The increasing demand for food safety and quality assurance has propelled the development of advanced technologies for nutrient detection and food adulteration prevention. Portable electrical sensors have emerged as a promising solution, offering rapid, accurate, and non-destructive analysis of food components. This paper presents the design and implementation of portable electrical sensors tailored for the detection of essential nutrients and identification of food adulterants. These sensors employ electrochemical, capacitive, and resistive transduction mechanisms, enabling the detection of a wide range of analytes, including vitamins, minerals, proteins, and common adulterants such as pesticides, heavy metals, and unauthorized additives. The sensors are designed for portability and ease of use, making them suitable for on-site testing in various settings, including farms, food processing plants, and retail environments. The integration of wireless communication modules allows real-time data transmission to centralized monitoring systems, facilitating timely decision-making and ensuring food safety compliance. The paper also explores the calibration techniques employed to enhance sensor accuracy, addressing challenges related to matrix effects and environmental variability. Furthermore, the study examines the scalability of these sensors for mass production and their potential integration into existing food safety monitoring frameworks. The results demonstrate that these portable electrical sensors offer a reliable, cost-effective, and user-friendly alternative to traditional laboratory-based analysis methods. By providing immediate feedback on nutrient content and the presence of adulterants, these sensors can play a crucial role in preventing food fraud, enhancing consumer confidence, and supporting regulatory enforcement. The study concludes by discussing future directions, including the development of multi-analyte sensors and the incorporation of machine learning algorithms for enhanced data interpretation and predictive analysis.