Detection and quantification of peanut contamination in garlic powder using NIR sensors and machine learning

Food contamination is a major concern for consumers and food businesses, especially when the contaminant is an allergen. This study focused on detecting and quantifying peanut powder in garlic powder using low-cost Near-Infrared sensors (S2.0–1550–1950 nm, and S2.5–2000–2450 nm) coupled with machine learning methods. Garlic and peanut powders of three different origins were allocated to different data sets, and 37 peanut contamination concentrations from 0.01% to 20% were studied. Samples were first assessed to determine if they were contaminated with peanut. Peanut concentration was then determined to be either low (0.01–1%) or high (2–20%). Finally, the peanut concentration was predicted. Classification accuracy of 100% was achieved when assessing models on an individual data set but declined when a second independent data set was used. In general, the models developed from the S2.0 sensor performed marginally better than those developed using the S2.5. Peanut concentration prediction models achieved Correlation Coefficient and Root Mean Square Error values of 70.8%, 0.49%, and 77.5%, 3.53% for low and high peanut concentrations, respectively. The results obtained from this study can be used to develop cost-effective contamination detection technologies for the food sector. •NIR sensors were utilised to evaluate peanut powder in garlic powder.•Powders of different origins and contamination concentrations (0.01–20%) studied.•Classification models achieved 100% prediction accuracy on data set 1.•Testing on data set 2 (different origin) reduced the accuracy to 85.1%.•PLSR to quantify peanut contamination achieved r values of 77.5%.