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

文献类型： 外文期刊

第一作者： Long, Yuan

作者： Long, Yuan;Huang, Wenqian;Wang, Qingyan;Fan, Shuxiang;Tian, Xi;Long, Yuan;Huang, Wenqian;Wang, Qingyan;Fan, Shuxiang;Tian, Xi;Long, Yuan;Huang, Wenqian;Wang, Qingyan;Fan, Shuxiang;Tian, Xi;Long, Yuan;Huang, Wenqian;Wang, Qingyan;Fan, Shuxiang;Tian, Xi

作者机构：

关键词： Raman hyperspectral imaging; Maize kernel; Fungal spore quantity; Textural features; Nondestructive detection

期刊名称：FOOD CHEMISTRY （ 影响因子：9.231； 五年影响因子：8.795 ）

ISSN： 0308-8146

年卷期： 2022 年 372 卷

页码：

收录情况： SCI

摘要： 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.

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