基于嗅觉受体多肽仿生微纤维传感器阵列的南美白对虾新鲜度检测

To achieve rapid, non-destructive and accurate detection of P. vannamei freshness, 12 micro-size and selective gas sensors would be developed for 6 characteristic odour components related to the freshness. The developed sensors were assembled into bionic olfaction array in a parallel sub-array method, and combined with appropriate data analysis algorithm to establish the freshness prediction model, which could resolve the complex volatile components of P. vannamei. With our previous gas sensor research based on biological microfiber, the miniaturization and arraying of the sensors could be realized using silk fibroin fiber/polyaniline composite gas-sensing microfibers. Selective response of the sensors to the characteristic odor components would be realized by olfactory receptor peptide (ORP). ORP would be prepared based on the interaction investigation between olfactory receptor protein and the characteristic odor molecules using molecular simulation technique, which would present the coordination site and corresponding peptide sequence. The completion of this project would demonstrate a new method for the accurate and reliable detection of shrimp freshness, provide enlightening thinking for the study of bionic olfactory in related fields.

为了达到快速、无损、准确检测南美白对虾新鲜度的目的，项目提出针对南美白对虾新鲜度相关的6种特征性气味成分研制12种微型、专用选择性气敏传感器，进而以并联子阵列形式组成仿生嗅觉阵列，并结合合适的数据分析方法建立新鲜度预测模型，实现南美白对虾复杂挥发性组分的特征分析。项目拟在已有基于生物微纤维的气敏传感器阵列研究基础上，利用蚕丝丝素纤维/聚苯胺复合物气敏微纤维实现传感器阵列的微型化和阵列化，利用嗅觉受体多肽实现传感器对于特征性气味成分的选择性响应。其中，通过分子模拟技术解析嗅觉受体蛋白与特征性气味分子的相互作用过程，并选取二者配合位点处的多肽序列制取嗅觉受体多肽。项目的完成将为虾类新鲜度准确、可靠检测提供新的手段，为相关领域仿生嗅觉研究提供新的思路。

针对肉品质量与安全相关的特征性气味组分，研制了多种微纳传感器用于肉品品质与安全检测，并通过分子对接和动力学模拟技术探究了嗅觉受体蛋白与小分子配体之间的相互作用机理，为新型高灵敏度传感器的设计提供理论支撑。具体内容包括以下四点：（1）基于微型金叉指电极和微纤维材料研制了多种新型微纳传感器，对氨气、三甲胺等典型气体表现出较高的灵敏度和响应速度，并成功组成传感器阵列应用于的肉品新鲜度检测；（2）基于拉曼静默区探针设计了三种高灵敏度、低成本农药残留检测体系，有效解决了食品基质的干扰问题；（3）基于多功能粘附蛋白设计了可更新生物传感器，有效解决了电化学电极的重复利用问题，提高了电极检测的稳定性、重复性和重现性，并应用于南美白对虾致病菌检测；（4）基于Alphafold2和Gromacs进行了嗅觉受体蛋白的三维结构预测和受体-配体分子动力学模拟，分析了其相互作用机制，为新型传感器的设计奠定了坚实基础。总体上，本研究针对新型高灵敏度传感器在食品质量与安全检测中的应用进行了广泛而深入的探索，但仍然有较多内容和难题需要进一步进行研究与解决，今后将在现有的研究基础上进一步开展更为深入的研究。