Journal articles: 'Moving Window PLS (mwPLS)'

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Pan, Tao, Wei Wei Chen, Zeng Hai Chen, and Jun Xie. "Waveband Selection for NIR Spectroscopy Analysis of Wastewater COD." Key Engineering Materials 480-481 (June 2011): 393–96. http://dx.doi.org/10.4028/www.scientific.net/kem.480-481.393.

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Waveband selection of near-infrared (NIR) spectroscopy analysis of wastewater chemical oxygen demand (COD) by moving window partial least squares (MWPLS) method with changeable size, the optimization of PLS factor was combined with MWPLS method. According to the prediction effect, the optimal model was selected, and the corresponding waveband, number of adopted wavelengths, PLS factor, RMSEP, RP were 820-850nm, 16, 13, 25.5 mg/L, and 0.968 respectively, which was obviously superior to the optimal PLS model on the whole spectral collecting region. The result shows that the MWPLS method can improve the model prediction effect, reduce model complexity, and provide valuable reference for designing special NIR spectrometer.

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Jiao, Long, Shan Bing, Xiaofeng Zhang, and Hua Li. "Interval partial least squares and moving window partial least squares in determining the enantiomeric composition of tryptophan by using UV-Vis spectroscopy." Journal of the Serbian Chemical Society 81, no. 2 (2016): 209–18. http://dx.doi.org/10.2298/jsc150227065j.

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The application of interval partial least squares (IPLS) and moving window partial least squares (MWPLS) to the enantiomeric analysis of tryptophan (Trp) was investigated. A UV-Vis spectroscopy method for determining the enantiomeric composition of Trp was developed. The calibration model was built by using partial least squares (PLS), IPLS and MWPLS respectively. Leave-one-out cross validation and external test validation were used to assess the prediction performance of the established models. The validation result demonstrates the established full-spectrum PLS model is impractical for quantifying the relationship between the spectral data and enantiomeric composition of L-Trp. On the contrary, the developed IPLS and MWPLS model are both practicable for modeling this relationship. For the IPLS model, the root mean square relative error (RMSRE) of external test validation and leave-one-out cross validation is 4.03 and 6.50 respectively. For the MWPLS model, the RMSRE of external test validation and leave-one-out cross validation is 2.93 and 4.73 respectively. Obviously, the prediction accuracy of the MWPLS model is higher than that of the IPLS model. It is demonstrated UV-Vis spectroscopy combined with MWPLS is a commendable method for determining the enantiomeric composition of Trp. MWPLS is superior to IPLS for selecting spectral region in UV-Vis spectroscopy analysis.

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Chen, Tao, Zhi Li, Fang Rong Hu, and Wei Mo. "Quantitative Analysis of Mixtures Using Terahertz Time-Domain Spectroscopy and Different PLS Algorithms." Advanced Materials Research 804 (September 2013): 23–28. http://dx.doi.org/10.4028/www.scientific.net/amr.804.23.

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This paper attempted the feasibility to determine component concentrations in multicomponent mixtures with terahertz time-domain spectroscopy (THz-TDS) combined with different partial least-squares regression (PLS) algorithms. First, THz absorbance spectra for 75 ternary mixtures of anhydrous theophylline, lactose monohydrate and magnesium stearate were investigated using THz-TDS in the frequency range from 0.1 to 3.0 THz, then four different PLS methods, including interval PLS (iPLS), backward interval PLS (biPLS), synergy interval PLS (siPLS) and moving window PLS (mwPLS), were employed to perform quantitative analysis of anhydrous theophylline concentrations in ternary mixtures. The performance of mwPLS model is the best in contrast to other PLS models and full spectrum PLS. The optimal model was achieved with higher correlation coefficient for calibration (RC) of 0.9842, higher correlation coefficient for prediction (RP) of 0.9851, lower root mean square error of cross-validation (RMSECV) of 3.8241, and lower root mean square error of prediction (RMSEP) of 4.1540. Experimental results demonstrate that THz spectroscopy combined with PLS algorithms could be successfully applied as an effective nondestructive tool for the quantitative analysis of component concentrations in multicomponent mixtures, and mwPLS is an ideal method for reducing the complexity and improving the performance of the model.

