Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration

Li, Hongdong; Liang, Yi‐Zeng; Xu, Qingsong; Cao, Dong‐Sheng

doi:10.1016/j.aca.2009.06.046

Cited by 1,219 publications

(602 citation statements)

References 44 publications

(42 reference statements)

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“…CARS, here combined with PLSR, aims at selecting key wavelengths with a physical meaning [38,43] in a computationally efficient procedure, which is based on the obtained values of regression coefficients. In detail, a series of m sampling runs (m equaled 50 in our case) is performed, each with two selection steps.…”

Section: Multivariate Calibrations Of Oc N Mbc and Hwec From Spectrmentioning

confidence: 99%

“…Although limited in size, the studied sample set covered a wide range of geological substrates, pH values, soil types and soil texture classes. For the multivariate modeling, we combined PLSR with three different methods of spectral variable selection that implement basically different search strategies for finding the optimal spectral variable subset-"competitive adaptive reweighted sampling" (CARS [43]), a method that "iteratively retains informative variables" (IRIV [39]) and a genetic algorithm (GA [44]). …”

Section: Introductionmentioning

confidence: 99%

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Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms

et al. 2017

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Abstract:We explored the potentials of both non-imaging laboratory and airborne imaging spectroscopy to assess arable soil quality indicators. We focused on microbial biomass-C (MBC) and hot water-extractable C (HWEC), complemented by organic carbon (OC) and nitrogen (N) as well-studied spectrally active parameters. The aggregation of different spectral variable selection strategies was used to analyze benefits for reachable estimation accuracies and to explore spectral predictive mechanisms for MBC and HWEC. With selected variables, quantification accuracies improved markedly for MBC (laboratory: RPD = 2.32 instead of 1.33 with full spectra; airborne: 2.35 instead of 1.80) and OC (laboratory: RPD = 3.08 instead of 2.36; airborne: 2.20 instead of 1.94). Patterns of selected variables indicated similarities between HWEC and OC, but significant differences between all other soil variables. This agreed to our results of indirect approaches in which both (i) wet-chemical data of OC and N and (ii) spectra fitted to measured OC and N values were used to estimate MBC and HWEC. Compared to these approaches, we found marked benefits of laboratory and airborne data for a direct spectral quantification of MBC (but not for HWEC). This suggests specificity of spectra for MBC, usable for the determination of this important soil parameter.

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Section: Multivariate Calibrations Of Oc N Mbc and Hwec From Spectrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms

et al. 2017

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“…Surface soil samples were collected from a 0-10 cm depth as composites of three to four cores in each field. Within three fields, sub-surface samples (10-20 cm depth in a field; [10][11][12][13][14][15][16][17][18][19][20][20][21][22][23][24][25][26][27][28][29][30], and 30-40 cm depth in two fields) were also collected. Thus, 62 soil samples were obtained.…”

Section: Introductionmentioning

confidence: 99%

Vis-NIR Spectroscopy and PLS Regression with Waveband Selection for Estimating the Total C and N of Paddy Soils in Madagascar

et al. 2017

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Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy with partial least squares (PLS) regression is a quick, cost-effective, and promising technology for predicting soil properties. The advantage of PLS regression is that all available wavebands can be incorporated in the model, while earlier studies indicate that PLS models include redundant wavelengths, and selecting specific wavebands can refine PLS analyses. This study evaluated the performance of PLS regression with waveband selection using Vis-NIR reflectance spectra to estimate the total carbon (TC) and total nitrogen (TN) in soils collected mainly from the surface of upland and lowland rice fields in Madagascar (n = 59; after outliers were removed). We used iterative stepwise elimination-based PLS (ISE-PLS) to estimate soil TC and TN and compared the predictive ability with standard full-spectrum PLS (FS-PLS). The predictive abilities were assessed using the coefficient of determination (R 2 ), the root mean squared error of cross-validation (RMSECV), and the residual predictive deviation (RPD). Overall, ISE-PLS using first derivative reflectance (FDR) showed a better predictive accuracy than ISE-PLS for both TC (R 2 = 0.972, RMSECV = 0.194, RPD = 5.995) and TN (R 2 = 0.949, RMSECV = 0.019, RPD = 4.416) in the soil of Madagascar. The important wavebands for estimating TC (12.59% of all wavebands) and TN (3.55% of all wavebands) were selected from all 2001 wavebands over the 400-2400 nm range using ISE-PLS. These findings suggest that ISE-PLS based on Vis-NIR diffuse reflectance spectra can be used to estimate soil TC and TN contents in Madagascar with an improved predictive accuracy.

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“…CARS used the Monte Carlo sampling method to evaluate the significance of each wavelength on the basis of RC absolute coefficients in PLSR model. Wavelengths with little influence due to established principles will be removed, and the next iteration always starts with the remaining variables until iterations are finished [44]. The use of the two variable selection methods can possibly improve model performances and meanwhile enable the development of simplified multispectral imaging systems, so that detection can meet industrial requirements.…”

Section: Reference Measurements Statistics and Spectral Data Processingmentioning

confidence: 99%

Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets

et al. 2018

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Cooking loss (CL) is a critical quality attribute directly relating to meat juiciness. The potential of the hyperspectral imaging (HSI) technique was investigated for non-invasively classifying and visualizing the CL of fresh broiler breast meat. Hyperspectral images of total 75 fresh broiler breast fillets were acquired by the system operating in the visible and near-infrared (VNIR, 400-1000 nm) range. Mean spectra were extracted from regions of interest (ROIs) determined by pure muscle tissue pixels. CL was firstly measured by calculating the weight loss in cooking, and then fillets were grouped into high-CL and low-CL according to the threshold of 20%. The classification methods partial least square-discriminant analysis (PLS-DA) and radial basis function-support vector machine (RBF-SVM) were applied, respectively, to determine the optimal spectral calibration strategy. Results showed that the PLS-DA model developed using the data, that is, first-order derivative (Der1) of VNIR full spectra, performed best with correct classification rates (CCRs) of 0.90 and 0.79 for the calibration and prediction sets, respectively. Furthermore, to simplify the optimal PLS-DA model and make it practical, effective wavelengths were individually selected using uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS). Through performance comparison, the CARS-PLS-DA combination was identified as the optimal method and the PLS-DA model built with 18 informative wavelengths selected by CARS resulted in good CCRs of 0.86 and 0.79. Finally, classification maps were created by predicting CL categories of each pixel in the VNIR hyperspectral images using the CARS-PLS-DA model, and the general CL categories of fillets were readily discernible. The overall results were encouraging and showed the promising potential of the VNIR HSI technique for classifying fresh broiler breast fillets into different CL categories.

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Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration

Cited by 1,219 publications

References 44 publications

Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms

Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms

Vis-NIR Spectroscopy and PLS Regression with Waveband Selection for Estimating the Total C and N of Paddy Soils in Madagascar

Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets

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