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Unfolded partial least squares/residual bilinearization combined with the successive projections algorithm for interval selection: enhanced excitation-emission fluorescence data modeling in the presence of the inner filter effect

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2015年第19期407卷 5649-5659页

作者： Gomes, Adriano de Araujo Schenone, Agustina V. Goicoechea, Hector C. de Araujo, Mario Cesar U. Univ Fed Paraiba CCEN Dept Quim Lab Automacao & Instrumentacao Quim Analit & Qui BR-58051970 Joao Pessoa Paraiba Brazil Univ Nacl Litoral Lab Desarrollo Analit & Quimiometria LADAQ Fac Bioquim & Ciencias Biol Catedra Quim Analit 1 Santa Fe Argentina

The use of the successive projections algorithm (SPA) for elimination of uninformative variables in interval selection, and unfold partial least squares regression (U-PLS) modeling of excitation-emission matrices (EEM), when under the inner filter effect (IFE) is reported for first time. Post-calibration residual bilinearization (RBL) was employed against events of unknown components in the test samples. The inner filter effect can originate changes in both the shape and intensity of analyte spectra, leading to trilinearity losses in both modes, and thus invalidating most multiway calibration methods. The algorithm presented in this paper was named iSPA-U-PLS/RBL. Both simulated and experimental data sets were used to compare the prediction capability during: (1) simulated EEM;and (2) quantitation of phenylephrine (PHE) in the presence of paracetamol (PAR) (or acetaminophen) in water samples. Test sets containing unexpected components were built in both systems [a single interference was taken into account in the simulated data set, while water samples were added with varying amounts of ibuprofen (IBU), and acetyl salicylic acid (ASA)]. The prediction results and figures of merit obtained with the new algorithm were compared with those obtained with U-PLS/RBL (without intervals selection), and with the well-known parallel factors analysis (PARAFAC). In all cases, U-PLS/RBL displayed better EEM handling capability in the presence of the inner filter effect compared with PARAFAC. In addition, iSPA-U-PLS/RBL improved the results obtained with the full U-PLS/RBL model, in this case demonstrating the potential of variable selection.

关键词： Interval selection successive projections algorithm Unfolded-partial least squares Second order calibration Inner filter effect

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A consensus successive projections algorithm - multiple linear regression method for analyzing near infrared spectra

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ANALYTICA CHIMICA ACTA 2015年第1期858卷 16-23页

作者： Liu, Ke Chen, Xiaojing Li, Limin Chen, Huiling Ruan, Xiukai Liu, Wenbin Wenzhou Univ Coll Phys & Elect Engn Informat Wenzhou Zhejiang Peoples R China

The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named "consensus SPA-MLR" (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Consensus model Variable selection successive projections algorithm Near infrared spectra Multiple linear regression

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Binary classification of chalcone derivatives with LDA or KNN based on their antileishmanial activity and molecular descriptors selected using the successive projections algorithm feature-selection technique

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EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES 2014年第1期51卷 189-195页

作者： Goodarzi, Mohammad Saeys, Wouter Ugulino de Araujo, Mario Cesar Harrop Galvao, Roberto Kawakami Heyden, Yvan Vander Katholieke Univ Leuven KULeuven Fac Biosci Engn Dept Biosyst B-3001 Heverlee Belgium Univ Fed Paraiba CCEN Dept Quim BR-58051970 Joao Pessoa PB Brazil Inst Tecnol Aeronaut Div Engn Eletron BR-12228900 Sao Jose Dos Campos SP Brazil Vrije Univ Brussel Ctr Pharmaceut Res CePhaR B-1090 Brussels Belgium

