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Selection Method of Quasi-Continuous Wavelength Combination with Applications to the Near-Infrared Spectroscopic Analysis of Soil Organic Matter

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APPLIED SPECTROSCOPY 2014年第3期68卷 263-271页

作者： Pan, Tao Li, Minmiao Chen, Jiemei Jinan Univ Key Lab Optoelect Informat & Sensing Technol Guangdong Higher Educ Inst Guangzhou 510632 Guangdong Peoples R China Chinese Acad Sci Inst Soil Sci State Key Lab Soil & Sustainable Agr Nanjing 210008 Jiangsu Peoples R China

Equidistant combination multiple linear regression (EC-MLR) for the quasi-continuous wavelength selection of spectroscopic analysis was proposed and successfully applied to the reagent-free determination of soil organic matter with near-infrared spectroscopy. For comparison, the continuous-mode moving window partial least squares (MWPLS) and the discrete-mode successive projections algorithm (SPA) were improved by considering the stability and applied to the same analysis object as well. All methods exhibited good effect, but the modeling accuracy, stability, and validation effect of EC-MLR were better than that of the other two methods. Compared with MWPLS, the optimal EC-MLR model contained only 16 wavelengths, and method complexity was substantially reduced. Compared with SPA-MLR, the optimal EC-MLR model could easily undergo spectral preprocessing to improve predictive capability. Moreover, appropriate equidistant discrete wavelength combination with EC-MLR corresponded to the spectral absorption band with proper resolution and can effectively overcome co-linearity interruption for the MLR model. Thus, the EC-MLR method has great potential in practical application and instrument design.

关键词： Equidistant combination multiple linear regression Moving window partial least squares successive projections algorithm Near-infrared spectroscopy Soil organic matter

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Using color histograms and SPA-LDA to classify bacteria

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2014年第24期406卷 5989-5995页

作者： de Almeida, Valber Elias da Costa, Gean Bezerra de Sousa Fernandes, David Douglas Goncalves Dias Diniz, Paulo Henrique Brandao, Deysiane Dantas de Medeiros, Ana Claudia Veras, Germano Univ Estadual Paraiba Ctr Ciencia & Tecnol LQAQ BR-58429500 Campina Grande Paraiba Brazil Univ Estadual Paraiba Ctr Ciencias Biol & Saude Lab Desenvolvimento & Ensaios Medicamentos LABDEM BR-58429500 Campina Grande Paraiba Brazil

In this work, a new approach is proposed to verify the differentiating characteristics of five bacteria (Escherichia coli, Enterococcus faecalis, Streptococcus salivarius, Streptococcus oralis, and Staphylococcus aureus) by using digital images obtained with a simple webcam and variable selection by the successive projections algorithm associated with Linear Discriminant Analysis (SPA-LDA). In this sense, color histograms in the red-green-blue (RGB), hue-saturation-value (HSV), and grayscale channels and their combinations were used as input data, and statistically evaluated by using different multivariate classifiers (Soft Independent Modeling by Class Analogy (SIMCA), Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Partial Least Squares Discriminant Analysis (PLS-DA) and successive projections algorithm-Linear Discriminant Analysis (SPA-LDA)). The bacteria strains were cultivated in a nutritive blood agar base layer for 24 h by following the Brazilian Pharmacopoeia, maintaining the status of cell growth and the nature of nutrient solutions under the same conditions. The best result in classification was obtained by using RGB and SPA-LDA, which reached 94 and 100 % of classification accuracy in the training and test sets, respectively. This result is extremely positive from the viewpoint of routine clinical analyses, because it avoids bacterial identification based on phenotypic identification of the causative organism using Gram staining, culture, and biochemical proofs. Therefore, the proposed method presents inherent advantages, promoting a simpler, faster, and low-cost alternative for bacterial identification.

