Hyperspectral Visible and Near-Infrared Determination of Copper Concentration in Agricultural Polluted Soils

Antonucci, Francesca; Menesatti, Paolo; Holden, Nicholas M.; Canali, E.; Giorgi, Stefano; Maienza, Anita; Stazi, Silvia Rita

doi:10.1080/00103624.2012.670348

Cited by 20 publications

(7 citation statements)

References 34 publications

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“…Therefore, the development of rapid and inexpensive techniques, such as hyperspectral spectrophotometry, for investigating heavy-metal soil contamination could be of great value (Cécillon et al 2009;Kuang and Mouazen 2011). As reported by Antonucci et al (2012) hyperspectral sensors can collect information as sets of images. Each image represents a range of the electromagnetic spectrum and is referred to as spectral band.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Visible–Near Infrared Determination of Arsenic Concentration in Soil

Stazi

Antonucci²,

Pallottino³

et al. 2014

Communications in Soil Science and Plant Analysis

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The development of rapid techniques, such as hyperspectral spectrophotometry, for investigating arsenic (As) soil contamination could be of great value with respect to conventional methods. This study was conducted to detect As concentrations in artificially polluted soils (from 25 to 1045 mg kg −1 ) through hyperspectral visible-near infrared spectrophotometry and to compare two multivariate statistical regression analyses: partial least squares and support vector machines. The correlation coefficient r is greater in the partial least squares in both model (0.93%) and test (0.87%) with respect to support vector machines (0.88% for the model and 0.82% for the test). The most important model variables extracted from the variable importance in projection scores resulted the absorption peaks at 580, 660, 715, and 780 nm. Bands in the visible spectra are not directly associated to As, but the metalloid can interact with the main spectrally active components of soil permitting to multivariate statistical models to screen As concentrations.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Visible–Near Infrared Determination of Arsenic Concentration in Soil

Stazi

Antonucci²,

Pallottino³

et al. 2014

Communications in Soil Science and Plant Analysis

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“…The model adopted for weight prediction was selected from 540 PLS linear regression models considering the combination among X pre-processings (Abs, Autoscale, Baseline, Detrend, Mean center, Median center, None, Normalize, Snv), y pre-processings (Autoscale, Median center, None) and number of LVs, from 1 to 20 (the pre-processing techniques are summarized in Ref. [23]). The PLS-R models were developed from a calibration set (training/evaluation set [24]) of 530 chips (75% of the 706-chip sub-sample) with 109 X-variables (10 size and 99 shape descriptors) and 1 y-variable (weight or mass).…”

Section: Multivariate Modelingmentioning

confidence: 99%

“…The models adopted for the size fraction prediction of chips were selected considering the combination between X preprocessing (Abs, Autoscale, Baseline, Detrend, Diff1, gls weighting, Groupscale, Log1suR, Mean center, Median center, Msc (mean), None, Normalize, Snv) and the number of LVs (the pre-processing techniques are summarized in Ref. [23]). There was no Y pre-processing.…”

Section: Multivariate Modelingmentioning

confidence: 99%

Automated determination of poplar chip size distribution based on combined image and multivariate analyses

Febbi

Menesatti²,

Costa³

et al. 2015

Biomass and Bioenergy

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“…In the last two decades, NIRS has become a well-known and effective analytical tool in agricultural and ecological research, but its application in analyzing soil properties is a relatively recent development (Malley et al, 2004). In soil science, NIRS is now widely used to develop the spectral calibration models for different soil parameters, such as soil texture (Shepherd and Walsh, 2002;Cozzolino and Moron, 2003;Summers et al, 2011;Viscarra Rossel and Webster, 2012), soil pH, extractable phosphorus, carbon, and nitrogen (Dunn et al, 2002;Islam et al, 2003Yang et al, 2011Yang et al, 2012), and potassium, calcium, magnesium, sulfur, boron, sodium, manganese, iron, chloride, zinc, and copper (Cohen et al, 2005;Ficklin et al, 2007;Viscarra Rossel et al, 2006;Antonucci et al, 2012;Viscarra Rossel and Webster, 2012). Because of the range of potential applications, NIRS is considered to be one of the most promising analytical techniques of the 21st century, from both cost and precision perspectives (Workman and Shenk, 2004).…”

Section: Peermentioning

confidence: 99%

Development of Near Infrared Spectral Models for Characterizing the Amy Soil Series

Bhandari

Ficklin

2014

Soil Horizons

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Physical and chemical properties of soils are routinely analyzed to assess site parameters and optimize ecological resources for precision farming, environmental monitoring, and soil survey and classification. Since existing standard laboratory methods are resource intensive, expensive, and time consuming, a convenient, rapid, economical, and ecologically viable technology is needed. The goal of this research was to evaluate the feasibility of using near infrared reflectance spectroscopy (NIRS) as an alternative analytical technique using 200 soil samples collected at depth increments of 0 to 15 and 15 to 30 cm in soils mapped as https://soilseries.sc.egov.usda.gov/OSD_Docs/A/AMY.html silt loam in southeastern Arkansas. At the depth of 0 to 15 cm, NIRS successfully predicted several soil parameters, including sand (R2 = 0.85), silt (R2 = 0.80), clay (R2 = 0.91), N (R2 = 0.75), C (R2 = 0.77), C/N ratio (R2 = 0.78), P (R2 = 0.80), K (R2 = 0.83), Ca (R2 = 0.92), Mg (R2 = 0.92), Mn (R2 = 0.93), and B (R2 = 0.87). Similarly, at the depth of 15 to 30 cm, sand (R2 = 0.86), silt (R2 = 0.89), clay (R2 = 0.96), N (R2 = 0.79), C (R2 = 0.82), C/N ratio (R2 = 0.76), P (R2 = 0.88), K (R2 = 0.93), Ca (R2 = 0.89), Mg (R2 = 0.89), Na (R2 = 0.87), Mn (R2 = 0.96), Zn (R2 = 0.95), and B (R2 = 0.95) were well predicted. These results demonstrate that NIRS is a nondestructive, fast, accurate, reliable, cost effective, and ecologically viable analytical technique to augment standard laboratory methods for routine analyses of soil properties.

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Hyperspectral Visible and Near-Infrared Determination of Copper Concentration in Agricultural Polluted Soils

Cited by 20 publications

References 34 publications

Hyperspectral Visible–Near Infrared Determination of Arsenic Concentration in Soil

Hyperspectral Visible–Near Infrared Determination of Arsenic Concentration in Soil

Automated determination of poplar chip size distribution based on combined image and multivariate analyses

Development of Near Infrared Spectral Models for Characterizing the Amy Soil Series

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