A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration

Yun, Yong‐Huan; Wang, Wei‐Ting; Tan, Minli; Liang, Yi‐Zeng; Li, Hongdong; Cao, Dong‐Sheng; Lü, Hongmei; Xu, Qingsong

doi:10.1016/j.aca.2013.11.032

Cited by 191 publications

(100 citation statements)

References 35 publications

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“…The possible benefits are increased estimation accuracies and more parsimonious and thus robust calibration models, which may improve the predictive ability for independent validation samples. Additionally, the selections of informative or key variables can be used to obtain insight in the underlying spectral predictive mechanisms [37][38][39]. However, we have to be aware that relevant mechanisms, e.g., for variables of soil organic matter, may vary from one soil population to another, each with a specific combination of spectrally meaningful factors such as soil texture or color [40,41].…”

Section: Introductionmentioning

confidence: 99%

“…In general, approaches have to compromise between an exhaustive search through the possible combinations of variables and computational efficiency [39]. Selections will not fully match but differ from one approach to another due to differences in the search procedure.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…This method was developed by considering the data matrix x which contains N samples in rows and p variables in columns, and y, of size 1 N × , denoting the measured property of interest [26]. Binary matrix sampling consists of generating a binary matrix M of dimension K P × which is assigned randomly with the number "1" or "0" to each column.…”

Section: Iriv Selection Methodsmentioning

confidence: 99%

“…Moreover, identification of effective wavelengths could offer possibilities to develop non-destructive macronutrient diagnosis devices for crops monitoring. Recently, new trends in chemometrics have highlighted IRIV-PLS methods which demonstrated superior performances in front of other successful variable selection algorithm particularly GA-PLS, MC-UVE-PLS and CARS [26]. IRIV method is part of MPA-based method.…”

Section: Introductionmentioning

confidence: 99%

Use of Fluorescence and Reflectance Spectra for Predicting Okra (<i>Abelmoschus esculentus</i>) Yield and Macronutrient Contents of Leaves

Dibi¹,

Bosson²,

Zobi³

et al. 2017

OJAppS

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In-field proximal sensing of most major crops nutrients still remains an economical and technical challenge. For this purpose, the use of effective multi-excitation fluorescence and reflectance wavelengths is explored in this work on Okra plant. Visible-near infrared (400 -1000 nm) reflectance and multi-fluorescence data were collected at leaf scale in a chemically fertilized field by using an USB spectrometer mounted with an Arduino-based LED driver clip. N, P, K and Ca content of samples leaves were measured using reference methods. Average pods yield and leaves macronutrients content were calibrated using IRIV-PLS regression after spectra pretreatments. Single informative wavelengths bands in reflectance, red and far-red fluorescences were selected for building yield and macronutrient content models. We showed that flowering stage was more suitable for yield prediction. Moderately useful macronutrient models were found in Ca content (RPD val = 1.93, r P = 0.818) and potassium content with RPD val = 1.8, r P = 0.88. P and N yielding prediction performance of RPD val = 1.61 (r P = 0.718 ) and RPD val = 1.46 (r P = 0.56) respectively were less accurate. This study demonstrates potentiality of fluorescence and reflectance spectroscopy for accurate estimation of leaf macronutrient content and crop yield. High selectivity obtained from resulted spectral bands could lead to the development of reliable, rapid and cost-effective devices for nutrient diagnosis.

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Incorporating PLS model information into particle swarm optimization for descriptor selection in QSAR/QSPR

Wang

Huang

Zhou

et al. 2015

Journal of Chemometrics

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As a representative paradigm of evolutionary algorithms, particle swarm optimization (PSO) has been combined with partial least square (PLS) (called PSO‐PLS) to select informative descriptors in quantitative structure‐activity/property relationship (QSAR/QSPR). However, one of the main limitations of PSO‐PLS is that it ignores PLS model information. In this paper, by incorporating the PLS model information into PSO‐PLS, we present a novel weighted sampling method (called WS‐PSO‐PLS) to choose the optimal descriptor subset. Due to the fact that the regression coefficients of the PLS model reflect the importance of descriptors in the model development, we firstly obtain the normalized regression coefficients by establishing the PLS model with all the descriptors. Afterward, weighted sampling is used to generate some individuals according to the aforementioned normalized regression coefficients. Finally, we employ some dimensions of the generated individuals to replace the corresponding dimensions of the individuals with poor quality in the population at each generation. WS‐PSO‐PLS has been assessed through three QSAR/QSPR datasets and the experimental results suggest that WS‐PSO‐PLS has the capability to effectively guide the search process by introducing the PLS model coefficients into PSO during the evolution and, therefore, performs better than PSO‐PLS. WS‐PSO‐PLS could be considered as a general and promising mechanism to introduce extra information to improve the performance of PSO for descriptor selection in QSAR/QSPR. Copyright © 2015 John Wiley & Sons, Ltd.

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A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration

Cited by 191 publications

References 35 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

Use of Fluorescence and Reflectance Spectra for Predicting Okra (<i>Abelmoschus esculentus</i>) Yield and Macronutrient Contents of Leaves

Incorporating PLS model information into particle swarm optimization for descriptor selection in QSAR/QSPR

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A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration

Cited by 191 publications

References 35 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

Use of Fluorescence and Reflectance Spectra for Predicting Okra (&lt;i&gt;Abelmoschus esculentus&lt;/i&gt;) Yield and Macronutrient Contents of Leaves

Incorporating PLS model information into particle swarm optimization for descriptor selection in QSAR/QSPR

Contact Info

Product

Resources

About

Use of Fluorescence and Reflectance Spectra for Predicting Okra (<i>Abelmoschus esculentus</i>) Yield and Macronutrient Contents of Leaves