Integrating ATR-FTIR and data-driven models to predict total soil carbon and nitrogen towards sustainable watershed management

Aslan-Sungur G., Evrendilek F., Karakaya N., Gungor K., Kilic S.

RESEARCH JOURNAL OF CHEMISTRY AND ENVIRONMENT, cilt.17, sa.6, ss.5-11, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 17 Sayı: 6
  • Basım Tarihi: 2013
  • Dergi Adı: RESEARCH JOURNAL OF CHEMISTRY AND ENVIRONMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.5-11
  • Anahtar Kelimeler: Artificial neural network, environmental monitoring, partial least square regression, soil management, INFRARED REFLECTANCE SPECTROSCOPY, PARTIAL LEAST-SQUARES, ORGANIC-CARBON, FRACTIONS, NITRATE
  • Akdeniz Üniversitesi Adresli: Hayır

Özet

The use of Attenuated Total Reflectance (ATR) is an alternative method in determining carbon (C), nitrogen (N) and other elemental contents of organic and inorganic soils for which diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy has been mostly utilized. In this study, the combined use of ATR-Fourier transform infrared (FTIR) spectroscopy and partial least square regression (PLSR) or artificial neural network (ANN) models in estimating total soil C and N have been explored which provide direct, rapid, economical and multiple in situ measurements. Total soil C and N data obtained from 153 soil samples across agricultural lands and analyzed using CNH elemental analyzer were used to build PLSR and ANN models as a function of ATR-FTIR spectrum ranges based on a training dataset with leave-one-out cross validation (LCV) and independent validation (IV) dataset that randomly constitute 67% and 33% of the entire dataset respectively.