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Yao, Li Jun, Jie Mei Chen, and Tao Pan. "Waveband Selection for NIR Spectroscopic Analysis of Zn²⁺ in Soil." Applied Mechanics and Materials 365-366 (August 2013): 737–40. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.737.

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Near-infrared (NIR) spectroscopy combined with moving window partial least squares (MWPLS) method was successfully applied to the waveband selection for the rapid chemical-free determination of Zn2+ in soil. Based on randomness and similarity, an effective approach was performed to obtain objective and practical models. The optimal MWPLS waveband was 1136-1252 nm, and the corresponding optimal number of PLS factors was 6. The validation root mean square error (V-SEP) and validation correlation coefficients (V-RP) of prediction were 15.658 mg kg-1 and 0.925, respectively. The Zn2+ prediction values of the validation samples are close to the measured values. The results provided a reliable NIR model and can serve as valuable references for designing the dedicated spectroscopic instruments.

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Pan, Tao, Qiang Ji, Jie Mei Chen, and Hua Zhou Chen. "FTIR/ATR Spectroscopy Applied to the Rapid Determination of Chemical Oxygen Demand of Wastewater." Key Engineering Materials 500 (January 2012): 820–25. http://dx.doi.org/10.4028/www.scientific.net/kem.500.820.

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A directly rapid quantification method for chemical oxygen demand (COD) of wastewater was established by Fourier transform infrared (FTIR) spectroscopy combined with attenuated total reflection (ATR) technology and moving window partial least squares (MWPLS) method with changeable parameters. All samples were firstly divided into the calibration set and the prediction set. And then according to the predicion effect, the optimal model was selected, and the corresponding waveband, number of adopted wavenumbers, PLS factor, root mean squared error of predication (RMSEP), correlation coefficient of predication (RP) were 3152-1109 cm-1, 1060, 8, 21.5 mg/L, and 0.981 respectively, which was obviously superior to the optimal PLS model on the whole spectral collecting region. The result shows that FTIR/ATR spectroscopy can be applied to the rapid determination of COD of wastewater, and the waveband selected by MWPLS method with changeable parameters has higher signal to noise ratio, which can effectively improve the precision of the predictive model.

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Xie, Jun, and Tao Pan. "Waveband Selection for FTIR/ATR Spectroscopy Analysis of Total Cholesterol in Human Serum." Advanced Materials Research 566 (September 2012): 712–15. http://dx.doi.org/10.4028/www.scientific.net/amr.566.712.

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Fourier transform infrared (FTIR) spectroscopy and attenuated total reflection (ATR) technology were successfully applied for the analysis of total cholesterol (TC) in human serum. An appropriate waveband with stability was selected by the moving window partial least-squares (MWPLS) regression through a rigorous modeling process. The selected waveband was 3065 cm-1 to 945 cm-1, the corresponding PLS factor was 15. The modeling effect M-SEPAve, M-SEPStd, M-RP,Ave, and M-RP,Std were 0.408 mmol/L, 0.068 mmol/L, 0.949, and 0.022, respectively. V-SEP and V-RP, were 0.419 mmol /L and 0.932, respectively. It indicated that the method has good prediction effect and stability, and provided valuable references for designing specialized spectroscopic instruments.

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Pan, Tao, and Peng Zhao Li. "Waveband Selection with Stability for FTIR/ATR Spectroscopic Analysis of COD in Sugar Refinery Wastewater." Advanced Materials Research 562-564 (August 2012): 220–23. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.220.

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Fourier transform infrared spectroscopy (FTIR) and attenuated total reflection (ATR) technology were successfully applied for the rapid no-reagent determination of chemical oxygen demand (COD) in sugar refinery wastewater. An appropriate waveband with stability was selected by the moving window partial least-squares (MWPLS) regression through a rigorous modeling process. The selected waveband was 3198 cm-1 to 2376 cm-1, the corresponding PLS factor was 5. The modeling effect M-SEPAve, M-RP,Ave, M-SEPStd, and M-RP,Std were 31.5 mg/L, 0.962, 3.9 mg/L, and 0.012, respectively. The validation effect V-SEP and V-RP were 31.9 mg/L and 0.965, respectively. It indicated that the method has good prediction effect and stability, and provided valuable references for designing specialized spectroscopic instruments.

