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Measurement of moisture content in lubricating oils of high-speed rail gearbox by Vis-NIR spectroscopy

OPTIK 2020年 224卷 165694-165694页

作者： Liu, Chenyang Tang, Xingjia Yu, Tao Wang, Taisheng Lu, Zhenwu Yu, Weixing Chinese Acad Sci R&D Ctr Precis Instruments & Equipment Changchun Inst Opt Fine Mech & Phys 3888 Dongnanhu Rd Changchun Jilin Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Spectral Imaging Technol 17 Xinxi Rd Xian 710119 Peoples R China

The moisture content in lubricating oil is one of the most important factors to reflect the health and effectiveness of it, thus to monitor the moisture content in lubricating oil in real time is crucial for high speed rail. In this paper, we developed a compact moisture-content monitoring system based on the visible-near-infrared (Vis-NIR) spectroscopy technology, which was shown to be able to determine the moisture content in lubricating oil in a fast, simple and accurate way. In this system, a reflection mode optical probe was developed for sending and receiving optical signals through the viewport of gear box. By employing the reflection optical probe, one can measure the spectrum of the lubricating oil in a fast way by simply putting the probe on viewport of gear box, and therefore has the potential to realize the real-time monitoring of the status of the lubricating oil. In order to verify the feasibility of the system, both reflection and transmission spectral of lubricating oil were taken. Partial least square regression (PLS) and back propagation neural network (BPNN) algorithms were used to establish the processing model. Modelling results show a good agreement in between two different probing modes. As a result, the effectiveness and reliability of the system have been proved, which provides a simple yet accurate method for real time monitoring the healthy status of the lubricating oil for the safe operation of the high-speed rail.

关键词： Vis-NIR spectroscopy Lubricating oils Moisture content The reflection probe successive projections algorithm Partial least square regression Back propagation neural network

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Screening analysis of biodiesel feedstock using UV-vis, NIR and synchronous fluorescence spectrometries and the successive projections algorithm

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TALANTA 2012年 97卷 579-583页

作者： Insausti, Matias Gomes, Adriano A. Cruz, Fernanda V. Pistonesi, Marcelo F. Araujo, Mario C. U. Galvao, Roberto K. H. Pereira, Claudete F. Band, Beatriz S. F. Univ Fed Paraiba Dept Quim Lab Automacao & Instrumentacao Quim Analit Quimio BR-58051970 Joao Pessoa Paraiba Brazil Univ Nacl Sur Sect Quim Analit INQUISUR CONICET FIA Lab RA-8000 Bahia Blanca Buenos Aires Argentina Inst Tecnol Aeronaut Div Engn Eletron Sao Jose Dos Campos SP Brazil Univ Fed Rural Pernambuco Dept Quim BR-52171900 Recife PE Brazil

This paper investigates the use of UV-vis, near infrared (NIR) and synchronous fluorescence (SF) spectrometries coupled with multivariate classification methods to discriminate biodiesel samples with respect to the base oil employed in their production. More specifically, the present work extends previous studies by investigating the discrimination of corn-based biodiesel from two other biodiesel types (sunflower and soybean). Two classification methods are compared, namely full-spectrum SIMCA (soft independent modelling of class analogies) and SPA-LDA (linear discriminant analysis with variables selected by the successive projections algorithm). Regardless of the spectrometric technique employed, full-spectrum SIMCA did not provide an appropriate discrimination of the three biodiesel types. In contrast, all samples were correctly classified on the basis of a reduced number of wavelengths selected by SPA-LDA. It can be concluded that UV-vis, NIR and SF spectrometries can be successfully employed to discriminate corn-based biodiesel from the two other biodiesel types, but wavelength selection by SPA-LDA is key to the proper separation of the classes. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Biodiesel UV-vis Near infrared and synchronous Fluorescence spectrometry Wavelength selection SIMCA successive projections algorithm Linear discriminant analysis

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A global model for the determination of prohibited addition in pesticide formulations by near infrared spectroscopy

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INFRARED PHYSICS & TECHNOLOGY 2020年 105卷 103191-000页

作者： Li, Qianqian Xie, Jianchun Zhang, Jixiong Yan, Hong Xiong, Yanmei Liu, Wei Min, Shungeng Beijing Univ Chinese Med Sch Chinese Mat Med Beijing 100029 Peoples R China Beijing Technol & Business Univ Beijing Lab Food Qual & Safety Beijing 100048 Peoples R China China Agr Univ Coll Sci Beijing 100193 Peoples R China Chongqing Grain & Oil Qual Supervis & Inspect Stn Chongqing 400026 Peoples R China

