Characterisation of non-viable whole barley, wheat and sorghum grains using near-infrared hyperspectral data and chemometrics

McGoverin, Cushla; Engelbrecht, Paulina; Geladi, Paul; Manley, Marena

doi:10.1007/s00216-011-5291-x

Cited by 64 publications

(31 citation statements)

References 15 publications

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“…enzymatic kits for a-amylase activity measurement, 14 antibody tests 15 and spectroscopic methods. [16][17][18] Despite the development of antibody-based tests for the measurement of a-amylase activity, the traditional HFN method is still regarded as the preferred one because it combines ease of use, reliability of results and wide use throughout the wheat and milling industry, especially for quality control and screening purposes. HFN requires analysis of a ground sample and cannot give information on single wheat kernels.…”

Section: Barnard Vanmentioning

confidence: 99%

Application of calibrations to hyperspectral images of food grains: example for wheat falling number

Caporaso

Whitworth

Fisk

2017

J. Spectral Imaging

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The presence of a few kernels with sprouting problems in a batch of wheat can result in enzymatic activity sufficient to compromise flour functionality and bread quality. This is commonly assessed using the Hagberg Falling Number (HFN) method, which is a batch analysis. Hyperspectral imaging (HSI) can provide analysis at the single grain level with potential for improved performance. The present paper deals with the development and application of calibrations obtained using an HSI system working in the near infrared (NIR) region (~900–2500 nm) and reference measurements of HFN. A partial least squares regression calibration has been built using 425 wheat samples with a HFN range of 62–318 s, including field and laboratory pre-germinated samples placed under wet conditions. Two different approaches were tested to apply calibrations: i) application of the calibration to each pixel, followed by calculation of the average of the resulting values for each object (kernel); ii) calculation of the average spectrum for each object, followed by application of the calibration to the mean spectrum. The calibration performance achieved for HFN (R2 = 0.6; RMSEC ~ 50 sRMSEP ~ 63 s) compares favourably with other studies using NIR spectroscopy. Linear spectral pre-treatments lead to similar results when applying the two methods, while non-linear treatments such as standard normal variate showed obvious differences between these approaches. A classification model based on linear discriminant analysis (LDA) was also applied to segregate wheat kernels into low (<250 s) and high (>250 s) HFN groups. LDA correctly classified 86.4% of the samples, with a classification accuracy of 97.9% when using an HFN threshold of 150 s. These results are promising in terms of wheat quality assessment using a rapid and non-destructive technique which is able to analyse wheat properties on a single-kernel basis, and to classify samples as acceptable or unacceptable for flour production.

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Section: Barnard Vanmentioning

confidence: 99%

Application of calibrations to hyperspectral images of food grains: example for wheat falling number

Caporaso

Whitworth

Fisk

2017

J. Spectral Imaging

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“…Existing methods to test seed viability include X-ray analysis [8][9][10], tetrazolium staining [11][12][13][14][15], a cut test [16][17][18], in some cases the careful extraction and sterile culture of zygotic embryos under aseptic in vitro conditions [10,19], but germination tests http://dx.doi.org/10.1016/j.jphotobiol.2015.02.015 1011-1344/Ó 2015 Elsevier B.V. All rights reserved. [11,20,21] are probably the most widely used.…”

Section: Introductionmentioning

confidence: 99%

Using hyperspectral imaging to determine germination of native Australian plant seeds

Nansen

Zhao

Dakin

et al. 2015

Journal of Photochemistry and Photobiology B: Biology

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a b s t r a c tWe investigated the ability to accurately and non-destructively determine the germination of three native Australian tree species, Acacia cowleana Tate (Fabaceae), Banksia prionotes L.F. (Proteaceae), and Corymbia calophylla (Lindl.) K.D. Hill & L.A.S. Johnson (Myrtaceae) based on hyperspectral imaging data. While similar studies have been conducted on agricultural and horticultural seeds, we are unaware of any published studies involving reflectance-based assessments of the germination of tree seeds. Hyperspectral imaging data (110 narrow spectral bands from 423.6 nm to 878.9 nm) were acquired of individual seeds after 0, 1, 2, 5, 10, 20, 30, and 50 days of standardized rapid ageing. At each time point, seeds were subjected to hyperspectral imaging to obtain reflectance profiles from individual seeds. A standard germination test was performed, and we predicted that loss of germination was associated with a significant change in seed coat reflectance profiles. Forward linear discriminant analysis (LDA) was used to select the 10 spectral bands with the highest contribution to classifications of the three species. In all species, germination decreased from over 90% to below 20% in about 10-30 days of experimental ageing. P 50 values (equal to 50% germination) for each species were 19.3 (A. cowleana), 7.0 (B. prionotes) and 22.9 (C. calophylla) days. Based on independent validation of classifications of hyperspectral imaging data, we found that germination of Acacia and Corymbia seeds could be classified with over 85% accuracy, while it was about 80% for Banksia seeds. The selected spectral bands in each LDA-based classification were located near known pigment peaks involved in photosynthesis and/or near spectral bands used in published indices to predict chlorophyll or nitrogen content in leaves. The results suggested that seed germination may be successfully classified (predicted) based on reflectance in narrow spectral bands associated with the primary metabolism function and performance of plants.

