Development of a qPCR-based method for the detection of arthropod pests in stored rice
Year: 2024
Type: article
Abstract: Stored products are vulnerable to arthropod pest infestations, threatening food security and causing production losses. Routine analyses are crucial for pest detection and management. Molecular diagnostic methods are increasing in the food industry for their sensitivity and reliability, but they have not yet been used to detect the presence of all potential arthropod pest species in storage environments. A method based on qPCR and the use of universal arthropod primers to detect arthropod infestations in stored rice has been developed. To determine the extent of the method, a specificity test was first conducted on species potentially found in grain commodities. Secondly, paddy and brown rice samples with different infestation densities of the most frequent pests were analyzed. The chosen universal arthropod primers successfully detected most of the pest species tested, and their combined use extended the efficacy of species detection. Results demonstrated that the developed method could accurately diagnose arthropod contamination in brown rice of the 10 most common pest species affecting this commodity with a high sensitivity (2.6 mg arthropod/500g rice). To avoid environmental DNA detection, it should be used in commodities with a certain degree of processing. Implementing this method could provide the food industry with a reliable tool for detecting pest contamination in rice and other stored products, enabling better decision-making capabilities that contribute to reducing regular pesticide applications. • The developed qPCR method can simultaneously detect multiple arthropod species. • This method detects the presence of the 10 most common pest species in rice. • Combining universal arthropod primer pairs increased the range of species detected. • The qPCR method is able to detect 0.0052 g of arthropods in 1 kg of rice.
Source: Food Control
Institution: Institute of Agrifood Research and Technology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $4,430
OpenAlex ID: W4405740182