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Predicting intramuscular fat content variations in boiled pork muscles by hyperspectral imaging using a novel spectral pre-processing technique

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LWT-FOOD SCIENCE AND TECHNOLOGY 2018年 94卷 119-128页

作者： Ma, Ji Pu, Hongbin Sun, Da-Wen South China Univ Technol Sch Food Sci & Engn Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Acad Contemporary Food Engn Guangzhou Higher Educ Mega Ctr Guangzhou 510006 Guangdong Peoples R China Guangzhou Higher Educ Mega Ctr Engn & Technol Res Ctr Guangdong Prov Intelligent Guangzhou 510006 Guangdong Peoples R China Univ Coll Dublin Natl Univ Ireland Agr & Food Sci Ctr Food Refrigerat & Computerized Food Technol Dublin 4 Ireland

For better predicting intramuscular fat contents in pork muscles using hyperspectral imaging, a novel correlation optimised warping (COW) technique was employed with the first derivative on the full spectra and the feature wavelengths selected by successive projections algorithm. Images of 104 pork longissimus dorsi samples cooked in boiling water for eight different periods were taken using a Vis-NIR (400-1000 nm) imaging system. Duplex method was used to divide the images into training and predicting sets. Reference measured intramuscular fat contents of each sample were correlated with the spectra extracted from ROI within the corresponding samples. Support vector regression models were developed and results proved positive effects by COW combined with first derivative transforms as spectral pre-processing techniques. The simplified model developed based on eight important wavelengths (403, 435, 438, 556, 586, 596, 739 and 951 nm) predicted accurately the intramuscular fat contents with R-P(2) of 0.9635 and RMSEP of 0.885 g/kg. Some other algorithms were used and listed as control algorithms to enhance data analysis, including Savitzky Golay (SG)-smoothing, standard normal variate (SNV), multiplicative scatter correction (MSC) and partial least squares regression (PLSR).

关键词： Intramuscular fat content Correlation optimised warping First derivative successive projections algorithm Support vector regression

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Mapping the Pungency of Green Pepper Using Hyperspectral Imaging

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FOOD ANALYTICAL METHODS 2018年第11期11卷 3042-3052页

作者： Rahman, Anisur Lee, Hoonsoo Kim, Moon S. Cho, Byoung-Kwan Chungnam Natl Univ Dept Biosyst Machinery Engn Coll Agr & Life Sci Daejeon 34134 South Korea Bangladesh Agr Univ Dept Farm Power & Machinery Mymensingh 2202 Bangladesh ARS Environm Microbial & Food Safety Lab USDA BARC East Powder Mill Rd Bldg 303 Beltsville MD 20705 USA

The pungency level of green peppers is dependent on the amounts of capsaicin and dihydrocapsaicin they contain. This study was conducted to develop a non-destructive method for the prediction and mapping of the capsaicin and dihydrocapsaicin contents in green pepper. Hyperspectral images of 200 total green peppers of three varieties were acquired in the wavelength range of 1000-1600nm, from which the mean spectra of each pepper variety were extracted. The reference capsaicin and dihydrocapsaicin contents of the samples were measured by high-performance liquid chromatography. Quantitative calibration models were built using partial least squares (PLS) regression with different spectral preprocessing techniques;the best performance was found by normalizing the preprocessed spectra with correlation coefficients (r(pred)) of 0.86 and 0.59, which showed the standard errors of prediction (SEPs) of 0.09 and 0.03mg/g for capsaicin and dihydrocapsaicin, respectively. Seventeen and 16 optimal wavebands were selected using the successive projections algorithm;r(pred) of 0.88 and 0.68 and SEPs of 0.084 and 0.027mg/g were obtained for capsaicin and dihydrocapsaicin, respectively, from the newly developed PLS calibration models using these optimal wavebands. The successive projections algorithm (SPA)-PLS model was used to map the capsaicin and dihydrocapsaicin contents of the green peppers. These maps provided detailed information on the pungency levels of the tested green peppers. The results of this study indicated that hyperspectral imaging is useful for the rapid and non-destructive evaluation of the pungency of green peppers.

