Wang Hui, Wang Yue, Hou Xiaopeng, Xiong Benhai
State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China.
Nanomaterials (Basel). 2020 Mar 7;10(3):479. doi: 10.3390/nano10030479.
The metabolic activity in plants or fruits is associated with volatile organic compounds (VOCs), which can help identify the different diseases. P-ethylphenol has been demonstrated as one of the most important VOCs released by the Phytophthora cactorum () infected strawberries. In this study, a bioelectronic nose based on a gas biosensor array and signal processing model was developed for the noninvasive diagnostics of the infected strawberries, which could overcome the limitations of the traditional spectral analysis methods. The gas biosensor array was fabricated using the single-wall carbon nanotubes (SWNTs) immobilized on the surface of field-effect transistor, and then non-covalently functionalized with different single-strand DNAs (ssDNA) through π-π interaction. The characteristics of ssDNA-SWNTs were investigated using scanning electron microscope, atomic force microscopy, Raman, UV spectroscopy, and electrical measurements, indicating that ssDNA-SWNTs revealed excellent stability and repeatability. By comparing the responses of different ssDNA-SWNTs, the sensitivity to P-ethylphenol was significantly higher for the s6DNA-SWNTs than other ssDNA-SWNTs, in which the limit of detection reached 0.13% saturated vapor of P-ethylphenol. However, s6DNA-SWNTs can still be interfered with by other VOCs emitted by the strawberries in the view of poor selectivity. The bioelectronic nose took advantage of the different sensitivities of different gas biosensors to different VOCs. To improve measure precision, all ssDNA-SWNTs as a gas biosensor array were applied to monitor the different VOCs released by the strawberries, and the detecting data were processed by neural network fitting (NNF) and Gaussian process regression (GPR) with high accuracy.
植物或果实中的代谢活性与挥发性有机化合物（VOCs）相关，这些化合物有助于识别不同的疾病。对羟基苯乙酮已被证明是感染草莓的恶疫霉释放的最重要的挥发性有机化合物之一。在本研究中，开发了一种基于气体生物传感器阵列和信号处理模型的生物电子鼻，用于对感染草莓进行无创诊断，该生物电子鼻可以克服传统光谱分析方法的局限性。气体生物传感器阵列是通过将单壁碳纳米管（SWNTs）固定在场效应晶体管表面制成的，然后通过π-π相互作用与不同的单链DNA（ssDNA）进行非共价功能化。使用扫描电子显微镜、原子力显微镜、拉曼光谱、紫外光谱和电学测量对ssDNA-SWNTs的特性进行了研究，结果表明ssDNA-SWNTs具有优异的稳定性和重复性。通过比较不同ssDNA-SWNTs的响应，s6DNA-SWNTs对乙基苯酚的灵敏度明显高于其他ssDNA-SWNTs，其检测限达到0.13%的乙基苯酚饱和蒸汽。然而，从选择性差的角度来看，s6DNA-SWNTs仍然会受到草莓释放的其他挥发性有机化合物的干扰。生物电子鼻利用了不同气体生物传感器对不同挥发性有机化合物的不同灵敏度。为了提高测量精度，将所有ssDNA-SWNTs作为气体生物传感器阵列用于监测草莓释放的不同挥发性有机化合物，并通过神经网络拟合（NNF）和高斯过程回归（GPR）对检测数据进行高精度处理。
