Study on identification of rice seeds by chemical oscillation fingerprints

Li, Guangyu; Yongling, Li; Zhang, Mingxiao

doi:10.1039/c5ra14126e

Cited by 3 publications

(3 citation statements)

References 13 publications

(9 reference statements)

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“…The ngerprint information contained in near-infrared spectra is attributed to the stretching vibrations of chemical bonds such as C-N, C-H, and N-H in organic molecules. 26,27 For example, the short peak at 995 nm may be related to the second overtone of N-H stretching. 11 The bands at 1133, 1164 and 1207 nm are attributed to the second overtone of C-H stretching.…”

Section: Selection Of Optimal Wavelengthsmentioning

confidence: 99%

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

Zhang

et al. 2019

RSC Adv.

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Section: Selection Of Optimal Wavelengthsmentioning

confidence: 99%

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

Zhang

et al. 2019

RSC Adv.

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“…However, machine vision technology could not achieve satisfactory results when the external characteristics such as appearance, morphology and texture of different varieties of seeds were little difference. Hence, the internal information could be considered to differentiate them, owing to the difference in the internal contents and components of different varieties of seeds [5,6]. Imaging and spectroscopic technology exactly reflect the internal characteristics of samples by their optical properties, which compensate for the shortcomings of machine vision technology.…”

Section: Introductionmentioning

confidence: 99%

Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics

Bao

et al. 2019

Applied Sciences

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The classification of wheat grain varieties is of great value because its high purity is the yield and quality guarantee. In this study, hyperspectral imaging combined with the chemometric methods was applied to explore and implement the varieties classification of wheat seeds. The hyperspectral images of all the samples covering 874–1734 nm bands were collected. Exploratory analysis was first carried out while using principal component analysis (PCA) and linear discrimination analysis (LDA). Spectral preprocessing methods including standard normal variate (SNV), multiplicative scatter correction (MSC), and wavelet transform (WT) were introduced, and their effects on discriminant models were studied to eliminate the interference of instrumental and environmental factors. PCA loading, successive projections algorithm (SPA), and random frog (RF) were applied to extract feature wavelengths for redundancy elimination owing to the possibility of existing redundant spectral information. Classification models were developed based on full wavelengths and feature wavelengths using LDA, support vector machine (SVM), and extreme learning machine (ELM). This optimal model was finally utilized to generate visualization map to observe the classification performance intuitively. When comparing with other models, ELM based on full wavelengths achieved the best accuracy up to 91.3%. The overall results suggested that hyperspectral imaging was a potential tool for the rapid and accurate identification of wheat varieties, which could be conducted in large-scale seeds classification and quality detection in modern seed industry.

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“…When the indicators of the samples are very similar, it usually fails to achieve the desired effect (Fayyazi, Abbaspour-Fard, Rohani, Monadjemi, & Sadrnia, 2017;Huang et al, 2016). Since the internal chemical composition content of the seeds of different varieties was different, therefore, the researchers consider using internal chemical information for classification (Li, Li, & Zhang, 2015;Zhang, Jiang, Liu, & He, 2017). Near-infrared (NIR) can be successfully used to investigate the chemical properties of complex food matrices and intact seeds or grains, such as corn (Esteve Agelet et al, 2012), soybeans (Ferreira, Galão, Pallone, & Poppi, 2014), and wheat kernels (Peiris & Dowell, 2011).…”

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Nondestructive identification of barley seeds varieties using hyperspectral data from two sides of barley seeds

Sun

Zhang

et al. 2021

J Food Process Engineering

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The identification of seed varieties is of significance to ensure seed purity. In this study, 1,600 barley seeds (400 per variety) were selected as the samples. By using a near-infrared hyperspectral imaging system, the hyperspectral cube data on the reverse and ventral sides of each barley seed were obtained. Three preprocessing methods (standard normal variate, multivariate scatter correction, and Savitzky-Golay first derivative) and three discriminant models (k-nearest neighbors, support vector machine [SVM], and random forest) were selected for preprocessing and modeling, respectively. The results indicated that the reverse spectral data preprocessed by multivariate scatter correction and the ventral spectral data preprocessed by Savitzky-Golay first derivative had the best modeling results. In order to further simplify the model, feature selection algorithm (successive projections algorithm) was used for feature wavelength selection. After multivariate data analysis, the combination of knearest neighbors and successive projections algorithm based on the reverse spectral data was selected as the best discriminant model. The accuracy of the calibration set and the prediction set were 95.52% and 93.71%, and the Kappa value were 0.9424 and 0.9156, respectively. The reverse spectral data of barley seeds of the validation set were used to verify the accuracy of the discriminant model and visualize the classification results. Finally, considering the practical application, the mixed spectral data containing different ratios of the reverse spectral data was analyzed, and satisfactory results were obtained. It can be concluded from this study that hyperspectral imaging technology was a very promising method for identification of barley seed varieties. Practical applicationsWith the circulation of the seed market, the traditional methods used for variety identification can no longer meet the needs of large-scale, high-quality production. As a nondestructive determination method, hyperspectral imaging technology combined machine vision and near-infrared spectroscopy. In this study, hyperspectral imaging technology was used to identify barley seed varieties, and satisfactory results were obtained. It can provide a theoretical reference for future multispectral online detection technology. | INTRODUCTIONHitherto, cereals are still the most important source of food in the world, which provide us with daily food and are the foundation of human nutrition (Kearney, 2010). Among them, barley (Hordeum vulgare L.) is the fourth largest cereal crop in terms of planting area (Han et al., 2017). Barley has a wide range of usage which can be used not only as feed, food, but also as a raw material for liquor brewing,

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Study on identification of rice seeds by chemical oscillation fingerprints

Abstract: A B–Z oscillation reaction can be maintained for several hours without interference from foreign substances. The oscillation reaction process changed when a sample was added, and a characteristic fingerprint was obtained.

Cited by 3 publications

References 13 publications

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics

Nondestructive identification of barley seeds varieties using hyperspectral data from two sides of barley seeds

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