关键词： Green pepper Pungency level Capsaicin Dihydrocapsaicin Hyperspectral imaging successive projections algorithm

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Automatic variable selection method and a comparison for quantitative analysis in laser-induced breakdown spectroscopy

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SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY 2018年 143卷 12-17页

作者： Duan, Fajie Fu, Xiao Jiang, Jiajia Huang, Tingting Ma, Ling Zhang, Cong Tianjin Univ State Key Lab Precis Measuring Technol & Instrume Tianjin 300072 Peoples R China Tsinghua Univ Elect Engn Dept Beijing 100084 Peoples R China

In this work, an automatic variable selection method for quantitative analysis of soil samples using laser-induced breakdown spectroscopy (LIBS) is proposed, which is based on full spectrum correction (FSC) and modified iterative predictor weighting-partial least squares (mIPW-PLS). The method features automatic selection without artificial processes. To illustrate the feasibility and effectiveness of the method, a comparison with genetic algorithm (GA) and successive projections algorithm (SPA) for different elements (copper, barium and chromium) detection in soil was implemented. The experimental results showed that all the three methods could accomplish variable selection effectively, among which FSC-mIPW-PLS required significantly shorter computation time (12s approximately for 40,000 initial variables) than the others. Moreover, improved quantification models were got with variable selection approaches. The root mean square errors of prediction (RMSEP) of models utilizing the new method were 27.47 (copper), 37.15 (barium) and 39.70 (chromium) mg/kg, which showed comparable prediction effect with GA and SPA (C) 2018 Elsevier B.V. All rights reserved.

关键词： LIBS Quantitative analysis Variable selection Genetic algorithm successive projections algorithm

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Rapid determination of moisture content in compound fertilizer using visible and near infrared spectroscopy combined with chemometrics

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INFRARED PHYSICS & TECHNOLOGY 2019年 102卷 103045-000页

作者： Wang, L. S. Wang, R. J. Lu, C. P. Wang, J. Huang, W. Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Anhui Peoples R China

Moisture is a very important index for quality control of compound fertilizer. In this study, a rapid method to determine moisture content in compound fertilizer using visible and near infrared (Vis-NIR) spectroscopy combined with chemometrics is proposed. Continuous wavelet transform (CWT) with Daubechies family was used to preprocess the spectra of compound fertilizer. CWT was optimized by comparison the performance of partial least squares regression (PLSR) models. For comparison, raw spectra and three common pre-processing transforms, namely multiplicative scatter correction (MSC), first derivative (FD) and second derivative (SD) with Savitzky-Golay (SG) smoothing, were also carried out. successive projections algorithm (SPA) was employed to select effective wavelengths with moisture under optimized continuous wavelet transforms. CWT-SPA-PLSR models were built with the effective wavelengths selected by SPA. Furthermore, CWT-SPA-LS-SVM model was developed with the best effective wavelengths. The results indicated that the optimized continuous wavelet transforms (db3 at scale 7, db4 at scale7 and db4 at scale 10) were superior;the best effective wavelengths were selected by SPA under db4 at scale 10;the performance of the CWT-SPA-LS-SVM model (R-2 = 0.8054, RMSEP = 0.1795, RPD = 2.2672) is better than that of CWT-SPA-PLSR model (R-2 = 0.7863, RMSEP = 0.1958, RPD = 2.0781). This study demonstrated visible and near infrared spectroscopy using continuous wavelet transform combined with successive projections algorithm and linear regression (PLSR) or nonlinear regression (LS-SVM) to determine moisture content in compound fertilizer was feasible.

关键词： Visible and near infrared spectroscopy Continuous wavelet transform successive projections algorithm Moisture content Compound fertilizer

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A modification of the successive projections algorithm for spectral variable selection in the presence of unknown interferents

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ANALYTICA CHIMICA ACTA 2011年第1期689卷 22-28页

作者： Carreiro Soares, Sofacles Figueredo Harrop Galvao, Roberto Kawakami Ugulino Araujo, Mario Cesar da Silva, Edvan Cirino Pereira, Claudete Fernandes Evangelista de Andrade, Stefani Fury Leite, Flaviano Carvalho Univ Fed Paraiba CCEN Dept Quim BR-58051970 Joao Pessoa PB Brazil Inst Technol & Aeronaut Div Engn Eletron BR-12228900 Sao Jose Dos Campos SP Brazil