Chalcones are naturally occurring aromatic ketones, which consist of an alpha-, beta-unsaturated carbonyl system joining two aryl rings. These compounds are reported to exhibit several pharmacological activities, including antiparasitic, antibacterial, antifungal, anticancer, immunomodulatory, nitric oxide inhibition and anti-inflammatory effects. In the present work, a Quantitative Structure-Activity Relationship (QSAR) study is carried out to classify chalcone derivatives with respect to their antileishmanial activity (active/inactive) on the basis of molecular descriptors. For this purpose, two techniques to select descriptors are employed, the successive projections algorithm (SPA) and the Genetic algorithm (GA). The selected descriptors are initially employed to build Linear Discriminant Analysis (LDA) models. An additional investigation is then carried out to determine whether the results can be improved by using a non-parametric classification technique (One Nearest Neighbour, INN). In a case study involving 100 chalcone derivatives, the INN models were found to provide better rates of correct classification than LDA, both in the training and test sets. The best result was achieved by a SPA-INN model with six molecular descriptors, which provided correct classification rates of 97% and 84% for the training and test sets, respectively. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Antileishmanial activity successive projections algorithm Linear Discriminant Analysis Genetic algorithm One Nearest Neighbour

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Rapid non-destructive identification of blueberry origin based on near infrared spectroscopy combined with wavelength selection

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INFRARED PHYSICS & TECHNOLOGY 2025年 145卷

作者： Wang, Guannan Wang, Na Dong, Ying Liu, Jinming Gao, Peng Hou, Rui Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn Daqing 163319 Heilongjiang Peoples R China Guangdong Prov Key Lab Intelligent Port Secur Insp Guangzhou 510700 Peoples R China Huangpu Customs Technol Ctr Sanyuan Rd 66 Dongguan 523000 Peoples R China

To realize the nondestructive identification of blueberry origin, near-infrared spectroscopy was used to obtain the original spectral data of blueberry. Given the problems of spectral bandwidth, severe overlap, and complicated information analysis in the collection of near-infrared spectral data, we integrated successive projection algorithm (SPA) and sparrow search algorithm (SSA) with partial least squares regression (PLS) and support vector machine (SVM), respectively, resulting in the construction of two wavelength selection (WS) models: SPAPLS and SSA-SVM for WS from blueberry spectral data, 30 and 148 wavelength variables were selected respectively. To further enhance the accuracy of blueberry origin identification, we incorporated SSA into both Optimal Latin hypercube idea and Osprey algorithm, creating a multi-strategy hybrid sparrow search algorithm (ZOSSA). This approach reduced the number of selected wavelengths from 148 to 36. Using wavelengths selected from three different techniques as input subsets, a blueberry origin recognition model is constructed by placing them separately into a support vector machine. The experimental results prove that the performance of the wavelength-optimized model is higher than that of the full spectra performance, and the wavelength variables screened by ZOSSA have the best effect. The wavelength variables identified by ZOSSA exhibit superior performance with an accuracy rate of 96.21%, precision rate of 95.12 %, recall rate of 94.78 %, and F1 score of 94.94 % on the test set;surpassing those obtained using SPA (89.39 %, 87.43 %, 88.72 %, and 88.08 %) as well as SSA (90.15 %, 87.90 %, 88.16 %, and 88.02 %). The method strikes a balance between selecting an appropriate number of wavelengths while maintaining high model performance levels;thus meeting requirements for fast, accurate, nondestructive origin identification not only for blueberries but also providing novel insights for identifying origins in other agricultural

关键词： Near-infrared spectroscopy Blueberry Sparrow search algorithm Wavelength selection Origin identification successive projections algorithm

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The successive projections algorithm for interval selection in PLS

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MICROCHEMICAL JOURNAL 2013年 110卷 202-208页

作者： Gomes, Adriano de Araujo Harrop Galvao, Roberto Kawakami Ugulino de Araujo, Mario Cesar Veras, Germano da Silva, Edvan Cirino Univ Fed Paraiba CCEN Dept Quim BR-58051970 Joao Pessoa Paraiba Brazil Inst Tecnol Aeronaut Div Engn Eletron BR-12228900 Sao Jose Dos Campos SP Brazil Univ Estadual Paraiba CCT Dept Quim BR-58429500 Campina Grande PB Brazil