关键词： Bacteria Digital images Classification Linear discriminant analysis successive projections algorithm

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Potential of hyperspectral imaging for non-invasive determination of mechanical properties of prawn (Metapenaeus ensis)

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JOURNAL OF FOOD ENGINEERING 2014年 136卷 64-72页

作者： Dai, Qiong Cheng, Jun-Hu Sun, Da-Wen Zeng, Xin-An S China Univ Technol Coll Light Ind & Food Sci Guangzhou 510641 Guangdong Peoples R China Natl Univ Ireland Univ Coll Dublin Agr & Food Sci Ctr Dublin 4 Ireland

The potential of using hyperspectral imaging in the wavelength region of visible and short-wave near infrared range (400-1000 nm) was investigated for the rapid and non-invasive determination of mechanical properties in different parts of prawns (Metapenaeus ensis). Hyperspectral cubes were acquired at the second and fourth segments of prawn and their mean spectral data were extracted. The quantitative relationship between the spectral data and their corresponding reference mechanical properties (measured by Instron universal testing machine) were established by partial least square regression (PLSR) and least-squares support vector machines (LS-SVM) algorithms, respectively. successive projections algorithm (SPA) was carried out to select the most important and effective wavelengths, which was an optimization process for improving the performance of established models. Then the optimized models were built using PLSR and LS-SVM based on the selected wavelengths and their performances were compared to find the best model for predicting mechanical properties of prawn. As a result, the SPA-LS-SVM model was considered as the best predictive model to determine the hardness, gumminess and chewiness values of prawn with correlation coefficient of prediction (R-p) of 0.8489, 0.8096, and 0.8596 and root mean square error of prediction (RMSEP) of 0.1465, 0.1445, and 0.1258, respectively. Visualization of distribution maps for the mechanical properties within the region of interests (ROI) of prawns was also presented. The overall results revealed that hyperspectral imaging technique had a great ability for predicting the mechanical properties of prawn rapidly and non-invasively. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Prawn Mechanical properties Hyperspectral imaging successive projections algorithm Least-squares support vector machines

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successive projections algorithm combined with uninformative variable elimination for spectral variable selection

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2008年第2期91卷 194-199页

作者： Ye, Shengfeng Wang, Dong Min, Shungeng China Agr Univ Coll Sci Beijing 100094 Peoples R China

The UVE - SPA method, successive projections algorithm (SPA) combined with uninformative variable elimination (UVE) is proposed as a novel variable selection approach for multivariate calibration. UVE is used to select informative variables, and SPA is followed to select variables with minimum redundant information from the informative variables. The proposed method was applied to near-infrared (NIR) reflectance data for analysis of nicotine in tobacco lamina and NIR transmission data for active pharmaceutical ingredient (API) in single tablet. On the aspect of elimination of uninformative variables, the effect of UVE using first derivative spectra was better than that of using raw spectra. In terms of variable selection, fewer variables with better performance were selected by UVE - SPA method than by direct SPA method. For NIR spectral analysis of nicotine in tobacco lamina, the number of variables selected from 3001 spectral variables reduced from 48 by direct SPA to 35 by UVE - SPA, and the root mean squared error of prediction set (RMSEP) of the corresponding MLR models decreased from 0.174 (%, mg/mg) to 0.160. For NIR spectral analysis of API in each tablet, the number of variables selected from 650 spectral variables reduced from 46 by direct SPA to 17 by UVE - SPA, and RMSEP of the corresponding multiple linear regression (MLR) models decreased from 0.842 (%, mg/mg) to 0.473. MLR model using variables selected by UVE - SPA had better prediction performance than full-spectrum partial least-squares (PLS) model, and comparable to PLS model of UVE. (c) 2007 Elsevier B.V. All rights reserved.

关键词： successive projections algorithm uninformative variable elimination variable selection near-infrared spectroscopy multivariate calibration

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An Efficient Procedure for Identifying the Prediction Model Between Residual Stress and Barkhausen Noise

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JOURNAL OF NONDESTRUCTIVE EVALUATION 2013年第4期32卷 341-349页

作者： Sorsa, Aki Leiviska, Kauko Santa-aho, Suvi Vippola, Minnamari Lepisto, Toivo Univ Oulu Control Engn Lab Oulu 90014 Finland Tampere Univ Technol Dept Mat Sci FIN-33101 Tampere Finland

Residual stress of case-hardened steel samples is predicted in this paper with the linear multivariable regression model. The development of the prediction model is based on the huge set of features calculated from the Barkhausen noise measurement signal among which the most suitable ones are chosen. The selection uses a genetic algorithm with leave-multiple-out cross-validation in the objective function. The original feature set contains collinear features that make the selection task even more complex. Thus a feature elimination procedure based on the successive projections algorithm is studied in this paper. Also the standard genetic algorithm is slightly modified to better serve the feature selection task. The obtained results are good showing that the proposed procedures suit well for residual stress predictions. Also the applied feature elimination procedure is applicable and can be safely used to reduce the dimensionality of the selection problem.