This work proposes a modification to the successive projections algorithm (SPA) aimed at selecting spectral variables for multiple linear regression (MLR) in the presence of unknown interferents not included in the calibration data set. The modified algorithm favours the selection of variables in which the effect of the interferent is less pronounced. The proposed procedure can be regarded as an adaptive modelling technique, because the spectral features of the samples to be analyzed are considered in the variable selection process. The advantages of this new approach are demonstrated in two analytical problems, namely (1) ultraviolet-visible spectrometric determination of tartrazine, allure red and sunset yellow in aqueous solutions under the interference of erythrosine, and (2) near-infrared spectrometric determination of ethanol in gasoline under the interference of toluene. In these case studies, the performance of conventional MLR-SPA models is substantially degraded by the presence of the interferent. This problem is circumvented by applying the proposed Adaptive MLR-SPA approach, which results in prediction errors smaller than those obtained by three other multivariate calibration techniques, namely stepwise regression, full-spectrum partial-least-squares (PLS) and PLS with variables selected by a genetic algorithm. An inspection of the variable selection results reveals that the Adaptive approach successfully avoids spectral regions in which the interference is more intense. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Variable selection successive projections algorithm Interferents Ultraviolet-visible and near-infrared spectrometry Colorants Gasoline

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Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine

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SENSORS 2019年第20期19卷 4355-4355页

作者： Li, Hongyang Jia, Shengyao Le, Zichun Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China China Jiliang Univ Coll Mech & Elect Engn Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Technol Coll Sci Hangzhou 310023 Zhejiang Peoples R China

Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900-1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction e ffect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE-ELM model performed better, having a correlation coefficient of prediction (r(p)), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE-ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization.

关键词： hyperspectral imaging soil total nitrogen partial least squares extreme learning machine uninformative variable elimination successive projections algorithm

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Thermogravimetric determination of L-ascorbic acid in non-effervescent formulations using multiple linear regression with temperature selection by the successive projections algorithm

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THERMOCHIMICA ACTA 2011年第1-2期526卷 200-204页

作者： Simencio Otero, Rosa Lucia Harrop Galvao, Roberto Kawakami Ugulino Araujo, Mario Cesar Gomes Cavalheiro, Eder Tadeu Univ Sao Paulo Inst Quim Sao Carlos BR-13566590 Sao Carlos SP Brazil CCDM UFSCar Sao Carlos SP Brazil Inst Tecnol Aeronaut Div Engn Eletron BR-12228900 Sao Jose Dos Campos SP Brazil Univ Fed Paraiba Dept Quim BR-58051970 Joao Pessoa PB Brazil

Changing experimental thermogravimetric conditions, such as heating rate and sample mass, may not be sufficient to eliminate the effect of interactions in pharmaceutical analysis. This motivates the investigation of a chemometric approach to determine active drugs in pharmaceutical formulations. The use of multiple linear regression (MLR) with temperatures selected by the successive projections algorithm (SPA) for determination of L-ascorbic acid (AA) in simulated non-effervescent formulations using microcrystalline cellulose as excipient is evaluated. For comparison, two other multivariate calibration methods, MLR with temperatures selected by genetic algorithm (GA) and partial least squares (PLS) using the entire range of temperatures were chosen. MLR-SPA provided the best predictions, in agreement with the expected AA concentration (correlation of 0.991 and root-mean-square error of 0.8%, m/m in the range 61.3-74.9%, m/m). No significant differences were found between the MLR-SPA values and those from iodimetric titration, according to t-test at 95% confidence level. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Thermogravimetry Multivariate calibration Temperature selection successive projections algorithm Analysis of pharmaceutical formulations

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Research of Nitrate Concentration Prediction Based on GA

Research of Nitrate Concentration Prediction Based on GA

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Conference on Ocean Optics and Information Technology / International Symposium on Optoelectronic Technology and Application (OTA)

作者： Wang Xueji Hu Bingliang Yu Tao Liu Qingsong Li Hongbo Fan Yao Chinese Acad Sci Xian Inst Opt & Precis Mech Lab Spectral Imaging Tech Xian 710119 Shaanxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (数字)9781510623439

ISBN: (纸本)9781510623439

High concentration of nitrate will cause many problems, such as water eutrophication and compromise of human beings' health. A fast and stable approach was applied to predict nitrate concentration in solutions using the dual optical active correction continuous spectrum analyzer designed by our research group. Firstly, standard normal variate (SNV) was used to pretreat the spectral data. Then characteristic wavelengths of spectral curve were selected by using successive projections algorithm (SPA) and genetic algorithm (GA) respectively. Finally, partial least-squares regression (PLSR) was applied to build the spectral prediction model to predict nitrate concentration, and coefficient of determination (R-2) and root mean square error of prediction (RMSEP) were introduced as the evaluation indicators of prediction models. For SNV-GA-PLS model, R-2=0.9966 and RMSEP=0.1712, which outperformed than SNV-UVE-SPA-PLS model (R-2=0.9896, RMSEP=0.3952). It demonstrated that he model which selects spectral characteristic wavelengths by GA can decrease the complexity of prediction model building and ensure the accuracy as well. Hence, SNV-GA-PLS model can be used to predict nitrate concentration in water with quick and steady performance.

