Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
Department of Plant Protection, Ferdowsi University of Mashhad, Mashhad, Iran.
J Sci Food Agric. 2018 Jul;98(9):3542-3550. doi: 10.1002/jsfa.8865. Epub 2018 Mar 22.
Fungal decay is a prevalent condition that mainly occurs during transportation of products to consumers (from harvest to consumption) and adversely affects postharvest operations and sales of citrus fruit. There are a variety of methods to control pathogenic fungi, including UV-assisted removal of fruit with suspected infection before storage, which is a time-consuming task and associated with human health risks. Therefore it is essential to adopt efficient and dependable alternatives for early decay detection. In this study, detection of orange decay caused by Penicillium genus fungi was examined using spectral imaging, a novel automated inspection technique for agricultural products.
The reflectance parameter (including mean reflectance) and reflectance distribution parameters (including standard deviation and skewness) of surfaces were extracted from decayed and rotten regions of infected samples and healthy regions of non-infected samples. The classification accuracy of rotten, decayed and healthy regions at 4 and 5 days after fungal inoculation was 98.6 and 100% respectively using the mean and skewness of 500 nm, 800 nm, 900 nm and modified normalized difference vegetation index (MNDVI) spectra.
Comparison of results between healthy and infected samples showed that early real-time detection of Penicillium digitatum using multispectral imaging was possible within the near-infrared (NIR) range. © 2018 Society of Chemical Industry.
真菌腐烂是一种普遍的现象，主要发生在产品从收获到消费者运输的过程中（从收获到消费），并对采后操作和柑橘类水果的销售产生不利影响。有多种方法可以控制病原菌真菌，包括在储存前使用紫外线辅助去除疑似感染的水果，这是一项耗时的任务，并与人类健康风险有关。因此，必须采用高效可靠的替代方法来进行早期腐烂检测。在这项研究中，使用光谱成像技术对由青霉属真菌引起的橙子腐烂进行了检测，这是一种用于农产品的新型自动化检测技术。
从感染样本的腐烂和腐烂区域以及未感染样本的健康区域提取了表面的反射率参数（包括平均反射率）和反射率分布参数（包括标准偏差和偏度）。在真菌接种后 4 天和 5 天，使用 500nm、800nm、900nm 和修正归一化差异植被指数（MNDVI）光谱的平均和偏度，对腐烂、腐烂和健康区域的分类准确率分别为 98.6%和 100%。
对健康和感染样本的结果进行比较表明，使用多光谱成像在近红外（NIR）范围内可以实时早期检测青霉属。 © 2018 化学工业协会。
