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食品品质可视化:利用多光谱成像的深紫外荧光传感技术检测和监测绿叶层上的食源性病原体
发表时间:2023-07-28 13:10:34点击:636
全世界对可持续和安全的农产品原材料的需求正在迅速增长。降低与新鲜农产品相关的食源性疾病风险是行业和学术研究人员多年来一直在努力解决的一项任务。迫切需要设计一种非侵入性光学检测系统来监测叶片表面的食源性病原体。用于评估病原体定植的侵入性技术,在叶菜产品上检测食源性病原体往往滞后的。深紫外荧光(DUVF)传感和可见-近红外多光谱成像(MSI)以前曾被用于监测植物对生物和非生物胁迫的相互作用。使用我们开发的病理系统用于监测在莴苣/菠菜等绿叶蔬菜中入侵的沙门氏菌和李斯特菌,研究表明,沙门氏菌/李斯特菌处理后,植物对叶绿素色素波动的反应迅速。研究还表明,通过叶面或根部补充沙门氏菌/李斯特菌的施用模式会改变ChlA反应。数据还显示,植物早期光合反应的哨兵反应可能对检测绿叶蔬菜上的食源性病原体至关重要。MSI表明,植物胁迫是可检测的,并且与植物上的细菌接种率成比例。研究可能会提供更好的实施策略和技术,以提高产量并降低与食源性细菌病原体污染相关的风险。
Deep ultraviolet fluorescence sensing with multispectral imaging to detect and monitor food-borne pathogens on the leafy green phyllosphere
Abstract
Demand for sustainable and safe raw agricultural commodities is growing rapidly worldwide. Reducing the risk of foodborne illnesses associated with fresh produce is a task which the industry and academic researchers have been struggling with for many years. There is an immediate need to devise a non-invasive optical detection system to monitor the food-borne pathogens on the leaf surface. The detection of foodborne pathogens on leafy produce is performed often too late because of the invasive techniques used to evalsuate the pathogen colonization. Use of deep ultraviolet fluorescence (DUVF) sensing and visible–near infrared multispectral imaging (MSI) has previously been used to monitor plant interactions against both biotic and abiotic stress regimes. Using the patho-system that we developed to monitor Salmonella sp. and Listeria sp. ingression in leafy greens such as lettuce/spinach, we show that plant response in terms of fluctuation of chlorophyll pigments post-Salmonella/Listeria treatment is rapid. We also show that the mode of application of Salmonella/Listeria via foliar or root supplementation changes the ChlA response. Our data also reveals that the plant sentinel response in terms of early photosynthetic response may be critical to detect food-borne pathogens on leafy greens. MSI demonstrated that plant stress was detectable and proportional to the bacterial inoculation rate on plants. Our research may lead to implementation of better strategies and technology to increase yield and reduce risks associated with contamination of foodborne bacterial pathogens.
Each plant was imaged once with the VideometerLab® 4 at each time point and three times.The VideometerLab® 4 (VL4) can gather foliar spectral images needed
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