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Pan, Tao, Zhen Tao Wu, and Jie Mei Chen. "Near-Infrared Spectroscopic Measurement of Organic Matter in Soil." Advanced Materials Research 499 (April 2012): 414–18. http://dx.doi.org/10.4028/www.scientific.net/amr.499.414.

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Near-infrared (NIR) spectroscopy was successfully applied to chemical free and rapid determination of the organic matter in soil, and moving window partial least square (MWPLS) combining with Savitzky-Golay (SG) smoothing was used to the selection of NIR waveband. Thirty-five samples were randomly selected from all 97 collected soil samples as the validation set. The remaining 62 samples were divided into similar modeling calibration set (37 samples) and modeling prediction set (25 samples) based on partial least square cross-validation predictive bias (PLSPB). The selected waveband was 1896 nm to 2138 nm; the SG smoothing parameters and PLS factor OD, DP, NSP and F were 2, 6, 71 and 15, respectively; the modeling effect M-SEP and M-RPwere 0.219% and 0.944, respectively; the validating effect V-SEP and V-RPwere 0.243% and 0.878, respectively. The result provided a reliable NIR model and valuable references for designing specialized NIR instruments.

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Liu, Manhua, Yangyang Wang, Yueping Jiang, Haitao Liu, Jingjing Chen, and Shao Liu. "Quantitation of Oxcarbazepine Clinically in Plasma Using Surfaced-Enhanced Raman Spectroscopy (SERS) Coupled with Chemometrics." Applied Spectroscopy 73, no. 7 (May 21, 2019): 801–9. http://dx.doi.org/10.1177/0003702819845389.

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Nondestructive, sensitive, near-real-time quantitative analysis approaches are gaining popularity and attention, especially in clinical diagnosis and detection. There is a need to propose an alternative scheme using surface-enhanced Raman spectroscopy (SERS) assisted by chemometrics to improve some defects existing using other analytical instruments to meet clinical demands. In this study, clinical drug oxcarbazepine (OXC) in human blood plasma has been quantified and detected using this method. Partial least squares regression (PLSR) modeling was employed to assess the relationship between full SERS spectral data and OXC concentration. The calibration set's correlation coefficient of the model is > 0.9, the result suggests that this method is favorable and feasible. Furthermore, other multivariate calibration algorithms like Monte Carlo cross-validation (MCCV) sample set partitioning based on joint XY distances (SPXY), adaptive iteratively reweighted penalized least squares (AIR–PLS), moving window partial least squares regression (MWPLS), and leave-one-out cross-validation were used to handle these spectral data to obtain an accurate predictive model. The results achieved in this study provide a possibility and availability for us to apply SERS in combination with chemometrics to diagnosis detection.

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10

Fu, Hai-Yan, Shuang-Yan Huan, Lu Xu, Li-Juan Tang, Jian-Hui Jiang, Hai-Long Wu, Guo-Li Shen, and Ru-Qin Yu. "Moving Window Partial Least-Squares Discriminant Analysis for Identification of Different Kinds of Bezoar Samples by near Infrared Spectroscopy and Comparison of Different Pattern Recognition Methods." Journal of Near Infrared Spectroscopy 15, no. 5 (October 2007): 291–97. http://dx.doi.org/10.1255/jnirs.743.

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Moving window partial least-squares (MWPLS) regression was coupled with near infrared (NIR) spectra as an interval selection method to improve the performance of partial least squares discriminant analysis (PLSDA) models. This method was applied to the identification of artificial bezoar, natural bezoar and artificial bezoar in natural bezoar and compared with some traditional pattern recognition methods, such as principal component analysis (PCA), linear discriminant analysis (LDA) and PLSDA. The introduction of MWPLS enhanced the performance of PLSDA model. The results obtained showed that moving window partial least-squares discriminant analysis (MWPLSDA) can extract wavelength intervals with useful information and build simple yet effective classification models that can significantly improve the classification accuracy. Then MWPLSDA was used to identify natural bezoar by geographical origin; a promising result was achieved. The work showed that MWPLSDA could be a promising method for quality analysis and discrimination of chinese medical herbs according to geographical origin.

