检索结果-内蒙古大学图书馆

Selection of characteristic wavelengths using SPA for laser induced fluorescence spectroscopy of mine water inrush

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019年 219卷 367-374页

作者： Hu, Feng Zhou, Mengran Yan, Pengcheng Li, Datong Lai, Wenhao Zhu, Song Wang, Yu Anhui Univ Sci & Technol Sch Elect & Informat Engn 168 Taifeng Rd Huainan 232001 Anhui Peoples R China

In the process of prevention and control of water inrush disaster, it is of great significance to identify the type of water inrush source for coal mine safety production accurately and quickly. The application of laser induced fluorescence (LIF) technology to identify the water inrush in coal mine broke the shortage of the traditional hydrochemical method, which could realize the accurate and rapid identification of water inrush types. Firstly, in order to avoid the influence of random variations of spectral data, four kinds of common pretreatment methods were analyzed and compared, and the moving average smoothing method was chosen to preprocess the original fluorescence spectral data. Then, for the purpose of selecting the appropriate sample division method to improve the predictive performance of the model, four common sample division methods were compared, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the samples into training set and test set. Further, the 10 characteristic wavelengths were selected by successive projections algorithm (SPA) to reduce the amount of data. Finally, the selected data was taken as input, the sigmoid function was selected as the activation function of extreme learning machine (ELM), and the number of hidden layer neurons was set to 34, which realized the construction of water source identification model. The prediction accuracy of ELM model for the training set and test set were 99.0% and 94.0%, respectively. In addition, the water samples collected at different time were mixed in the same way to form the independent verification set, and the prediction accuracy of the ELM water source identification model for independent verification set was 91.5%. The results shown that it was feasible to select the characteristic wavelengths of fluorescence spectra by using the SPA. The data of 10 characteristic wavelengths could fully represent the effective information of whole band spectrum. And i

关键词： Laser induced fluorescence Characteristic wavelengths successive projections algorithm Mine water inrush

来源：评论

学校读者我要写书评

暂无评论

Simultaneous identification of the wood types in aged cachacas and their adulterations with wood extracts using digital images and SPA-LDA

引用

FOOD CHEMISTRY 2019年 273卷 77-84页

作者： de Sousa Fernandes, David Douglas de Almeida, Valber Elias Fontes, Marcelo Marques Ugulino de Araujo, Mario Cesar Veras, Germano Goncalves Dias Diniz, Paulo Henrique Univ Fed Paraiba Dept Quim POB 5093 BR-58051970 Joao Pessoa PB Brazil Inst SENAI Tecnol Dist Ind BR-58082004 Joao Pessoa PB Brazil Univ Estadual Paraiba Ctr Ciencia & Tecnol Dept Quim BR-58429500 Campina Grande PB Brazil Univ Fed Oeste Bahia Nucleo Quim Campus Reitor Edgard SantosRua Bertioga 892 BR-47810059 Barreiras BA Brazil

Cachaca is a sugarcane-derived alcoholic spirit exclusively produced in Brazil. It can be aged in barrels made from different types of wood, similar to other distilled beverages. The choice of wood type promotes different effects on color, flavor, aroma and consequently the price of cachaca, favoring fraudulent activities. This paper proposes the simultaneous identification of different wood types in aged cachacas and their adulterations with wood extracts using a digital-image based methodology employing color histograms obtained from digital images associated with pattern recognition methods, without any sample preparation step. Linear Discriminant Analysis, coupled with successive projections algorithm for variable selection (SPA-LDA), obtained the best results, reaching accuracy, sensitivity, and specificity rates higher than 90.0% in the test set. This can be a rapid and reliable tool to prevent fraudulent labeling;ensuring that what is on the label reflects the quality of aged cachacas, affording security to consumers and regulatory agencies.

关键词： Cachaca Aging Wood types Wood extracts Adulteration successive projections algorithm

来源：评论

学校读者我要写书评

暂无评论

Prediction and visualisation of S-ovalbumin content in egg whites using hyperspectral images

引用

INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2019年第1期22卷 1077-1086页

作者： Fu, Dan Dan Wang, Qiao Hua Ma, Mei Hu Ma, Yi Xiao Vong, Chin Nee Huazhong Agr Univ Coll Engn Wuhan Hubei Peoples R China Huazhong Agr Univ Natl Res & Dev Ctr Egg Proc Wuhan Hubei Peoples R China Huazhong Agr Univ Minist Agr Key Lab Agr Equipment Middle & Lower Reaches Yang Wuhan Hubei Peoples R China Huazhong Agr Univ Coll Food Sci & Technol Wuhan Hubei Peoples R China Univ Missouri Coll Agr Food & Nat Resources Div Food Syst & Bioengn Columbia MO USA

