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

被引:51
|
作者
Johnson, Joel B. [1 ]
机构
[1] CQUniversity, Sch Hlth Med & Appl Sci, North Rockhampton, Qld 4701, Australia
关键词
Post-harvest insect pests; Food security; Rice weevil (Sitophilus oryzae L.); Chemometrics; Non-invasive analysis; SITOPHILUS-ORYZAE COLEOPTERA; RHYZOPERTHA-DOMINICA F; SINGLE WHEAT KERNELS; AUTOMATED DETECTION; POPULATION-DENSITY; RAPID ASSESSMENT; PRODUCT INSECTS; INFESTATION; RICE; FRAGMENTS;
D O I
10.1016/j.jspr.2019.101558
中图分类号
Q96 [昆虫学];
学科分类号
摘要
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. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页数:10
相关论文
共 50 条
  • [31] Triticale moisture and protein content prediction by near-infrared spectroscopy (NIRS)
    Igne, B.
    Gibson, L. R.
    Rippke, G. R.
    Schwarte, A.
    Hurburgh, C. R., Jr.
    CEREAL CHEMISTRY, 2007, 84 (04) : 328 - 330
  • [32] Using Internet of Things (IoT), Near-Infrared Spectroscopy (NIRS), and Hyperspectral Imaging (HSI) to Enhance Monitoring and Detection of Grain Pests in Storage and Handling Operators
    Crepon, Katell
    Cabacos, Marine
    Bonduelle, Felix
    Ammari, Faten
    Faure, Marlene
    Maudemain, Severine
    AGRICULTURE-BASEL, 2023, 13 (07):
  • [33] EVALUATION OF BULK FODDER QUALITY BY NEAR-INFRARED RED SPECTROSCOPY (NIRS)
    MIKA, V
    PAUL, C
    ROSTLINNA VYROBA, 1989, 35 (10): : 1109 - 1114
  • [34] Pharmaceutical Applications of Separation of Absorption and Scattering in Near-Infrared Spectroscopy (NIRS)
    Shi, Zhenqi
    Anderson, Carl A.
    JOURNAL OF PHARMACEUTICAL SCIENCES, 2010, 99 (12) : 4766 - 4783
  • [36] Prediction of bioactive compounds in barley by near-infrared reflectance spectroscopy (NIRS)
    Albanell, Elena
    Martinez, Mariona
    De Marchi, Massimo
    Manuelian, Carmen L.
    JOURNAL OF FOOD COMPOSITION AND ANALYSIS, 2021, 97
  • [37] NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy
    Chul, Jong
    Tak, Sungho
    Jang, Kwang Eun
    Jung, Jinwook
    Jang, Jaeduck
    NEUROIMAGE, 2009, 44 (02) : 428 - 447
  • [38] Use of near-infrared spectroscopy (NIRS) during carotid endarterectomy (CEA)
    Amory, DW
    Chen, B
    Benni, P
    Hardman, D
    McCann, RL
    ANESTHESIA AND ANALGESIA, 1998, 86 (04): : SCA86 - SCA86
  • [39] Use of near-infrared reflectance spectroscopy (NIRS) in palaeoecological studies of peat
    McTiernan, KB
    Garnett, MH
    Mauquoy, D
    Ineson, P
    Couteaux, MM
    HOLOCENE, 1998, 8 (06): : 729 - 740
  • [40] Characterization of the Freshness of Pork by Near-Infrared Spectroscopy (NIRS) and Ensemble Learning
    Tan, Cheng
    Chen, Hui
    Zeng, Miao
    Xue, Zhi
    ANALYTICAL LETTERS, 2024,