Detection of Scab‐Damaged Hard Red Spring Wheat Kernels by Near‐Infrared Reflectance

Abstract

Cereal Chem. 81(5):643-649Scab (Fusarium head blight) is a fungal disease that has become increasingly prevalent in North American wheat during the past 15 years. It is of concern to growers, processors, and the consumers because of depressed yields, poor flour quality, and the potential for elevated concentrations of the mycotoxin, deoxynivalenol (DON). Both wheat breeder and wheat inspector must currently deal with the assessment of scab in harvested wheat by manual human inspection. The study described herein examined the accuracy of a semi-automated wheat scab inspection system that is based on near-infrared (NIR) reflectance (1,000-1,700 nm) of individual kernels. Using statistical classification techniques such as linear discriminant analysis and nonparametric (k-nearest-neighbor) classification, upper limits of accuracy for NIR-based classification schemes of ≈88% (cross-validation) and 97% (test) were determined. An exhaustive search of the most suitable wavelength pairs for the spectral difference, [log(1/R) λ1 -log(1/R) λ2 ], revealed that the slope of the lowwavelength side of a broad carbohydrate absorption band (centered at ≈1,200 nm) was very effective at discriminating between healthy and scab-damaged kernels with test set accuracies of 95%. The achieved accuracy levels demonstrate the potential for the use of NIR spectroscopy in commercial sorting and inspection operations for wheat scab.

An empirical evaluation of three vibrational spectroscopic methods for detection of aflatoxins in maize

“…Preprocessed Raman, FT-NIR, and FTIR spectra of ground maize samples were used to classify aflatoxin contaminated samples based on the level of aflatoxin contamination using the following classification algorithms: linear discriminant analysis (LDA), k-nearest neighbour (KNN), and partial least squares discriminant analysis (PLSDA) (Delwiche & Hareland, 2004;Dowell, Pearson, Maghirang, Xie, & Wicklow, 2002;Johnson, 1998;Lee et al, 2013). Aflatoxin contaminated maize samples were assigned to one of the following groups: Group 1 for below 20 lg/kg (considered as ''negative''), Group 2 for 20-200 lg/kg, Group 3 for 300-450 lg/kg, Group 4 for 550-700 lg/kg, and Group 5 for above 850 lg/kg.…”

Principal Component Analysis in the Development of Optical and Imaging Spectroscopic Inspections for Agricultural / Food Safety and Quality

“…Vibrational spectroscopy is an alternative approach, since it can be applied directly to the solid grain in the state of singlekernel and ground without any DON extraction steps (Delwiche & Hareland, 2004;Delwiche, 2008). In general, grain contains a large portion of moisture that, in turn, yields intense and broad water bands in both the IR and NIR regions and can substantially hide other useful bands attributable to protein and carbohydrate species.…”

Detection of fungal infection and Ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

“…NIR hyperspectral imaging techniques have also been used to detect pre-and postharvest fungal infection in raw grains, detection of scab and other mold damaged wheat kernels (Delwiche, 2003), detection of scab damaged hard red spring wheat kernels (Delwiche and Hareland, 2004), detection of A. glaucus, A. niger and Penicillium spp. infected wheat kernels , classification of sound and Fusarium damaged wheat kernels (Peiris et al, 2009), detection of fungal-infected corn kernels (Tallada et al, 2011), fungal development in maize kernels , quantification of ergot bodies in cereals (Vermeulen et al, 2012), differentiation between species and strains of members of the genus Fusarium , detection of Fusarium head blight in wheat kernels (Barbedo et al, 2015) and detection of Fusarium damaged oats (Tekle et al, 2015).…”