Department of Chemistry and Institute for Soldier Nanotechnologies , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge Massachusetts 02139 , United States.
Department of Biological Engineering , Massachusetts Institute of Technology , Cambridge Massachusetts 02139 , United States.
ACS Sens. 2019 Aug 23;4(8):2101-2108. doi: 10.1021/acssensors.9b00825. Epub 2019 Jul 24.
Successful identification of complex odors by sensor arrays remains a challenging problem. Herein, we report robust, category-specific multiclass-time series classification using an array of 20 carbon nanotube-based chemical sensors. We differentiate between samples of cheese, liquor, and edible oil based on their odor. In a two-stage machine-learning approach, we first obtain an optimal subset of sensors specific to each category and then validate this subset using an independent and expanded data set. We determined the optimal selectors via independent selector classification accuracy, as well as a combinatorial scan of all 4845 possible four selector combinations. We performed sample classification using two models-a -nearest neighbors model and a random forest model trained on extracted features. This protocol led to high classification accuracy in the independent test sets for five cheese and five liquor samples (accuracies of 91% and 78%, respectively) and only a slightly lower (73%) accuracy on a five edible oil data set.
成功地通过传感器阵列识别复杂气味仍然是一个具有挑战性的问题。在此，我们报告了使用 20 个基于碳纳米管的化学传感器阵列进行稳健的、特定于类别的多类时间序列分类。我们根据气味来区分奶酪、酒和食用油的样本。在两阶段机器学习方法中，我们首先获得每个类别的最佳传感器子集，然后使用独立且扩展的数据集来验证该子集。我们通过独立选择器分类准确性以及所有 4845 种可能的四个选择器组合的组合扫描来确定最佳选择器。我们使用两种模型-最近邻模型和基于提取特征训练的随机森林模型来进行样本分类。该方案在五个奶酪和五个酒样本的独立测试集中实现了高分类准确性（分别为 91%和 78%），而在五个食用油数据集上的准确性仅略低（73%）。
