Four bioactive compounds in turmeric (Curcuma longa L.) have been identified using a gas sensor array based on a molecularly imprinted polymer-quartz crystal microbalance (MIP-QCM). Four QCM sensors coated with MIPs were used to analyse the performance of the array sensor toward target compounds, namely ar-turmerone, curlone, ethyl-p-methoxycinnamate and, tumerone, at different concentrations. In this experiment, nine samples of Curcuma longa odour (CL₁, CL₂, CL₃, CL₄, CL₅, CL₆, CL₇, CL₈, and CL₉) were exposed in a MIP-QCM sensor chamber. These analytes have been previously examined using gas chromatography-mass spectroscopy (GC-MS) to ensure the presence of the target compounds. GC-MS chromatograms indicated that the concentrations of the target compounds in nine analytes were distinct. The frequency change in the response due to the adsorption of the target compound with the selective layer coated on the QCM sensor was used as the sensor response. The performance of the MIP-QCM sensor array exhibited a higher response and better sensitivity and selectivity to turmeric odour with a high concentration of target compounds (CL₁, CL₂, CL₃, CL₄, CL₅ and CL₆) than the turmeric odour dominated by non-target compounds (CL₇, CL₈ and CL₉). Principal component analysis (PCA) and backpropagation neural network (BPNN) were employed to analyse the sensor responses. The visualisation of the PCA score plot shows that the MIP-QCM array sensor performed highly in distinguishing the turmeric odour based on the composition of target compounds. The BPNN classifier reached an accuracy of 98.41% and 96.29% for categorising the samples using training data sets and testing data sets, respectively.

中文翻译：

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使用基于分子印迹聚合物石英晶体微量天平的气体传感器阵列识别和检测姜黄（Curcuma longa L.）中的生物活性化合物

已经使用基于分子印迹聚合物-石英晶体微量天平 (MIP-QCM) 的气体传感器阵列鉴定了姜黄 ( Curcuma longa L.) 中的四种生物活性化合物。四个涂有 MIP 的 QCM 传感器用于分析阵列传感器对目标化合物的性能，即不同浓度的姜黄酮、卷曲酮、对甲氧基肉桂酸乙酯和姜黄酮。在本实验中，九种姜黄气味样品（CL ₁、CL ₂、CL ₃、CL ₄、CL ₅、CL ₆、CL ₇、CL ₈和CL ₉) 暴露在 MIP-QCM 传感器室中。这些分析物之前已使用气相色谱-质谱 (GC-MS) 进行过检查，以确保目标化合物的存在。GC-MS 色谱图表明，九种分析物中目标化合物的浓度不同。由于目标化合物与涂覆在 QCM 传感器上的选择性层的吸附引起的响应频率变化被用作传感器响应。MIP-QCM 传感器阵列的性能对高浓度目标化合物（CL ₁、CL ₂、CL ₃、CL ₄、CL ₅和CL _6）的姜黄气味表现出更高的响应和更好的灵敏度和选择性) 而不是由非目标化合物 (CL ₇、CL ₈和 CL ₉ )主导的姜黄气味。采用主成分分析 (PCA) 和反向传播神经网络 (BPNN) 来分析传感器响应。PCA 得分图的可视化显示 MIP-QCM 阵列传感器在根据目标化合物的组成区分姜黄气味方面表现出色。BPNN 分类器分别使用训练数据集和测试数据集对样本进行分类，准确率分别达到 98.41% 和 96.29%。