Discrimination of transgenic soybean seeds by terahertz spectroscopy

Liu, Wei; Liu, Changhong; Chen, Feng; Yang, Jian‐Bo; Zheng, Lei

doi:10.1038/srep35799

Cited by 32 publications

(8 citation statements)

References 35 publications

(36 reference statements)

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“…This result agrees with the work of other researchers [49,50]. However, in some other studies, MSC was not the optimal pretreatment method; rather, these studies reported optimal pretreatment methods of median filter smoothing [51], SG [52] t-distributed stochastic neighborhood embedding (t-SNE) [53], and SNV [54,55]. In the hyperspectral identification of wheat [56] and maize [57] seed varieties, the best models for identifying seed varieties were the 5 point and 3 time smoothing and SNV.…”

Section: Resultssupporting

confidence: 89%

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Zhu

Chao

Zhang

et al. 2019

Sensors

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Hyperspectral imaging is a nondestructive testing technology that integrates spectroscopy and iconology technologies, which enables us to quickly obtain both internal and external information of objects and identify crop seed varieties. First, the hyperspectral images of ten soybean seed varieties were collected and the reflectance was obtained. Savitzky-Golay smoothing (SG), first derivative (FD), standard normal variate (SNV), fast Fourier transform (FFT), Hilbert transform (HT), and multiplicative scatter correction (MSC) spectral reflectance pretreatment methods were used. Then, the feature wavelengths and feature information of the pretreated spectral reflectance data were extracted using competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and principal component analysis (PCA). Finally, 5 classifiers, Bayes, support vector machine (SVM), k-nearest neighbor (KNN), ensemble learning (EL), and artificial neural network (ANN), were used to identify seed varieties. The results showed that MSC-CARS-EL had the highest accuracy among the 90 combinations, with training set, test set, and 5-fold cross-validation accuracies of 100%, 100%, and 99.8%, respectively. Moreover, the contribution of spectral pretreatment to discrimination accuracy was higher than those of feature extraction and classifier selection. Pretreatment methods determined the range of the identification accuracy, feature-selective methods and classifiers only changed within this range. The experimental results provide a good reference for the identification of other crop seed varieties.

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Section: Resultssupporting

confidence: 89%

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Zhu

Chao

Zhang

et al. 2019

Sensors

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“…The resulting Tray has a broad application prospect in object imaging, medical [118]  Detection of a carbamate insecticide in food matrices [119]  Moisture content in wheat grain [120] and wafers [121].  Pesticide detection in food powders [122]  Antibiotic detection in food and feed matrices [123]  Discrimination of transgenic crops [130] diagnosis, environmental detection and communication. However, in the frequency range of terahertz radiation, the absorption and group velocity dispersion (GVD) of water vapor controls the propagation of THz waves in the atmosphere [147].…”

Section: B Terahertz Radiationmentioning

confidence: 99%

State-of-the-art in terahertz sensing for food and water security – A comprehensive review

Ren

Zahid

Fan

et al. 2019

Trends in Food Science & Technology

119

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Past few years have witness a dramatic change in the field of terahertz (THz) technology. The recent advancements in the technology for generation, manipulation and detection exploiting THz radiation have brought revolution in the field. Many researchers around the world have been inspired by the potential of invaluable new applications of THz sensing for food and water contamination detection. The microbial pollution in water and food is one the crucial issues with regard to the sanitary state for drinking water and daily consumption of food. To address this risk, the detection of microbial contamination is of utmost importance since the consumption of insanitary or unhygienic food can lead to catastrophic illness. This paper presents a first-time review of the open literature focused on the advances in the THz sensing for microbiological contamination of food and water and state-of-theart network architectures, applications, industrial trends and recent developments. Finally, open challenges and future research directions are presented with in the field.

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“…In the past decade, new emerging approach of THz spectroscopy – THz time-domain spectroscopy (TDS) in combination with chemometrics techniques in particular 6 – showed a significant breakthrough in application for food quality inspection 12 . A number of studies has recently reported promising results that are addressed to the discrimination of transgenic crops 13,14 , early wheat grains germination detection 15 , the determination of tea types with the protected geographical indications 16 , as well as the discrimination between edible oils and used frying oils 17 . Recently, Yin et al .…”

Section: Introductionmentioning

confidence: 99%

Non-destructive inspection of food and technical oils by terahertz spectroscopy

Karaliūnas

Nasser

Urbanowicz

et al. 2018

Sci Rep

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Quality control and non-destructive monitoring are of notable interest of food and pharmaceutical industries. It relies on the ability of non-invasive inspection which can be employed for manufacturing process control. We hereby apply terahertz (THz) time-domain spectroscopy as non-destructive technique to monitor pure and degraded oils as well as hydrocarbon chemicals. Significant differences in the spectra of refractive index (RI) and absorption coefficient arising from the presence of ester linkages in the edible and technical oils were obtained. Explicit increase from 1.38 to 1.5 of the RI in all THz spectrum range was observed in hydrocarbons and mono-functional esters with the increase of molar mass. This fact is in contrast of RI dependence on molar mass in multi-functional esters, such as Adipate or vegetable oils, where it is around 1.54. Degradation products, Oleic Acid (OA) and water in particular, lead only to some changes in absorption coefficient and RI spectra of vegetable oils. We demonstrate that complex colloidal and supramolecular processes, such as dynamics of inverse micelles and oil hydrolysis, take part during oil degradation and are responsible for non-uniform dependence of optical properties on extent of degradation.

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Discrimination of transgenic soybean seeds by terahertz spectroscopy

Cited by 32 publications

References 35 publications

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

State-of-the-art in terahertz sensing for food and water security – A comprehensive review

Non-destructive inspection of food and technical oils by terahertz spectroscopy

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