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No abstract

An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of conformal prediction. In offline mode, the conformal predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity.

Volatile organic compounds (VOCs) in breath can be used as biomarkers to identify early stages of lung cancer. Herein, we report a disposable colorimetric array that has been constructed from diverse chemo-responsive colorants. Distinguishable difference maps were plotted within 4 min for specifically targeted VOCs. Through the consideration of various chemical interactions with VOCs, the arrays successfully discriminate between 20 different volatile organic compounds in breath that are related to lung cancer. VOCs were identified either with the visualized difference maps or through pattern recognition with an accuracy of at least 90%. No uncertainties or errors were observed in the hierarchical cluster analysis (HCA). Finally, good reproducibility and stability of the array was achieved against changes in humidity. Generally, this work provides fundamental support for construction of simple and rapid VOC sensors. More importantly, this approach provides a hypothesis-free array method for breath testing via VOC profiling. Therefore, this small, rapid, non-invasive, inexpensive, and visualized sensor array is a powerful and promising tool for early screening of lung cancer. Graphical abstract A disposable colorimetric array has been developed with broadly chemo-responsive dyes to incorporate various chemical interactions, through which the arrays successfully discriminate 20 VOCs that are related to lung cancer via difference maps alone or chemometrics within 4 min. The hydrophobic porous matrix provides good stability against changes in humidity.