Genetic algorithm artificial neural network in near infrared spectroscopic quantification

The implantation of a genetic algorithm (GA) in quantitating components of interest in near infrared spectroscopic analysis could improve the predictive ability of a regression model. Thus, this study investigates the feasibility of a single layer Artificial Neuron Network (ANN) that trained with Levenberg-Marquardt (SLM) coupled with GA in predicting the boiling point of diesel fuel and the blood hemoglobin using near infrared spectral data. The proposed model was compared with a well-known model of Partial Least Squares (PLS) with and without Genetic Algorithm. Results show that the proposed model achieved the best results with root mean square error of prediction (RMSEP) of 3.6734 and correlation coefficient of 0.9903 for the boiling point, and RMSEP of 0.2349 and correlation coefficient of 0.9874 among PLS with and without GA, and SLM without GA. Findings suggest that the proposed SLMGA is insusceptible to the number of iterations when the SLM was trained with excessive iteration after the optimal iteration number. This indicates that the proposed model is capable of avoiding overfitting issue that due to excessive training iteration.

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Genetic Algorithm Artificial Neural Network in Near Infrared Spectroscopic Quantification

APTIKOM Journal on Computer Science and Information Technologies ◽

10.11591/aptikom.j.csit.125 ◽

2018 ◽

Vol 3 (3) ◽

pp. 84-91

Author(s):

Hasan Ali Gamal Al-Kaf ◽

Kim Seng Chia ◽

Musaed Al-subari

Keyword(s):

Genetic Algorithm ◽

Correlation Coefficient ◽

Boiling Point ◽

Near Infrared ◽

Single Layer ◽

Predictive Ability ◽

Neuron Network ◽

Proposed Model ◽

Infrared Spectroscopic ◽

Infrared Spectral

The implantation of a genetic algorithm (GA) in quantitating components of interest in near infrared spectroscopic analysis could improve the predictive ability of a regression model. Thus, this study investigates the feasibility of a single layer Artificial Neuron Network (ANN) that trained with Levenberg-Marquardt (SLM) coupled with GA in predicting the boiling point of diesel fuel and the blood hemoglobin using near infrared spectral data. The proposed model was compared with a well-known model of Partial Least Squares (PLS) with and without Genetic Algorithm. Results show that the proposed model achieved the best results with root mean square error of prediction (RMSEP) of 3.6734 and correlation coefficient of 0.9903 for the boiling point, and RMSEP of 0.2349 and correlation coefficient of 0.9874 among PLS with and without GA, and SLM without GA. Findings suggest that the proposed SLM-GA is insusceptible to the number of iterations when the SLM was trained with excessive iteration after the optimal iteration number. This indicates that the proposed model is capable of avoiding overfitting issue that due to excessive training iteration.

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Rapid identification and assay of crude oils based on moving-window correlation coefficient and near infrared spectral library

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2011.01.006 ◽

2011 ◽

Vol 107 (1) ◽

pp. 44-49 ◽

Cited By ~ 26

Author(s):

Xiao-Li Chu ◽

Yu-Peng Xu ◽

Song-Bai Tian ◽

Jing Wang ◽

Wan-Zhen Lu

Keyword(s):

Correlation Coefficient ◽

Near Infrared ◽

Rapid Identification ◽

Crude Oils ◽

Moving Window ◽

Spectral Library ◽

Infrared Spectral

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Detection of illegally added drugs in dietary supplements by near-infrared spectral imaging

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545814500321 ◽

2014 ◽

Vol 07 (06) ◽

pp. 1450032 ◽

Cited By ~ 1

Author(s):

Rui Zhang ◽

Lihui Yin ◽

Shaohong Jin

Keyword(s):

Dietary Supplements ◽

Correlation Coefficient ◽

False Positive ◽

Near Infrared ◽

Spectral Imaging ◽

Test Results ◽

Infrared Spectral ◽

Positive Results

The application to detect illegally added drugs in dietary supplements by near-infrared spectral imaging was studied with the focus on nifedipine, diclofenac and metformin. The method is based on near-infrared spectral images correlation coefficient to detect illegally added drugs. The results comply 100% with HPLC methods test results with no false positive results.

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Variable Selection Using Genetic Algorithm for Analysis of Near-Infrared Spectral Data Using Partial Least Squares

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference ◽

10.1109/iembs.2005.1616633 ◽

2005 ◽

Cited By ~ 1

Author(s):

Chit Siang Soh ◽

Kok Meng Ong ◽

P. Raveendran

Keyword(s):

Genetic Algorithm ◽

Variable Selection ◽

Least Squares ◽

Spectral Data ◽

Partial Least Squares ◽

Near Infrared ◽

Infrared Spectral

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Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar

Remote Sensing ◽

10.3390/rs11050506 ◽

2019 ◽

Vol 11 (5) ◽

pp. 506 ◽

Cited By ~ 10

Author(s):

Kensuke Kawamura ◽

Yasuhiro Tsujimoto ◽

Tomohiro Nishigaki ◽

Andry Andriamananjara ◽

Michel Rabenarivo ◽

...

