Improvement of NIR model by fractional order Savitzky–Golay derivation (FOSGD) coupled with wavelength selection

Tong, Peijin; Du, Yukou; Zheng, Kaiyi; Wu, Ting; Wang, Jiajun

doi:10.1016/j.chemolab.2015.02.017

Cited by 59 publications

(34 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The application of the derivative method in VIS-NIR spectra is able to eliminate background noises, sharpen spectral features, improve the spectral resolution of overlapped peaks and extract important spectral information [13,54,55]. Generally, 1-order derivative stands for the slope of the reflectance spectra, and 2-order derivative stands for the curvature of the reflectance spectra [56].…”

Section: Discussionmentioning

confidence: 99%

“…Fractional order derivative (FOD) algorithm, as a mathematical method for analysis of the reflectance spectra, allows interpolating between integer order derivatives and can extract more subtle details from the spectral signal. Recently, FOD is gaining popularity in many scientific fields, especially in signal preprocessing [13,14]. To date, few studies have used FOD algorithm in soil VIS-NIR spectral field [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Hong

Chen

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Visible and near-infrared (VIS-NIR) spectroscopy has been extensively applied to estimate soil organic matter (SOM) in the laboratory. However, if field/moist VIS-NIR spectra can be directly applied to estimate SOM, then much of the time and labor would be avoided. Spectral derivative plays an important role in eliminating unwanted interference and optimizing the estimation model. Nonetheless, the conventional integer order derivatives (i.e., the first and second derivatives) may neglect some detailed information related to SOM. Besides, the full-spectrum generally contains redundant spectral variables, which would affect the model accuracy. This study aimed to investigate different combinations of fractional order derivative (FOD) and spectral variable selection techniques (i.e., competitive adaptive reweighted sampling (CARS), elastic net (ENET) and genetic algorithm (GA)) to optimize the VIS-NIR spectral model of moist soil. Ninety-one soil samples were collected from Central China, with their SOM contents and reflectance spectra measured. Support vector machine (SVM) was applied to estimate SOM. Results indicated that moist spectra differed greatly from dried ground spectra. With increasing order of derivative, the spectral resolution improved gradually, but the spectral strength decreased simultaneously. FOD could provide a better tool to counterbalance the contradiction between spectral resolution and spectral strength. In full-spectrum SVM models, the most accurate estimation was achieved by SVM model based on 1.5-order derivative spectra, with validation R 2 = 0.79 and ratio of the performance to deviation (RPD) = 2.20. Of all models studied (different combinations of FOD and variable selection techniques), the highest validation model accuracy for SOM was achieved when applying 1.5 derivative spectra and GA method (validation R 2 = 0.88 and RPD = 2.89). Among the three variable selection techniques, overall, the GA method yielded the optimal predictability. However, due to its long computation time, one alternative was to use CARS method. The results of this study confirm that a suitable combination of FOD and variable selection can effectively improve the model performance of SOM in moist soil.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Hong

Chen

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Therefore, wavelength selection is a crucial step in NIR analysis . Improved analytical performance has been reported upon variable selection, which supports the continuing interest in this chemometric activity …”

Section: Introductionmentioning

confidence: 89%

A heuristic and parallel simulated annealing algorithm for variable selection in near‐infrared spectroscopy analysis

Shi

Zhang

et al. 2016

Journal of Chemometrics

View full text Add to dashboard Cite

A new heuristic and parallel simulated annealing algorithm was proposed for variable selection in near‐infrared spectroscopy analysis. The algorithm employs a parallel mechanism to enhance the search efficiency, a heuristic mechanism to generate high‐quality candidate solutions, and the concept of Metropolis criterion to estimate accuracy of the candidate solutions. Several near‐infrared datasets have been evaluated under the proposed new algorithm, with partial least squares leading to improved analytical figures of merit upon wavelength selection. Improved robust and predictive regression models were obtained by the new algorithm. The method could also be helpful in other chemometric activities such as classification or quantitative structure‐activity relationship problems.

show abstract

“…NIR technology can meet this requirement. NIR spectroscopy is widely used in the fermentation, food, agricultural, petrochemical and pharmacological industries due to its rapid, noninvasive and economic advantages (H. Jiang et al, ; Tong, Du, Zheng, Wu, & Wang, ). In their study, do Nascimento, de Macedo, dos Santos, and de Oliveira () reported that NIRS can be utilized for actual‐time detecting of process factors in alcoholic fermentation.…”

Section: Introductionmentioning

confidence: 99%

Rapid detection model of Bacillus subtilis in solid‐state fermentation of rapeseed meal

Zheng

Jiang

et al. 2019

Journal of Food Safety

View full text Add to dashboard Cite

The main objective of this research was to monitor variety in the content of Bacillus subtilis 10160 during solid‐state fermentation (SSF) of rapeseed meal by near infrared (NIR) spectroscopy and chemometrics. Observations showed the coefficient of determination (R2) value was 0.9401 and root‐mean‐square deviation was 0.639 log (CFU/g) for viable cell content by synergies between four intervals 5,203.003–5,600.267, 5,604.124–6,001.388, 6,807.487–7,204.751 and 8,411.972–8,809.235 cm−1. The determination coefficient of prediction (Rp2) and root‐mean‐square deviation of prediction in an external validation set could reach 0.9532 and 0.628 log (CFU/g) by the viable B. subtilis model. These findings suggest that rapid detection of B. subtilis in SSF is achieved by the combination of synergy interval partial least squares and NIR spectroscopy.

show abstract

Improvement of NIR model by fractional order Savitzky–Golay derivation (FOSGD) coupled with wavelength selection

Cited by 59 publications

References 53 publications

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

A heuristic and parallel simulated annealing algorithm for variable selection in near‐infrared spectroscopy analysis

Rapid detection model of Bacillus subtilis in solid‐state fermentation of rapeseed meal

Contact Info

Product

Resources

About