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11

Pan, Tao, Minmiao Li, Jiemei Chen, and Haiyan Xue. "Quantification of glycated hemoglobin indicator HbA1c through near-infrared spectroscopy." Journal of Innovative Optical Health Sciences 07, no. 04 (July 2014): 1350060. http://dx.doi.org/10.1142/s1793545813500600.

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A new strategy for quantitative analysis of a major clinical biochemical indicator called glycated hemoglobin (HbA1c) was proposed. The technique was based on the simultaneous near-infrared (NIR) spectral determination of hemoglobin (Hb) and absolute HbA1c content (Hb • HbA1c) in human hemolysate samples. Wavelength selections were accomplished using the improved moving window partial least square (MWPLS) method for stability. Each model was established using an approach based on randomness, similarity, and stability to obtain objective, stable, and practical models. The optimal wavebands obtained using MWPLS were 958 to 1036 nm for Hb and 1492 to 1858 nm for Hb • HbA1c, which were within the NIR overtone region. The validation root mean square error and validation correlation coefficients of prediction (V-SEP, V-R P ) were 3.4 g L-1 and 0.967 for Hb, respectively, whereas the corresponding values for Hb • HbA1c were 0.63 g L-1 and 0.913. The corresponding V-SEP and V-R P were 0.40% and 0.829 for the relative percentage of HbA1c. The experimental results confirm the feasibility for the quantification of HbA1c based on simultaneous NIR spectroscopic analyses of Hb and Hb • HbA1c.

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12

Chen, Jiemei, Tao Pan, Guisong Liu, Yun Han, and Dingxing Chen. "Selection of stable equivalent wavebands for near-infrared spectroscopic analysis of total nitrogen in soil." Journal of Innovative Optical Health Sciences 07, no. 04 (July 2014): 1350071. http://dx.doi.org/10.1142/s1793545813500715.

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The selection of stable wavebands for the near-infrared (NIR) spectroscopic analysis of total nitrogen (TN) in soil was accomplished by using an improved moving window partial least squares (MWPLS) method. A new modeling approach was performed based on randomness, similarity and stability, which produced an objective, stable and practical model. Based on the MWPLS method, a search was in the overall scanning region from 400 to 2498 nm, and the optimal waveband was identified to be 1424 to 2282 nm. A model space that includes 41 wavebands that are equivalent to the optimal waveband was then proposed. The public range of the 41 equivalent optimal wavebands was 1590 to 1870 nm, which contained sufficient TN information. The wavebands of 1424 to 2282 nm, 1590 to 1870 nm, and the long-NIR region 1100 to 2498 nm all achieved satisfactory validation effects. However, the public waveband of 1590 to 1870 nm had only a minimum number of wavelengths, which significantly reduced the method complexity. Various equivalent wavebands serve as guidelines for designing spectroscopic instruments. These wavebands could address the restrictions of position and the number of wavelengths in instrument design.

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Lu, Bo, Ivan Castillo, Leo Chiang, and Thomas F. Edgar. "Industrial PLS model variable selection using moving window variable importance in projection." Chemometrics and Intelligent Laboratory Systems 135 (July 2014): 90–109. http://dx.doi.org/10.1016/j.chemolab.2014.03.020.

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Du, Y. P., Y. Z. Liang, J. H. Jiang, R. J. Berry, and Y. Ozaki. "Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares and searching combination moving window partial least squares." Analytica Chimica Acta 501, no. 2 (January 2004): 183–91. http://dx.doi.org/10.1016/j.aca.2003.09.041.

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Chen, Jiemei, Zhiwei Yin, Yi Tang, and Tao Pan. "Vis-NIR spectroscopy with moving-window PLS method applied to rapid analysis of whole blood viscosity." Analytical and Bioanalytical Chemistry 409, no. 10 (February 21, 2017): 2737–45. http://dx.doi.org/10.1007/s00216-017-0218-9.

