Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice

Shi, Shijie; Tang, Zihan; Ma, Yingying; Cao, Cougui; Jiang, Yang

doi:10.1080/10408398.2023.2284246

Cited by 2 publications

(3 citation statements)

References 170 publications

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“…Researchers began delving into the unexplored realms of te rahertz spectroscopy, leading to a gradual increase in the application of this technology [59][60][61]. Terahertz spectroscopy offers numerous advantages [62,63]. THz produces mini mal energy release during the detection process, making nondestructive sample detection possible.…”

Section: Terahertz Spectroscopy (Thz)mentioning

confidence: 99%

Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review

Xu,

Guo,

Liang

et al. 2024

Foods

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Fruits and vegetables are an important part of our daily diet and contain low-content components that are crucial for our health. Detecting these components accurately is of paramount significance. However, traditional detection methods face challenges such as complex sample processing, slow detection speed, and the need for highly skilled operators. These limitations fail to meet the growing demand for intelligent and rapid detection of low-content components in fruits and vegetables. In recent years, significant progress has been made in intelligent rapid detection technology, particularly in detecting high-content components in fruits and vegetables. However, the accurate detection of low-content components remains a challenge and has gained considerable attention in current research. This review paper aims to explore and analyze several intelligent rapid detection techniques that have been extensively studied for this purpose. These techniques include near-infrared spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy, and terahertz spectroscopy, among others. This paper provides detailed reports and analyses of the application of these methods in detecting low-content components. Furthermore, it offers a prospective exploration of their future development in this field. The goal is to contribute to the enhancement and widespread adoption of technology for detecting low-content components in fruits and vegetables. It is expected that this review will serve as a valuable reference for researchers and practitioners in this area.

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Section: Terahertz Spectroscopy (Thz)mentioning

confidence: 99%

Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review

Xu,

Guo,

Liang

et al. 2024

Foods

View full text Add to dashboard Cite

show abstract

“…Spectral imaging technology [19], as a non-contact rapid detection means, has been widely used in research related to rice detection [19]. Among them, fluorescence hyper-spectral [20] imaging technology combines the advantages of hyperspectral technology and digital imaging technology, which can effectively make up for the shortcomings of traditional rice identification methods.…”

Section: Introductionmentioning

confidence: 99%

“…This energy is emitted in the form of fluorescence, from which the spectral camera captures the image information and obtains a fluorescence hyperspectral image of the sample [21]. The principle of fluorescence hyperspectral imaging is similar to that of hyperspectral imaging [19]. However, the difference is that, in fluorescence hyperspectral imaging, the spectral camera receives the fluorescence intensity emitted by the sample's fluorescent substance rather than the sample's reflected light [20].…”

Section: Introductionmentioning

confidence: 99%

The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning

Kang,

Fan,

Zhan

et al. 2024

Molecules

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A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475–1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.

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Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice

Cited by 2 publications

References 170 publications

Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review

Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review

The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning

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