A novel algorithm for spectral interval combination optimization

Song, Xianfeng; Huang, Yue; Yan, Hong; Xiong, Yanmei; Min, Shungeng

doi:10.1016/j.aca.2016.10.041

Cited by 71 publications

(24 citation statements)

References 54 publications

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“…VISSA is an optimization algorithm for the selection of variable, based on weighted binary matrix sampling and model population analysis strategies (Deng et al., 2014). Its advantages include overcoming the uncertainty of a single model, generating synergistic/combination effects, and avoiding the elimination of important variables by mistake during the optimization process (Song et al., 2016). Up to now, it was mainly employed to deal with complex NIR datasets.…”

Section: Discussionmentioning

confidence: 99%

VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury

et al. 2020

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Metabolomics is an emerging tool to uncover the complex pathogenesis of disease, as well as the multitargets of traditional Chinese medicines, with chemometric analysis being a key step. However, conventional algorithms are not suitable for directly analyzing data at all times. The variable iterative space shrinkage approach-partial least squares-discriminant analysis, a novel algorithm for data mining, was first explored to screen metabolic varieties to reveal the multitargets of Xuefu Zhuyu decoction (XFZY) against traumatic brain injury (TBI) by the 7th day. Rat plasma from Sham, Vehicle, and XFZY groups was used for gas chromatography/mass spectrometry-based metabolomics. This method showed an improved discrimination ability (area under the curve ¼ 93.64%). Threonine, trans-4-hydroxyproline, and creatinine were identified as the direct metabolic targets of XFZY against TBI. Five metabolic pathways affected by XFZY in TBI rats, were enriched using Metabolic Pathway Analysis web tool (i.e., phenylalanine, tyrosine, and tryptophan biosynthesis; phenylalanine metabolism; galactose metabolism; alanine, aspartate, and glutamate metabolism; and tryptophan metabolism). In conclusion, metabolomics coupled with variable iterative space shrinkage approach-partial least squares-discriminant analysis model may be a valuable tool for identifying the holistic molecular mechanisms involved in the effects of traditional Chinese medicine, such as XFZY.

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Section: Discussionmentioning

confidence: 99%

VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury

et al. 2020

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“…ICO is a method based on the appearance frequency of each interval (the weight) at the last iteration . The main principles are summarized as follows: (i) the whole spectra was generated into N equal‐width intervals and M random combinations were obtained via weighted bootstrap sampling (WBS), (ii) a ratio (α) of optimal interval combinations was extracted from the M random combinations according to root mean square error of cross‐validation (RMSECV), (iii) the appearance of frequencies (the weight) was calculated and intervals with weight over 0.5 were considered to be the optimum intervals and (iv) the optimal intervals were selected for modeling.…”

Section: Methodsmentioning

confidence: 99%

“…A variety of single wavelength selection methods have been raised including uninformative variable elimination (UVE), Monte Carlo based UVE (MC‐UVE), the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), the variable iterative space shrinkage approach (VISSA), the genetic algorithm (GA), simulated annealing (SA) and so on. Following the principles of selecting a spectral band, many interval wavelength selection methods have been put forward, such as interval PLS (iPLS), synergy iPLS (siPLS), backward iPLS (biPLS), moving window PLS (MWPLS), interval combination optimization (ICO) and so on. Recently, a series of random variable selection methods has been proposed, such as random forest, particle swarm optimization (PSO), grey wolf and the Fisher optimal partitions algorithm .…”

Section: Introductionmentioning

confidence: 99%

Spectral interval optimization on rapid determination of prohibited addition in pesticide by ATR‐FTIR

Huang

Song

et al. 2019

Pest Management Science

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BACKGROUND Acetamiprid, as a low‐toxicity pesticide, has already been extensively used to increase plant production and quality. Although fipronil has been prohibited, it is usually illicitly added to acetamiprid due to its particular insecticidal action and effect, so it is highly desirable to obtain a rapid and effective method to detect its concentration. Mid‐infrared spectroscopy (MIR) combined with two variable selection methods, interval combination optimization (ICO) and interval partial least squares (iPLS), were used to determinate the prohibited addition of fipronil. RESULTS The full spectra for both ICO and iPLS were divided into 40 equal‐width intervals. Consequently, 45 and 135 characteristic variables were extracted from ICO and iPLS to establish the models. Compared with iPLS, the ICO model acquired a more suitable spectral region and as a result gained a higher prediction accuracy. Specifically, the ICO method selected the characteristic wavelengths ascribed to CF and CN (in five‐membered heterocyclics), iPLS chose the intervals associated with CF and SO. CONCLUSION Results revealed that MIR combined with ICO could be efficiently used for rapid identification of illegal addition and had great potential to provide on‐site pesticide quality control. © 2018 Society of Chemical Industry

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“…The MPA has been classified into single variable model population analysis and interval model population analysis. The former includes random frog (RF) [14], iteratively retains informative variables (IRIV) [15], variable iterative space shrinkage approach (VISSA) [16], iteratively variable subset optimization (IVSO) [17], CARS [18], stability competitive adaptive reweighted sampling (SCARS) [19], sampling error profile analysis LASSO (SEPA-LASSO) [20], BOSS [4] and SBOSS [5]; while the latter includes interval random frog (iRF) [21], interval variable iterative space shrinkage approach (iVISSA) [22], interval combination optimization (ICO) [23] and fisher optimal subspace shrinkage (FOSS) [6]. Moreover, selecting the variables on near-infrared spectroscopy by utilizing models that hybridize two or more different techniques was recommended in [12].…”

Section: Related Workmentioning

confidence: 99%

A Bootstrapping Soft Shrinkage Approach and Interval Random Variables Selection Hybrid Model for Variable Selection in Near-Infrared Spectroscopy

et al. 2020

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A novel algorithm for spectral interval combination optimization

Cited by 71 publications

References 54 publications

VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury

VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury

Spectral interval optimization on rapid determination of prohibited addition in pesticide by ATR‐FTIR

A Bootstrapping Soft Shrinkage Approach and Interval Random Variables Selection Hybrid Model for Variable Selection in Near-Infrared Spectroscopy

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