Application of the combination method based on RF and LE in near infrared spectral modeling

Zhang, Xiaowen; Chen, Zhengguang; Jiao, Feng

doi:10.1016/j.saa.2022.122247

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…Within the context of the RF algorithm, the iterative computational procedure unfolds through three primary stages for a spectral variable X . Here, the n rows correspond to the sample count, the p columns denote the variables, and the corresponding target matrix Y is composed of n×1 variables ( Zhang et al., 2023 ). The sequence of steps is as follows:…”

Section: Methodsmentioning

confidence: 99%

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy

Li,

Zhou,

Zhang

et al. 2024

Front. Plant Sci.

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Widespread adoption of machine-picked cotton in China, the impurity content of seed cotton has increased significantly. This impurity content holds direct implications for the valuation of seed cotton and exerts a consequential influence on the ensuing quality of processed lint and textiles. Presently, the primary approach for assessing impurity content in seed cotton primarily depends on semi-automated testing instruments, exhibiting suboptimal detection efficiency and not well-suited for the impurity detection requirements during the purchase of seed cotton. To address this challenge, this study introduces a seed cotton near-infrared spectral (NIRS) data acquisition system, facilitating the rapid collection of seed cotton spectral data. Three pretreatment algorithms, namely SG (Savitzky-Golay convolutional smoothing), SNV (Standard Normal Variate Transformation), and Normalization, were applied to preprocess the seed cotton spectral data. Cotton-Net, a one-dimensional convolutional neural network aligned with the distinctive characteristics of the seed cotton spectral data, was developed in order to improve the prediction accuracy of seed cotton impurity content. Ablation experiments were performed, utilizing SELU, ReLU, and Sigmoid functions as activation functions. The experimental outcomes revealed that after normalization, employing SELU as the activation function led to the optimal performance of Cotton-Net, displaying a correlation coefficient of 0.9063 and an RMSE (Root Mean Square Error) of 0.0546. In the context of machine learning modeling, the LSSVM model, developed after Normalization and Random Frog algorithm processing, demonstrated superior performance, achieving a correlation coefficient of 0.8662 and an RMSE of 0.0622. In comparison, the correlation coefficient of Cotton-Net increased by 4.01%. This approach holds significant potential to underpin the subsequent development of rapid detection instruments targeting seed cotton impurities.

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Section: Methodsmentioning

confidence: 99%

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy

Li,

Zhou,

Zhang

et al. 2024

Front. Plant Sci.

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“…The total number of spectral variables is 600. We selected the pharmaceutical tablets activity for spectroscopy modeling and prediction [12]. The spectra for the first 50 tablet samples are shown in Figure 1A.…”

Section: Public Datasetmentioning

confidence: 99%

“…Admittedly, this enables spectral data to contain more information. Still, highdimensional data also increases noise and redundant information, posing challenges for data storage, processing, and analysis, seriously affecting the accuracy and stability of the model [12][13][14]. Therefore, it has become a common consensus among researchers to obtain spectral analysis models with stronger predictive performance and stability through efficient feature selection methods.…”

Section: Introductionmentioning

confidence: 99%

Rapid discrimination of urine specific gravity using spectroscopy and a modified combination method based on SPA and spectral index

Yang,

Cai,

et al. 2023

Journal of Biophotonics

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To achieve high‐accuracy urine specific gravity discrimination and guide the design of four‐waveband multispectral sensors. A modified combination strategy was attempted to be proposed based on the successive projections algorithm (SPA) and the spectral index (SI) in the present study. First, the SPA was used to select four spectral variables in the full spectra. Second, the four spectral variables were mathematically transformed by SI to obtain SI values. Then, SPA gradually fusions the SI values and establishes models to identify USG. The results showed that the SPA can screen out the four characteristic wavelengths related to the measured sample attributes. SIs can be used to improve the performance of constructed prediction models. The best model only involves four spectral variables and 1 SI value, with high accuracy (91.62%), sensitivity (0.9051), and specificity (0.9667). The results reveal that m‐SPA‐SI can effectively distinguish USG and provide design guidance for 4‐wavelength multispectral sensors.

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A 1D-CNN model for the early detection of citrus Huanglongbing disease in the sieve plate of phloem tissue using micro-FTIR

Yang,

et al. 2024

Chemometrics and Intelligent Laboratory Systems

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Application of the combination method based on RF and LE in near infrared spectral modeling

Cited by 10 publications

References 10 publications

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy

Rapid discrimination of urine specific gravity using spectroscopy and a modified combination method based on SPA and spectral index

A 1D-CNN model for the early detection of citrus Huanglongbing disease in the sieve plate of phloem tissue using micro-FTIR

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