maize kernel was determined by Raman hyperspectral imaging coupled with chemometrics. Spectral and textural feature of a single maize kernel were extracted to describe the mold. Characteristic Raman shifts were screened to optimize the detection model. This technique provided an in-situ alternative for the detection of fungal spore quantity of a single maize kernel. Abstract: Maize mildew is a common phenomenon and it is essential to detect the mildew of a single maize kernel and prevent mildew from spreading around. In this study, a line-scanning Raman hyperspectral imaging system was applied to detect fungal spore quantity of a single maize kernel. Raman spectra were extracted while textural features were obtained to depict the maize mildew. Three kinds of modeling algorithms were used to establish the quantitative model to determine the fungal spore quantity of a single maize kernel. Then competitive adaptive reweighted sampling (CARS) was used to optimize characteristic variables. The optimal detection model was established with variables selected from the combination of Raman spectra and textural variance feature by PLSR. Results indicated that it was feasible to detect the fungal spore quantity of a single maize kernel by Raman hyperspectral technique. The study provided an in-situ and nondestructive alternative to detect fungal spore quantity.