Vis/Near- and Mid- Infrared Spectroscopy for Predicting Soil N and C at a Farm Scale

Yang, Haiqing; Mouazen, Abdul Mounem

doi:10.5772/36393

Cited by 21 publications

(20 citation statements)

References 20 publications

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“…It might be practicable to use the visible and near-infrared (vis-NIR) spectroscopy for the characterization of biochar properties, although using IR range (4000∼400 cm −1 ) may produce higher performance than vis-NIR range, as shown in other reports [9,10].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Biochar Properties Affected by Different Pyrolysis Temperatures Using Visible-Near-Infrared Spectroscopy

Yang

Sheng

2012

ISRN Spectroscopy

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Rapid characterization of biochar for energy and ecological purpose utilization is fundamental. In this work, visible and nearinfrared (vis-NIR) spectroscopy was used to measure ash, volatile matter, fixed carbon contents, and calorific value of three types of biochar produced from pine wood, cedar wood, and cotton stalk, respectively. The vis-NIR spectroscopy was also used to discriminate biochar feedstock types and pyrolysis temperature. Prediction result shows that partial least squares (PLS) regression calibrating the spectra to the values of biochar properties achieved very good or excellent performance with coefficient of determination (R 2 ) of 0.86∼0.91 and residual prediction deviation (RPD) of 2.58∼3.32 for ash, volatile matter, and fixed carbon, and good prediction with R 2 of 0.81 and RPD of 2.30 for calorific value. Linear discrimination analysis (LDA) of the principal components (PCs) produced from PCA of wavelength matrix shows that three types of biochar can be successfully discriminated with 95.2% accuracy. The classification of biochar with different pyrolysis temperatures can be conducted with 69% accuracy for all three types and 100% accuracy for single type of cotton stalk. This experiment suggests that the vis-NIR spectroscopy is promising as an alternative of traditionally quantitative and qualitative analysis of biochar properties.

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Section: Introductionmentioning

confidence: 99%

Characterization of Biochar Properties Affected by Different Pyrolysis Temperatures Using Visible-Near-Infrared Spectroscopy

Yang

Sheng

2012

ISRN Spectroscopy

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“…Diffuse Reflectance Spectroscopy (DRS) could address these needs by predicting soil properties using their spectroscopic signatures in the ultraviolet-visibleinfrared (UV-Vis-IR) domain. Various approaches have been tested to relate UV-Vis-IR spectra to many soil parameters, such as soil organic matter (SOM), total organic carbon (TOC), total carbon, total nitrogen, texture, as well as biological properties (Baumgardner et al 1985, Henderson et al 1992, Ben-Dor 2002, Viscarra Rossel et al 2006a, Zornoza et al 2008, Yang & Mouazen 2012, Heinze et al 2013, Conforti et al 2015.…”

Section: Introductionmentioning

confidence: 99%

“…The latter goal has particular importance for high dimensional regressions, where few variables which correctly predict the true response have to be identified among thousands of possible predictors. In this context, techniques such as the Bayesian variable selection approaches (Brown et al 2001) or the Minimum Average Variance Estimation (MAVE - Amato et al 2006), as well as methods that produce sparse solutions like the LASSO (Zhao et al 2013), could be used to reduce the dimensionality of the data. Most regression techniques try to address both goals at the same time, although this is not prerequisite: Paul et al (2008) recently proposed a new approach -called "pre-conditioning" -that uses two different methods to address the relative goals.…”

Section: Introductionmentioning

confidence: 99%

Chemometric technique performances in predicting forest soil chemical and biological properties from UV-Vis-NIR reflectance spectra with small, high dimensional datasets

