Discrimination between durum and common wheat kernels using near infrared hyperspectral imaging

Vermeulen, Philippe; Suman, Michele; Pierna, Juan Antonio Fernández; Baeten, Vincent

doi:10.1016/j.jcs.2018.10.001

Cited by 39 publications

(16 citation statements)

References 17 publications

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“…Hyperspectral imaging conveys analysis of the images obtained by combining multiple wavelength ranges. Wheat images with different wavelengths have been subject to research through various methods and classifications for estimation of its protein, gluten and moisture value, virtuousness as well as its identity [2,3]. The following briefly discusses previous research on wheat.…”

Section: Related Workmentioning

confidence: 99%

Wheat kernels classification using visible-near infrared camera based on deep learning

Özkan¹,

Seke²,

Işık³

2021

Pamukkale J Eng Sci

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Section: Related Workmentioning

confidence: 99%

Wheat kernels classification using visible-near infrared camera based on deep learning

Özkan¹,

Seke²,

Işık³

2021

Pamukkale J Eng Sci

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“…They obtained a classification rate of up to 94% when separating two nonvitreous classes, and a 100% separation when separating vitreous and nonvitreous kernels. More recently, Vermeulen et al, 69 were able to discriminate with high level of confidence between durum and common wheat analyzed using hyperspectral imaging kernel by kernel. For this, different criteria were combined in a data fusion based methodology including visual and morphological information, the protein content calculated with a reference method as well as the NIR spectral profile.…”

Section: Cereal Grainsmentioning

confidence: 99%

Chemometrics in NIR Hyperspectral Imaging: Theory and Applications in the Agricultural Crops and Products Sector

Pierna

Vermeulen

Eylenbosch

et al. 2020

Comprehensive Chemometrics

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This article was originally published in Comprehensive Chemometrics, 2nd edition, published by Elsevier, and the attached copy is provided by Elsevier for the author's benefit and for the benefit of the author's institution, for non-commercial research and educational use including without limitation use in instruction at your institution, sending it to specific colleagues who you know, and providing a copy to your institution's administrator.

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“…In the case of durum wheat, NIR is accepted as a useful method to determine moisture and protein content (method EN 15948:2015) 5 . This technology has also been applied as a predictive test to evaluate the quality (i.e., test weight, hardness, semolina yield, and yellow pigment) of early generations of grain in breeding programs, 6 to classify vitreous and non‐vitreous kernels, 7 and to quantify the degree of adulteration of durum wheat flour with common bread wheat flour 8,9 . In the last decade, the NIR has been applied to predict the technical quality of semolina, 10,11 and the technique was proposed for the in‐line determination of moisture content in pasta immediately after the extrusion process 12 …”

Section: Introductionmentioning

confidence: 99%

Application of near‐infrared handheld spectrometers to predict semolina quality

Cecchini¹,

Antonucci²,

Costa³

et al. 2020

J Sci Food Agric

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BACKGROUND Durum wheat semolina is the best raw material for pasta production and its protein content and gluten strength are essential for cooking quality. The need to develop rapid methods to speed up quality control makes near‐infrared spectroscopy (NIR) a useful method that is widely accepted in the cereal sector. In this study, two non‐destructive and rapid technologies, a low‐cost sensor providing a short wavelength NIR range (swNIR: 700–1100 nm) and a handheld NIR spectrometer (NIR: 1600–2400 nm), were employed to evaluate semolina quality. The spectra data were correlated with chemical (protein content) and rheological parameters (i.e., Gluten Index, Alveograph®, Sedimentation test, GlutoPeak®). A partial least squares (PLS) model was used to compare the efficacy of swNIR and NIR. RESULTS The protein content was the reference parameter that correlated best with the spectra data and provided the best regression model (r model = 0.9788 for NIR and 0.9561 for swNIR). GlutoPeak indices also correlated well with spectral data, particularly with swNIR spectra. A provisional multivariate model was applied to classify semolina samples in quality classes by using their spectra. Better modeling efficiency was obtained for swNIR. CONCLUSION The results highlighted the advantages of a pocket‐sized low cost sensor (swNIR), which is easier to use directly at the sample source than laboratory instruments or more expensive portable devices. © 2020 Society of Chemical Industry

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Discrimination between durum and common wheat kernels using near infrared hyperspectral imaging

Cited by 39 publications

References 17 publications

Wheat kernels classification using visible-near infrared camera based on deep learning

Wheat kernels classification using visible-near infrared camera based on deep learning

Chemometrics in NIR Hyperspectral Imaging: Theory and Applications in the Agricultural Crops and Products Sector

Application of near‐infrared handheld spectrometers to predict semolina quality

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