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.
Highlights
Nowadays, the industry is evolving towards the digital age to increase its efficiency and productivity, leading to the development of Industry 4.0 [1]
APPLICATION OF THE Wavelength Shift of the Resonant Mode (WSRM) METHOD Following the methodology exposed before for the WSRM method and the cross-validation strategy, all responses in S − si, the training set, for the Deionized Water (DIW)-ethanol experiment are reduced to a single point corresponding to the minimum resonant mode
Analyzing this figure and due to the high linearity of the data, a linear model should be considered because otherwise, we could incur in an overfitting issue, which is a situation of concern in Machine Learning (ML)
Summary
The industry is evolving towards the digital age to increase its efficiency and productivity, leading to the development of Industry 4.0 [1] This advanced industrial approach allows an increment of the digital data with which to cre-. Attending to a relevant field as the agro-food industry, this innovative trend was successfully applied when exploring novel approaches for monitoring fluid properties [4], [5]. Focusing on this field of knowledge, there are two usual approaches of fluid characterization for a sample in the.
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