Generally, a pesticide was determined by a specific model, so different models were established for different pesticides as the backgrounds between pesticides formulations were with relatively huge discrepancy. It is expected to develop a more adaptive model which could not only improve the efficiency for prohibited addition detection but also afford an extensive application of the target pesticide. Moreover, less time would be spent on maintaining the model and thus the model supervision ability would be improve. In this paper, a global model for the determination of fipronil in a mixed matrix of imidacloprid and cypermethrin formulations was developed. The global model was established by means of two chemometrics methods, namely: full spectrum partial least squares (PLS) and competitive adaptive reweighted sampling-successive projections algorithm (CARS-SPA). Furthermore, the global model was investigated by the following two aspects: algorithm strategies of PLS and CARS-SPA, and spectroscopy interpretation. Results revealed that CARS-SPA combined with NIR could be efficiently used for rapid identification of prohibited addition in imidacloprid and cypermethrin formulations. Simultaneously, this study provided a theoretical support in the aspect of developing a global model for quantitative analysis.

关键词： Global model Competitive adaptive reweighted sampling successive projections algorithm Partial least squares Quantitative analysis

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Digital image-based tracing of geographic origin, winemaker, and grape type for red wine authentication

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FOOD CHEMISTRY 2020年第0期312卷 126060-000页

作者： de Lima, Carlos Monteiro Sousa Fernandes, David Douglas Pereira, Giuliano Elias Gomes, Adriano de Araujo Ugulino de Araujo, Mario Cesar Goncalves Dias Diniz, Paulo Henrique Univ Fed Paraiba Dept Quim POB 5093 BR-58051970 Joao Pessoa Paraiba Brazil Empresa Brasileira Pesquisa Agr Ctr Pesquisa Agr Trop Semiarido EMBRAPA BR-56302970 Petrolina PE Brazil Univ Fed Rio Grande do Sul Inst Quim BR-90650001 Porto Alegre RS Brazil Univ Fed Oeste Bahia Programa Posgrad Quim Pura & Aplicada BR-47810059 Barreiras BA Brazil

This work proposes the development of a simple, fast, and inexpensive methodology based on color histograms (obtained from digital images), and supervised pattern recognition techniques to classify red wines produced in the Sao Francisco Valley (SFV) region to trace geographic origin, winemaker, and grape variety. PCA-LDA coupled with HSI histograms correctly differentiated all of the SFV samples from the other geographic regions in the test set;SPA-LDA selecting just 10 variables in the Grayscale + HSI histogram achieved 100% accuracy in the test set when classifying three different SFV winemakers. Regarding the three grape varieties, SPA-LDA selected 15 variables in the RGB histogram to obtain the best result, misclassifying only 2 samples in the test set. Pairwise grape variety classification was also performed with only 1 misclassification. Besides following the principles of Green Chemistry, the proposed methodology is a suitable analytical tool;for tracing origins, grape type, and even (SFV) winemakers.

关键词： Red wine Cabernet Sauvignon Syrah Touriga Nacional Geographical origin indication Color histogram Digital images successive projections algorithm

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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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Moisture spectral characteristics and hyperspectral inversion of fly ash-filled reconstructed soil

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021年 253卷 119590-119590页

作者： Xia, Ke Xia, Shasha Shen, Qiang Yang, Bin Song, Qiang Xu, Yunfei Zhang, Shiwen Zhou, Xu Zhou, Yan Anhui Univ Sci & Technol Sch Geomat Huainan 232001 Peoples R China Anhui Univ Sci & Technol Sch Earth & Environm Huainan 232001 Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing 211100 Peoples R China Minist Nat Resource Land Consolidat & Rehabil Ctr Beijing 100035 Peoples R China

