A solid-phase porphyrin and boron-dipyrromethene sensing platform for the infestation detection of two main hidden pests in rice

• Published in: Sensors and actuators. B, Chemical Chemical, 2022-08, Vol.364, p.131843, Article 131843
• Main Authors: He, Peihuan, Yang, Wenjing, Ali, Shujat, Lin, Hao, Jiang, Hui, Shi, Zhaoxia, Li, Huanhuan, Chen, Quansheng
• Format: Article
• Language: English
• Subjects: Pest detection in rice; Supervised K-Nearest Neighbor model; The solid-phase porphyrin and boron-dipyrromethene sensors; Unsupervised principal component analysis; Volatile organic compounds
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2022.131843

Hidden pests are the unpleasant contaminants in rice and the main reason for the quantity and quality loss during rice storage. In this work, an innovative detection strategy applying the developed solid-phase porphyrin and boron-dipyrromethene sensing platform based on volatile organic compounds (VOCs), was proposed for the infestation determination of rice weevil (RW) and maize weevil (MW) in rice. The gas chromatography-mass spectrometry analysis exhibited for the first time content of VOCs decreased with increasing pest infection. The solid-phase sensing platform was developed based on a colorimetric sensor array of porphyrin and boron-dipyrromethene dyes. And the principle and response signals of the fabricated sensing platform showed the sensors respond accordingly with the pest infestation degrees of rice, concordant with the VOC changes. Unsupervised Principal Component Analysis completely distinguished clean rice from other samples with different degrees of infestation, and distinguished RW-infected rice from MW-infected rice. Furthermore, the qualitative discriminant analysis model of pest infection degree and species identification was established based on supervised K-Nearest Neighbor using the response signal of the solid-phase sensing platform, with an accuracy rate of 88% and 100%, respectively. The results suggested the proposed sensing platform for pest detection in rice has a high application prospect. •VOCs of rice are efficient indicators for detecting pest infestation.•CSA signals of the pest-infected rice are in accord with the VOCs dynamics.•Supervised KNN exhibit great prediction for infestation degree and species of pest.•Clean rice samples are completely distinguished from the infected rice using the CSA.•VOCs dynamics of the pest-infected rice are revealed for the first time.