Kyushu University Department of Electronics Graduate School of Information Science and Electrical Engineering 744 Motooka, Fukuoka 819-0395, Japan..
Kyushu University Department of Electronics Graduate School of Information Science and Electrical Engineering 744 Motooka, Fukuoka 819-0395, Japan.
Talanta. 2015 Mar;134:105-119. doi: 10.1016/j.talanta.2014.09.049. Epub 2014 Nov 10.
In present work, a novel quartz crystal microbalance (QCM) sensor array has been developed for prompt identification of primary aldehydes in human body odor. Molecularly imprinted polymers (MIP) are prepared using the polyacrylic acid (PAA) polymer matrix and three organic acids (propenoic acid, hexanoic acid and octanoic acid) as template molecules, and utilized as QCM surface coating layer. The performance of MIP films is characterized by 4-element QCM sensor array (three coated with MIP layers and one with pure PAA for reference) dynamic and static responses to target aldehydes: hexanal, heptanal, and nonanal in single, binary, and tertiary mixtures at distinct concentrations. The target aldehydes were selected subsequent to characterization of body odor samples with solid phase-micro extraction gas chromatography mass spectrometer (SPME-GC-MS). The hexanoic acid and octanoic acid imprinted PAA exhibit fast response, and better sensitivity, selectivity and reproducibility than the propenoic acid, and non-imprinted PAA in array. The response time and recovery time for hexanoic acid imprinted PAA are obtained as 5 s and 12 s respectively to typical concentrations of binary and tertiary mixtures of aldehydes using the static response. Dynamic sensor array response matrix has been processed with principal component analysis (PCA) for visual, and support vector machine (SVM) classifier for quantitative identification of target odors. Aldehyde odors were identified successfully in principal component (PC) space. SVM classifier results maximum recognition rate 79% for three classes of binary odors and 83% including single, binary, and tertiary odor classes in 3-fold cross validation.
在本工作中，开发了一种新型的石英晶体微天平（QCM）传感器阵列，用于快速识别人体气味中的初级醛。采用聚丙烯酸（PAA）聚合物基质和三种有机酸（丙烯酸、己酸和辛酸）作为模板分子制备分子印迹聚合物（MIP），并用作 QCM 表面涂层。通过四元 QCM 传感器阵列（三个涂有 MIP 层，一个涂有纯 PAA 作为参比）对目标醛（己醛、庚醛和壬醛）在单、双和三元混合物中的动态和静态响应来表征 MIP 薄膜的性能在不同浓度下。选择目标醛是在固相微萃取气相色谱质谱联用（SPME-GC-MS）对体臭样本进行特征分析之后进行的。在阵列中，与丙烯酸和非印迹 PAA 相比，己酸和辛酸印迹的 PAA 表现出快速响应和更好的灵敏度、选择性和重现性。己酸印迹 PAA 的响应时间和恢复时间分别为 5 s 和 12 s，用于典型浓度的二元和三元混合物的静态响应。使用主成分分析（PCA）对动态传感器阵列响应矩阵进行处理，用于目标气味的可视化，以及支持向量机（SVM）分类器用于定量识别。醛气味在主成分（PC）空间中成功识别。SVM 分类器在 3 倍交叉验证中，对三类二元气味的最大识别率为 79%，包括单、双和三元气味类别的识别率为 83%。