To explore problems with the fast estimation method of moisture content (MC) in reconstructed soil under human disturbance, this paper used a fly ash-filled reconstructed soil region as the research object and obtained experimental data by Fieldspec4 high spectrometry and the laboratory drying method. The response characteristics of MC were analyzed from the original spectral data that underwent mathematical transformation and the spectral index data, and a corresponding inversion model was established. Combined with the successive projections algorithm (SPA), the model was optimized with a better fitting effect, and the optimal inversion model was obtained. The results showed that the composition of soil and fly ash were different, resulting in obvious differences in the shape of the spectral curve, but both had large moisture absorption peaks near 1420 nm and 1920 nm. After mathematical transformation, the correlation between the spectral reflectance and MC was enhanced, in which the absolute value of the maximum correlation between the soil moisture content (SMC) was 0.839, and the absolute value of the maximum correlation between the fly ash moisture content (FMC) was 0.801. Among them, the first-order differential of multivariate scattering correction (MSC') and the first-order differential of logarithm ((lgR)') had higher fitting accuracy for FMC and SMC, respectively. The scale and sensitivity of significance variables were greatly improved based on the spectral index of two-band operation. Better FMC and SMC models were constructed based on the difference soil index (DSI) under mathematical transformation, and R-2 were 0.73 and 0.87, respectively. After SPA optimization, the predictive ability of the model was further improved, in which the predictive accuracy R-2 of FMC and SMC reached up to 0.87 and 0.96, respectively, and the RPD was greater than 3. This shows that the DSI model based on MSC' and (lgR)' combined with the SPA method can be used as an effec

关键词： Hyperspectral Fly ash-filled reconstructed soil Moisture content Mathematical transformation Spectral index successive projections algorithm

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Measurement of potentially toxic elements in the soil through NIR, MIR, and XRF spectral data fusion

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 187卷 106257-106257页

作者： Li, Fang Xu, Li You, Tianyan Lu, Anxiang Beijing Res Ctr Agr Stand & Testing Beijing 100097 Peoples R China Jiangsu Univ Sch Agr Engn Inst Agr Engn Zhenjiang 212013 Jiangsu Peoples R China

This paper aims to investigate the feasibility combination of X-ray fluorescence (XRF), near-infrared (NIR), and mid-infrared (MIR) sensors for the detection of seven key monitoring elements in the soil. Two strategies for data fusion were adopted: (i) the XRF characteristic bands were fused with all spectral data of NIR and MIR separately, and (ii) the XRF characteristic bands were fused with the characteristic bands of NIR and MIR, respectively. Also, different feature extraction methods were compared. The best feature extraction methods for XRF-NIR and XRFMIR models were principal component analysis (PCA) and successive projections algorithm (SPA). The modeling results showed that strategy (ii) showed better performance, and the XRF-MIR model provided more accurate results than the XRF-NIR model. Both XRF-MIR and XRF-NIR methods improved the accuracy of As, Cr, Cu, Ni, and Zn. The XRF-MIR obtained the best predictions, and the determination coefficients (R2) were 0.93, 0.98, 0.98, 0.95, and 0.98. For Pb and Cd, the measurement could be obtained by XRF alone, and the corresponding R2 were both 0.98. The results confirmed that sensor fusion can effectively improve the accuracy of the spectrometer in detecting metal elements in the soil.

关键词： X-ray fluorescence Near-infrared Mid-infrared successive projections algorithm Partial least square regression

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Assessment of calibration methods for nitrogen estimation in wet and dry soil samples with different wavelength ranges using near-infrared spectroscopy

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 186卷 106181-106181页

作者： Wang, Qinqin Zhang, Hao Li, Fadong Gu, Congke Qiao, Yunfeng Huang, Siyuan Insentek Co Ltd 2 Beixin RdEast Third Ring Rd Beijing 100027 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100094 Peoples R China

Choosing an appropriate wavelength range, extracting optimal wavelength variables, and selecting suitable statistical analysis methods are of great importance for improving the prediction accuracy of soil nitrogen (N) with near-infrared (NIR) spectroscopy. In this study, the prediction performances of two different wavelength ranges, a short wavelength range (SWR) of 900-1,700 nm and a full wavelength range (FWR) of 900-2,500 nm, are evaluated for the measurement of soil N content. Spectral scanning is performed on wet and dry-sieve soil samples to assess the effect of moisture on the prediction performance of soil N. Two calibration methods, a commonly used linear partial least squares regression (PLSR) and a nonlinear back propagation neural network (BPNN), are used. To understand if it is possible to reduce the number of wavelength variables without decreasing prediction accuracy, we introduce a successive projection algorithm (SPA) to extract wavelength variables that are minimally redundant. The results show that models developed within FWR outperform those developed within SWR, regardless of wet or dry soil conditions, which can be attributed to the presence of more spectral information related to soil N in FWR. Compared with PLSR, BPNN is a better choice for predicting soil N, because BPNN models provide higher accuracy. The best prediction performance is achieved by BPNN method in FWR using a SPA with Rp2 = 0.93, RMSEP = 0.0297% and RPD = 4.00 of wet soil samples, and Rp2 = 0.99, RMSEP = 0.0132% and RPD = 8.76 of dry soil samples. Additionally, we demonstrate that using the SPA algorithm significantly reduces the number of wavelength variables while maintaining high prediction accuracy. The characteristic wavelengths selected by the SPA algorithm follow the principle of material spectral absorption. It is worth noting that dry soil conditions lead to superior performance over wet soil conditions for the measurement of soil N, which can be attributed to the r

