An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter

José L. Safanelli, Jonathan Sanderman, Dellena Bloom, Katherine Todd-Brown, Leandro L. Parente, Tomislav Hengl, Sean Adam, Franck Albinet, Eyal Ben-Dor, Claudia M. Boot, James H. Bridson, Sabine Chabrillat, Leonardo Deiss, José A.M. Demattê, M. Scott Demyan, Gerd Dercon, Sebastian Doetterl, Fenny van Egmond, Rich Ferguson, Loretta G. GarrettMichelle L. Haddix, Stephan M. Haefele, Maria Heiling, Javier Hernandez-Allica, Jingyi Huang, Julie D. Jastrow, Konstantinos Karyotis, Megan B. Machmuller, Malefetsane Khesuoe, Andrew Margenot, Roser Matamala, Jessica R. Miesel, Abdul M. Mouazen, Penelope Nagel, Sunita Patel, Muhammad Qaswar, Selebalo Ramakhanna, Christian Resch, Jean Robertson, Pierre Roudier, Marmar Sabetizade, Itamar Shabtai, Faisal Sherif, Nishant Sinha, Johan Six, Laura Summerauer, Cathy L. Thomas, Arsenio Toloza, Beata Tomczyk-Wójtowicz, Nikolaos L. Tsakiridis, Bas van Wesemael, Finnleigh Woodings, George C. Zalidis, Wiktor R. Żelazny

Research output: Contribution to journalArticlepeer-review


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 NSSC-KSSL 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.

Original languageEnglish (US)
Article number116724
StatePublished - Dec 2023


  • Calibration transfer
  • Chemometrics
  • Ring trial
  • Soil spectroscopy
  • Spectral standardization

ASJC Scopus subject areas

  • Soil Science


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