关键词： Quantitative modelling Variable selection Genetic algorithm successive projections algorithm

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Discrimination of edible olive oils by means of synchronous fluorescence spectroscopy with multivariate data analysis

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GRASAS Y ACEITES 2013年第4期64卷 425-431页

作者： Dankowska, A. Malecka, M. Kowalewski, W. Poznan Univ Econ Fac Commod Sci Poznan Poland Adam Mickiewicz Univ Fac Math & Comp Sci Poznan Poland

The potential of fluorescence spectroscopy for the classification of olive oils was investigated. Synchronous fluorescence spectra were collected in the region of 240-700 nm with the wavelength intervals of 10, 30, 60 and 80 nm. successive projection algorithm (SPA) was applied for the determination of representative wavelengths while the linear discriminant analysis (LDA) method was used to classify olive oils. The classification error of the method was low (0,9-6,4%) for measurements collected at all wavelength intervals. The best classification accuracy was obtained for synchronous fluorescence intensities acquired at 10 selected wavelengths with the wavelength interval equal to 10 nm.

关键词： Extra virgin olive oil Pomace olive oil Refined olive oil successive projections algorithm Synchronous fluorescence spectroscopy

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A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2008年第1期92卷 83-91页

作者： Galvao, Roberto Kawakami Harrop Ugulino Araujo, Mario Cesar Fragoso, Wallace Duarte Silva, Edvan Cirino Jose, Gledson Emidio Carreiro Soares, Sofacles Figueredo Paiva, Henrique Mohallem 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 EMBRAER BR-12227901 Sao Jose Dos Campos SP Brazil

The successive projections algorithm (SPA) is a variable selection technique designed to minimize collinearity problems in multiple linear regression (MLR). This paper proposes a modification to the basic SPA formulation aimed at further improving the parsimony of the resulting MLR model. For this purpose, an elimination procedure is incorporated to the algorithm in order to remove variables that do not effectively contribute towards the prediction ability of the model as indicated by an F-test. The utility of the proposed modification is illustrated in a simulation study, as well as in two application examples involving the analysis of diesel and com samples by near-infrared (NIR) spectroscopy. The results demonstrate that the number of variables selected by SPA can be reduced without significantly compromising prediction performance. In addition, SPA is favourably compared with classic Stepwise Regression and full-spectrum PLS. A graphical user interface for SPA is available at ***/similar to kawakami/spa/. (C) 2008 Elsevier B.V. All rights reserved.

关键词： multiple linear regression variable selection successive projections algorithm near-infrared spectrometry diesel analysis com analysis

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Feasibility study of near infrared spectroscopy with variable selection for non-destructive determination of quality parameters in shell-intact cottonseed

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INDUSTRIAL CROPS AND PRODUCTS 2013年第1期43卷 654-660页

作者： Huang, Zhuangrong Sha, Sha Rong, Zhengqin Chen, Jinhong He, Qiuling Khan, Daud M. Zhu, Shuijin Zhejiang Univ Coll Agr & Biotechnol Dept Agron Hangzhou 310029 Zhejiang Peoples R China Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310029 Zhejiang Peoples R China Kohat Univ Sci & Technol Dept Biotechnol & Genet Engn Kohat 26000 Pakistan

The potential of near infrared (NIR) spectroscopy for non-destructive determination of quality parameters including oil and protein contents in shell-intact cottonseed was investigated. Linear partial least squares (PLS) and nonlinear least-squares support vector machine (LS-SVM) methods were used to develop the calibration models to determinate the protein and oil contents. Moreover, as variable selection techniques, the Monte Carlo uninformative variable elimination (MC-UVE) and the successive projections algorithm (SPA) were applied to improve the predictive ability of the model. Finally, the MC-UVE-LS-SVM models show the best prediction performance. The coefficient of determination (R-2), residual predictive deviation (RPD) and root mean squares error of prediction (RMSEP) were 0.959, 4.871 and 0.977 for protein, and 0.950, 4.429 and 0.834 for oil, respectively. The results indicate that NIR technology could be very useful for the rapid quality analysis of shell-intact cottonseed avoiding the need of grinding. Furthermore, the variable selection of MC-UVE can provide more robust and accurate calibration models than SPA. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Cottonseed Non-destructive Near infrared spectroscopy Least-squares support vector machine Monte Carlo uninformative variable elimination successive projections algorithm

