A Piezoelectric Microelectrode Arrays System for Real-Time Monitoring of Bacterial Contamination in Fresh Milk

Piezoelectric sensing of glucose oxidase activity of Aspergillus niger spores pretreated by different methods

The development of Aspergillus niger (A. niger) spores as glucose oxidase (GOD) biocatalysts to produce gluconic acid is highly anticipated in the food industry. Herein, a piezoelectric sensor (PIS) method has been developed for the detection of GOD activity and better application of rapid screening of GOD activity in A. niger spores. The GOD activity detection is based on GOD catalyzing β-d-glucose to produce gluconic acid, which results in frequency shift changes recorded by the PIS device in real-time. Using the PIS method, the kinetic parameter 6.5 mg/mL, the correlation equation υ₀=31.92C_GOD+1.04, the recoveries (89.4%-93.9%, and their RSDs were all within 6.1%) and the optimal GOD activity in A. niger spores under different treatment conditions was obtained. Compared with the classical methods, the proposed method is accurate, rapid, convenient and does not require additional reagents. It has a broad range of potential applications for exploring new GOD biocatalysts.

Disposable all-printed electronic biosensor for instantaneous detection and classification of pathogens

A novel disposable all-printed electronic biosensor is proposed for a fast detection and classification of bacteria. This biosensor is applied to classify three types of popular pathogens: Salmonella typhimurium, and the Escherichia coli strains JM109 and DH5-α. The proposed sensor consists of inter-digital silver electrodes fabricated through an inkjet material printer and silver nanowires uniformly decorated on the electrodes through the electrohydrodynamic technique on a polyamide based polyethylene terephthalate substrate. The best sensitivity of the proposed sensor is achieved at 200 µm teeth spaces of the inter-digital electrodes along the density of the silver nanowires at 30 × 10³/mm². The biosensor operates on ±2.5 V and gives the impedance value against each bacteria type in 8 min after sample injection. The sample data are measured through an impedance analyzer and analyzed through pattern recognition methods such as linear discriminate analysis, maximum likelihood, and back propagation artificial neural network to classify each type of bacteria. A perfect classification and cross-validation is achieved by using the unique fingerprints extracted from the proposed biosensor through all the applied classifiers. The overall experimental results demonstrate that the proposed disposable all-printed biosensor is applicable for the rapid detection and classification of pathogens.