Adulteration in milk is a common scenario for gaining extra profit, which may cause severe harmful effects on humans. The qualitative spectroscopic technique provides a better solution for detecting the toxic contents of milk and foodstuffs. All the available spectroscopic methods for milk adulterant detection are based on laboratory-based with costly equipment. This laboratory-based detection takes a long time and is more expensive, which may not be afforded by a common man. To overcome this issue, this research work involves the design and development of a low-cost, portable, multispectral, AI-based, non-destructive spectroscopic sensor system that can be used to detect the milk adulterant in real-time. The designed sensor system uses the spectroscopic method with wavelength ranges from (410-940nm) which consists of three different bands Ultraviolet (UV), visible, and Infra-Red(IR) spectrum to improve the accuracy of detection. The sensor system is connected to the internet via the developed IoT application module, which displays the detected adulterant results in a dedicated web page designed for this purpose. This IoT application enables the adulterant detected results published on the internet immediately with location information for bringing transparency. Adulterant detection problem is formulated as a classification problem and solved by machine learning algorithms of a decision tree, Naive Bayes, linear discriminant analysis, support vector machine and neural network model. The average accuracy of linear discriminant analysis, support vector machine, Naive Bayes, decision tree and neural network model are obtained as 88.1%, 90%, 90%, 91.7% and 92.7% respectively. Genetic algorithm framework is formulated for hyperparameter tuning of neural network model which improved the accuracy from 92.7% to 100%. The model is trained for five different classes of four adulterants, namely Sodium Salicylate, Dextrose, Hydrogen Peroxide, Ammonium Sulphate, and one pure milk sample.

Growing organic food is becoming a challenging task with increasing demand. Food fraud activity has increased considerably with the increase in population growth. Consumers cannot visually distinguish between conventional and organically grown food products. Spectroscopic methodologies are presented to identify chemicals in food, thereby identifying organic and conventional food. Such spectroscopic techniques are laboratory-based, take more time to produce an outcome, and are costlier. Thus, this research designed a portable, low-cost multispectral sensor system to discriminate between organic and conventional vegetables. The designed multispectral sensor system uses a wavelength range (410 nm–940 nm) that includes three bands, namely visible (VIS), ultraviolet (UV) and near-infrared (NIR) spectra, to enhance the accuracy of detection. Tomato, brinjal and green chili samples are employed for the experiment. The organic and conventional discrimination problem is formulated as a classification problem and solved through random forest (RF) and neural network (NN) models, which achieve 92% and 89% accuracy, respectively. A two-stage enhancement mechanism is proposed to improve accuracy. In the first stage, the fuzzy logic mechanism generates additional feature sets. Ant colony optimization (ACO) algorithm-based parameter tuning and feature selection are employed in the second stage to enhance accuracy further. This two-stage improvement mechanism results in 100% accuracy in discriminating between organic and conventional vegetable samples. The detected adulterant is displayed on a web page through an IoT-developed application module to be accessed from anywhere.

Edible oils plays significant role in everyday life of human being consumption which provides better nutrition. Among worldwide, edible oil has more demand such as olive oil which are branded and it is more expensive when compared with other vegetable oils. In recent years adulteration becomes a part of global issue. To gain additional profit for the vendor edible oils are being adulterated which become major problem for consumers health. It cause severe several problems and affects human health. Mixing the cheaper vegetable oil with pure oils are also a kind of adulteration. Therefore, sensitive, rapid, low cost suitable method is required to detect the presence of adulteration in the oil samples. This review work focuses on various recent oil adulteration detection methods for qualitative analysis. Spectroscopic, imaging, DNA based evaluation and sensor techniques are delineated for the oil adulteration determination. Gas Chromatography is the widely used conventional technique for adulteration determination. Among the spectroscopic models Attenuation Total Reflectance- Fourier Transform Infrared spectroscopy performs better identification for mixed oil samples.

One of the basic necessities of human beings to survive on the earth is food. Nowadays, consuming nutritious food is challenging. The production of food and its consumption rate is increasing year by year. World Health Organisation (WHO) estimates that around 9.2 billion metric tons of foods need to be produced annually as per 2024 population rate. On the other hand, 600 million people undergo food-borne illness due to contamination due to pesticides. Consumption of good quality food items remains a global concern. It is necessary to give importance to food safety measures during production and processing. Integrating artificial intelligence and machine learning with food processing helps for better predict and optimize production to maintain safety and quality. These advanced technologies can detect toxic contents, microbial contamination, chemical residues, and allergens in real time, enabling preventive measures. The developed algorithms are becoming more sustainable, resilient, and provide safety for the end consumers.