Hyperspectral Visible-Near Infrared Determination of Arsenic Concentration in Soil

被引:21
|
作者
Stazi, Silvia Rita [1 ]
Antonucci, Francesca [2 ]
Pallottino, Federico [2 ]
Costa, Corrado [2 ]
Marabottini, Rosita [1 ]
Petruccioli, Maurizio [1 ]
Menesatti, Paolo [2 ]
机构
[1] Univ Tuscia, Dipartimento Innovaz Sistemi Biol Agroalimentari, Viterbo, Italy
[2] Consiglio Ric & Sperimentaz Agr, Unita Ric Ingn Agr, I-00015 Rome, Italy
关键词
partial least squares; hyperspectral imaging; Arsenic; support vector machines; soil contamination; SUPPORT VECTOR MACHINES; DIFFUSE-REFLECTANCE SPECTROSCOPY; ESTUARINE SEDIMENTS; ORGANIC-MATTER; HEAVY-METALS; RAPID METHOD; FIELD; REGRESSION; CALIBRATION; PREDICTION;
D O I
10.1080/00103624.2014.954716
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
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.
引用
收藏
页码:2911 / 2920
页数:10
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