This study proposed a method using hyper-spectral imaging technology in determining eggs' quality in term of freshness from a biochemical perspective by estimating the S-ovalbumin content. This method has the potential in assessing eggs' quality rapidly and non-destructively. Hyper-spectral image of egg was captured using a hyper-spectral imaging system and regression model was built to estimate the S-ovalbumin content. The successive projections algorithm (SPA) was used to select significant wavebands followed by building a partial least squares regression (PLSR) model and a multiple linear regression (MLR) model. The MLR model could predict S-ovalbumin content better than PLSR model with a higher correlation coefficient (0.922) and lower root mean square error (0.086) of the calibration set, a higher correlation coefficient (0.911) and lower root mean square error (0.119) of the validation set, and a higher residual predictive deviation (2.348). The regression equation from the MLR model was used to compute each pixel of the image in the validation set and visualisation of S-ovalbumin content distribution in the egg was obtained using pseudo-color image. The findings implied that the proposed hyper-spectral imaging system with the regression model developed has the potential in determining and visualising the eggs' quality.

关键词： Chicken eggs hyper-spectral imaging S-ovalbumin content successive projections algorithm visualisation

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Simultaneous determination of goat milk adulteration with cow milk and their fat and protein contents using NIR spectroscopy and PLS algorithms

引用

LWT-FOOD SCIENCE AND TECHNOLOGY 2020年 127卷 109427-109427页

作者： dos Santos Pereira, Elainy Virginia de Sousa Fernandes, David Douglas Ugulino de Araujo, Mario Cesar Goncalves Dias Diniz, Paulo Henrique Sucupira Maciel, Maria Ines Univ Fed Paraiba Technol Ctr Food Sci & Technol Postgrad Program PPGCTA Joao Pessoa 58051900 Paraiba Brazil State Univ Paraiba CCT Dept Chem NUPEA Campina Grande 58429500 Paraiba Brazil Univ Fed Paraiba CCEN Dept Chem Joao Pessoa 58051970 Paraiba Brazil Fed Univ West Bahia Pure & Appl Chem Postgrad Program Barreiras 47810059 BA Brazil Univ Fed Rural Pernambuco Food Sci & Technol Postgrad Program Recife 52171900 PE Brazil

This work employed NIR spectroscopy and PLS algorithms for the identification and quantification of goat milk adulteration by adding cow milk, besides the determination of their fat and protein contents. Since cow milk can represent a health risk to allergic consumers regardless its amount, PLS-DA was able to identify cow milk additions in goat milk as low as 1.0154 g/100g, likewise the non-adulterated goat and cow milk samples, achieving a 100% of correct classification. For quantification purposes, the successive projections algorithm for interval selection in PLS (iSPA-PLS) provided the best results for the determination of both adulteration and fat contents, while PLS gave better results for the protein quantification. Despite the great similarity of both natural dairy matrices and their intrinsic variability, the prediction results provided suitable values with high correlation coefficients and low RMSEP and REP values, with RPD values higher than 3. Therefore, the proposed methodology proved to be a useful, fast and non-destructive tool for screening the quality of goat milk in terms of its adulteration with cow milk, in addition to the quantification of its fat and protein contents.

关键词： Goat milk Cow milk Adulteration NIR spectroscopy successive projections algorithm

来源：评论

学校读者我要写书评

暂无评论

Qualitative and quantitative analysis based on digital images to determine the adulteration of ketchup samples with Sudan I dye

引用

FOOD CHEMISTRY 2020年 328卷 127101-127101页

作者： Giselle Reile, Cynthia Susana Rodriguez, Maria de Sousa Fernandes, David Douglas de Araujo Gomes, Adriano Goncalves Dias Diniz, Paulo Henrique Vanesa Di Anibal, Carolina Univ Nacl Sur Dept Quim INQUISUR UNS CONICET Av Alem 1253 B8000CPB Bahia Blanca Buenos Aires Argentina Univ Fed Paraiba Dept Quim BR-58051970 Joao Pessoa Paraiba Brazil Univ Fed Rio Grande do Sul Inst Quim BR-90650001 Porto Alegre RS Brazil Univ Fed Oeste Bahia Nucleo Quim Campus Reitor Edgard SantosRua Bertioga 892 BR-47810059 Barreiras BA Brazil

