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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #419861
Research Project: Advancement of Sensing Technologies for Food Safety and Security Applications

Location: Environmental Microbial & Food Safety Laboratory

Title: Investigating the detection of peanuts in chopped nut products using hyperspectral imaging systems

Author
item VEGA-CASTELLOTE, MIGUEL - University Of Cordova (UCO), Spain
item SANCHEZ, MARIA-TERES - University Of Cordova (UCO), Spain
item Kim, Moon
item HWANG, CHANSONG - University Of Maryland
item PEZEZ-MARIN, DELORES - University Of Cordova (UCO), Spain

Submitted to: Journal of Food Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/25/2024
Publication Date: 10/28/2024
Citation: Vega-Castellote, M., Sanchez, M., Kim, M.S., Hwang, C., Pezez-Marin, D. 2024. Investigating the detection of peanuts in chopped nut products using hyperspectral imaging systems. Journal of Food Engineering. 338: 112378. https://doi.org/10.1016/j.jfoodeng.2024.112378.
DOI: https://doi.org/10.1016/j.jfoodeng.2024.112378

Interpretive Summary: Consumption or contact with peanuts can cause severe health problems for consumers with peanut allergies. With the increase in prevalence of peanut allergies that has occurred over the past two decades, accurately identifying the presence of peanuts in commercial nut products prior to sale is critical for preventing both public health problems and economic losses. This research investigated imaging methods using visible and short-wave infrared light to detect peanut pieces present in chopped tree-nut products (almonds, hazelnuts, and walnuts). The results showed that the imaging-based methods could produce excellent classification results and are viable for potential incorporation into commercial operations to detection of peanuts present among chopped tree-nut products to prevent peanut allergy reactions in consumers and economic losses from allergen-based recalls for food processors.

Technical Abstract: The intentional presence or cross-contamination of peanuts in other nut products can result in severe health problems for consumers allergic to peanuts. It is therefore essential to identify the presence of these allergenic compounds in commercialized nut products prior to their sale. For this purpose, we assessed the performance of a visible near infrared (Vis-NIR) and a shortwave infrared (SWIR) hyperspectral imaging (HSI) systems working in the spectral regions 419-1007 nm and 842-2532 nm, respectively, to identify peanut pieces in different chopped nuts (almonds, hazelnuts and walnuts). Two strategies were evaluated to create the training and validation sets. In Strategy I, these sets were composed of spectra belonging to individual pixels, whereas in Strategy II, the mean spectrum of each individual piece of nut was used. We used partial least squares discriminant analysis (PLS-DA) to develop the classification models, and the results were assessed by means of the values obtained for the sensitivity, specificity, and non-error rate (NER) statistics. The external validation procedure showed excellent classification results, with a NER of 98.3 % and 99.8 % for the Vis-NIR and SWIR systems, respectively, for Strategy I, and 100 % for both systems when Strategy II was followed. These results therefore confirm the viability of using HSI technology together with multivariate classification methods to detect peanut pieces in other chopped-nut products.