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Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging

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FOOD CHEMISTRY 2013年第1期141卷 389-396页

作者： Kamruzzaman, Mohammed ElMasry, Gamal Sun, Da-Wen Allen, Paul Natl Univ Ireland Univ Coll Dublin Agr & Food Sci Ctr Sch Biosyst Engn Dublin 4 Ireland TEAGASC AFRC Dublin 15 Ireland

The purpose of this study was to develop and test a hyperspectral imaging system (900-1700 nm) to predict instrumental and sensory tenderness of lamb meat. Warner-Bratzler shear force (WBSF) values and sensory scores by trained panellists were collected as the indicator of instrumental and sensory tenderness, respectively. Partial least squares regression models were developed for predicting instrumental and sensory tenderness with reasonable accuracy (R-cv = 0.84 for WBSF and 0.69 for sensory tenderness). Overall, the results confirmed that the spectral data could become an interesting screening tool to quickly categorise lamb steaks in good (i.e. tender) and bad (i.e. tough) based on WBSF values and sensory scores with overall accuracy of about 94.51% and 91%, respectively. successive projections algorithm (SPA) was used to select the most important wavelengths for WBSF prediction. Additionally, textural features from Gray Level Co-occurrence Matrix (GLCM) were extracted to determine the correlation between textural features and WBSF values. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Sensory analysis Warner-Bratzler Shear force Near-infrared hyperspectral imaging successive projections algorithm

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Classification of blue pen ink using infrared spectroscopy and linear discriminant analysis

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MICROCHEMICAL JOURNAL 2013年 109卷 122-127页

作者： Silva, Carolina Santos Lins Borba, Flavia de Souza Pimentel, Maria Fernanda Coelho Pontes, Marcio Jose Honorato, Ricardo Saldanha Pasquini, Celio Univ Fed Pernambuco Dept Quim Fundamental BR-50740521 Recife PE Brazil Univ Fed Pernambuco Dept Engn Quim BR-50740521 Recife PE Brazil Univ Fed Paraiba Dept Quim BR-58059900 Joao Pessoa Paraiba Brazil Univ Estadual Campinas Inst Quim Campinas SP Brazil

Attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy associated to linear discriminant analysis (LDA) was employed to perform classification of blue pen ink according to types and brands, in a nondestructive way. To build a representative data set, blue pens of 3 types, namely ballpoint (5 brands), roller ball (2 brands) and gel (3 brands) were purchased from local dealers. Ten different pens, representing the best seller of each brand, were purchased, making a total of 100 pens. Circular areas were painted five times with each pen and spectra were taken in 2 different locations, using a Universal Attenuated Total Reflectance accessory (UATR), within the range of 4000 to 650 cm(-1). Three types of paper were employed: two brands of A4 sulfite paper (paper 1 and paper 2) and one recycled paper (paper 3). The genetic algorithm (GA), stepwise formulation (SW) and successive projections algorithm (SPA) were employed to select spectral variables employed in LDA. LDA models were built using the blue pen ink spectra obtained from paper 1. Three test sets were employed using the blue pen ink spectra obtained from papers 1, 2 and 3, in order to evaluate the influence of the paper on the predictions. The LDA models used to classify the pens according to their type (gel, rollerball and ballpoint) achieved a correct classification rate of 100% in the test set composed of blue pen ink spectra obtained from paper 1, using GA and SPA. Using SW, the rate achieved was 99.5%. For paper 2, SPA, GA and SW provided 100%, 97.3% and 93.8% of correct classification, respectively. For paper 3, SPA, GA and SW achieved a correct prediction rate of 100%, 100% and 94.9%, respectively. LDA models for classifications of pens according to their brand were 100% correct in their classification when the test set was composed of blue pen ink spectra obtained from papers 1 and 2. For the test set composed of blue pen ink spectra obtained from paper 3, LDA-SPA, LDA-GA and LD

关键词： Infrared reflectance spectroscopy Blue ink pen identification Linear discriminant analysis successive projections algorithm Genetic algorithm Stepwise selection

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