Automated Detection of Single Wheat Kernels Containing Live or Dead Insects Using Near–infrared Reflectance Spectroscopy

Abstract

An automated near-infrared (NIR) reflectance system was used over a two-month storage period to detect single wheat kernels that contained live or dead internal rice weevils at various stages of growth. Correct classification of sound kernels plus kernels containing live pupae, large larvae, medium-sized larvae, and small larvae averaged 94%, 93%, 84%, and 63%, respectively. Pupae + large larvae calibrations were developed for live (day 1) and dead (days 7, 14, 28, 42, and 56) internal insects. Validation results showed that the live pupae +live large larvae calibration correctly classified 86% to 96% of dead pupae + dead large larvae validation samples. The dead pupae + dead large larvae calibration correctly detected the presence of live pupae + live large larvae with an accuracy of 92% to 93%. Thus, wheat kernels containing either live or dead insects can be used to develop calibrations for detecting both live and dead insects in wheat. These findings will impact how calibration sample sets can be handled. Results indicated that immediate sample processing for creating calibrations may no longer be necessary; internal insects can be killed and calibrations created at a later time without sacrificing accuracy. Additionally, laboratories can share these same calibration samples to save time and resources.

Near-infrared spectroscopy and imaging in food quality and safety

“…Comparable prediction performance was achieved for both dispersive and FT-NIR systems. Maghirang et al reported an automated NIR reflectance sensor for detection of single wheat kernels containing insects [149]. Their findings suggest that calibration models built with wheat samples having dead internal insects can be used to detect live insects inside wheat kernel without sacrificing accuracy.…”

Identification of Damaged Wheat Kernels and Cracked-Shell Hazelnuts with Impact Acoustics Time-Frequency Patterns

“…Several methods have been studied to tackle the problem of detecting insect-damaged wheat kernels, including x-ray imaging (Karunakkaran et al, 2003), NIR spectroscopy (Maghirang et al, 2003;Dowell et al, 1998), and carbon dioxide measurements (Bruce et al,. 1982).…”

Applying singular value decomposition technique for quantifying the insects in commercial Thai Hommali Rice from NIR Spectrum

“…The NIR spectroscopy technique measures the chemical composition of biological materials using di®use re°ec-tance or transmittance of the samples at several wavelengths including detection of insect or piece of insect in whole single grain, ground grain and bulk grain samples. [11][12][13][14][15][16][17] However, NIR spectroscopy detection method also has weaknesses such as the complex development of robust calibration models, and inconsistency across several individual instruments. Most of the commercial NIR instruments are inconvenient for practical use in the¯eld.…”