Abstract
Smells are known to be composed of thousands of chemicals with various concentrations, and thus, the extraction of specific information from such a complex system is still challenging. Herein, we report for the first time that the nanomechanical sensing combined with machine learning realizes the specific information extraction, e.g. alcohol content quantification as a proof-of-concept, from the smells of liquors. A newly developed nanomechanical sensor platform, a Membrane-type Surface stress Sensor (MSS), was utilized. Each MSS channel was coated with functional nanoparticles, covering diverse analytes. The smells of 35 liquid samples including water, teas, liquors, and water/EtOH mixtures were measured using the functionalized MSS array. We selected characteristic features from the measured responses and kernel ridge regression was used to predict the alcohol content of the samples, resulting in successful alcohol content quantification. Moreover, the present approach provided a guideline to improve the quantification accuracy; hydrophobic coating materials worked more effectively than hydrophilic ones. On the basis of the guideline, we experimentally demonstrated that additional materials, such as hydrophobic polymers, led to much better prediction accuracy. The applicability of this data-driven nanomechanical sensing is not limited to the alcohol content quantification but to various fields including food, security, environment, and medicine.
Highlights
Quantification is an important process in most of the analyses
We demonstrate that a sensor array combined with a machine learning technique can be utilized to derive quantitative information, e.g. alcohol content, from the smells of various liquors (Fig. 1)
Titania-based hybrid nanoparticles (NPs) with different surface functionalities were prepared through the hydrolysis and co-condensation reaction of two alkoxides, i.e. titanium tetraisopropoxide (TTIP) and various silane coupling reagents, combined with a multi-step microfluidic approach which we reported previously[17]
Summary
Preparation of silica/titania hybrid nanoparticles with different surface functionalities. In addition to C18-STNPs and Phenyl-STNPs, we utilized two commercial hydrophobic polymers: polysulfone and polycaprolactone (in Supplementary Figures (Figures S5, S6 and S7) and Supplementary Note sections, all the data including measured responses, and the training results by using each polymer are shown.) In this case, since the features of a signal by each polymer were expressed by the four parameters explained above, the 16 parameters were obtained from the four sensor channels in the MSS. The red points are the unknown liquors: red wine (12%), imo-shochu (25%), and whisky (40%)
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