Detection of different densities of Ephestia kuehniella pest on white flour at different larvae instar by an electronic nose system

Warehouse pests reduce the quantity, quality, and health of storage products. These parameters are protected by detecting and controlling related pests. Ephestia kuehniella (E. kuehniella) causes intense damage to the storage products, such as flour, almond, date and cereals. Thus, diagnosing pest densities for preventing and monitoring them by online alarming systems is important. The present study was designed to detect pest densities in white flour. For this purpose, an electronic nose (E-nose) system was applied by MOS sensors for pest density detection. PCA/LDA multivariate statistical models were built and relevant performances were compared for different instars of larvae. LDA provided the highest prediction abilities on the fifth instar with an accuracy of 90%. According to results, this system had the capability to differentiate between the pests densities, and thus the detection accuracy reflected the ability of the E-nose system for such purposes. (C) 2019 Elsevier Ltd. All rights reserved.