Ultrasensitive bacterial sensing using a disposable all-in-one amperometric platform with self-noise cancellation.

The early detection of very low bacterial concentrations is key to minimize the healthcare and safety issues associated with microbial infections, food poisoning or water pollution. In amperometric integrated circuits for electrochemical sensors, flicker noise is still the main bottleneck to achieve ultrasensitive detection with small footprint, cost-effective and ultra-low power instrumentation. Current strategies rely on autozeroing or chopper stabilization causing negative impacts on chip size and power consumption. This work presents a 27-µW potentiostatic-amperometric Delta-Sigma modulator able to cancel its own flicker noise and provide a 4-fold improvement in the limit of detection. The 2.3-mm2 all-in-one CMOS integrated circuit is glued to an inkjet-printed electrochemical sensor. Measurements show that the limit of detection is 15 pArms, the extended dynamic range reaches 110 dB and linearity is R2 = 0.998. The disposable device is able to detect, in less than 1h, live bacterial concentrations as low as 102 CFU/mL from a 50-µL droplet sample, which is equivalent to 5 microorganisms.

Portable Electronic Tongue Based on Microsensors for the Analysis of Cava Wines.

Cava is a quality sparkling wine produced in Spain. As a product with a designation of origin, Cava wine has to meet certain quality requirements throughout its production process; therefore, the analysis of several parameters is of great interest. In this work, a portable electronic tongue for the analysis of Cava wine is described. The system is comprised of compact and low-power-consumption electronic equipment and an array of microsensors formed by six ion-selective field effect transistors sensitive to pH, Na⁺, K⁺, Ca2+, Cl-, and CO32-, one conductivity sensor, one redox potential sensor, and two amperometric gold microelectrodes. This system, combined with chemometric tools, has been applied to the analysis of 78 Cava wine samples. Results demonstrate that the electronic tongue is able to classify the samples according to the aging time, with a percentage of correct prediction between 80% and 96%, by using linear discriminant analysis, as well as to quantify the total acidity, pH, volumetric alcoholic degree, potassium, conductivity, glycerol, and methanol parameters, with mean relative errors between 2.3% and 6.0%, by using partial least squares regressions.