13TSB_AgriFood Measurement of Biochemical Traits in Fresh Produce using Imaging Technologies

13TSB_AgriFood 使用成像技术测量新鲜农产品的生化特性

• 批准号: BB/L017431/1
• 负责人: Georgios Koutsidis
• 金额: $ 39.81万
• 依托单位: Northumbria University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FL017431%2F1
• 关键词: 13TSB_AgriFood; Measurement; Biochemical; Traits; Fresh

The research to be conducted at the University of Northumbria relates to the biochemical characterisation of the ripening process across a range of tomato cultivars. Tomatoes are one of the most widespread crops in the world with an annual production in excess of 145 million tonnes per annum with a number of cultivars being commercially explored. Through this project we aim to identify and link specific commercially important attributes of tomatoes with biochemical functions at the molecular level. The content of important compounds, metabolites and proteins, that contribute to quality characteristics of tomatoes such as flavour, colour, micronutrients, firmness and taste will be determined and where possible linked to specific sequences of genes that encode such traits. This will allow plant breeders and growers to fully exploit the genetic make-up of existing varieties and develop novel cultivars with enhanced quality characteristics through cross breeding (non GMO approaches). The recenly published tomato genome database will be used in this project to drive information and build Biochemical data will be correlated with sensory data in order to identify cause and effect relationships as those are experienced by consumers. Working together with our industrial partners we also aim to develop tools that could be used by plant breeders, growers and retailers to assess quality. Such tools include, the use of digital and hyperspectal imaging to identify and quantify in a non-invasive manner important quality characteristics. Most of the tools used by the industry currently relate to either fairly cost effective measurements that lack though the necessary detail or particularly advanced methodologies that are both laborious and expensive to conduct. Our approach will lead to the development of cost effective tools that could improve the current methodologies while either complementing or replacing chemical analysis. An example of the application of such tools is in the determination of the ripening stage of tomatoes. Currently this is achieved either by the determination of the sugar content and/or in certain instances by the determination of colour through subjective observations which are complemented by the use of printed colour charts. Those charts are typically generic in nature and provide very limited information (the user is asked to assess the colour of tomatoes by comparing it to printed pictures). The use of digital imaging offers the possibility to extract more information and objectively map the evolution of colour through the ripening stage. Moreover, the use of hyperspectral imaging, (otherwise known as chemical imaging) will assist in extracting even more information. For example using this technology it is possible to determine the sugar content and firmness of fruits in a non-invasive manner. Such tools could then be deployed in the field, pack houses and retailers to more accurately and non-invasively determine the quality of tomatoes. Through our approaches we aim to create methodologies, technologies, databases and mathematical models that will be able to predict the quality of tomatoes based on digital and hyperspectal imaging and /or targeted genomic tests while at the same time enhance our knowledge in the field and create workflows that will allow the development of new tomato cultivars with enhanced quality characteristics.

这项研究将在诺森比亚大学进行，涉及一系列番茄品种成熟过程的生化特征。西红柿是世界上最广泛种植的作物之一，年产量超过1.45亿吨，其中许多品种正在进行商业开发。通过这个项目，我们的目标是在分子水平上识别和连接西红柿的特定商业重要属性和生化功能。重要的化合物，代谢物和蛋白质的含量，有助于西红柿的质量特征，如味道，颜色，微量营养素，硬度和味道将被确定，并在可能的情况下与编码这些特征的特定基因序列联系起来。这将使植物育种者和种植者能够充分利用现有品种的基因组成，并通过杂交育种（非转基因方法）开发具有更高质量特征的新品种。最近发布的番茄基因组数据库将用于该项目，以驱动信息和构建生化数据将与感官数据相关联，以确定消费者所经历的因果关系。与我们的工业合作伙伴一起，我们还致力于开发可用于植物育种者，种植者和零售商评估质量的工具。这些工具包括使用数字和高光谱成像，以非侵入性的方式识别和量化重要的质量特征。目前，该行业使用的大多数工具要么是成本效益相当高的测量，但缺乏必要的细节，要么是特别先进的方法，既费力又昂贵。我们的方法将导致开发具有成本效益的工具，可以改进当前的方法，同时补充或取代化学分析。应用这种工具的一个例子是确定西红柿的成熟阶段。目前，这是通过测定糖含量和/或在某些情况下通过主观观察来测定颜色来实现的，这种主观观察是通过使用印刷颜色图表来补充的。这些图表通常是一般性质的，提供的信息非常有限（用户被要求通过与印刷图片进行比较来评估西红柿的颜色）。数字成像的使用提供了提取更多信息的可能性，并客观地描绘了色彩在成熟阶段的演变。此外，使用高光谱成像（也称为化学成像）将有助于提取更多的信息。例如，使用这种技术，可以以非侵入性的方式确定水果的糖含量和硬度。这样的工具可以在田间、包装房和零售商中使用，以更准确、非侵入性地确定西红柿的质量。通过我们的方法，我们的目标是创建方法、技术、数据库和数学模型，这些方法、技术、数据库和数学模型将能够基于数字和高光谱成像和/或靶向基因组测试来预测西红柿的质量，同时增强我们在该领域的知识，并创建工作流程，从而开发具有更高质量特征的新番茄品种。