@article{01HMED64TYPA8N1B7KE4QACP4S,
  abstract     = {{Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost-effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and operations still hamper further integration and interoperability across mid-infrared (MIR) soil spectral libraries. In this study, we conducted a large-scale ring trial experiment to understand the lab-to-lab variability of multiple MIR instruments. By developing a systematic evaluation of different mathematical treatments with modeling algorithms, including regular preprocessing and spectral standardization, we quantified and evaluated instruments' dissimilarity and how this impacts internal and shared model performance. We found that all instruments delivered good predictions when calibrated internally using the same instruments' characteristics and standard operating procedures by solely relying on regular spectral preprocessing that accounts for light scattering and multiplicative/additive effects, e.g., using standard normal variate (SNV). When performing model transfer from a large public library (the USDA NSSCKSSL MIR library) to secondary instruments, good performance was also achieved by regular preprocessing (e. g., SNV) if both instruments shared the same manufacturer. However, significant differences between the KSSL MIR library and contrasting ring trial instruments responses were evident and confirmed by a semi-unsupervised spectral clustering. For heavily contrasting setups, spectral standardization was necessary before transferring prediction models. Non-linear model types like Cubist and memory-based learning delivered more precise estimates because they seemed to be less sensitive to spectral variations than global partial least square regression. In summary, the results from this study can assist new laboratories in building spectroscopy capacity utilizing existing MIR spectral libraries and support the recent global efforts to make soil spectroscopy universally accessible with centralized or shared operating procedures.}},
  articleno    = {{116724}},
  author       = {{Safanelli, José L. and Sanderman, Jonathan and Bloom, Dellena and Todd-Brown, Katherine and Parente, Leandro L. and Hengl, Tomislav and Adam, Sean and Albinet, Franck and Ben-Dor, Eyal and Boot, Claudia M. and Bridson, James H. and Chabrillat, Sabine and Deiss, Leonardo and Demattê, José A.M. and Scott Demyan, M. and Dercon, Gerd and Doetterl, Sebastian and van Egmond, Fenny and Ferguson, Rich and Garrett, Loretta G. and Haddix, Michelle L. and Haefele, Stephan M. and Heiling, Maria and Hernandez-Allica, Javier and Huang, Jingyi and Jastrow, Julie D. and Karyotis, Konstantinos and Machmuller, Megan B. and Khesuoe, Malefetsane and Margenot, Andrew and Matamala, Roser and Miesel, Jessica R. and Mouazen, Abdul and Nagel, Penelope and Patel, Sunita and Qaswar, Muhammad and Ramakhanna, Selebalo and Resch, Christian and Robertson, Jean and Roudier, Pierre and Sabetizade, Marmar and Shabtai, Itamar and Sherif, Faisal and Sinha, Nishant and Six, Johan and Summerauer, Laura and Thomas, Cathy L. and Toloza, Arsenio and Tomczyk-Wójtowicz, Beata and Tsakiridis, Nikolaos L. and van Wesemael, Bas and Woodings, Finnleigh and Zalidis, George C. and Żelazny, Wiktor R.}},
  issn         = {{0016-7061}},
  journal      = {{GEODERMA}},
  keywords     = {{Soil spectroscopy,Ring trial,Chemometrics,Calibration transfer,Spectral standardization,ANALYTICAL QUALITY,SOIL,REFLECTANCE,PREDICTION,SPECTROSCOPY,PERFORMANCE,REGRESSION,LIBRARY}},
  language     = {{eng}},
  pages        = {{14}},
  title        = {{An interlaboratory comparison of mid-infrared spectra acquisition : instruments and procedures matter}},
  url          = {{http://doi.org/10.1016/j.geoderma.2023.116724}},
  volume       = {{440}},
  year         = {{2023}},
}

Altmetric

View in Altmetric

Web of Science

Times cited: