A Novel Data Processing Technique for Expert Resonant Nano-Pillars Transducers: A Case Study Measuring Ethanol in Water and Wine Liquid Matrices

Abstract

This paper proposes a novel readout methodology for improving the performance of Refractive Index (RI) based photonic transducers. Specifically, the authors focus on an optical transducer reported recently in the literature, the so-called Resonant Nano-Pillars (RNPs) transducer. The readout signal for this transducer is usually obtained based on the Wavelength Shift of the Resonant Mode (WSRM), which identifies a single point from the signal, such as the minimum of a resonant mode, whose wavelength shift or intensity value has a correlation with the RI of the media, and, therefore, with the monitored chemical component. This work proposes a novel spectral analysis through Principal Component Analysis (PCA), later inferring the property of interest by regression techniques. To evaluate the performance of the proposal, the authors mimic an agro-food experiment emulating a fermentation process as a proof of concept by measuring the ethanol concentration over time in two liquids: Deionized Water (DIW) and White Wine (WW). The authors compare both methods by inferring the ethanol concentration in the two experiments. As a result, the authors demonstrated experimentally that the proposal significantly outperformed the WSRM method, reporting an improvement of the Limit of Detection (LoD) of up to 140 times. Moreover, this PCA method can be also applied to many other biochemical sensing systems and transducers.

Keywords

• Chemical and biological sensors
• optical sensors
• optical resonators
• machine learning
• principal component analysis
• expert sensors