Raman spectroscopy

Chemometrics

Biology

Chromatography

Computer science

Botany

Physics

Kernel (algebra)

Pattern recognition (psychology)

Mathematics

Optics

Hyperspectral imaging

Artificial intelligence

Combinatorics

Chemistry

Biological system

Machine learning

Mildew

Food Chemistry

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.

Integration of textural and spectral features of Raman hyperspectral imaging for quantitative determination of a single maize kernel mildew coupled with chemometrics