Sudan I is a synthetic-azo dye commonly used to adulterate foods to increase sensory appearance. However, it is banned due to its carcinogenic, mutagenic and genotoxic properties, which represent a serious risk to human health. Thus, this paper proposes a feasibility study to identify and quantify Sudan I dye in ketchup samples using colour histograms (obtained from digital images) and multivariate analysis. The successive projections algorithm coupled with linear discriminant analysis (SPA-LDA) classified correctly all samples, while the partial least squares coupled with SPA for interval selection (iSPA-PLS) quantified adequately the adulterant, attaining values of RMSEP of 11.64 mg kg(-1), R-2 of 0.96, RPD of 5.28, REP of 13.63% and LOD of 39.45 mg kg(-1). Therefore, the proposed methodology provides a simple, fast, inexpensive, promising analytical tool for the screening of both the quality and safety of ketchup samples. As a consequence, it can help to protect the consumer's health.

关键词： Ketchup Adulteration Sudan I dye Colour histograms Multivariate analysis successive projections algorithm

来源：评论

学校读者我要写书评

暂无评论

THE DEVELOPMENT OF PORTABLE DETECTOR FOR APPLES SOLUBLE SOLIDS CONTENT BASED ON VISIBLE AND NEAR INFRARED SPECTRUM

引用

INMATEH-AGRICULTURAL ENGINEERING 2020年第3期62卷 277-288页

作者： Peng, Fa Liu, ShuangXi Jiang, Hao Liu, XueMei Mu, JunLin Wang, JinXing Beijing Agr Equipment Res Ctr Beijing 100097 Peoples R China Shandong Agr Univ Coll Mech & Engn Tai An 271018 Shandong Peoples R China

In order to detect the soluble solids content of apples quickly and accurately, a portable apple soluble solids content detector based on USB2000 + micro spectrometer was developed. The instrument can communicate with computer terminal and mobile app through network port, Bluetooth and other ways, which can realize the rapid acquisition of apple spectral information. Firstly, the visible/near-infrared spectrum data and soluble solids content information of 160 apple samples were collected;secondly, the spectral data preprocessing methods were compared, and the results showed that the prediction model of sugar content based on partial least square (PLS) method after average smoothing preprocessing was accurate. The correlation coefficient (RP) and root mean square error (RMSEP) of the prediction model were 0.902 and 0.589 *Brix, respectively. Finally, on the basis of average smoothing preprocessing, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the wavelength of spectral data, and PLS model was constructed based on the selected 17 characteristic wavelengths, which can increase the accuracy of soluble solids content prediction model, increase the RP to 0.912, and reduce RMSEP to 0.511 *Brix. The portable visible/nearinfrared spectrum soluble solids prediction model based on the instrument and method has high accuracy, and the detector can quickly and accurately measure the soluble solids content of apple.

关键词： Apple soluble solids Visible/near infrared spectroscopy Portable Adaptive Reweighted algorithm successive projections algorithm

来源：评论

学校读者我要写书评

暂无评论

Voltammetric e-Tongue Based on a Single Sensor and Variable Selection for the Classification of Teas

引用

FOOD ANALYTICAL METHODS 2018年第7期11卷 1958-1968页

作者： Rodrigues, Dayvison R. de Oliveira, Diana S. M. Pontes, Marcio J. C. Lemos, Sherlan G. Univ Fed Paraiba Dept Quim CP 5093 BR-58051970 Joao Pessoa PB Brazil

This work presents a simple and low-cost strategy to obtain voltammetric tongues by using a single voltammetric sensor aided by variable selection techniques. A usual electronic tongue consists of an array of physical sensors followed by data compression before pattern recognition modeling. Alternatively, a single voltammetric sensor can also act as an array of pseudo-sensors-applied potentials in a voltammogram-that can be properly selected to perform a given discrimination. The applicability of this strategy was evaluated in the discrimination of teas. Teas prepared for immediate intake (simulating a home-made tea cup) were analyzed with staircase voltammetry at an epoxy-graphite electrode. Voltammograms were submitted to variable selection previously to linear discriminant analysis (LDA) to identify the applied potentials that improve discrimination of teas according to variety, country of origin, and manufacturer. successive projections algorithm (SPA), genetic algorithm (GA), and stepwise (SW) formulation were the variable selection techniques evaluated. Best results were achieved with SPA/LDA models, with correct classification rates for the prediction set close to 100%.