The successive projections algorithm (SPA) is aimed at selecting a subset of variables with small multicollinearity and suitable prediction power for use in Multiple Linear Regression (MLR). The resulting SPA-MLR models have advantages in terms of simplicity and ease of interpretation as compared to latent-variable models, such as Partial-Least-Squares (PIS). However, PLS tends to be less sensitive to instrumental noise. The present paper proposes an extension of SPA to combine the noise-reduction properties of PLS with the possibility of discarding non-informative variables in SPA. For this purpose, SPA is modified in order to select intervals of variables for use in PLS. The proposed iSPA-PLS algorithm is evaluated in two case studies involving near-infrared spectrometric analysis of wheat and beer extract samples. As compared to full-spectrum PLS, the resulting iSPA-PLS models exhibited better performance in terms of both cross-validation and external prediction. On the other hand, iSPA-PLS and SPA-MLR presented similar cross-validation performance, but the iSPA-PLS models clearly outperformed SPA-MLR in the external prediction. Such results indicate that iSPA-PLS may be more robust with respect to differences between the external prediction set and the calibration set used in the cross-validation procedure. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Variable selection iPLS successive projections algorithm Partial Least Squares NIR spectrometry

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Primary assessment of macronutrients in durian (CV Monthong) leaves using near infrared spectroscopy with wavelength selection

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 304卷 123398页

作者： Phanomsophon, Thitima Jaisue, Natthapon Worphet, Akarawhat Tawinteung, Nukoon Khurnpoon, Lampan Lapcharoensuk, Ravipat Krusong, Warawut Pornchaloempong, Pimpen Sirisomboon, Panmanas Inagaki, Tetsuya Ma, Te Tsuchikawa, Satoru King Mongkuts Inst Technol Ladkrabang Sch Engn Dept Agr Engn Bangkok Thailand King Mongkuts Inst Technol Ladkrabang Sch Agr Technol Dept Plant Prod Technol Bangkok Thailand King Mongkuts Inst Technol Ladkrabang Sch Food Ind Div Fermentat Technol Bangkok Thailand King Mongkuts Inst Technol Ladkrabang Sch Engn Dept Food Engn Bangkok Thailand Nagoya Univ Grad Sch Bioagr Sci Nagoya Japan

Farmers would be able to regulate fertilization and produce quality durian if they knew the nutrient concentration in durian leaves. A long period of time for traditional nutritional content determination is needed. Therefore, near-infrared spectroscopy is a good method for nondestructive and quick nutrient content evaluation. The leaf sample matrices (fresh leaves, dried ground leaves, and dried ground leaf pellets) were scanned by Fourier transform near-infrared (FT-NIR) with a wavelength of 12,500-3,600 cm- 1. Regression models were developed using partial least squares (PLS) with full wavelength, short wavelength, and selected wavelength by successive projections algorithm (SPA). In this study, the model for N and K concentration was acceptable and the prediction was considered good but for P content not had succeeded. As a result, the PLS-SPA model using fresh leaf samples for evaluating N content in durian leaves exhibited performance of r2 = 0.852, SEP = 0.14%, RPD = 2.63 and bias = -0.020%. The PLS-SPA model using dried ground leaf samples for evaluating K content in durian leaves exhibited performance of r2 = 0.820, SEP = 0.13%, RPD = 2.36 and bias = 0.006%. This research found that it is possible to apply NIR waves to predict N and K concentrations in durian leaves. It is not necessary to predict directly from the wavelengths associated with -N or -K bonds. Instead, NIR can measure them indirectly from the bonding of proteins, which are products formed by N and K. In addition, selecting the wavelength that is related to the value to be measured can produce results that are not significantly different from using full or short wavelengths. These models can assist farmers in rapidly predicting N and K content in durian leaves for immediate fertilizer adjustment.

