用于检测空气中微量化学物质的传感器已经得到广泛开发,因为它们可以识别化学物质的来源。这些传感技术还用于确定新鲜食品的品种和新鲜度,并检测爆炸物,危险化学品,环境激素,和使用呼出气体的疾病。然而,仍然需要快速开发响应复杂环境的便携式和高灵敏度传感器。这里,我们展示了一种有效的方法,用于优化基于M13噬菌体的多阵列比色传感器,以进行多个同时分类。苹果,由于分布品种众多,难以分类,被选择用于对目标进行分类。M13被用来使用自模板工艺制造多阵列比色传感器,因为由M13体组成的主要外壳蛋白p8的化学性质可以通过基因工程来操纵以响应各种目标物质。二十个传感器单元,由不同类型的操纵M13组成,当它们暴露于与苹果相关的目标物质时,由于光子晶体状纳米结构的变化而表现出颜色变化。对于苹果品种(100%),以高精度实现了最优传感器组合的分类成功率,四种标准香料(100%),同时老化(84.5%)。我们希望这种优化技术可以用于快速传感器开发,能够在各个领域进行多个同时分类。比如医学诊断,危险环境监测,和食品工业,需要开发传感器来应对由各种目标组成的复杂环境。
Sensors for detecting infinitesimal amounts of chemicals in air have been widely developed because they can identify the origin of chemicals. These sensing technologies are also used to determine the variety and freshness of fresh food and detect explosives, hazardous chemicals, environmental hormones, and diseases using exhaled gases. However, there is still a need to rapidly develop portable and highly sensitive sensors that respond to complex environments. Here, we show an efficient method for optimising an M13 bacteriophage-based multi-array colourimetric sensor for multiple simultaneous classifications. Apples, which are difficult to classify due to many varieties in distribution, were selected for classifying targets. M13 was adopted to fabricate a multi-array colourimetric sensor using the self-templating process since a chemical property of major coat protein p8 consisting of the M13 body can be manipulated by genetic engineering to respond to various target substances. The twenty sensor units, which consisted of different types of manipulated M13, exhibited colour changes because of the change of photonic crystal-like nanostructure when they were exposed to target substances associated with apples. The classification success rate of the optimal sensor combinations was achieved with high accuracy for the apple variety (100%), four standard fragrances (100%), and aging (84.5%) simultaneously. We expect that this optimisation technique can be used for rapid sensor development capable of multiple simultaneous classifications in various fields, such as medical diagnosis, hazardous environment monitoring, and the food industry, where sensors need to be developed in response to complex environments consisting of various targets.