Spectroscopic quantitation of tetrazolium formazan in nano-toxicity assay with interval-based partial least squares regression and genetic algorithm

Mahanty, Biswanath; Yoon, Soon-Uk; Kim, Chang-Gyun

doi:10.1016/j.chemolab.2016.03.012

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…For the backward iPLS (BiPLS) algorithm, the dataset is split into a given number of intervals; the PLS models are then calculated with each interval left out in a sequence; that is, if n intervals are chosen, then each model is based on n − 1 intervals that exclude one interval at a time. The first omitted interval gives the poorest performing model with respect to RMSECV [ 21 , 22 ].…”

Section: Methodsmentioning

confidence: 99%

Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology

Xia

Zhang

Weng

et al. 2017

International Journal of Analytical Chemistry

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Hyperspectral imaging (HSI) technology has increasingly been applied as an analytical tool in fields of agricultural, food, and Traditional Chinese Medicine over the past few years. The HSI spectrum of a sample is typically achieved by a spectroradiometer at hundreds of wavelengths. In recent years, considerable effort has been made towards identifying wavelengths (variables) that contribute useful information. Wavelengths selection is a critical step in data analysis for Raman, NIRS, or HSI spectroscopy. In this study, the performances of 10 different wavelength selection methods for the discrimination of Ophiopogon japonicus of different origin were compared. The wavelength selection algorithms tested include successive projections algorithm (SPA), loading weights (LW), regression coefficients (RC), uninformative variable elimination (UVE), UVE-SPA, competitive adaptive reweighted sampling (CARS), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), and genetic algorithms (GA-PLS). One linear technique (partial least squares-discriminant analysis) was established for the evaluation of identification. And a nonlinear calibration model, support vector machine (SVM), was also provided for comparison. The results indicate that wavelengths selection methods are tools to identify more concise and effective spectral data and play important roles in the multivariate analysis, which can be used for subsequent modeling analysis.

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Section: Methodsmentioning

confidence: 99%

Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology

Xia

Zhang

Weng

et al. 2017

International Journal of Analytical Chemistry

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“…The PLS regression model was developed on the calibration set using the scikit-learn package (version 0.23.2) in Python . The effective spectral variables were retrieved by dimensional reduction, which extract a certain number of uncorrelated latent variables (LVs) from intercorrelated variables in the spectrum . The root mean square error (RMSE) and regression coefficient ( R 2 ) are the main indicators of the robustness and the predictive ability of the model .…”

Section: Discussionmentioning

confidence: 99%

“…25 The effective spectral variables were retrieved by dimensional reduction, which extract a certain number of uncorrelated latent variables (LVs) from intercorrelated variables in the spectrum. 26 The root mean square error (RMSE) and regression coefficient (R 2 ) are the main indicators of the robustness and the predictive ability of the model. 27 An internal leave-one-out cross-validation was employed to choose the optimum number of LVs and evaluate the robustness of the model.…”

Section: Materials Andmentioning

confidence: 99%

Hairpin-Structured Magnetic SERS Sensor for Tetracycline Resistance Gene tetA Detection

Sun

et al. 2020

Anal. Chem.

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Antibiotic resistance genes (ARGs) have become emerging environmental contaminants, and the effective on-site detection of ARGs is urgently needed. Herein, we constructed a hairpin-structured magnetic sensor for the analysis of a widespread ARG, tetA, using surface-enhanced Raman scattering (SERS). The SERS sensor was assembled by immobilizing core-satellite structured Fe 3 O 4 @SiO 2 -Au with single-stranded DNA in a folded hairpin structure. The SERS sensor exhibited good sensitivity and specificity for the detection of laboratory-synthesized tetA ssDNA fragments. In addition, this SERS strategy is the first of its kind to be employed for monitoring environmental samples in the field, with a limit of detection reaching as low as 25 copies μL −1 . Univariate and multivariate linear regression equations verify the practicability of the SERS sensor for quantitative tetA determination, showing the prospect for an amplification-free alternative platform for sensitive and reliable on-site detection of ARGs in the environment.

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“…Recently, based on the PLS algorithm, some variable selection methods have been developed including interval partial least-squares (iPLS) (Saudland et al, 2000 ), backward interval partial least-square (BiPLS) (Leardi and Nørgaard, 2004 ) and synergy interval partial least-squares (SiPLS) (Munck et al, 2001 ), etc. Many studies have confirmed the efficiency of these variable selection methods for improving model performance (Chen et al, 2008 ; Di et al, 2010 ; Wu et al, 2013a ; Mahanty et al, 2016 ).…”

Section: Introductionmentioning

confidence: 93%

Pharmaceutical Analysis Model Robustness From Bagging-PLS and PLS Using Systematic Tracking Mapping

Zhao

Huang

et al. 2018

Front. Chem.

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Our work proved that processing trajectory could effectively obtain a more reliable and robust quantitative model compared with the step-by-step optimization method. The use of systematic tracking was investigated as a tool to optimize modeling parameters including calibration method, spectral pretreatment and variable selection latent factors. The variable was selected by interval partial least-squares (iPLS), backward interval partial least-square (BiPLS) and synergy interval partial least-squares (SiPLS). The models were established by Partial least squares (PLS) and Bagging-PLS. The model performance was assessed by using the root mean square errors of validation (RMSEP) and the ratio of standard error of prediction to standard deviation (RPD). The proposed procedure was used to develop the models for near infrared (NIR) datasets of active pharmaceutical ingredients in tablets and chlorogenic acid of Lonicera japonica solution in ethanol precipitation process. The results demonstrated the processing trajectory has great advantages and feasibility in the development and optimization of multivariate calibration models as well as the effectiveness of bagging model and variable selection to improve prediction accuracy and robustness.

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Spectroscopic quantitation of tetrazolium formazan in nano-toxicity assay with interval-based partial least squares regression and genetic algorithm

Cited by 8 publications

References 40 publications

Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology

Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology

Hairpin-Structured Magnetic SERS Sensor for Tetracycline Resistance Gene tetA Detection

Pharmaceutical Analysis Model Robustness From Bagging-PLS and PLS Using Systematic Tracking Mapping

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