Keyword(s):

Genetic Algorithm ◽

Least Squares ◽

Partial Least Squares ◽

Near Infrared ◽

Soil Phosphorus ◽

Predictive Ability ◽

Lowland Rice ◽

Pls Regression ◽

Proximal Sensing ◽

Extractable P

As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for crop production in the tropics—is still a challenging task. PLS regression with waveband selection can improve the predictive ability of a calibration model, and a genetic algorithm (GA) has been widely applied as a suitable method for selecting wavebands in laboratory calibrations. To develop a laboratory-based proximal sensing method, this study investigated the potential to use GA-PLS regression analyses to estimate oxalate-extractable P in upland and lowland soils from laboratory Vis-NIR reflectance data. In terms of predictive ability, GA-PLS regression was compared with iterative stepwise elimination PLS (ISE-PLS) regression and standard full-spectrum PLS (FS-PLS) regression using soil samples collected in 2015 and 2016 from the surface of upland and lowland rice fields in Madagascar (n = 103). Overall, the GA-PLS model using first derivative reflectance (FDR) had the best predictive accuracy (R2 = 0.796) with a good prediction ability (residual predictive deviation (RPD) = 2.211). Selected wavebands in the GA-PLS model did not perfectly match wavelengths of previously known absorption features of soil nutrients, but in most cases, the selected wavebands were within 20 nm of previously known wavelength regions. Bootstrap procedures (N = 10,000 times) using selected wavebands also confirmed the improvements in accuracy and robustness of the GA-PLS model compared to those of the ISE-PLS and FS-PLS models. These results suggest that soil oxalate-extractable P can be predicted from Vis-NIR spectroscopy and that GA-PLS regression has the advantage of tuning optimum bands for PLS regression, contributing to a better predictive ability.

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Quantitative near-infrared spectroscopic analysis of trimethoprim by artificial neural networks combined with modified genetic algorithm

Chemical Research in Chinese Universities ◽

10.1007/s40242-014-3410-x ◽

2014 ◽

Vol 30 (4) ◽

pp. 582-586 ◽

Cited By ~ 1

Author(s):

Hongyan Shan ◽

Yanqun Fei ◽

Yanfu Huan ◽

Guodong Feng ◽

Qiang Fei

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Artificial Neural Networks ◽

Near Infrared ◽

Spectroscopic Analysis ◽

Infrared Spectroscopic Analysis ◽

Modified Genetic Algorithm ◽

Infrared Spectroscopic ◽

Artificial Neural

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Using Genetic Algorithm Neural Network on Near Infrared Spectral Data for Ripeness Grading of Oil Palm (Elaeis guineensis Jacq.) Fresh Fruit

Information Processing in Agriculture ◽

10.1016/j.inpa.2016.10.001 ◽

2016 ◽

Vol 3 (4) ◽

pp. 252-261 ◽

Cited By ~ 3

Author(s):

Divo Dharma Silalahi ◽

Consorcia E. Reaño ◽

Felino P. Lansigan ◽

Rolando G. Panopio ◽

Nathaniel C. Bantayan

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Spectral Data ◽

Oil Palm ◽

Near Infrared ◽

Elaeis Guineensis ◽

Fresh Fruit ◽

Infrared Spectral ◽

Elaeis Guineensis Jacq

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Geographical origin discrimination and polysaccharides quantitative analysis of Radix codonopsis with micro near-infrared spectrometer engine

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545818500049 ◽

2017 ◽

Vol 11 (01) ◽

pp. 1850004 ◽

Cited By ~ 4

Author(s):

Jiayue Wang ◽

Tongtong Li ◽

Hailong Yang ◽

Tian Hu ◽

Lei Nie ◽

...

Keyword(s):

Quality Control ◽

Quantitative Analysis ◽

Correlation Coefficient ◽

Near Infrared ◽

Mean Squared Error ◽

Raw Materials ◽

Nir Spectroscopy ◽

Predictive Ability ◽

Calibration Model ◽

Infrared Spectrometer

At present, Tradition Chinese Medicine (TCM) industry in China is in the stage from the empirical development to industrial production. Near infrared (NIR) spectroscopy has been widely used in the quality control of TCM’s modernization with its characteristics including rapidness, nondestruction, simplicity, economy, and so on. In this study, as one type of a portable micro NIR spectrometer, Micro NIR 1700 was used to establish the qualitative models for identification of geographical region and authenticity of Radix codonopsis based on discriminant analysis (DA) method. Both of the DA models had better predictive ability with 100% accuracy. In addition, a method for rapid quantitative analysis of polysaccharide in Radix codonopsis was also developed based on Micro NIR 1700 spectrometer with partial least-squares (PLS) algorithm. In the PLS calibration model, the NIR spectra of samples were pretreated with different preprocessing methods and the spectral region was selected with different variable selection methods as well. The performance of the final PLS model was evaluated according to correlation coefficient of calibration ([Formula: see text]), correlation coefficient of prediction ([Formula: see text]), root mean squared error of cross validation (RMSECV), and root mean squared of prediction (RMSEP). The values of [Formula: see text], [Formula: see text], RMSECV, and RMSEP were 0.9775, 0.9602, 2.496, and 2.734[Formula: see text]g/mL, respectively. This work demonstrated that micro infrared spectrometer could be more convenient and rapid for quality control of Radix codonopsis, and the presented models would be a useful reference for quality control of other similar raw materials of TCM.

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