Detection and identification of five common internal grain insect pests by multiplex PCR

Abstract Consumer demands for better quality food have led to research on new tools aimed at early detection of insect pests in agro food industries. In these industries, internal grain feeders are the most concerning pests because of being the first colonizers of stored grain and transmitting harmful micro-organisms, such as fungi and bacteria, which affect both food quality and human health. The immature stages of these cosmopolitan pests develop and feed inside the grain kernels, easily evading visual analysis in food industries. To avoid the consequent underestimation of contamination by internal pest species, a multiplex PCR approach for the detection and identification of the five most concerning primary pests that develop and feed hidden inside the grain kernels ( Rhyzopertha dominica , Sitophilus granarius , S. oryzae , S. zeamais and Sitotroga cerealella ) has been developed. Results have demonstrated that the designed protocol can be used for the diagnosis of grain contamination with high sensitivity (0.1 pupa/kilo of rice, except for R. dominica 10 pupae/kilo). This tool proved to be specific when 46 other species potentially present in grain commodities were tested, and to detect all developmental stages of S. zeamais in different kinds of grain (barley, maize, oat, spelt, rice and wheat) and pasta (macaroni). Detection was even possible when grain was treated with CO 2 . Finally, in order to confirm its applicability in food industries, this method has also been tested in real commercial grain samples from a pasta mill. The multiplex PCR method presented here could be of great help when making commercial decisions aimed at satisfying the current market demands.

• Publication year

  2018

• Venue

  Food Control

• Publication date

  2018-02-01

• Fields of study

  Agricultural and Food Sciences, Biology

• Identifiers
  DOI 10.1016/J.FOODCONT.2017.08.002
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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