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Jaworski, Aleksander, Hanna Wikiel, and Kazimierz Wikiel. "Online Monitoring of Copper Damascene Electroplating Bath by Voltammetry: Selection of Variables for Multiblock and Hierarchical Chemometric Analysis of Voltammetric Data." International Journal of Electrochemistry 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/4289517.

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The Real Time Analyzer (RTA) utilizing DC- and AC-voltammetric techniques is an in situ, online monitoring system that provides a complete chemical analysis of different electrochemical deposition solutions. The RTA employs multivariate calibration when predicting concentration parameters from a multivariate data set. Although the hierarchical and multiblock Principal Component Regression- (PCR-) and Partial Least Squares- (PLS-) based methods can handle data sets even when the number of variables significantly exceeds the number of samples, it can be advantageous to reduce the number of variables to obtain improvement of the model predictions and better interpretation. This presentation focuses on the introduction of a multistep, rigorous method of data-selection-based Least Squares Regression, Simple Modeling of Class Analogy modeling power, and, as a novel application in electroanalysis, Uninformative Variable Elimination by PLS and by PCR, Variable Importance in the Projection coupled with PLS, Interval PLS, Interval PCR, and Moving Window PLS. Selection criteria of the optimum decomposition technique for the specific data are also demonstrated. The chief goal of this paper is to introduce to the community of electroanalytical chemists numerous variable selection methods which are well established in spectroscopy and can be successfully applied to voltammetric data analysis.

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Li, Zhe, Yi-Shan Lee, Junghui Chen, and Yongwu Qian. "Developing variable moving window PLS models: Using case of NOx emission prediction of coal-fired power plants." Fuel 296 (July 2021): 120441. http://dx.doi.org/10.1016/j.fuel.2021.120441.

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Chen, Jiemei, Tian Ai, Tao Pan, Lijun Yao, and Fenggeng Xia. "AO–MW–PLS method applied to rapid quantification of teicoplanin with near-infrared spectroscopy." Journal of Innovative Optical Health Sciences 10, no. 01 (January 2017): 1650029. http://dx.doi.org/10.1142/s1793545816500292.

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Teicoplanin (TCP) is an important lipoglycopeptide antibiotic produced by fermenting Actinoplanes teichomyceticus. The change in TCP concentration is important to measure in the fermentation process. In this study, a reagent-free and rapid quantification method for TCP in the TCP–Tris–HCl mixture samples was developed using near-infrared (NIR) spectroscopy by focusing our attention on the fermentation process for TCP. The absorbance optimization (AO) partial least squares (PLS) was proposed and integrated with the moving window (MW) PLS, which is called AO–MW–PLS method, to select appropriate wavebands. A model set that includes various wavebands that were equivalent to the optimal AO–MW–PLS waveband was proposed based on statistical considerations. The public region of all equivalent wavebands was just one of the equivalent wavebands. The obtained public regions were 1540–1868[Formula: see text]nm for TCP and 1114–1310[Formula: see text]nm for Tris. The root-mean-square error and correlation coefficient for leave-one-out cross validation were 0.046[Formula: see text]mg mL[Formula: see text] and 0.9998[Formula: see text]mg mL[Formula: see text] for TCP, and 0.235[Formula: see text]mg mL[Formula: see text] and 0.9986[Formula: see text]mg mL[Formula: see text] for Tris, respectively. All the models achieved highly accurate prediction effects, and the selected wavebands provided valuable references for designing specialized spectrometers. This study provided a valuable reference for further application of the proposed methods to TCP fermentation broth and to other spectroscopic analysis fields.

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Liu, Yande, Yanrui Zhou, and Yuanyuan Pan. "Online quantitative analysis of soluble solids content in navel oranges using visible-near infrared spectroscopy and variable selection methods." Journal of Innovative Optical Health Sciences 07, no. 06 (October 21, 2014): 1350065. http://dx.doi.org/10.1142/s179354581350065x.

