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

Shi, Jiyong; Hu, Xuetao; Zhang, Xiaobo; Zhao, Jiewen; Zhang, Wen; Huang, Xiaowei; Zhu, Yaodi; Li, Zhihua; Xu, Yanhua

doi:10.1002/cem.2812

Cited by 11 publications

(3 citation statements)

References 46 publications

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“…After hyperspectral image acquisition, spectral data of cucumber leaves in the calibration set and prediction set was extracted by defining an region of interest (ROI) (50×50pixels) in the center region of hyperspectral images. Partial least squares (PLS), interval partial least squares ( iPLS), simulated annealing -Interval partial least squares (SA-iPLS) were employed to build chlorophyll calibration models based on the extracted spectral data and reference chlorophyll concentrations determined using 9 high performance liquid chromatography (HPLC) (Shi et al, 2016;Shi et al, 2012).…”

Section: Building Calibration Modelsmentioning

confidence: 99%

Nondestructive diagnostics of magnesium deficiency based on distribution features of chlorophyll concentrations map on cucumber leaf

Shi

Zhai

et al. 2019

Journal of Plant Nutrition

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A new and nondestructive method for diagnosing magnesium (Mg) deficiency based on chlorophyll concentration distribution features of cucumber leaves was proposed. Mg deficient cucumber plants and Control plants were grown under non-soil conditions with special nutrient supply. Cucumber leaves were employed to collect hyperspectral images using a visible and near infrared (VIS/NIR) hyperspectral imaging system (400-900 nm) and determine reference chlorophyll concentrations using high performance liquid chromatography (HPLC). An optimal chlorophyll concentration calibration model (Rp = 0.9087) was constructed and used to detect chlorophyll distribution maps of Mg deficient leaves and Control leaves. Results shown that chlorophyll content distributed more unevenly on Mg deficient leaves than Control leaves. The Standard Deviation (SD) value of the chlorophyll content at all the pixels on a chlorophyll distribution map was calculated for Mg deficient diagnostics. An Mg deficiency diagnostics model with satisfied performance (diagnostic rate 93.33%) was obtained.The result indicated the SD value of chlorophyll concentrations on the whole cucumber leaf could be employed to diagnose Mg deficiency nondestructively.

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Section: Building Calibration Modelsmentioning

confidence: 99%

Nondestructive diagnostics of magnesium deficiency based on distribution features of chlorophyll concentrations map on cucumber leaf

Shi

Zhai

et al. 2019

Journal of Plant Nutrition

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“…Several methods based on optimization algorithms have been proposed with the criterion of root‐mean‐squared error of cross‐validation (RMSECV) or root‐mean‐squared error of prediction (RMSEP) of an independent validation set. Such methods include simulated annealing, genetic algorithm, ant colony optimization, and particle swarm optimization . These methods are fairly straightforward to understand and usually produce satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

Combination of heuristic optimal partner bands for variable selection in near‐infrared spectral analysis

Jin

Cui

Cai

et al. 2017

Journal of Chemometrics

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Variable selection plays a critical role in the analysis of near-infrared (NIR) spectra. A method for variable selection based on the principle of the successive projection algorithm (SPA) and optimal partner wavelength combination (OPWC) was proposed for NIR spectral analysis. The method determines a number of knot variables with sufficient independence by SPA, and candidate variable bands with a definite width are defined. The cooperative effect of the bands is then evaluated with the partial least squares regression model by using the method of OPWC. The performance of the proposed method was compared with those of SPA, OPWC, randomization test, competitive adaptive reweighted sampling, and Monte Carlo uninformative variable elimination by using NIR datasets for pharmaceutical tablets, corn, and soil. The results show that the proposed method can select informative variable bands with a cooperative effect and improves the model for quantitative analysis.KEYWORDS near-infrared spectroscopy, optimal partner wavelength combination, spectral band selection, successive projection algorithm, variable selection Journal of Chemometrics. 2018;32:e2971.

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“…First of all, the variable selection is important in many fields, such as spectroscopy [7,8], QSPR [9,10], and other fields [11,12]. The selection of molecular descriptors largely determines the quality of the QSPR model [13][14][15]. The step of molecular descriptor screening aims to reflect more structural information so that there is no noise in the descriptors.…”

mentioning

confidence: 99%

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

Zhang

Liu

et al. 2018

Applied Sciences

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Featured Application:The proposed hybrid intelligent model can be applied in engineering design, material performance prediction, numerical calculation, and the prediction of physical and chemical properties.Abstract: A quantitative structure-property relationship (QSPR) model is proposed to explore the relationship between the pKa of various compounds and their structures. Through QSPR studies, the relationship between the structure and properties can be obtained. In this study, a novel chaos-enhanced accelerated particle swarm algorithm (CAPSO) is adopted to screen molecular descriptors and optimize the weights of back propagation artificial neural network (BP ANN). Then, the QSPR model based on CAPSO and BP ANN is proposed and named the CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm was a reliable method for screening molecular descriptors. The five molecular descriptors obtained by the CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed that the CAPSO BP ANN model exhibited good performance in predicting the pKa values of various compounds. The absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the high prediction accuracy. The proposed hybrid intelligent model can be applied in engineering design and the prediction of physical and chemical properties.The establishment of the QSPR model mainly involves the following steps: acquisition of experimental data, construction and optimization of the molecular structure, calculation and screening of molecular descriptors, establishment and verification of the model, etc. First of all, the variable selection is important in many fields, such as spectroscopy [7,8], QSPR [9,10], and other fields [11,12]. The selection of molecular descriptors largely determines the quality of the QSPR model [13][14][15]. The step of molecular descriptor screening aims to reflect more structural information so that there is no noise in the descriptors. Many methods have been developed to screen molecular descriptors and can be mainly divided into two categories [16][17][18]. The first category includes the common methods, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), and forward/backward/bi-directional stepwise multiple linear regression (MLR). The second includes the modern search algorithms, such as genetic algorithm (GA), simulated annealing algorithm (SA), ant colony algorithm (AC), particle swarm optimization (PSO), and other swarm intelligence algorithms [7,11,[19][20][21]. The common methods are the most simple and efficient, but their overall performances are low in complex nonlinear problems. The modern search algorithms based on the optimization strategy have obvious advantages and can search for optimal variables and deal with complex large data points. The model establishment is important in the QSPR study and commonly used QSPR model...

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

Cited by 11 publications

References 46 publications

Nondestructive diagnostics of magnesium deficiency based on distribution features of chlorophyll concentrations map on cucumber leaf

Nondestructive diagnostics of magnesium deficiency based on distribution features of chlorophyll concentrations map on cucumber leaf

Combination of heuristic optimal partner bands for variable selection in near‐infrared spectral analysis

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

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