Identifying Stored-Grain Insects Using Near-Infrared Spectroscopy

February 1999 DOWELL ET AL.: NIR IDENTIFICATION OF INSECT SPECIES 169
occurring at the CH 3 overtones (1,130 and 1,670 nm)
supports the conclusions indicated by the PLS factors.
Thus, each insect species appears to have molecules
with unique vibrational characteristics that may be
caused by the unique mixture of hydrocarbon molecules
and other lipid classes.
Spectra of the chitin hexamer and ground insect
cuticle had absorbance peaks around 1,400Ð1,500 nm.
Neither the correlation plots nor the factor weight
plots indicated that this wavelength range was useful.
Thus, it appears that the NIR system may have detected
differences in cuticular lipids between species,
but that the chitin within the cuticle did not contribute
to classiÞcations. Other compounds contained in
insect cuticle that could be contributing to classiÞcation
include protein, catachols, pigments, and oxalates
(Kramer et al. 1995).
When classifying Þeld insects using calibrations developed
from laboratory stock colonies, 100% of the C.
ferrugineus were correctly classed as secondary insects
and in the correct genus. The A. advena were not
included in the original calibration; however, 80%
were correctly classed by the model as secondary
insects. For the R. dominica, 67% were correctly
classed as primary insects. Of the R. dominica correctly
classed as primary, 83% were placed in the correct
genus. Of those placed in the correct genus, 100% were
placed in the correct species. The calibration developed
using laboratory colonies appears to classify Þeld
insects with reasonable accuracy, especially considering
that the conÞguration for illuminating and viewing
insects had changed between the original calibration
and subsequent testing of Þeld insects.
Insects that enter traps can be counted electronically
(Shuman and Weaver 1996). However, integration
of Þltered NIR sensors within an insect trap could
provide an automated means of not only counting the
trapped insects but also identifying the insect to type
or species. This type of timely information concerning
pest insect populations in stored grain would certainly
be an advantage when implementing control strategies.
Computer vision, which uses digitized images from
cameras and provides information about object color,
shape, and size, could provide an alternate means of
identifying insect species. Although computer vision
could likely be integrated into an insect trap, it poses
additional problems of proper lighting, shadows, insect
presentation, image segmentation (Zayas and
Flinn 1997).
In summary, our results showed that NIR spectroscopy
coupled with PLS or neural network spectral
analysis techniques can be used to classify the 11 insect
species examined in this study, with primary and secondary
insects being classed with 99% accuracy. The
unique composition of cuticular lipids in the different
beetles may be partially responsible for the classiÞcations
achieved with this system. Although we were
not able to classify all tested insects to the species level
with high accuracy, identiÞcation to species is not
necessarily required for making pest management decisions
in grain storages. IdentiÞcation to genus or
identiÞcation as a primary or secondary pest is usually
sufÞcient. In addition to stored grain insects, we believe
that this technology could be used for rapid,
automated identiÞcation of many other organisms.
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Received for publication 3 February 1998; accepted 22 September
1998.