Assessment of Food Adulteration and Its Determinants in Gurugram and South West District of Delhi, India
Jai Parkash *
Animal Science, KVK Delhi, India.
Ram Sagar
KVK, New Delhi, India.
Rakesh Kumar
Animal Science, KVK Delhi, India.
Brijesh Yadav
KVK, New Delhi, India.
Suman
DPG College, Gurugram, India.
Kailash
Animal Science, KVK Delhi, India.
Gitam Singh
College of Agriculture, Madhav University Pindwara Sirohi Rajasthan–307032, India.
DK Rana
KVK, New Delhi, India.
*Author to whom correspondence should be addressed.
Abstract
This study investigates the prevalence and drivers of food adulteration in Gurugram (Haryana) and South West District of Delhi between 2022 and 2024. The primary objectives were to (1) identify commonly adulterated food items sold in retail and informal markets, (2) determine the types of adulterants and their frequencies, (3) analyse spatial and temporal patterns, and (4) propose actionable interventions for regulators and stakeholders. A stratified sampling design collected 360 food samples across both regions (milk and dairy products, edible oils, spices, honey, jaggery/gur, and sugar). Samples were analysed using a combination of field screening tests, physicochemical assays, and confirmatory instrumental methods (GC–MS, HPLC, FTIR). Results indicate an overall adulteration prevalence of 38%, with highest rates observed in spices (50%), milk (45%), and jaggery/gur (40%). Common adulterants included starch and diluted water in milk, cheaper vegetable oils in packaged edible oils, sugar syrups in honey, metanil yellow and synthetic colors in spices, and sugar addition/cane molasses in gur. Socioeconomic drivers identified were price differentials, seasonal demand, weak traceability, and limited vendor awareness. The paper concludes with recommendations for enhanced surveillance, point-of-sale rapid testing, supply-chain traceability, consumer education, and targeted enforcement.
Keywords: Food adulteration, Gurugram, South West Delhi, spices, milk adulteration, GC–MS, public health, FSSAI