Qu Maozhen, An Changqing, Cheng Fang, Zhang Jun
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310027, China.
College of Mechanical and Electrical Engineering, Jiaxing Nanhu University, Jiaxing 314001, China.
Foods. 2024 Sep 27;13(19):3087. doi: 10.3390/foods13193087.
() in maize poses a threat to grain security. Current non-destructive detection methods face limited practical applications in grain quality detection. This study aims to understand the optical properties and volatileomics of -contaminated maize. Specifically, the transmission and reflection spectra (wavelength range of 200-1100 nm) were used to explore the optical properties of -contaminated maize. Volatile organic compounds (VOCs) of -contaminated maize were determined by headspace solid phase micro-extraction with gas chromatography-tandem mass spectrometry. The VOCs of normal maize were mainly alcohols and ketones, while the VOCs of severely contaminated maize became organic acids and alcohols. The ultraviolet excitation spectrum of maize showed a peak redshift as fungi grew, and the intensity decreased in the 400-600 nm band. Peak redshift and intensity changes were observed in the visible/near-infrared reflectance and transmission spectra of -contaminated maize. Remarkably, optical imaging platforms based on optical properties were developed to ensure high-throughput detection for single-kernel maize. The developed imaging platform could achieve more than 80% classification accuracy, whereas asymmetric polarization imaging achieved more than 93% prediction accuracy. Overall, these results can provide theoretical support for the cost-effective preparation of low-cost gas sensors and high-prediction sorting equipment for maize quality detection.
玉米中的()对粮食安全构成威胁。当前的无损检测方法在谷物质量检测中的实际应用有限。本研究旨在了解受()污染玉米的光学特性和挥发性组学。具体而言,利用透射和反射光谱(波长范围为200 - 1100 nm)来探究受()污染玉米的光学特性。采用顶空固相微萃取结合气相色谱 - 串联质谱法测定受()污染玉米的挥发性有机化合物(VOCs)。正常玉米的VOCs主要为醇类和酮类,而重度污染玉米的VOCs则变为有机酸和醇类。随着真菌生长,玉米的紫外激发光谱显示峰值红移,且在400 - 600 nm波段强度降低。在受()污染玉米的可见/近红外反射和透射光谱中观察到峰值红移和强度变化。值得注意的是,基于光学特性开发了光学成像平台,以确保对单粒玉米进行高通量检测。所开发的成像平台分类准确率可达80%以上,而非对称偏振成像预测准确率超过93%。总体而言,这些结果可为低成本制备用于玉米质量检测的气体传感器和高预测分选设备提供理论支持。