Utilizing plant-based biogenic volatile organic compounds (bVOCs) to detect aflatoxin in peanut plants, pods, and kernels

Abstract

Mycotoxins and particularly aflatoxin are a concern for peanut growers, processors, and consumers. Aflatoxins are responsible for approximately 25 % of liver cancer cases worldwide, while also costing millions of dollars in lost revenue on an annual basis. Current detection techniques based on high pressure liquid chromatography (HPLC) are time, labor and cost intensive. Peanut product can be held multiple days before testing is complete, costing processors additional revenue loss. Over the past few years, The Georgia Tech Research Institute (GTRI) has been investigating the use of plant-based biogenic volatile organic compounds (bVOCs) for monitoring the status of peanut plants. Initially, GTRI utilized these bVOCs to monitor for heat/drought stress in peanut plants. They quickly saw very promising indications of the ability of bVOCs to monitor other parameters in these plants. More recently, the team has started to investigate the of use of bVOCs to monitor aflatoxin development in plants, pods, and kernels pre- and post-harvest. A field trial for detection of aflatoxin using bVOCs was conducted in August–September of 2020 where three test groups were prepared: plants treated with Aspergillus fungus; plants treated with Afla-Guard (biocontrol agent); plants not treated – acting as a control group. Plant-based bVOCs were collected from the plants before treatment, and once a week post treatment using Stir Bar Sorptive Extraction (SBSE) devices or Twisters®. Each Twister® was then analyzed via gas chromatography–mass spectrometry (GC/MS). Pods from tested plants were harvested and sent to GTRI where bVOCs were collected and analyzed using GC/MS. Several statistical analysis and machine learning techniques were applied to all the collected GC/MS data. It was found using only bVOCs, that Random Forest classification performed well for the analysis of the pod and kernel samples with an F1 score of 0.80. On the other hand, Linear Discriminate Analysis (LDA) was only able to correctly classify 50 % of plant-based samples solely on bVOCs alone, which may be due to training models developed using original labels assuming no cross contamination at the field level. These results indicate the potential for bVOC screening for aflatoxin as an important way to lower impacts to growers, shellers, and consumers.