关键词： Tea Voltammetric electronic tongue Multivariate classification successive projections algorithm

来源：评论

学校读者我要写书评

暂无评论

Non-destructive prediction of protein content in wheat using NIRS

引用

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2018年 189卷 463-472页

作者： Ye, Dandan Sun, Laijun Zou, Borui Zhang, Qian Tan, Wenyi Che, Wenkai Heilongjiang Univ Coll Heilongjiang Prov Key Lab Elect Engn Harbin Heilongjiang Peoples R China Heilongjiang Univ Room 503Bldg A874 Xuefu Rd Harbin 150080 Heilongjiang Peoples R China

A steady and accurate model used for quality detection depends on precise data and appropriate analytical methods. In this study, the authors applied partial least square regression (PLSR) to construct a model based on the spectral data measured to predict the protein content in wheat, and proposed a new method, global search method, to select PLSR components. In order to select representative and universal samples for modeling, Monte Carlo cross validation (MCCV) was proposed as a tool to detect outliers, and identified 4 outlier samples. Additionally, improved simulated annealing (ISA) combined with PLSR was employed to select most effective variables from spectral data, the data's dimensionality reduced from 100 to 57, and the standard error of prediction (SEP) decreased from 0.0716 to 0.0565 for prediction set, as well as the correlation coefficients (R-2) between the, predicted and actual protein content of wheat increased from 0.9989 to 0.9994. In order to reduce the dimensionality of the data further, successive projections algorithm (SPA) was then used, the combination of these two methods was called ISA-SPA. The results indicated that calibration model built using ISA-SPA on 14 effective variables achieved the optimal performance for prediction of protein content in wheat comparing with other developed PLSR models (ISA or SPA) by comprehensively considering the accuracy, robustness, and complexity of models. The coefficient of determination increased to 0.9986 and the SEP decreased to 0.0528, respectively. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Partial least square regression Monte Carlo cross validation Outliers PLSR components Improved simulated annealing successive projections algorithm

来源：评论

学校读者我要写书评

暂无评论

Estimation of foliar nitrogen of rubber trees using hyperspectral reflectance with feature bands

引用

INFRARED PHYSICS & TECHNOLOGY 2019年 102卷 103021-000页

作者： Guo, Peng-Tao Li, Mao-Fen Luo, Wei Cha, Zheng-Zao Chinese Acad Trop Agr Sci Rubber Res Inst Haikou 571101 Hainan Peoples R China Chinese Acad Trop Agr Sci Hainan Prov Key Lab Pract Res Trop Crops Informat Inst Sci & Tech Informat Haikou 571101 Hainan Peoples R China

Leaf nitrogen is an indispensable parameter for simulating biogeochemical cycling in ecosystems. However, detailed information about leaf nitrogen of rubber trees (Hevea brasiliensis) in the tropical regions is rare. Aims of the current study were to determine the feature bands of foliar nitrogen of rubber trees and to examine the ability of these bands to estimate leaf nitrogen concentration (LNC). In this work, a hybrid variable selection method namely competitive adaptive reweighted sampling (CARS) combined with successive projections algorithm (SPA) (CARS-SPA) was used to extract feature bands for predicting LNC of rubber trees. Two hundred leaf samples were gathered from five fields through April to November (tapping season) in 2014. These samples were divided into calibration dataset (n = 140) and prediction dataset (n = 60) using the Kennard-Stone algorithm. Sixty bands were determined from the first derivative spectrum using the CARS-SPA. Among these bands, not only the commonly used absorption features of chlorophyll, protein, and the red edge position were included, but also those of starch, cellulose, and lignin were selected. All the 60 bands were used as input variables for the partial least squares regression (PLSR) and artificial neural networks (ANN) models to estimate LNC of rubber trees. The determination coefficient of calibration (r(c)(2)) and prediction (r(p)(2)), and root mean square error of calibration (RMSEC) and prediction (RMSEP), as well as normalized RMSEC (nRMSEC) and normalized RMSEP (nRMSEP) were employed to evaluate performances of these models. Results indicated that CARS-SPA-ANN (r(c)(2) = 0.82, RMSEC = 0.24%, nRMSEC = 7.76%;r(p)(2) = 0.78, RMSEP = 0.22%, nRMSEP = 6.73%) outperformed the other selected models except CARS-PLSR (r(c)(2) = 0.91, RMSEC = 0.17%, nRMSEC = 5.46%;r(p)(2) = 0.80, RMSEP = 0.21%, nRMSECP = 6.44%). However, CARS-SPA-ANN contained much less bands and was more stable than CARS-PLSR. In conclusion, hyperspectral

关键词： Leaf nitrogen concentration Artificial neural networks Competitive adaptive reweighted sampling successive projections algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：