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

Pan, Tao; Li, Minmiao; Chen, Jiemei

doi:10.1366/13-07088

Cited by 54 publications

(28 citation statements)

References 30 publications

(29 reference statements)

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“…The selection of a reasonable F is necessary but di±cult. 10,12,17,18,23 Each waveband corresponded to a unique combination of parameters (I, NÞ ¼ ðI 0 , N 0 ); the optimal F was determined according to the following expression:…”

Section: Integrated Ao-mw-pls Methodsmentioning

confidence: 99%

“…Given that NIR absorption strength is weak, most sample types can be measured directly without preprocessing and reagents. The NIR spectroscopic analysis is a rapid and reagentless technique that has been proven to be a powerful analytical tool for use in agriculture, [9][10][11][12][13] food, 14,15 environment, 16,17 medicine, 18,19 pharmaceuticals, 20 and so on. However, to the best of our knowledge, a quanti¯cation method for TCP using NIR spectroscopy has not been developed yet.…”

Section: Introductionmentioning

confidence: 99%

“…However, waveband selection is necessary because the prediction e®ect of PLS is di±cult to improve when the signal-to-noise ratio of a waveband is not adequately high. 10,12,17,18 PLS analysis combined with waveband selection is important for spectral analysis of complex systems. Moving window PLS (MW-PLS), a well-performed and PLS-based method, is proven as an e®ective method for waveband selection in the analysis of many objects.…”

Section: Introductionmentioning

confidence: 99%

“…Moving window PLS (MW-PLS), a well-performed and PLS-based method, is proven as an e®ective method for waveband selection in the analysis of many objects. [10][11][12]17,18,23 Absorbance is another important factor to consider because high absorbance leads to strong spectral absorption and noise interference, whereas low absorbance results in missing certain information. Each wavelength corresponds to an absorbance value in a spectrum.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

AO–MW–PLS method applied to rapid quantification of teicoplanin with near-infrared spectroscopy

Chen

Pan

et al. 2017

J. Innov. Opt. Health Sci.

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Teicoplanin (TCP) is an important lipoglycopeptide antibiotic produced by fermenting Actinoplanes teichomyceticus. The change in TCP concentration is important to measure in the fermentation process. In this study, a reagent-free and rapid quanti¯cation method for TCP in the TCP-Tris-HCl mixture samples was developed using near-infrared (NIR) spectroscopy by focusing our attention on the fermentation process for TCP. The absorbance optimization (AO) partial least squares (PLS) was proposed and integrated with the moving window (MW) PLS, which is called AO-MW-PLS method, to select appropriate wavebands. A model set that includes various wavebands that were equivalent to the optimal AO-MW-PLS waveband was proposed based on statistical considerations. The public region of all equivalent wavebands was just one of the equivalent wavebands. The obtained public regions were 1540-1868 nm for TCP and 1114-1310 nm for Tris. The root-mean-square error and correlation coe±cient for leave-oneout cross validation were 0.046 mg mL À1 and 0.9998 mg mL À1 for TCP, and 0.235 mg mL À1 and 0.9986 mg mL À1 for Tris, respectively. All the models achieved highly accurate prediction e®ects, and the selected wavebands provided valuable references for designing specialized spectrometers. This study provided a valuable reference for further application of the proposed methods to TCP fermentation broth and to other spectroscopic analysis¯elds.

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Section: Integrated Ao-mw-pls Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

AO–MW–PLS method applied to rapid quantification of teicoplanin with near-infrared spectroscopy

Chen

Pan

et al. 2017

J. Innov. Opt. Health Sci.

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“…Near-infrared (NIR) spectroscopy as a simple and quick tool has been e®ectively utilized in various elds for quantitative and qualitative analysis, such as agriculture, [1][2][3][4][5][6] food, 7-9 environment, 10,11 biomedicine, [12][13][14][15][16] petroleum industry, 17 and so on. 18 Spectral discriminant analysis is a methodology that uses computer pattern recognition to identify and classify samples based on the collected spectral data.…”

Section: Introductionmentioning

confidence: 99%

Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

Yao

Pan

et al. 2018

J. Innov. Opt. Health Sci.

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The moving-window bis-correlation coe±cients (MW-BiCC) was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and -thalassemia with visible and nearinfrared (Vis-NIR) spectroscopy. The well-performed moving-window principal component analysis linear discriminant analysis (MW-PCA-LDA) was also conducted for comparison. A total of 306 transgenic (positive) and 150 nontransgenic (negative) leave samples of sugarcane were collected and divided to calibration, prediction, and validation. The di®use re°ection spectra were corrected using Savitzky-Golay (SG) smoothing with¯rst-order derivative (d ¼ 1), third-degree polynomial (p ¼ 3) and 25 smoothing points (m ¼ 25). The selected waveband was 736-1054 nm with MW-BiCC, and the positive and negative validation recognition rates (V REC þ , V REC À Þ were 100%, 98.0%, which achieved the same e®ect as MW-PCA-LDA. Another example, the 93 -thalassemia (positive) and 148 nonthalassemia (negative) of human hemolytic samples were collected. The transmission spectra were corrected using SG smoothing with d ¼ 1, p ¼ 3 and m ¼ 53. Using MW-BiCC, many best wavebands were selected (e.g., 1116-1146, 1794-1848 and 2284-2342 nm). The V REC þ and V REC À were both 100%, which achieved the same e®ect as MW-PCA-LDA. Importantly, the BiCC only required calculating correlation coe±cients between the spectrum of prediction sample and the average spectra of two types of calibration samples. Thus, BiCC was very simple in algorithm, and expected to obtain more applications. The results¯rst con¯rmed the feasibility of distinguishing -thalassemia and normal control samples by NIR spectroscopy, and provided a promising simple tool for large population thalassemia screening.

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Organic matter estimation of surface soil using successive projection algorithm

et al. 2021

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Soil organic matter (SOM) affects circulation and stability of the soil C pool, and plays a key role in global C change and local ecological balance. Timely and accurate acquisition of large‐scale farmland soil organic carbon (SOC) information is of great significance to the study of SOC reserves and distribution, farmland soil ecological security, and scientific crop management. In this study, we have evaluated the potentiality of visible‐near‐infrared spectroscopy to along with machine learning algorithms to predict SOM content. At present, soil characterization is mainly focused on hyperspectral data measured by remote‐sensing spectral index and spectrometer. This article aims to establish a statistical machine‐learning model to study the efficiency of spectral data to comprehensively evaluate SOM. The results show that after transforming the different forms of the original spectrum and combining with successive projection algorithm (SPA), the characteristic wavelength of organic matter at 350–2,500 nm can be effectively extracted. A combination of spectroscopy and chemometrics techniques can be used as a practical, rapid, low‐cost, and quantitative method to evaluate SOM. Based on SPA, partial least squares regression, support vector machine, random forest, a SOM prediction model with optimal characteristic wavelength is constructed. The best spectral processing method is continual removal combined with SPA, the validation coefficient of determination, root mean square error prediction, and ratio of performance deviation values are 0.82, 1.12, and 2.87, respectively.

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

Cited by 54 publications

References 30 publications

AO–MW–PLS method applied to rapid quantification of teicoplanin with near-infrared spectroscopy

AO–MW–PLS method applied to rapid quantification of teicoplanin with near-infrared spectroscopy

Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

Organic matter estimation of surface soil using successive projection algorithm

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