Application of visible-near-infrared hyperspectral imaging technology coupled with wavelength selection algorithm for rapid determination of moisture content of soybean seeds

Guo, Zhen; Zhang, Jing; Yin, Xiang; Guo, Yangming; Sun, Xia; Jin, Chengqian

doi:10.1016/j.jfca.2022.105048

Cited by 19 publications

(5 citation statements)

References 36 publications

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“…From the observation of Figure 3a, 400–420 nm and 960–980 nm were the two primary absorption bands in the rice spectrum. The absorption characteristic in the range of 400–420 nm was related to the protein content of rice (Que et al, 2023; Ziegler et al, 2018), and the absorption characteristic in the range of 960–980 nm was associated with the O–H stretching second overtone of water molecules (Guo et al, 2023; Yao, Sun, Tang, et al, 2021; Yao, Sun, Zhang, et al, 2021). Generally, spectral data obtained from hyperspectral images are accompanied by noise, so spectral data preprocessing is necessary before establishing the model.…”

Section: Resultsmentioning

confidence: 99%

Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with MSLPP–ESMA–SVR model

Zhong,

Sun,

Yao

et al. 2024

Journal of Food Safety

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Moisture content detection has guiding significance for the storage and quality detection of rice. To detect moisture content rapidly and non‐destructively, hyperspectral imaging technology (400‐1000 nm) was employed to analyze rice with different moisture content, and Savitzky–Golay mixed standard normalized variable algorithm (SG‐SNV) was used for spectral data pretreatment. Furthermore, a modified supervised locality preserving projections (MSLPP) method was proposed to extract spectral features. The modeling results showed that MSLPP had better spectral feature extraction performance. Finally, to improve prediction accuracy, the equilibrium slime mold algorithm (ESMA) was introduced to obtain the optimal parameters (c, g) of the support vector regression (SVR) model. And MSLPP–ESMA–SVR model had higher prediction accuracy and stronger robustness, with R2p reaching 0.9755 and root mean square error of prediction reaching 0.8597%. Therefore, hyperspectral imaging technology combined with MSLPP–ESMA–SVR model is feasible to detect rice moisture content.

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Section: Resultsmentioning

confidence: 99%

Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with MSLPP–ESMA–SVR model

Zhong,

Sun,

Yao

et al. 2024

Journal of Food Safety

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show abstract

“…To mitigate the issue of data collinearity, the use of SPA is recommended. Recent studies have reported positive results using IVISSA-SPA for secondary extraction of spectral data [ 32 , 33 ].…”

Section: Methodsmentioning

confidence: 99%

Maturity Classification of Rapeseed Using Hyperspectral Image Combined with Machine Learning

Feng,

Chen,

Song

et al. 2024

Plant Phenomics

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Oilseed rape is an important oilseed crop planted worldwide. Maturity classification plays a crucial role in enhancing yield and expediting breeding research. Conventional methods of maturity classification are laborious and destructive in nature. In this study, a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms. Initially, hyperspectral images were captured for 3 distinct ripeness stages of rapeseed, and raw spectral data were extracted from the hyperspectral images. The raw spectral data underwent preprocessing using 5 pretreatment methods, namely, Savitzky–Golay, first derivative, second derivative (D2nd), standard normal variate, and detrend, as well as various combinations of these methods. Subsequently, the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling, successive projection algorithm (SPA), iterative spatial shrinkage of interval variables (IVISSA), and their combination algorithms, respectively. The classification models were constructed using the following algorithms: extreme learning machine, k -nearest neighbor, random forest, partial least-squares discriminant analysis, and support vector machine (SVM) algorithms, applied separately to the full wavelength and the feature wavelengths. A comparative analysis was conducted to evaluate the performance of diverse preprocessing methods, feature wavelength selection algorithms, and classification models, and the results showed that the model based on preprocessing-feature wavelength selection-machine learning could effectively predict the maturity of rapeseed. The D2nd-IVISSA-SPA-SVM model exhibited the highest modeling performance, attaining an accuracy rate of 97.86%. The findings suggest that rapeseed maturity can be rapidly and nondestructively ascertained through hyperspectral imaging.

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“…Wang et al proposed an improved binary particle swarm optimization algorithm (IBPSO) to select the characteristic waves of fused RAMAN and NIR spectral data [21]. Guo et al compared five wavelength selection algorithms to select the characteristic wavelengths and established the interval variable iterative spatial shrinkage method and the original spectrum-based continuous projection algorithm (IVISSA-SPA) to perform wavelength selection for one-dimensional sequences [22]. However, these methods are quite tedious processes and may ignore the hidden information within the 1D serial data that has an important impact on the model results.…”

Section: Introductionmentioning

confidence: 99%

The rapid detection method of brown tide algae concentration based on unthresholded recurrence plots and improved broad learning system

Chen,

Dong,

et al. 2024

Meas. Sci. Technol.

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In recent years, there have been frequent outbreaks of harmful algal blooms (HAB) in coastal areas, which have caused serious economic losses to the local community. Therefore, accurate and rapid prediction of microalgal concentrations is necessary for early warning and countermeasures before the occurrence of HAB. This paper presents a model for predicting microalgae concentration based on unthresholded recurrence plots (UTRPs) combined with an improved broad learning system (BLS). Spectral data acquisition of algae species at different concentrations using l-induced fluorescence spectroscopy. Then, the 1D spectral data are dimensionally lifted by UTRPs transformation, recurrence plots (RPs) transformation can fully extract the internal information of 1D sequence data, and at the same time, UTRPs avoids the influence of artificially selected thresholds on the feature transformation results of traditional (RPs). Finally, a lightweight flat network BLS was used for microalgae concentration regression prediction, at the same time the BLS regularization method was improved. When comparing the two most commonly used deep learning regression models and integrated learning models, UTRPs and elastic net (UTRPs-ENBLS) achieves convincing results.

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Application of visible-near-infrared hyperspectral imaging technology coupled with wavelength selection algorithm for rapid determination of moisture content of soybean seeds

Cited by 19 publications

References 36 publications

Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with MSLPP–ESMA–SVR model

Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with MSLPP–ESMA–SVR model

Maturity Classification of Rapeseed Using Hyperspectral Image Combined with Machine Learning

The rapid detection method of brown tide algae concentration based on unthresholded recurrence plots and improved broad learning system

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