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“…The NIR hyperspectral imaging has been utilized to detect different quality parameters associated with cereals, oilseeds and pulses such as detection of different wheat classes , fungal infection in wheat (Singh et al, wheat (Singh et al, 2009b), fungal infection in canola , fungal infection in pulses , insect infestation in mungbean (Kaliramest et al, 2013), and insect infestation in soybean (Chelladurai et al, 2014). NIR hyperspectral imaging has been used to detect barley kernel viability (McGoverin et al, 2011) and pregerminated barley . The fungal infection and mycotoxin detection using NIR hyperspectral imaging in barley kernels has not been reported in the literature.…”

Section: Discussionmentioning

confidence: 99%

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

Senthilkumar

Jayas

White

et al. 2016

Journal of Stored Products Research

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Fungal infection and mycotoxin contamination in agricultural products are a serious food safety issue. The detection of fungal infection and mycotoxin contamination in food products should be in a rapid way. A Near-Infrared (NIR) hyperspectral imaging system was used to detect fungal infection in 2013 crop year canola, wheat, and barley at different periods after inoculation and different concentration levels of ochratoxin A in wheat and barley. Artificially fungal infected (Fungi: Aspergillus glaucus, Penicillium spp.) kernels of canola, wheat and barley, were subjected to single kernel imaging after 2, 4, 6, 8, and 10 weeks post inoculation in the NIR region from 1000 to 1600 nm at 61 evenly distributed wavelengths at 10 nm intervals.The acquired image data were in the three-dimensional hypercube forms, and these were transformed into two-dimensional data. The two-dimensional data were subjected to principal component analysis to identify significant wavelengths based on the highest principal component factor loadings. Wavelengths 1100, 1130, 1250, and 1300 nm were identified as significant for detection of fungal infection in canola kernels, wavelengths 1280, 1300, and 1350 nm were identified as significant for detection of fungal infection in wheat kernels, and wavelengths 1260, 1310, and 1360 nm were identified as significant for detection of fungal infection in barley kernels. The linear, quadratic and Mahalanobis statistical discriminant classifiers differentiated healthy canola kernels with > 95% and fungal infected canola kernels with > 90% classification accuracy. All the three classifiers discriminated healthy wheat and barley kernels with > 90% and fungal infected wheat and barley kernels with > 80% classification accuracy.ii The wavelengths 1300, 1350, and 1480 nm were identified as significant for detection of ochratoxin A contaminated wheat kernels, and wavelengths 1310, 1360, 1480 nm were identified as significant for detection of ochratoxin A contaminated barley kernels. All the three statistical classifiers differentiated healthy wheat and barley kernels and ochratoxin A contaminated wheat and barley kernels with a classification accuracy of 100%. The classifiers were able to discriminate between different durations of fungal infections in canola, wheat, and barley kernels with classification accuracy of more than 80% at initial periods (2 weeks) of fungal infection and 100% at the later periods of fungal infection. Different concentration levels of ochratoxin A contamination in wheat and barley kernels were discriminated with a classification accuracy of > 98% at ochratoxin A concentration level of ≤ 72 ppb in wheat kernels and ≤ 140 ppb in barley kernels and with 100% classification accuracy at higher concentration levels.iii

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Characterisation of non-viable whole barley, wheat and sorghum grains using near-infrared hyperspectral data and chemometrics

Cited by 64 publications

References 15 publications

Application of calibrations to hyperspectral images of food grains: example for wheat falling number

Application of calibrations to hyperspectral images of food grains: example for wheat falling number

Using hyperspectral imaging to determine germination of native Australian plant seeds

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

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