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Quantitative Analysis of Total Amino Acid in Barley Leaves under Herbicide Stress Using Spectroscopic Technology and Chemometrics

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SENSORS 2012年第10期12卷 13393-13401页

作者： Bao, Yidan Kong, Wenwen He, Yong Liu, Fei Tian, Tian Zhou, Weijun Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Coll Agr & Biotechnol Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Cyrus Tang Ctr Sensor Mat & Applicat Hangzhou 310058 Zhejiang Peoples R China

Visible and near infrared (Vis/NIR) spectroscopy were employed for the fast and nondestructive estimation of the total amino acid (TAA) content in barley (Hordeum vulgare L.) leaves. The calibration set was composed of 50 samples;and the remaining 25 samples were used for the validation set. Seven different spectral preprocessing methods and six different calibration methods (linear and nonlinear) were applied for a comprehensive prediction performance comparison. successive projections algorithm (SPA) and regression coefficients (RC) were applied to select effective wavelengths (EWs). The results indicated that the latent variables-least-squares-support vector machine (LV-LS-SVM) model achieved the optimal performance. The prediction results by LV-LS-SVM with raw spectra were achieved with a correlation coefficients (r) = 0.937 and root mean squares error of prediction (RMSEP) = 0.530. The overall results showed that the NIR spectroscopy could be used for determination of TAA content in barley leaves with an excellent prediction precision;and the results were also helpful for on-field monitoring of barley growing status under herbicide stress during different growth stages.

关键词： visible and near infrared spectroscopy barley total amino acid variable selection successive projections algorithm least squares-support vector machine

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Application of Visible and Near-Infrared Hyperspectral Imaging to Determine Soluble Protein Content in Oilseed Rape Leaves

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SENSORS 2015年第7期15卷 16576-16588页

作者： Zhang, Chu Liu, Fei Kong, Wenwen He, Yong Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Zhejiang Peoples R China

Visible and near-infrared hyperspectral imaging covering spectral range of 380-1030 nm as a rapid and non-destructive method was applied to estimate the soluble protein content of oilseed rape leaves. Average spectrum (500-900 nm) of the region of interest (ROI) of each sample was extracted, and four samples out of 128 samples were defined as outliers by Monte Carlo-partial least squares (MCPLS). Partial least squares (PLS) model using full spectra obtained dependable performance with the correlation coefficient (r(p)) of 0.9441, root mean square error of prediction (RMSEP) of 0.1658 mg/g and residual prediction deviation (RPD) of 2.98. The weighted regression coefficient (Bw), successive projections algorithm (SPA) and genetic algorithm-partial least squares (GAPLS) selected 18, 15, and 16 sensitive wavelengths, respectively. SPA-PLS model obtained the best performance with r(p) of 0.9554, RMSEP of 0.1538 mg/g and RPD of 3.25. Distribution of protein content within the rape leaves were visualized and mapped on the basis of the SPA-PLS model. The overall results indicated that hyperspectral imaging could be used to determine and visualize the soluble protein content of rape leaves.

关键词： hyperspectral imaging soluble protein content weighted regression coefficient successive projections algorithm genetic algorithm-partial least squares

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Nondestructive detection of apple crispness via optical fiber spectroscopy based on effective wavelengths

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FOOD SCIENCE & NUTRITION 2019年第11期7卷 3654-3663页

作者： Ni, Fupeng Zhu, Xiaowen Gu, Fang Hu, Yaohua Northwest A&F Univ Coll Mech & Elect Engn 22 Xinong Rd Yangling 712100 Shaanxi Peoples R China Minist Agr Key Lab Agr Internet Things Yangling Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligen Yangling Shaanxi Peoples R China

Crispness is regarded as a significant quality index for apples. Currently, destructive sensory evaluation is the accepted method used to detect apple crispness, making it essential to develop a method that can detect apple crispness in a nondestructive manner. In this study, spectroscopy was proposed as the nondestructive technique for detecting apples' crispness, ultimately obtaining a spectral reflectance curve between 450 nm and 1,000 nm. In order to simplify and improve modeling efficiency, successive projections algorithm (SPA) and x-loading weights (x-LW) methods were used to select the most effective wavelengths. Partial least squares (PLS) algorithm, radial basis neural networks (RBNN), and multilayer perceptron neural networks (MLPNN) methods were used to establish the models and to predict the crispness of "Fuji" and "Qinguan" apple varieties. Based on the full wavelength (FW), the prediction accuracy of the PLS model for "Fuji" and "Qinguan" apple varieties was 92.05% and 95.87%, respectively. The effective wavelengths selected via SPA for the "Fuji" apple variety were 450.41 nm, 476.80 nm, 677.75 nm, and 750.72 nm, and the effective wavelengths selected via x-LW for the "Qinguan" apple variety were 542.51 nm, 544.79 nm, 676.96 nm, and 718.29 nm. The prediction accuracy of the PLS model based on effective wavelengths for "Fuji" and "Qinguan" apple varieties reached 91.31% and 96.41%, respectively. Compared with the RBNN model, the MLPNN model achieved better prediction results for both "Fuji" and "Qinguan" apples, with the prediction accuracy reaching 97.8% and 99.9%, respectively. Based on the above findings, effective wavelength selection and MLPNN modeling were able to detect apple crispness with the highest accuracy. Overall, it can be concluded that the less effective wavelengths are conducive to developing an instrument for crispness detection.