关键词： Near infrared spectroscopy Primary macronutrients Durian leaf successive projections algorithm

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Dapsone determination in tablets to leprosy treatment using a portable NIR spectrometer

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JOURNAL OF MOLECULAR STRUCTURE 2024年 1299卷

作者： Santos, Jocimar da Silva Diniz, Paulo Henrique Gonsalves Dias Sobrinho, Jose Lamartine Soares Soares, Monica Felts de La Roca Univ Fed Pernambuco Dept Pharm Nucleo Controle Qualidade Medicamentos & Correlato Ave Prof Arthur de Sa BR-50740521 Recife PE Brazil Univ Fed Oeste Bahia UFOB Programa Posgrad Quim Pura & Aplicada POSQUIPA BR-47808021 Barreiras BA Brazil

Leprosy is a neglected disease with limited technological advancements in the development of new formulations and analytical methods. The pharmaceutical industry offers several analytical alternatives for drug quality control;however, they are expensive, environmentally harmful, and labor-intensive. To address this, the United States Pharmacopoeia recommends the use of high-performance liquid chromatography (HPLC). In such a context, there arises a need for eco-friendly analytical approaches, such as near-infrared (NIR) spectroscopy, which offers the advantages of being non-destructive and eliminating the need for chemicals and solvents. Thus, in this study, we initially developed and validated an HPLC method for the quantification of dapsone in tablets. The method demonstrated selectivity, linearity, precision, accuracy, and very low limits of detection and quantification. Subsequently, the dapsone contents determined by HPLC in manipulated and industrial tablets were used as reference values to construct chemometric models based on NIR spectra acquired using a portable device. Partial Least Squares (PLS) and the successive projections algorithm for interval selection coupled with PLS (iSPA-PLS) were then employed. Only the models constructed with linear baseline-corrected (LBC) spectra yielded significant results, with a ratio of performance to deviation (RPD) higher than 3. LBC/iSPA-PLS was the best model, selecting 3 out of 5 divided intervals, incorporated analytical information related to the aromatic rings and amino groups present in the dapsone molecule. This model achieved a relative prediction error of only 2.78 and 2.54 % for both the manufactured and industrialized tablets, respectively, and a limit of detection of 5.38 mg per 100 g of tablet. Therefore, the proposed methodology can be hereafter applied to the quality control of dapsone tablets, even in the presence of non-modeled excipients.

关键词： Vibrational spectroscopy Chemometrics Multivariate calibration successive projections algorithm Quality control Hansen's disease

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Neural Networks Calibration Model Optimizing of Wheat Protein Based on successive projections algorithm

Neural Networks Calibration Model Optimizing of Wheat Protei...

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3rd International Conference on Frontiers of Manufacturing Science and Measuring Technology (ICFMM 2013)

作者： Mao, Xiaodong Sun, Laijun Hao, Gang Xu, Lulu Hui, Guangyan Heilongjiang Univ Coll Heilongjiang Prov Key Lab Elect Engn Harbin Peoples R China

ISBN: (纸本)9783037858462

In order to reduce computational complexity of modeling and improve the model's robustness and prediction accuracy, successive projections algorithm is used in the Neural Networks calibration modeling of wheat protein. Firstly, the spectral data is pretreated with first-order differential method and SNV method, and then a representative set of calibration samples are selected by SPXY algorithm. Secondly, making use of successive projections algorithm(SPA) to extract sensitive wave points of the original spectrum and the pretreated spectrum, and then the neural networks calibration model of wheat protein is established. The results show that the calibration model based on successive projections algorithm has a fast convergence speed and high accuracy, both of which are better than the calibration model established with the original data. Root mean square error of prediction(RMSEP) and prediction correlation coefficient(r) are 1.3332 and 0.94319 respectively, which can basically complete the grain reserves and the food processing profession division and the breeding preliminary generation.