Bellino¹,

Colombo²,

Iovieno³

et al. 2016

iForest

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(1) Chemometric analysis applied to diffuse reflectance spectroscopy is increasingly proposed as an effective and accurate methodology to predict soil physical, chemical and biological properties. Its effectiveness, however, largely varies in relation to the calibration techniques and the specific soil properties. In addition, the calibration of UV-Vis-NIR spectra usually requires large datasets, and the identification of techniques suitable to deal with small sample sizes and high dimensionality problems is a primary challenge. In order to investigate the predictability of many soil chemical and biological properties from a small dataset and to identify the most suitable techniques to deal with this type of problems, we analysed 20 top soil samples of three different forests (Fagus sylvatica, Quercus cerris and Quercus ilex) in southern Apennines (Italy). Diffuse reflectance spectra were recorded in the UV-Vis-NIR range (200-2500 nm) and 22 chemical and biological properties were analysed. Three different calibration techniques were tested, namely the Partial Least Square Regression (PLSR), the combinations wavelet transformation/Elastic net and wavelet transformation/Supervised Principal Component (SPC) regression/ Least Absolute Shrinkage and Selection Operator (LASSO), a kind of preconditioned LASSO. Calibration techniques were applied to both raw spectra and spectra subjected to wavelet shrinkage filtering, in order to evaluate the influence on predictions of spectra denoising. Overall, SPC/LASSO outperformed the other techniques with both raw and denoised spectra. Elastic net produced heterogeneous results, but outperformed SPC/LASSO for total organic carbon, whereas PLSR produced the worst results. Spectra denoising improved the prediction accuracy of many parameters, but worsen the predictions in some cases. Our approach highlighted that: (i) SPC/LASSO (and Elastic net in the case of total organic carbon) is especially suitable to calibrate spectra in the case of small, high dimensional datasets; and (ii) spectra denoising could be an effective technique to improve calibration results.

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“…Soil, as in the argument as part of road surrounding material, where not included as this identification due to the fact that it mainly consists of sands, clay and silt. Where the main contributor of silt is carbon material from vegetation natural fibers that noticeable from the infra red imaging [24]. A study was conducted in looking at different ingredients of organic carbon, clay and sands/aggregates in showing variation of the spectral characteristics range from 500nm to 2500nm [25].…”

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confidence: 99%

Urban Road Materials Identification using Narrow Near Infrared Vision System

Ipung

Tjandrasa

2017

IJECE

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An urban road materials vision system using narrow band near infrared imaging indexes were proposed. This proposed imaging indexes were enhancement for previous work on autonomous multispectral road sensing method. Each urban road material has different near infrared spectral patterns which is as the base of its spectral identification. The new proposed imaging indexes, which using similar formula of NDVI, was normalized with narrow band near infrared spectrum range of 720nm to 1000nm of wavelength, were used to identify concretes, aggregates/sands/rocks, clay, natural dry fibers and bitumen/asphalt that make up most of urban road materials. This paper proposes imaging indexes evaluation from experiment results to identify those urban road materials. There were seven narrow band optical filter sets with the center spectrum at 710nm, 730nm, 750nm, 800nm, 870nm, 905nm and 970nm. Normalization band used was 720nm using high pass optical filter. The proposed multi-spectral imaging indexes were able to show the potential to classify the selected urban road materials, another approach may need to clearly distinguish between concrete and aggregates. The comparison to the previous imaging indexes (NDVI, NDGR, NDBR) were presented that used for urban road materials identification. Keyword:Imaging index Narrow band optical filter NDBR NDGR NDVI Near infrared vision system Urban road materials

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Vis/Near- and Mid- Infrared Spectroscopy for Predicting Soil N and C at a Farm Scale

Cited by 21 publications

References 20 publications

Characterization of Biochar Properties Affected by Different Pyrolysis Temperatures Using Visible-Near-Infrared Spectroscopy

Characterization of Biochar Properties Affected by Different Pyrolysis Temperatures Using Visible-Near-Infrared Spectroscopy

Chemometric technique performances in predicting forest soil chemical and biological properties from UV-Vis-NIR reflectance spectra with small, high dimensional datasets

Urban Road Materials Identification using Narrow Near Infrared Vision System

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