关键词： apple crispness artificial neural network effective wavelengths optical fiber spectroscopy partial least squares method successive projections algorithm

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Nondestructive determination of nutritional information in oilseed rape leaves using visible/near infrared spectroscopy and multivariate calibrations

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Science China(Information Sciences) 2011年第3期54卷 598-608页

作者： LIU Fei, NIE PengCheng, HUANG Min, KONG WenWen & HE Yong College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China 1. College of Biosystems Engineering and Food Science Zhejiang University Hangzhou 310029 China

Nitrogen, phosphorus and potassium content are the three most important nutritional parameters for growing oilseed rape. We investigated visible and near infrared (Vis/NIR) spectroscopy combined with chemometrics for the fast and nondestructive determination of nutritional information in oilseed rape leaves. A total of 154 leaf samples were collected, with 104 randomly selected as the calibration set, and the remaining 50 samples used as the validation set. The performance of eight different preprocessing methods was compared in partial least squares (PLS) models. Some effective wavelengths selected by a successive projections algorithm (SPA) were also used to develop linear SPA-PLS, nonlinear back propagation neural network (BPNN), and nonlinear least squares-support vector machine (LS-SVM) models to determine nutritional information. The best prediction models were DOSC-PLS for nitrogen with r=0.9743 and RMSEP=0.1459, DOSC-SPA-BPNN for phosphorus with r=0.7054 and RMSEP=0.0594, and DOSC-SPA-BPNN for potassium with r=0.9380 and RMSEP=0.1788. The prediction precision for nitrogen and potassium determinations was acceptable for further practical applications, whereas further studies are needed to improve the prediction precision for phosphorus. The results indicated that Vis/NIR spectroscopy is feasible for nondestructive determination of nutritional information in oilseed rape leaves. It also provided an alternative technique for detecting other growth information about oilseed rape leaves.

关键词： visible and near infrared spectroscopy oilseed rape nutritional information successive projections algorithm direct orthogonal signal correction chemometrics

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Determination of Protein Content of Auricularia auricula Using Near Infrared Spectroscopy Combined with Linear and Nonlinear Calibrations

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JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2009年第11期57卷 4520-4527页

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

Near infrared (NIR) spectroscopy was investigated to determine the protein content of Auricularia auricula (commonly called black woody ear or tree ear) using partial least-squares (PLS), multiple linear regression (MLR), and least-squares-support vector machine (LS-SVM). The performances of different preprocessing were compared including Savitzky-Golay (SG) smoothing, standard normal variate, multiplicative scatter correction (MSC), first derivative, second derivative, and direct orthogonal signal correction. A successive projections algorithm (SPA) was applied for relevant effective wavelengths selection. The combinations of various pretreatment and calibration methods were compared based on the prediction performance. The optimal full-spectrum PLS model was achieved by raw spectra, whereas the optimal SPA-MLR, SPA-PLS, and SPA-LS-SVM models were achieved by MSC spectra. The best prediction performance was achieved by the SPA-LS-SVM model, with correlation coefficients (0 = 0.9839 and a root mean squares error of prediction (RMSEP) = 0.16. The results indicated that NIR spectroscopy combined with SPA-LS-SVM was the most successful to determine the protein content of A. auricula.

关键词： Near infrared spectroscopy direct orthogonal signal correction successive projections algorithm least-squares-support vector machine Auricularia auricula protein content

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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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Determination of invert syrup adulterated in acacia honey by terahertz spectroscopy with different spectral features

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JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2020年第5期100卷 1913-1921页

作者： Liu, Wen Zhang, Yuying Li, Ming Han, Donghai Liu, Wenjie Xiangtan Univ Sch Chem Engn Xiangtan Peoples R China Market Supervis & Adm Xihu Dist Hangzhou Peoples R China Tianjin Univ Commerce Tianjin Key Lab Food Biotechnol Coll Biotechnol & Food Sci Tianjin Peoples R China China Agr Univ Coll Food Sci & Nutr Engn 17 Tsinghua East Rd Beijing 100083 Peoples R China

BACKGROUND Invert syrup is a common adulterant in honey falsification, thus generating risk for consumers. Most of the methods developed are tedious and time-consuming for manufactures and consumers. However, terahertz spectroscopy provides analytical information in a simple, rapid, and environmentally friendly manner. Subsequently, 3 kinds of terahertz spectroscopic characteristics data, the absorption coefficient, the slope of the absorption coefficient spectra, and the area of the absorption coefficient spectra, were employed for determination of acacia honey adulterated with invert syrup. RESULTS Single linear regression (SLR) models with different terahertz spectroscopic features were adopted to predict the syrup adulterant proportion in acacia honey. The best SLR model used the area of the absorption coefficient, displaying an adjusted correlation coefficient of 0.985 and a root-mean-square error of 3.201. Meanwhile, multiple linear regression (MLR) models using a successive projections algorithm for variables selection were implemented. The MLR model considered the integral area of the absorption coefficient spectra, as the inputs yielded the best result with less variables selected, higher Rc2 and Rp2, lower root-mean-square error of calibration and prediction, as well as higher residual predictive deviation. CONCLUSIONS The results indicate terahertz spectroscopy combined with the integral area of the absorption coefficient spectra is reliable enough for invert syrup proportion quantification in acacia honey and is also a rapid and nondestructive determination method for other honey adulterants. (c) 2019 Society of Chemical Industry