关键词： Nitrate concentration Ultraviolet/ visible spectral technology Genetic algorithm successive projections algorithm

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Nondestructive Determination of Citric Acid Using successive projections algorithm and Spectroscopic Techniques

Nondestructive Determination of Citric Acid Using Successive...

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International Conference on Components, Packaging and Manufacturing Technology

作者： Liu, Fei He, Yong Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310029 Zhejiang Peoples R China

ISBN: (纸本)9780878492138

successive projections algorithm (SPA) combined with least square-support vector machine (LS-SVM) was investigated to determine the citric acid of lemon vinegar by 13 wavelengths within visible/near infrared (Vis/NIR) spectral region. Five concentration levels (100%, 80%, 60%, 40% and 20%) of lemon vinegar were prepared, and the calibration set consisted of 150 samples, validation set consisted of 75 samples and the remaining 75 samples for prediction set. After the comparison of different preprocessing such as smoothing, standard normal variate and derivative, SPA was applied to extract the effective wavelengths to reduce the redundancies and collinearity of variables, and the multiple linear regression (MLR) models were developed compared with partial least squares (PLS) models. Simultaneously, the selected wavelengths were used as the inputs of LS-SVM, and a new proposed combination of SPA-LS-SVM model was developed. The results indicated that SPA-LS-SVM achieved the optimal prediction performance, and the correlation coefficient (r) and root mean square error of prediction (RMSEP) were 0.9894 and 0.0623, respectively. An excellent prediction precision was obtained. The overall results demonstrated that it was feasible to utilize Vis/NIR spectroscopy to predict the citric acid of lemon vinegar, and SPA-LS-SVM model achieved the optimal prediction precision. This study supplied a feasible method for the process monitoring of fruit vinegar manufacture and fermentation.

关键词： Visible/near infrared spectroscopy successive projections algorithm least square-support vector machine lemon vinegar nondestructive testing

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Varietal classification and antioxidant activity prediction of Osmanthus fragrans Lour. flowers using UPLC-PDA/QTOF-MS and multivariable analysis

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FOOD CHEMISTRY 2017年第0期217卷 490-497页

作者： Zhou, Fei Peng, Jiyu Zhao, Yajing Huang, Weisu Jiang, Yirong Li, Maiquan Wu, Xiaodan Lu, Baiyi Zhejiang Univ Coll Biosyst Engn & Food Sci Fuli Inst Food SciZhejiang Key Lab Agrofood Proc Minist AgrKey Lab Agrofood Risk AssessmentZheji Hangzhou 310058 Zhejiang Peoples R China Zhejiang Econ & Trade Polytech Dept Appl Technol Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Anal Ctr Agrobiol & Environm Sci Hangzhou 310058 Zhejiang Peoples R China

This study was aimed to classify the varieties and predict the antioxidant activity of Osmanthus fragrans flowers by UPLC-PDA/QTOF-MS and multivariable analysis. The PLS-DA model successfully classified the four varieties based on both the 21 identified compounds and the effective compounds. For the antioxidant activity prediction, PLS performed well to predict the antioxidant activity of O. fragrans flowers. Furthermore, acteoside, suspensaside A, ligustroside, forsythoside A, phillygenin and caffeic acid were selected as effective compounds by UVE-SPA for prediction. On the basis of effective compounds, PLS, MLR and PCR were applied to establish the calibration models. The UVE-SPA MLR model was the optimal method to predict the antioxidant activity values with R-p of 0.9200, 0.9010 and 0.8905 for DPPH, ABTS and FRAP assays, respectively. The results revealed that the UPLC-PDA/QTOF-MS combined with chemometrics could be a new method to classify the varieties and predict the antioxidant activity of O. fragrans flowers. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Osmanthus fragrans Lour. flower Varietal classification Antioxidant activity Partial least squares Multiple linear regression successive projections algorithm Uninformative variable elimination Effective compound

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