An overview of near-infrared spectroscopy (NIRS) for the detection of insect pests in stored grains. (March 2020)

• Record Type: Journal Article
• Authors: Johnson, Joel B.
• Is Part Of: Journal of stored products research. Volume 86(2020)
• Publication Date: 2020-03
• Subjects: Post-harvest insect pests -- Food security -- Rice weevil (Sitophilus oryzae L.) -- Chemometrics -- Non-invasive analysis; Food -- Storage -- Periodicals; Farm produce -- Storage -- Diseases and injuries -- Periodicals; Entomology -- Periodicals; Food Contamination -- Periodicals; Food Preservation -- Periodicals; Insect Control -- Periodicals; Aliments -- Entreposage -- Périodiques; Produits agricoles -- Entreposage -- Maladies et dommages -- Périodiques; Electronic journals; 631.568
• DOI: 10.1016/j.jspr.2019.101558
• Languages: English
• ISSNs: 0022-474X

Abstract

Abstract: Applications of near-infrared spectroscopy for measuring various aspects of grain quality have expanded rapidly in recent years. One application that could be of particular use to growers and industry is the detection of insect pests across a range of stored grains. This prospect was first reported over 20 years ago, but the accuracy of this technique does not currently meet FDA standards for the quantification of insect fragments in bulk wheat and flour samples. When considering bulk samples, near-infrared spectroscopy may be suitable for identifying the presence of infestation in samples, followed by flotation testing to provide an accurate quantitative value. Much higher accuracy has been found for the detection of pest species at the single-kernel level. With faster spectrophotometers and kernel sorting systems, single-kernel analysis is likely to be utilised more in the future and could even render bulk analysis of samples redundant. This technology could allow for the detection and identification of pest species in every single kernel of a representative grain sample. The development and application of more sensitive spectrophotometers, such as FT-NIR (Fourier transform near infrared) and more powerful chemometric data analysis techniques are also likely to provide significant improvements, through allowing the minute chemical differences present in bulk infested grains to be accurately detected and quantified. Highlights: NIRS shows good accuracy for detecting insect infestations in individual grains. Poorer accuracy is found at detecting infestations in bulk samples. Rapid single kernel characterisation systems may render bulk analysis redundant. New developments include hyperspectral imaging, FT-NIR and improved data analysis.