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Variable selection is applied widely for visible-near infrared (Vis-NIR) spectroscopy analysis of internal quality in fruits. Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content (SSC) in navel oranges. Moving window partial least squares (MW-PLS), Monte Carlo uninformative variables elimination (MC-UVE) and wavelet transform (WT) combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges. The performances of these methods were compared for modeling the Vis-NIR data sets of navel orange samples. Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation coefficient (r) of 0.89 and lower root mean square error of prediction (RMSEP) of 0.54 at 5 fruits per second. It concluded that Vis-NIR spectroscopy coupled with WT-MC-UVE may be a fast and effective tool for online quantitative analysis of SSC in navel oranges.

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Liu, Yang, Haikuan Feng, Jibo Yue, Zhenhai Li, Xiuliang Jin, Yiguang Fan, Zhihang Feng, and Guijun Yang. "Estimation of Aboveground Biomass of Potatoes Based on Characteristic Variables Extracted from UAV Hyperspectral Imagery." Remote Sensing 14, no. 20 (October 13, 2022): 5121. http://dx.doi.org/10.3390/rs14205121.

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Aboveground biomass (AGB) is an important indicator for crop-growth monitoring and yield prediction, and accurate monitoring of AGB is beneficial to agricultural fertilization management and optimization of planting patterns. Imaging spectrometer sensors mounted on unmanned aerial vehicle (UAV) remote-sensing platforms have become an important technical method for monitoring AGB because the method is convenient, rapidly collects data and provides image data with high spatial and spectral resolution. To confirm the feasibility of UAV hyperspectral remote-sensing technology to estimate AGB, this study acquired hyperspectral images and measured AGB data over the potato bud, tuber formation, tuber growth, and starch-storage periods. The canopy spectrum obtained in each growth period was smoothed by using the Savitzky–Golay filtering method, and the spectral-reflection feature parameters, spectral-location feature parameters, and vegetation indexes were extracted. First, a Pearson correlation analysis was performed between the three types of characteristic spectral parameters and AGB, and the spectral parameters that reached a significant level of 0.01 in each growth period were selected. Next, the spectral parameters reaching a significance of 0.01 were optimized and screened by moving window partial least squares (MWPLS), Monte Carlo uninformative variable elimination (MC-UVE), and random frog (RF) methods, and the final model parameters were determined according to the thresholds of the root mean square error of cross-validation (RMSEcv), the reliability index, and the selected probability. Finally, the three optimal characteristic spectral parameters and their combinations were used to estimate the potato AGB in each growth period by combining the partial least squares regression (PLSR) and Gaussian process regression (GPR) methods. The results show that, (i) ranked from high to low, vegetation indexes, spectral-location feature parameters, and spectral-reflection feature parameters in each growth period are correlated with the AGB, and these correlations all first improve and then degrade in going from the budding period to the starch-storage period. (ii) The AGB estimation model based on the characteristic variables screened by the three methods in each growth period is most accurate with RF, less so with MC-UVE, and least accurate with MWPLS. (iii) Estimating the AGB with the same variables combined with the PLSR method in each growth period is more accurate than the corresponding GPR method, but the estimations produced by the two methods both show a trend of first improving and then worsening from the budding period to the starch-accumulation period. The accuracy of the estimation models constructed by PLSR and GPR from high to low is based on comprehensive variables, vegetation indexes, spectral-location feature parameters and spectral-reflection feature parameters. (iv) When combined with the RF-PLSR method to estimate AGB in each growth period, the best R2 values are 0.65, 0.68, 0.72, and 0.67, the corresponding RMSE values are 167.76, 162.98, 160.77, and 169.24 kg/hm2, and the corresponding NRMSE values are 19.76%, 16.01%, 15.04%, and 16.84%. The results of this study show that a variety of characteristic spectral parameters may be extracted from UAV hyperspectral images, that the RF method may be used for optimizing and screening, and that PLSR regression provides accurate estimates of the potato AGB. The proposed approach thus provides a rapid, accurate, and nondestructive way to monitor the growth status of potatoes.

