Modified self‐adaptive model for improving the prediction accuracy of soil organic matter by laser‐induced breakdown spectroscopy

Abstract Soil organic matter (SOM) significantly influences soil fertility, biology, and the global C cycle. Laser‐induced breakdown spectroscopy (LIBS) is attractive, as it can measure SOM accurately and quickly with minimal sample preparation and effective cost. Our earlier self‐adaptive (SA) model based on individual distances between LIBS soil spectra coupled with partial least squares (PLS) was favorable for SOM prediction. To optimize this model by including soil identification via the use of a hybrid distance between the spectra, two hybrid distance SA models to enhance SOM predictions from LIBS spectra in 250 Chinese topsoils were built and studied. The models used LIBS soil spectra and SOM content measured by the traditional potassium dichromate oxidation method for calibration. The performance of these models for the validation set was compared with the conventional no‐identification PLS model and SA‐PLS models. The modified models that used (a) a hybrid of Euclidean distance (ED) and angle cosine (SA xc –PLS), and (b) a hybrid of ED and the distance of response y (SA xy –PLS) improved SOM predictions , with a R 2 of 0.908, a RMSE of 5.65 g kg −1 , and a residual prediction deviation (RPD) of 2.91 in the validation set for SA xc –PLS; and a R 2 of 0.900, a RMSE of 5.49 g kg −1 , and a RPD of 2.99 in the validation set for SA xy –PLS. The improved performance mainly resulted from simultaneous consideration of diversity and correlation of the spectra. Therefore, modified SA models hold great potential for SOM prediction, especially for different soils.