Detection and quantification of the insect pest Rhyzopertha dominica (F.) (Coleoptera: Bostrichidae) in rice by qPCR |
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| Affiliation: | 1. IRTA, Ctra Cabrils km 2, 08348 Cabrils, Barcelona, Spain;2. USDA-ARS North Central Agricultural Research Services, Brookings, SD 57006, USA;1. Biotechnology Research Center, Food Analysis and Treatment Research Group, P. O. Box 30313, Tripoli, Libya;2. Aalto University, School of Chemical Technology, Department of Biotechnology and Chemical Technology, P. O. Box 16100 (Kemistintie 1), FIN-00076, Aalto, Finland;3. University of Sharjah, SREE Department, P. O. Box 27272, United Arab Emirates;4. Department of Food Engineering and Technology, Institute of Chemical Technology, Nathalal Parekh Marg, Matunga, Mumbai 400019, India;1. Plant Biotechnology Laboratory, Department of Biotechnology and Bioinformatics, Sambalpur University, Jyoti Vihar, 768019, Odisha, India;2. Plant Molecular Breeding and Functional Genomics Laboratory, Department of Biotechnology, Central University of Rajasthan, NH-8, Bandarsindri, 305817, Kishangarh, Ajmer, Rajasthan, India;3. Department of Bioscience and Bioinformatics, Khallikote University, Berhampur, 760001, Odisha, India;1. IRTA, Ctra. Cabrils km 2, Cabrils, Barcelona, Spain;2. Departament d’Entomologia, Centre de Protecció Vegetal i Biotecnologia (CPVB), Institut Valencià d’Investigacions Agràries (IVIA), Apartat Oficial, 46113 Montcada, València, Spain;3. Unitat Associada d’Entomologia Universitat Jaume I (UJI) – IVIA, UJI, Campus del Riu Sec, 12071 Castelló de la Plana, Spain;4. Centre de Tecnologia Postcollita (CTP), IVIA, Apartat Oficial, 46113 Montcada, València, Spain;1. Ahi Evran University, Faculty of Agriculture, Ba?ba??, 40100, K?r?ehir, Turkey;2. Ahi Evran University, Department of Environmental Engineering, Ba?ba??, 40100, K?r?ehir, Turkey;1. Department of Medical Parasitology, Wannan Medical College, Wuhu 241002, Anhui, China;2. School of Public Health, Wannan Medical College, Wuhu 241002, Anhui, China |
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| Abstract: | The early detection of insects during grain storage and processing remains a major issue for the cereal industry, especially when immature stages are hidden inside the grain kernels. For this reason, we developed a qPCR method to detect and quantify one of the main pests of stored products in rice: the coleopteran internal feeder Rhyzopertha dominica. For that purpose, a specific primer set was designed to amplify artificial infestations of this pest in rice. Then, using a regression model, a standard curve was generated that correlated individuals to adult equivalent DNA quantity (inverse of the Ct value). Results revealed that the designed primer set was specific for R. dominica when tested against the other 4 common internal feeders in grain. The technique showed to be accurated (DNA was detected in more than 73% of the samples) and sensitive to insect presence (i.e. from 0.02 adults, 0.1 3rd instar to pupae or 13 egg to 2nd instar detectable per kg of rice). Moreover, the detection of R. dominica was strongly associated with a given infestation size: DNA quantity increased along with the size of the population. The use of the described qPCR protocol in grain and milling factories may enhace the critical detection and quantification of R. dominica populations in raw materials and processed food. |
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| Keywords: | Adult equivalent Insect detection Lesser grain borer Hidden infestation Grain Molecular analysis Standard curve |
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