Detecting Dye-Contaminated Vegetables Using Low-Field NMR Relaxometry

Dyeing vegetables with harmful compounds has become an alarming public health issue over the past few years. Excessive consumption of these dyed vegetables can cause severe health hazards, including cancer. Copper sulfate, malachite green, and Sudan red are some of the non-food-grade dyes widely used on vegetables by untrusted entities in the food supply chain to make them look fresh and vibrant. In this study, the presence and quantity of dye-based adulteration in vegetables are determined by applying H-1-nuclear magnetic resonance (NMR) relaxometry. The proposed technique was validated by treating some vegetables in-house with different dyes and then soaking them in various solvents. The resulting solutions were collected and analyzed using NMR relaxometry. Specifically, the effective transverse relaxation time constant, T-2,T-eff, of each solution was estimated using a Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence. Finally, the estimated time constants (i.e., measured signatures) were compared with a library of existing T-2,T-eff data to detect and quantify the presence of unwanted dyes. The latter consists of data-driven models of transverse decay times for various concentrations of each water-soluble dye. The time required to analyze each sample using the proposed approach is dye-dependent but typically no longer than a few minutes. The analysis results can be used to generate warning flags if the detected dye concentrations violate widely accepted standards for food dyes. The proposed low-cost detection approach can be used in various stages of a produce supply chain, including consumer household.

Automated summary

Dyeing vegetables with harmful compounds has become a concerning public health issue in recent years, as excessive consumption of dyed vegetables can lead to severe health hazards such as cancer. This study validates the presence and quantity of dye-based adulteration in vegetables using proton nuclear magnetic resonance (NMR) technology, and proposes a low-cost detection method that can be used in various stages of the produce supply chain.