Direct non-thermal plasma treatment for the sanitation of fresh corn salad leaves: Evaluation of physical and physiological effects and antimicrobial efficacy

Current problems with outbreaks of serious infections caused by human pathogens on fresh-cut greens highlight the need for new, optimized postharvest sanitation treatments to effectively meet recent food safety standards. In contrast to various chemical treatments, non-thermal plasma (NTP) has a high potential as a gentle target sanitation technique. However, possible interactions between NTP and the physiology of treated fresh products have not been investigated in detail. Here, chlorophyll fluorescence image analysis (CFIA) was used to study the potential impacts of non-thermal plasma on the photosynthetic activity of highly perishable corn salad leaves as a model produce. For this purpose, an atmospheric pressure plasma jet, driven at radio frequency, and transforming argon with flow rates of 20 L min⁻¹ into non-thermal plasma at 10, 20, 30, and 40 W generator power was applied for various times to the surface of corn salad leaves. Thermographic measurements indicated maximum temperatures of 39.0 °C, 44.4 °C, 60.1 °C, and 66.0 °C, respectively, on treated leaf surfaces. CFIA revealed that treatment at moderate generator power of 20 W for up to 1 min was the maximum setting for quality retention. Furthermore, the microbial inactivation efficiency of the plasma jet system at these operating parameters was successfully tested on Escherichia coli bacteria, inoculated on corn salad surface at 10⁷ cfu cm⁻² and 10⁴ cfu cm⁻². At 20 W, bacteria with lower initial load could be inactivated by 3.6 (±0.6) log-cycles within 15 s of treatment duration; whereas at the higher initial load of 10⁷ cfu cm⁻², bacteria were reduced by 2.1 (±0.2) log-cycles after 30 s.     

High-resolution fluorodeoxyglucose positron emission tomography and magnetic resonance imaging findings of a pituitary microtumor in a dog

A 16-year-old, castrated, male English cocker spaniel dog was presented due to generalized alopecia. Routine clinical pathology, endocrine and abdominal ultrasonography results were consistent with a diagnosis of pituitary-dependent hyperadrenocorticism. The adenohypophyseal lesion was clearly visualized on both 3 T and 7 T magnetic resonance imaging (MRI) of the pituitary gland. Although biochemical and MRI findings were consistent with a functional pituitary microtumor, a pituitary lesion was not detected using ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET). This report firstly describes the application of high-resolution FDG-PET to a spontaneous pituitary microtumor in a dog.     

A microwave tomographic system for wood characterization in the forest products industry

Abstract

Nondestructive testing and evaluation techniques able to extract information about the internal structure of the samples under test are very important in the wood industry. Microwave imaging systems have been considered for a long time promising apparatuses for this task. In this framework, approaches exploiting the full scattering phenomena for creating images of the distributions of the dielectric properties of the targets have been developed in the last few years. In this paper, a prototype of microwave tomographic system is presented and several experimental validation confirming its suitability for the use in the wood and forest product industry are reported.     

基于高光谱图谱融合技术的英德红茶等级快速无损判别

茶叶等级评价是检测茶叶品质的一项重要技术指标。通过提取红茶高光谱成像技术下的图像特征和光谱特征,构建一种基于图谱融合方法、适用于英德红茶等级评价的快速无损判别模型。首先制备3种不同等级的红茶样本,采用t分布-随机近邻嵌入和主成分分析对光谱数据进行降维可视化分析,然后从影响内在品质角度用连续投影法提取每种化学值的特征波长,通过多模型共识策略和竞争性自适应重加权算法-连续投影法筛选得出表征其内在品质的最佳特征波长组合,并建立基于遗传算法优化支持向量机的等级判别模型;其模型的训练集准确率为88%,预测集准确率为78.33%。为了融合外形纹理差异,先提取最佳特征波长组合对应的高光谱图像;采用图像掩膜消除背景的干扰和采用图像主成分分析消除多波长图像间的冗余信息,然后采用灰度共生矩阵和局部二值化算法提取主成分前三维主成分图像与特征光谱融合,并建立基于特征融合的遗传算法优化支持向量机等级判别模型,且基于第三主成分图像特征融合模型判别效果最佳,训练集准确率提升至98%,预测集准确率提升至96.67%。     

融合多光谱成像与深度学习的作物植株叶绿素检测系统研究

为了满足田间作物长势快速检测与指导变量管理的需求,以玉米为例设计了基于多光谱成像的田间作物植株叶绿素检测系统,包括可见光(RGB)和近红外(Near-infrared, NIR)图像采集模块、主控处理器模块、模型加速模块、显示及电源模块,用于实现玉米植株智能识别与叶绿素指标一体化检测。首先,采集玉米苗期和拔节期冠层图像数据集,比较了植株冠层实例分割与株心目标检测两种深度学习模型,构建了基于MobileDet+SSDLite(Single shot multibox detector lite)轻量化网络的玉米植株定位检测模型,实现玉米植株识别。其次,提取被识别的植株株心RGB-NIR图像,开展RGB和NIR图像匹配与分割,提取R、G、B和NIR灰度值计算植被指数,使用SPXY算法(Sample set portioning based on joint X-Y distances)和连续投影算法(Successive projections algorithm, SPA)分别对数据集进行样本划分及特征变量筛选,选择高斯过程回归(Gaussian process regression, ...     

脂质分析技术及其在植物种子脂质 代谢调控研究中的应用进展

脂质是一类具有独特生理功能活性的化合物,参与调节多种植物应答非生物胁迫过程,对维持植 物组织中生理动态平衡至关重要。脂质组学（Lipidomics）自 2003 年被提出后,已迅速发展成为对脂质整体系 统分析的一门新兴学科,为传统代谢组学注入新的技术支撑,有助于阐明脂类物质在植物中的代谢调控机制。 同时,质谱成像技术因其具有无标记、非特异性、高灵敏度、多物质同时分析等优势,被广泛应用到植物组织 中各类脂质分子的空间分布研究。介绍了脂质组学和质谱成像技术的研究现状,重点综述脂质分析技术在植物 种子脂质代谢调控研究中的最新进展,特别是新兴质谱成像技术在植物种子中脂类物质的成像应用。脂质组学 和质谱成像技术作为目前多组学技术的重要补充,将为植物代谢途径和调控机制的深入探究提供新的契机。亚 微米级高空间分辨率质谱成像技术的不断发展,将进一步推动植物在空间分辨水平的脂质代谢调控网络的深入 解析和前沿应用研究。