A review on hybrid strategy-based wavelength selection methods in analysis of near-infrared spectral data

Fu, Jiashun; Yu, Hai‐Dong; Chen, Zhe; Yun, Yong‐Huan

doi:10.1016/j.infrared.2022.104231

Cited by 34 publications

(21 citation statements)

References 106 publications

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“…As a fast variable selection method, CARS algorithm was proposed according to the principle of “survival of the fittest” in Darwin’s theory of evolution. Due to the randomness of the MC sampling method in the CARS algorithm, in order to ensure the reliability of the model, each set MC sampling number was ran 500 times, respectively, and a 10-fold cross-validation method was used to take the wavelength corresponding to the minimum RMSECV value in all PLSR models as the optimal variable [ 35 ]. Figure 6 a shows the average weight distribution of CARS.…”

Section: Resultsmentioning

confidence: 99%

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A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

Dong

Bi²,

Hao³

et al. 2022

Biosensors

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Alanine (Ala), as the most important free amino acid, plays a significant role in food taste characteristics and human health regulation. The feasibility of using near–infrared hyperspectral imaging (NIR–HSI) combined with two–dimensional correlation spectroscopy (2D–COS) analysis to predict beef Ala content quickly and nondestructively is first proposed in this study. With Ala content as the external disturbance condition, the sequence of chemical bond changes caused by synchronous and asynchronous correlation spectrum changes in 2D–COS was analyzed, and local sensitive variables closely related to Ala content were obtained. On this basis, the simplified linear, nonlinear, and artificial neural network models developed by the weighted coefficient based on the feature wavelength extraction method were compared. The results show that with the change in Ala content in beef, the double-frequency absorption of the C-H bond of CH2 in the chemical bond sequence occurred prior to the third vibration of the C=O bond and the first stretching of O-H in COOH. Furthermore, the wavelength within the 1136–1478 nm spectrum range was obtained as the local study area of Ala content. The linear partial least squares regression (PLSR) model based on effective wavelengths was selected by competitive adaptive reweighted sampling (CARS) from 2D–COS analysis, and provided excellent results (R2C of 0.8141, R2P of 0.8458, and RPDp of 2.54). Finally, the visual distribution of Ala content in beef was produced by the optimal simplified combination model. The results show that 2D–COS combined with NIR–HSI could be used as an effective method to monitor Ala content in beef.

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Section: Resultsmentioning

confidence: 99%

“…This shows that the local features selected by 2D–COS contained a large number of features, and the correlation between features was large. In the case of local data, the disappearance of some features would not affect the detection and matching of other features [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

Dong

Bi²,

Hao³

et al. 2022

Biosensors

View full text Add to dashboard Cite

show abstract

“…All of these methods require the support of a large amount of data to discover similar patterns between different data domains. At the same time, the quality of the data can cause large interference with the above methods, which is why numerous spectral preprocessing methods ( Zhen et al., 2008 ) and feature band selection methods ( Fu et al., 2022 ) are proposed to reduce the interference as much as possible, which requires a large number of comparison experiments, and the cost of model application is increased. The proposed hybrid model has a feature extractor, which can exclude the interfering bands in the training and reduce the dependence of the model on the quality of the original data; meanwhile, the depth model can learn the underlying information in the data during the training process, which reduces the demand of the model on the sample size; the introduction of the fine-tuning and TrAdaBoost.R2 methods makes it have a certain self-renewal capability.…”

Section: Discussionmentioning

confidence: 99%

Section: Comparison Of Model Predictive Abilitymentioning

confidence: 99%

Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning

Zhang

Liu

2022

Front. Plant Sci.

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Wood density, as a key indicator to measure wood properties, is of weighty significance in enhancing wood utilization and modifying wood properties in sustainable forest management. Visible–near-infrared (Vis-NIR) spectroscopy provides a feasible and efficient solution for obtaining wood density by the advantages of its efficiency and non-destructiveness. However, the spectral responses are different in wood products with different moisture content conditions, and changes in external factors may cause the regression model to fail. Although some calibration transfer methods and convolutional neural network (CNN)-based deep transfer learning methods have been proposed, the generalization ability and prediction accuracy of the models still need to be improved. For the prediction problem of Vis-NIR wood density in different moisture contents, a deep transfer learning hybrid method with automatic calibration capability (Resnet1D-SVR-TrAdaBoost.R2) was proposed in this study. The disadvantage of overfitting was avoided when CNN processes small sample data, which considered the complex exterior factors in actual production to enhance feature extraction and migration between samples. Density prediction of the method was performed on a larch dataset with different moisture content conditions, and the hybrid method was found to achieve the best prediction results under the calibration samples with different target domain calibration samples and moisture contents, and the performance of models was better than that of the traditional calibration transfer and migration learning methods. In particular, the hybrid model has achieved an improvement of about 0.1 in both R2 and root mean square error (RMSE) values compared to the support vector regression model transferred by piecewise direct standardization method (SVR+PDS), which has the best performance among traditional calibration methods. To further ascertain the generalizability of the hybrid model, the model was validated with samples collected from mixed moisture contents as the target domain. Various experiments demonstrated that the Resnet1D-SVR-TrAdaBoost.R2 model could predict larch wood density with a high generalization ability and accuracy effectively but was computation consuming. It showed the potential to be extended to predict other metrics of wood.

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“…This hybrid strategy can avoid random combinations of variable selection methods and overcome the inability of a single method to achieve optimal selection and combination for a large number of variables. 57–59…”

Section: Methodsmentioning

confidence: 99%

A hybrid wavelength selection strategy-based quantitative analysis model for LIBS data from standard ground samples of the Curiosity rover on Mars

Yao

Jian-ping

2022

J. Anal. At. Spectrom.

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A review on hybrid strategy-based wavelength selection methods in analysis of near-infrared spectral data

Cited by 34 publications

References 106 publications

A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning

A hybrid wavelength selection strategy-based quantitative analysis model for LIBS data from standard ground samples of the Curiosity rover on Mars

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