The increasing demand for food production has necessitated
the
development of sensitive and reliable methods of analysis, which allow
for the optimization of storage and distribution while ensuring food
safety. Methods to quantify and monitor volatile and biogenic amines
are key to minimizing the waste of high-protein foods and to enable
the safe consumption of fresh products. Novel materials and device
designs have allowed the development of portable and reliable sensors
that make use of different transduction methods for amine detection
and food quality monitoring. Herein, we review the past decade’s
advances in volatile amine sensors for food quality monitoring. First,
the role of volatile and biogenic amines as a food-quality index is
presented. Moreover, a comprehensive overview of the distinct amine
gas sensors is provided according to the transduction method, operation
strategies, and distinct materials (e.g., metal oxide semiconductors,
conjugated polymers, carbon nanotubes, graphene and its derivatives,
transition metal dichalcogenides, metal organic frameworks, MXenes,
quantum dots, and dyes, among others) employed in each case. These
include chemoresistive, fluorometric, colorimetric, and microgravimetric
sensors. Emphasis is also given to sensor arrays that record the food
quality fingerprints and wireless devices that operate as radiofrequency
identification (RFID) tags. Finally, challenges and future opportunities
on the development of new amine sensors are presented aiming to encourage
further research and technological development of reliable, integrated,
and remotely accessible devices for food-quality monitoring.
The development of simple detection methods aimed at widespread screening and testing is crucial for many infections and diseases, including prostate cancer where early diagnosis increases the chances of cure considerably. In this paper, we report on genosensors with different detection principles for a prostate cancer specific DNA sequence (PCA3). The genosensors were made with carbon printed electrodes or quartz coated with layer-by-layer (LbL) films containing gold nanoparticles and chondroitin sulfate and a layer of a complementary DNA sequence (PCA3 probe). The highest sensitivity was reached with electrochemical impedance spectroscopy with the detection limit of 83 pM in solutions of PCA3, while the limits of detection were 2000 pM and 900 pM for cyclic voltammetry and UV–vis spectroscopy, respectively. That detection could be performed with an optical method is encouraging, as one may envisage extending it to colorimetric tests. Since the morphology of sensing units is known to be affected in detection experiments, we applied machine learning algorithms to classify scanning electron microscopy images of the genosensors and managed to distinguish those exposed to PCA3-containing solutions from control measurements with an accuracy of 99.9%. The performance in distinguishing each individual PCA3 concentration in a multiclass task was lower, with an accuracy of 88.3%, which means that further developments in image analysis are required for this innovative approach.
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