Work

Smart density and viscosity sensing based on edge machine learning and piezoelectric MEMS for edible oil monitoring

Year: 2025

Type: article

Abstract: The new industry requires embedded sensor systems that can monitor processes and address issues quickly and intelligently. These devices must operate efficiently at any stage of the product’s lifecycle, using minimal resources and low energy. Integrating sensors with AI via microcontrollers is thus commercially significant and highly relevant. In this work, we introduce a compact, intelligent, and portable device based on a piezoelectric resonator microelectromechanical system (MEMS) and discrete electronic circuits managed by a microcontroller unit (MCU), capable of estimating the physical properties of fluids using deep learning algorithms deployed on the MCU, without relying on cloud-based computations. By analyzing multiple MEMS resonances in a training and calibration process with blends of various vegetable edible oils, we obtained valuable insights into the liquid properties. Various combinations of hyperparameters of a convolutional neural network were examined to optimize the model’s performance in terms of calibration and resolution errors. The system is employed to monitor oil mixtures, addressing a critical industrial issue such as olive oil adulteration. By measuring viscosity, our system can detect blends of pure olive oil with other vegetable oils in concentrations as low as 2%, with calibration and resolution errors of 0.47% and 0.14 mPa s for viscosity, and 0.0331% and 9 . 25 ⋅ 1 0 − 5 g/mL for density. These results are comparable to other commercial laboratory instruments, offering a low-cost, portable and precise alternative. • Portable device with MEMS resonators and MCU estimates fluid properties using CNN. • Cost-effective sensor production using MEMS manufacturing techniques. • MCU integrates signal generation, power and complex calculations in one instrument. • AI enhances performance, handles complex tasks and analyzes multi-peak spectra. • Highly precise detection of olive oil adulteration at concentrations as low as 2

Source: Sensors and Actuators A Physical

DOI: 10.1016/j.sna.2025.116258

Authors: Víctor Corsino, Víctor Ruiz-Díez, José Luis Sánchez-Rojas

Institution: University of Castilla-La Mancha

Language: English

Topic: Acoustic Wave Resonator Technologies

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,750