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Economically viable remote sensing of foodborne contaminants using minimalistic chemical reagents and simultaneous automation calls for a concrete integration of a chemical detection strategy with artificial intelligence. In a first of its kind, we report the ultrasensitive detection of citrinin and associated mycotoxins like aflatoxin B1 and ochratoxin A using an Alizarin Red S (ARS) and cystamine-derived carbon dot (CD) that aptly amalgamate with machine learning algorithms for automation. The photoluminescence response of the CD as a function of various solvents and pH is used to generate array channels that are further modulated in the presence of the mycotoxins whose digital images were acquired to determine pixelation, essentially creating a barcode. The barcode was fed to machine learning algorithms that actualize and intertwine convoluted databases, demonstrating Extreme Gradient Boosting (XGBoost) as the optimized model out of eight algorithms tested. Spiked samples of wheat, rice, gram, maize, coffee, and milk were used to evaluate the testing model where an exemplary accuracy of 100% even at 10 pmol of mycotoxin concentration was achieved. Most importantly, the coexistence of mycotoxins could also be detected through the CD array and XGBoost synergy hinting toward a broader scope of the developed methodology for smart detection of foodborne contaminants.

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Waste (or waste) is useless or unusable materials or components that are discarded after principal use. Sometimes, it is a defective article and of no use. In the modern outlook, waste may be a valuable substance subject to an appropriate operation or action on the waste. In the context of waste management RRR (reduce, reuse and recycle) model may be followed appropriately.Pollution from waste is aesthetically unpleasing and results in large amounts of litter in our communities which can cause health problems. Plastic bags and discarded ropes and strings can be very dangerous to birds and other animals. This indicator addresses waste production and disposal, plastic waste, paper waste, food waste, and recycling. Solid waste can be divided into two categories: general waste and hazardous waste.Jhargram Municipality is one of the fastest developing Municipalities in West Bengalwith a population of 61,682 anda population density of 2882.33 /sq km in 2011 but at the present near about 92000. This Municipality has around 19775 houses, markets, apartments and offices, colleges, hospitals, and schools which generate 79 Metric Ton of solid waste per day. The study has been carried out to select solid waste disposal sites and to find out the suitable management strategies forthe solid waste system by using Geographical Information System techniques. Manually field observation and expert opinion have been taken for disposal site selection thematic layers are prepared using Remote Sensing and GIS technique. Finally, theGeographicalInformation System technique was applied in Arc GIS Software to identify the disposal sites. The leading problem is the open dumping of solid waste without recycling, which resulted in environmental pollution for thisjhargram Municipality. The auditor diagnoses the prevailing waste disposal policies and suggests the best way to combat the problems. It is therefore essential that any environmentally responsible institution and authority examine its waste processing practices. Keeping the objective of the audit the following study will be limited to the waste generated on the residence campus and surroundings.The study has been decided with some productive management strategies which may be helpful to the local people as well as municipal authority.