Desenvolvimento de procedimentos analíticos e aplicações IoT utilizando sensores óticos para detecção de adulterações em alimentos e contaminação de água

Abstract

DIY (Do It Yourself) practices have make possible to build personalized, affordable, portable, and easy-to-use analytical devices adapted to various analytical techniques. To improve the capabilities of these devices, IoT (Internet of Things) integration is taking place, making the devices internet-connected and able to collect and transmit data in real-time, facilitating remote monitoring and analysis. With this in mind, three devices were built to meet specific requirements regarding analytical demands and, from an environmental perspective, in line with the principle of green chemistry. Firstly, based on the Griess method, a portable device was built for the colorimetric determination of nitrite in water. An APDS 9960 RGB sensor was used as the detector, and a green LED (λ=530 nm) as the light source. An ESP 32 board powered by a rechargeable lithium battery was used as a microcontroller and a router for transmitting data to the smartphone by Wi-Fi. The signals were monitored by selecting the green channel. The procedure achieved a detection limit of 2 μg L−1 , intra-day and inter-day precision of 1.6 and 2.4 %, respectively. The green LED showed greater sensitivity and precision compared to the UV-VIS method. The proposed device has a working autonomy of 7 hours, making it a viable alternative for in situ nitrite determination. The second device aimed to determine formaldehyde in milk, based on the Hantzsch reaction, which consists of formaldehyde reacting with acetylacetone to form 3,5-diacetyl-1,4-dihydrolutidine (DDL). The method used an AS 7341 sensor to take reflectance and fluorescence measurements of DDL at 415 nm and 515 nm, respectively. The procedure showed a high correlation coefficient, a limit of detection of 27 µg L-1 and a precision of 0.12 % for reflectance measurements. For fluorescence, the detection limit was 30 µg L-1, and the precision was 0.92 %. These results guarantee the reliability and accuracy of this approach, making it a valuable tool for analyzing formaldehyde in milk samples. Finally, the third paper presented a method for quantifying glycerol in wine samples, employing a portable optical device using the AS 7341 sensor as a detector. The chemical strategy involved the oxidation of glycerol using periodate, followed by the formation of DDL through a reaction with acetylacetone. The optimized procedure demonstrated adequate analytical performance for the determination of glycerol in wine samples, showed a high correlation coefficient (r = 0.998), low limit of detection (0.050 mg L-1 ) and good precision (RSD = 0.1 %). These characteristics make it suitable for routine glycerol analysis in the wine industry and tackling challenges related to wine adulteration and counterfeiting. These studies have demonstrated the effectiveness and versatility of using color sensors for different analytical applications, with the advantages of portability, low cost and potential for integration with IoT technologies.