A novel quantification technique is presented for electronic nose (E-nose), which is based on a double-step strategy combined with hierarchical classifier (HC) and partial least squares regression (PLSR). With the tree structure of HC, the complexity of classifier training process can be reduced in the case of unbalanced samples. For each level of the class hierarchy, the extreme learning machine-based artificial neural network (ELM-ANN) is applied for classification. In order to improve the classification performance of ELM-ANN, the multiple time-domain features are selected as training inputs, and a novel optimization method of the number of hidden layer neurons is given. To validate the effectiveness of the presented quantification technique, an E-nose system is designed to quantify the gases including six toxic gases (hydrogen sulfide, carbon monoxide, ammonia, toluene, formaldehyde, acetone) and three kinds of binary gas mixtures. This presented hierarchical classifier has demonstrated outstanding performance for the identification of target gases, such as the macro-averaged precision for unlabeled data is improved from 80% to 92% compared with non-hierarchical classifiers. Furthermore, an excellent performance of concentration estimation is obtained utilizing PLSR, where average values of the coefficient of determination for training and test samples are equal to 0.957 and 0.927, respectively. Overall, our work demonstrates that the proposed approach is applicable in E-nose-based odor quantification.

提出了一种用于电子鼻 (E-nose) 的新型量化技术，该技术基于双步策略结合分层分类器 (HC) 和偏最小二乘回归 (PLSR)。使用HC的树结构，可以在样本不平衡的情况下降低分类器训练过程的复杂度。对于类层次结构的每个级别，应用基于极限学习机器的人工神经网络（ELM-ANN）进行分类。为了提高ELM-ANN的分类性能，选取多个时域特征作为训练输入，给出了一种新的隐层神经元个数优化方法。为了验证所提出的量化技术的有效性，设计了一个电子鼻系统来量化包括六种有毒气体（硫化氢、一氧化碳、氨、甲苯、甲醛、丙酮）和三种二元气体混合物。这种分层分类器在识别目标气体方面表现出优异的性能，例如与非分层分类器相比，未标记数据的宏观平均精度从 80% 提高到 92%。此外，利用 PLSR 获得了出色的浓度估计性能，其中训练和测试样本的确定系数的平均值分别等于 0.957 和 0.927。总的来说，我们的工作表明所提出的方法适用于基于电子鼻的气味量化。这种分层分类器在识别目标气体方面表现出优异的性能，例如与非分层分类器相比，未标记数据的宏观平均精度从 80% 提高到 92%。此外，利用 PLSR 获得了出色的浓度估计性能，其中训练和测试样本的确定系数的平均值分别等于 0.957 和 0.927。总的来说，我们的工作表明所提出的方法适用于基于电子鼻的气味量化。这种分层分类器在识别目标气体方面表现出优异的性能，例如与非分层分类器相比，未标记数据的宏观平均精度从 80% 提高到 92%。此外，利用 PLSR 获得了出色的浓度估计性能，其中训练和测试样本的确定系数的平均值分别等于 0.957 和 0.927。总的来说，我们的工作表明所提出的方法适用于基于电子鼻的气味量化。其中训练和测试样本的确定系数的平均值分别等于 0.957 和 0.927。总的来说，我们的工作表明所提出的方法适用于基于电子鼻的气味量化。其中训练和测试样本的确定系数的平均值分别等于 0.957 和 0.927。总的来说，我们的工作表明所提出的方法适用于基于电子鼻的气味量化。