The method of calibration model transfer by optimizing wavelength combinations based on consistent and stable spectral signals

Zhang, Liguo; Li, Yongqi; Huang, Wen Jiang; Ni, Lei; Ge, Jiong

doi:10.1016/j.saa.2019.117647

Cited by 29 publications

(13 citation statements)

References 39 publications

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“…The methods that are popular in small dataset processing may not be suitable for applications on large and complex datasets. Thus, some common methods such as SPA-PLS, UVE-PLS, and the methods based on loading weights of latent variables of PLS ( Li et al, 2007 , 2019 ; Zhang et al, 2017 , 2020 ; Li and Hui, 2019 ) were not evaluated in this study. The UFS is a typical unsupervised feature selector.…”

Section: Discussionmentioning

confidence: 99%

Wheat Kernel Variety Identification Based on a Large Near-Infrared Spectral Dataset and a Novel Deep Learning-Based Feature Selection Method

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Wheat Kernel Variety Identification Based on a Large Near-Infrared Spectral Dataset and a Novel Deep Learning-Based Feature Selection Method

et al. 2020

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“…Li et al [31] proposed a new relative error analysis (REA) wavelength selection method based on relative errors, reducing redundant variables between delivery models, and calibration transfer of different fruit products was realized. Zhang et al [32] combined screening wavelengths with consistent and stable signals (SWCSS) algorithm with traditional feature selection methods to further filter the feature wavelengths used for calibration transfer and improve the accuracy of the model. When using these methods to select features, only the relevant analysis is performed on the wavelength of the master and slave instruments, but the influence of the instability of the wavelength drift is not considered, and some of them are too complicated, which reduces the efficiency of calibration transfer.…”

Section: Introductionmentioning

confidence: 99%

Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer

Zhang

et al. 2022

Sensors

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For conventional near-infrared spectroscopy (NIR) technology, even within the same sample, the NIR spectral signal can vary significantly with variation of spectrometers and the spectral collection environment. In order to improve the applicability and application of NIR prediction models, effective calibration transfer is essential. In this study, a stability-analysis-based feature selection algorithm (SAFS) for NIR calibration transfer is proposed, which is used to extract effective spectral band information with high stability between the master and slave instruments during the calibration transfer process. The stability of the spectrum bands shared between the master and slave instruments is used as the evaluation index, and the genetic algorithm was used to select suitable thresholds to filter out the spectral feature information suitable for calibration transfer. The proposed SAFS algorithm was applied to two near-infrared datasets of corn oil content and larch wood density. Simultaneously, its calibration transfer performances were compared with two classical feature selection methods. The effects of different preprocessing algorithms and calibration transfer algorithms were also assessed. The model with the feature variables selected by the SAFS obtained the best prediction. The SAFS algorithm can simplify the spectral data to be transferred and improve the transfer efficiency, and the universality of the SAFS allows it to be used to optimize calibration transfer in various situations. By combining different preprocessing and classic feature selection methods with this, the sensitivity of the correlation between spectral data and component information are improved significantly, as well as the effect of calibration transfer, which will be deeply developed.

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“…The model transfer algorithm used can be divided into methods with standard samples and methods without standard samples according to whether one-to-one correspondence of standard spectra should be collected on all instruments (Zhang et al 2020). The model transfer algorithm with standard samples must take a certain number of samples to form a standard sample set, such as Slope/Bias (S/B) (Du et al 2011;Zhao et al 2019), direct standardization (DS) (Parrott et al 2022) and piecewise direct standardization (PDS) (Bergman et al 2006;Peng et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Transfer strategy for near infrared analysis model of holocellulose and lignin based on improved slope/bias algorithm

Wang

Xiong

et al. 2022

BioRes

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Model transfer techniques in near infrared spectroscopy are important for avoiding duplicate modeling, sharing samples and data resources, and reducing the human and material consumption required for modeling. Use of the slope/bias correction algorithm (S/B) based on screening wavelengths with consistent and stable signals (SWCSS) for model transfer is a new strategy. To enable sharing of near infrared analysis models of pulp holocellulose and lignin content in two different types of spectroscopic instruments, a combined SWCSS-S/B algorithm was proposed. The stable and consistent wavelengths between the spectroscopic instruments screened by the SWCSS method reduced the differences between the instruments, thereby improving the universality and transmission accuracy of the S/B method. The SWCSS-S/B based model transfer method reduced the predicted standard deviation RMSEP of holocellulose and lignin contents of the samples measured on the target spectrometer of the from 5.4686 and 7.6823 to 1.2133 and 1.3494, respectively. This result showed a significant improvement in the transfer effect compared to the SWCSS and S/B correction results alone, and the prediction of holocellulose was better than that of the prediction effect of lignin. The method has fewer wavelength variables involved in model transfer, fast transfer speed, and high prediction accuracy, which provides a new solution for the wide application of NIR analytical models.

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The method of calibration model transfer by optimizing wavelength combinations based on consistent and stable spectral signals

Cited by 29 publications

References 39 publications

Wheat Kernel Variety Identification Based on a Large Near-Infrared Spectral Dataset and a Novel Deep Learning-Based Feature Selection Method

Wheat Kernel Variety Identification Based on a Large Near-Infrared Spectral Dataset and a Novel Deep Learning-Based Feature Selection Method

Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer

Transfer strategy for near infrared analysis model of holocellulose and lignin based on improved slope/bias algorithm

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