关键词： wheat protein near infrared spectroscopy successive projections algorithm neural networks

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SSA optimized back propagation neural network model for dam displacement monitoring based on long-term temperature data

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EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2023年第4期27卷 1617-1643页

作者： Yu, Xin Li, Junjie Kang, Fei Dalian Univ Technol Fac Infrastruct Engn Sch Hydraul Engn 2 Linggong Rd Dalian 116024 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn P Nanjing Peoples R China

Featured with the harmonic sinusoidal function to reflect temperature effects, the hydrostatic-season-time (HST) model is often used to monitor the concrete gravity dam health, but it does not take account of the effects of environment temperatures in real-term and has flaws especially when applied in conditions of significant temperature variations. A model of Sparrow Search algorithm optimized error Back Propagation neural network (SSA-BP) based on the hydrostatic-temperature-time (HTT) model is proposed in this paper for predicting the concrete gravity dam displacement using the long-term environment temperature variable sets to reflect temperature effects. successive projections algorithm (SPA) is used for the first time for feature selection on long-term temperature variables to further optimize the model (as SPA-SSA-BP). Through a case study with the practical observed data from a reality high concrete gravity dam, the effectiveness of the new model is verified, suggesting that HTT-based SSA-BP models have better performance than HST with the best result obtained when using the 2-year long variable sets. The SSA-BP model has much lower error in predicting the concrete dam displacement than Multiple Linear Regression (MLR). The arithmetic speed and prediction accuracy of the SPA-SSA-BP model is optimized as it can minimize the collinearity among feature variables in the long-term HTT variable sets, bring down the input variable dimension close to the level of HST, and diminish the redundant data information.

关键词： Dam behavior modeling structural health monitoring sparrow search algorithm temperature simulation back propagation neural network successive projections algorithm

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Spatial analysis of hyperspectral images for detecting adulteration levels in bon-sorkh (Allium jesdianum L.) seeds: Application of voting classifiers

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SMART AGRICULTURAL TECHNOLOGY 2025年 10卷

作者： Fathi, Golshid Mireei, Seyed Ahmad Jafari, Mehrnoosh Sadeghi, Morteza Karimmojeni, Hassan Nazeri, Majid Isfahan Univ Technol Coll Agr Dept Biosyst Engn Esfahan *** Iran Isfahan Univ Technol Coll Agr Dept Agron & Plant Breeding Esfahan *** Iran Univ Kashan Fac Phys Dept Laser & Photon Kashan *** Iran

Bon-sorkh (Allium jesdianum L.) is a flowering plant with various reported beneficial properties. The rising price of bon-sorkh seeds has led some opportunists to distribute counterfeit seeds. This study assessed the feasibility of hyperspectral imaging to detect different adulteration levels (10%, 20%, 30%, 40%, and 50%) in bon-sorkh seeds adulterated with shallot seeds, the most similar to bon-sorkh based on spectral features. Hyperspectral images of pure and adulterated seed samples were acquired in the spectral range of 400-1000 nm. After image preprocessing using median blur and bilateral filters, pixel-wise classification models were developed using artificial neural networks, random forest, and voting classifiers to detect pure bon-sorkh and shallot seeds. A hard voting strategy was employed in the voting classifier, combining gradient boosting and histogram gradient boosting models. Additionally, competitive adaptive reweighted sampling (CARS), the successive projections algorithm, and extreme gradient boosting were used for feature selection to identify efficient wavelengths. Despite the promising performance of artificial neural networks in distinguishing pure bon-sorkh and shallot seeds, the voting classifiers demonstrated better generalization in classifying pure test images and were therefore applied to detect adulteration levels and identify the spatial distribution of bon-sorkh and shallot seeds. The results indicated the superiority of the voting model based on the median blur filter, which distinguished various adulteration levels with a total root mean squares error (RMSE) of 3.94%. Among the different feature selection methods, CARS-selected features performed best, with the corresponding voting model satisfactorily detecting adulteration levels, resulting in a RMSE of 6.69%. This study highlights the potential of hyperspectral imaging combined with voting classifiers to detect varying levels of shallot adulteration in bon-sorkh seeds.

关键词： Shallot adulterant Random forest Histogram gradient boosting Competitive adaptive reweighted sampling successive projections algorithm

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