关键词： Soil nitrogen Partial least squares regression Back propagation neural network successive projections algorithm

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In-situ authentication of goat milk in terms of its adulteration with cow milk using a low-cost portable NIR spectrophotometer

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MICROCHEMICAL JOURNAL 2021年 163卷 105885-105885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 Grad Program PPGCTA BR-58051900 Joao Pessoa Paraiba Brazil State Univ Paraiba CCT NUPEA BR-58429500 Campina Grande Paraiba Brazil Univ Fed Paraiba Dept Chem CCEN BR-58051970 Joao Pessoa Paraiba Brazil Fed Univ Western Bahia Pure & Appl Chem Grad Program BR-47810059 Barreiras BA Brazil Univ Fed Rural Pernambuco Food Sci & Technol Grad Program BR-52171900 Recife PE Brazil

This work aimed at the use of a miniaturized near-infrared (NIR) spectrophotometer for the in-situ goat milk authentication in terms of its adulteration by adding cow milk. For this, One-Class Partial Least Squares (OC-PLS), PLS for Discriminant Analysis (PLS-DA), and the successive projections algorithm for interval selection in PLS-DA (iSPA-PLS-DA) were employed. OC-PLS misclassified 1 pure goat milk sample as adulterated and 49 adulterated sample as pure in the test set, while PLS-DA misclassified 1 adulterated sample as pure goat milk and 2 pure goat milk samples as adulterated in the test set. On the other hand, the best predictive ability was achieved by iSPA-PLS-DA when using the spectra pre-processed with smoothing by 7-point window moving mean and baseline offset correction, classifying correctly 100% of the pure goat milk samples and misclassifying only one adulterated sample in the test set. This result reinforces the fact of the selection of intervals by SPA provided a more parsimonious model, with less latent variables than the other models, since it selects only the most relevant and interpretable chemical information. Therefore, the proposed methodology represents a promise, nondestructive, fast, and low-cost tool for screening the authentication of raw goat milk, also helping to support future decisions by the regulatory agencies to prevent this kind of fraud directly in the production local.

关键词： Dairy product Quality control Near-infrared spectroscopy successive projections algorithm Pattern recognition

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Optical Spectroscopy Sequential Wavelength Selection Using a Higher Leverage Approach

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2021年第6期5卷 1-4页

作者： Aquino, Bernardo Gupta, Vijay Howard, Scott Univ Notre Dame Dept Elect Engn Notre Dame IN 46556 USA

The problem of minimizing wavelength usage and, therefore, decreasing measurement time in spectroscopy experiments has been studied over the years, under many different scenarios. These solutions offer optimal or suboptimal wavelengths for systems with substances. In this letter, we propose a deterministic algorithm for wavelength selection for substance detection and classification of spectral data, generated by overdetermined systems, that is, when the number of wavelengths is greater than the number of substances. This type of problem is common when the increasing popular low signal-to-noise portable thermal sensors are used. A cardinality penalizing constrained multivariate regression is also proposed as the criterion for evaluating the mean-squared error. As a practical example, the spectrum of five different substances [i.e., 1) lignin, 2) cellulose, 3) petroleum jelly, 4) pentaerythritol tetranitrate, and 5) hexamine] were chosen as a proof of concept to the proposed method and compared with a similar method and were shown to achieve lower mean-squared error.

关键词： Mathematical model Wavelength measurement Minimization Sensors Linear regression Spectroscopy Optimization Sensor signal processing higher leverage spectroscopy successive projections algorithm variable selection

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