Bridging the gap between soil spectroscopy and traditional laboratory: Insights for routine implementation

[Display omitted] •We analyzed the analytical quality of 36 commercial laboratories from Brazil and Paraguay.•Outlying attributes in spectral clusters detected by adjusted method to the data distribution.•Spectra-based systematic sampling had attributes equally distributed among the subsets.•Nearly 25% laboratories had the potential to reduce the need for laboratory analysis by at least 50%. We need innovative solutions to help laboratories to rapidly characterize the soils and adopt internal quality controls with lower costs and ecosystem impacts. Soil spectroscopy emerged as an alternative to wet chemistry. However, soil laboratories still do not widely use this technology in their routines, mainly due to the lack of: i) standards and protocols, ii) spectral libraries, iii) capacity in spectral methods, and iv) professionals with expertise in chemometrics. Therefore, we aimed to provide basic guidelines for the use of soil spectra in laboratory routines regarding internal quality control and samples selection for analysis and prediction. We used 350–2500 nm spectra and unsupervised random forests to compute proximity matrices and cluster spectrally similar soil samples for outlier detection using an adjusted method for skewed distribution, which allowed us to establish an internal quality control. Then, we used the most important wavelengths from unsupervised random forest to order soil samples by laboratory and subset them into different set sizes for training and testing prediction models for clay, sand, organic matter cation exchange capacity and base saturation. Our results indicated that internal quality control based on soil spectra and unsupervised analysis can be implemented for laboratory routines. Spectra-based soil samples selection and attributes predictions can reduce the need for laboratory analysis by half. Soil spectroscopy can become an important alternative to improve traditional analytical results, save time, reduce costs, and mitigate environmental impact with acceptable accuracy. Soil data and full codes from this study are publicly available at https://github.com/raulpoppiel/urssa.