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Li, Zhongbing, Wei Pang, Haibo Liang, Guihui Chen, Xinyu Zheng, and Pengbo Ni. "Multicomponent Alkane IR Measurement System Based on Dynamic Adaptive Moving Window PLS." IEEE Transactions on Instrumentation and Measurement, 2022, 1. http://dx.doi.org/10.1109/tim.2022.3195257.

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Yao, Lijun, Xiaowen Shi, Tao Pan, and Jiemei Chen. "Wavelength Selection Method Based on Absorbance Value Optimization to Near-Infrared Spectroscopic Analysis." Frontiers in Physics 9 (May 27, 2021). http://dx.doi.org/10.3389/fphy.2021.663573.

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Regarding absorption spectrum, high absorption corresponds to low light transmittance and relatively loud noise, whereas low absorption corresponds to low information content, which interferes with the modeling of spectral analysis. Appropriate absorbance level is necessary to improve spectral information content and reduces noise level. In this study, based on the selection of the upper and lower bounds of absorbance, the absorbance value optimization partial least squares (AVO-PLS) method was proposed for appropriate wavelength model selection. Near-infrared spectroscopic analysis of hyperlipidemia indicators, namely, total cholesterol (TC), and triglyceride (TG), was conducted to validate the predicted performance of AVO-PLS. Well-performed wavelength selection methods, namely, moving-window PLS (MW-PLS) of continuous type-and successive projections algorithm (SPA) of discrete type, were also conducted for comparison. The spectra were first corrected using Savitzky–Golay smoothing. Modeling was performed based on the multiple partitioning of calibration and prediction sets to avoid data over-fitting and achieve parameter stability. The selected absorbance ranged from 0.45 to 0.86 for TC and from 0.45 to 0.92 for TG, and the corresponding waveband combinations were 1,376–1,388 and 1,560–1840 nm for TC and 1,376–1,390 and 1,552–1,846 nm for TG. Among them, the waveband combination of TG covers TC’s one, and can be used for the high-precision cooperativity analysis of the two indicators. Using the independent validation samples, the RMSEP and RP of 0.164 mmol l−1 and 0.990 for TC and 0.096 mmol l−1 and 0.997 for TG were obtained by the cooperativity model. And the sensitivity and specificity for hyperlipidemia were 98.0 and 100%, respectively. These values were better than those of MW-PLS and SPA. Importantly, the proposed AVO-PLS is a novel multi-band optimization approach for improving prediction performance and applicability. This method is expected to obtain more applications.

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Liang, Haibo, Fenglong Luo, Jinhong Guo, and Xing Zuo. "Study on measurement method of two-component fluid proportion infrared spectrum based on HDC-PSAM." Measurement Science and Technology, November 7, 2022. http://dx.doi.org/10.1088/1361-6501/aca0b2.

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Abstract When traditional infrared spectroscopy technology measures and analyzes the quantitative research of fluid, due to the complex band composition of infrared spectrum, weak absorption intensity and spectral signal, it is easy to be disturbed during measurement and increase the error, making the quantitative analysis of fluid inaccurate and difficult to achieve requirements for accurate analysis of fluids. In order to solve this problem, this paper uses the self-developed experimental apparatus, and takes water-based drilling fluid and diesel oil as the research objects, and proposes a new method for quantitative research of two-component proportional fluids based on HDC-PSAM. A large number of experiments are carried out on water-based drilling fluids, diesel and water mixtures, and a large number of spectral experimental data are preprocessed using moving window smoothing and normalization methods. Extracting effective features with Hybrid dilated convolution (HDC) and Pyramid self-attention modules (PSAM) , combined with Partial Least Squares Algorithm (PLS) to model fluid mixtures to achieve quantification of fluid mixtures calculate. The experimental results show that this method can reduce the influence of disturbance during measurement, has higher accuracy and stability, and effectively improves the measurement accuracy of infrared spectroscopy for quantitative analysis of fluids. At the same time, this method provides a strong technical support for the analysis of other fluids and the measurement of fluid mixture by using infrared spectroscopy technology.

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Journal articles on the topic 'Moving Window PLS (mwPLS)'

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