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

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.

[1]  Yanxia Wang,et al.  A wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared spectral quantitative analysis , 2015 .

[2]  Ron Wehrens,et al.  Wavelength selection with Tabu Search , 2003 .

[3]  Feng Qian,et al.  Online updating of NIR model and its industrial application via adaptive wavelength selection and local regression strategy , 2014 .

[4]  L Delattre,et al.  Validation of manufacturing process of Diltiazem HCl tablets by NIR spectrophotometry (NIRS). , 2007, Journal of pharmaceutical and biomedical analysis.

[5]  Kaiyi Zheng,et al.  Stability competitive adaptive reweighted sampling (SCARS) and its applications to multivariate calibration of NIR spectra , 2012 .

[6]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[7]  John H. Kalivas,et al.  Further investigation on a comparative study of simulated annealing and genetic algorithm for wavelength selection , 1995 .

[8]  R. Boggia,et al.  Genetic algorithms as a strategy for feature selection , 1992 .

[9]  J. Kister,et al.  Monitoring of the evolution of an industrial compost and prediction of some compost properties by NIR spectroscopy. , 2009, The Science of the total environment.

[10]  Lutgarde M. C. Buydens,et al.  Improvement of PLS model transferability by robust wavelength selection , 1998 .

[11]  Kimito Funatsu,et al.  Genetic algorithm‐based wavelength selection method for spectral calibration , 2011 .

[12]  B. Nicolai,et al.  Postharvest quality of apple predicted by NIR-spectroscopy: Study of the effect of biological variability on spectra and model performance , 2010 .

[13]  Zou Xiaobo,et al.  Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of ‘Fuji’ apple based on different PLS models , 2007 .

[14]  Franco Allegrini,et al.  A new and efficient variable selection algorithm based on ant colony optimization. Applications to near infrared spectroscopy/partial least-squares analysis. , 2011, Analytica chimica acta.

[15]  Wang,et al.  Sequencing Mixed-model Production Systems by Modified Multi-objective Genetic Algorithms , 2010 .

[16]  Non-destructive prediction of hardening pericarp disorder in intact mangosteen by near infrared transmittance spectroscopy , 2011 .

[17]  Jorge Otávio Trierweiler,et al.  NIR pre-selection data using modified changeable size moving window partial least squares and pure spectral chemometrical modeling with ant colony optimization for wheat flour characterization , 2015 .

[18]  Sanjay Bisht,et al.  Hybrid Genetic-simulated Annealing Algorithm for Optimal Weapon Allocation in Multilayer Defence Scenario , 2004 .

[19]  A. Versari,et al.  Prediction of colloidal stability in white wines using infrared spectroscopy , 2011 .

[20]  R. Fahmy,et al.  Sustained release dosage forms dissolution behavior prediction: a study of matrix tablets using NIR spectroscopy. , 2009, International journal of pharmaceutics.

[21]  M. T. Sze,et al.  A genetic algorithm of determining cycle time for printed circuit board assembly lines , 2001, Eur. J. Oper. Res..

[22]  John H. Kalivas,et al.  Global optimization by simulated annealing with wavelength selection for ultraviolet-visible spectrophotometry , 1989 .

[23]  Xueguang Shao,et al.  Application of latent projective graph in variable selection for near infrared spectral analysis , 2012 .

[24]  Roman M. Balabin,et al.  Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data. , 2011, Analytica chimica acta.

[25]  Zongyu Shu,et al.  Simulated Annealing with a Hybrid Local Search for Solving the Traveling Salesman Problem , 2015 .

[26]  S. Sokhansanj,et al.  Variability of biomass chemical composition and rapid analysis using FT-NIR techniques , 2010 .

[27]  Xueguang Shao,et al.  Multivariate calibration of near-infrared spectra by using influential variables , 2012 .

[28]  István Bársony,et al.  Comparison of algorithms used for evaluation of ellipsometric measurements random search, genetic algorithms, simulated annealing and hill climbing graph-searches , 2000 .

[29]  Xueguang Shao,et al.  A wavelength selection method based on randomization test for near-infrared spectral analysis , 2009 .

[30]  John H. Kalivas,et al.  Simulated‐annealing‐based optimization algorithms: Fundamentals and wavelength selection applications , 1995 .

[31]  D. Massart,et al.  Elimination of uninformative variables for multivariate calibration. , 1996, Analytical chemistry.

[32]  Ludovic Duponchel,et al.  Parallel genetic algorithm co-optimization of spectral pre-processing and wavelength selection for PLS regression , 2011 .

[33]  Zou Xiaobo,et al.  Variables selection methods in near-infrared spectroscopy. , 2010, Analytica chimica acta.

[34]  W. Cai,et al.  A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra , 2008 .

[35]  Å. Rinnan,et al.  Application of near infrared reflectance (NIR) and fluorescence spectroscopy to analysis of microbiological and chemical properties of arctic soil , 2007 .

[36]  V. A. McGlone,et al.  Prediction of storage disorders of kiwifruit (Actinidia chinensis) based on visible-NIR spectral characteristics at harvest , 2004 .

[37]  A. Peirs,et al.  Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review , 2007 .

[38]  Hongdong Li,et al.  Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. , 2009, Analytica chimica acta.

[39]  Zehui Shao,et al.  An Effective Simulated Annealing Algorithm for Solving the Traveling Salesman Problem , 2009 .

[40]  J. F. Martín.  Optical path length and wavelength selection using Vis/NIR spectroscopy for olive oil's free acidity determination , 2015 .

[41]  M. Holmes,et al.  Near Infrared Quantitative Analysis of Total Flavonoid Content in Fresh Ginkgo Biloba Leaves Based on Different Wavelength Region Selection Methods and Partial Least Squares Regression , 2012 .

[42]  Ting Wu,et al.  Improvement of NIR model by fractional order Savitzky–Golay derivation (FOSGD) coupled with wavelength selection , 2015 .

[43]  Ramachandra Raghavendra,et al.  Particle swarm optimization based fusion of near infrared and visible images for improved face verification , 2011, Pattern Recognit..