关键词： terahertz spectroscopy invert syrup adulteration single linear regression multiple linear regression successive projections algorithm

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UV-Vis spectrometric classification of coffees by SPA-LDA

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FOOD CHEMISTRY 2010年第1期119卷 368-371页

作者： Carvalho Polari Souto, Urijatan Teixeira Coelho Pontes, Marcio Jose Silva, Edvan Cirino Harrop Galvao, Roberto Kawakami Ugulino Araujo, Mario Cesar Castriani Sanches, Fatima Aparecida Silva Cunha, Francisco Antonio Ribeiro Oliveira, Maria Socorro Univ Fed Paraiba Dept Quim Lab Automacao & Instrumentacao Quim Analit Quimio BR-58051970 Joao Pessoa Paraiba Brazil Inst Tecnol Aeronaut Div Engn Eletron Sao Jose Dos Campos SP Brazil

UV-Vis spectrometry and chemometric techniques were used to classify aqueous extracts of Brazilian ground roast coffee with respect to type (caffeinated/decaffeinated) and conservation state (expired and non-expired shelf-life). Two classification methods were compared: soft independent modelling of class analogy (SIMCA) and linear discriminant analysis (LDA) with wavelength selection by the successive projections algorithm (SPA). The best results were obtained by SPA-LDA, which correctly classified all test samples. The classification accuracy of this model remained high (96%) even after the introduction of artificial spectral noise. These results suggest that UV-Vis spectrometry and SPA-LDA modelling provide a promising alternative for assessment of conservation state and decaffeination condition of coffee samples. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Coffee classification UV-Vis spectrometry Linear discriminant analysis successive projections algorithm Soft independent modelling of class analogy

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Determining the quality of insulating oils using near infrared spectroscopy and wavelength selection

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MICROCHEMICAL JOURNAL 2011年第2期98卷 254-259页

作者： Coelho Pontes, Marcio Jose Jose Rocha, Alexandre Magno Pimentel, Maria Fernanda Pereira, Claudete Fernandes Univ Fed Paraiba Dept Quim Lab Automacao & Instrumentacao Quim Analit Quimio BR-58051970 Joao Pessoa PB Brazil Univ Fed Rural Pernambuco Dept Quim Recife PE Brazil Univ Fed Pernambuco Dept Engn Quim Recife PE Brazil

This study presents an analytical method for determining interfacial tension and relative density in insulating oils using near infrared spectrometry (NIR). Five different strategies of regression were evaluated: partial least squares (PLS) with significant regression coefficients selected by jack-knife algorithm;interval PLS (iPLS);multiple linear regression (MLR) with variable selection by genetic algorithm (MLR/GA), successive projections algorithm (MLR/SPA) and stepwise strategy (SR/MLR). The overall results point to MLR/SPA as the best modeling strategy. The strategy is simpler and uses fewer spectral variables. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Insulating oils Near infrared spectrometry Interfacial tension Relative density Interval partial least squares successive projections algorithm Genetic algorithm Stepwise strategy Multiple linear regression

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A Weak Signal Extraction Method for Human Blood Glucose Noninvasive Measurement using Near Infrared Spectroscopy

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JOURNAL OF INFRARED MILLIMETER AND TERAHERTZ WAVES 2009年第11期30卷 1191-1204页

作者： Li, Li-Na Li, Qing-Bo Zhang, Guang-Jun Beihang Univ Sch Instrumentat Sci & Optoelect Engn Key Lab Educ Minist Beijing 100191 Peoples R China

Background interference from optical absorption of matrix components, low spectral selectivity and low spectral sensitivity are the main interference factors for human blood glucose noninvasive measurement using near infrared (NIR) spectroscopy. In order to extract the weak glucose concentration information, a modified uninformative variable elimination (mUVE) method combined with successive projections algorithm (SPA) named as mUVE-SPA, is proposed. mUVE is used to eliminate matrix background and high-frequency noise by wavelet multi-resolution technology. SPA is followed to select variables with minimum colinearity by projection algorithm in a vector space. The proposed method was applied in two NIR spectra data sets (plasma samples experiment in vitro and human blood glucose noninvasive measurement experiment in vivo) respectively. The performance and adaptability of the proposed strategy were discussed. The results indicate that the proposed hybrid method can give an alternative path to extract weak glucose information and yield more parsimonious models with higher precision.

关键词： Uninformative variable elimination successive projections algorithm Blood glucose noninvasive measurement Near infrared spectroscopy

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