Optimizing setup of scan number in FTIR spectroscopy using the moment distance index and PLS regression: application to soil spectroscopy

Abstract

Vibrational spectroscopy such as Fourier-transform infrared (FTIR), has been used successfully for soil diagnosis owing to its low cost, minimal sample preparation, non-destructive nature, and reliable results. This study aimed at optimizing one of the essential settings during the acquisition of FTIR spectra (viz. Scans number) using the standardized moment distance index (SMDI) as a metric that could trap the fine points of the curve and extract optimal spectral fingerprints of the sample. Furthermore, it can be used successfully to assess the spectra resemblance. The study revealed that beyond 50 scans the similarity of the acquisitions has been remarkably improved. Subsequently, the effect of the number of scans on the predictive ability of partial least squares regression models for the estimation of five selected soil properties (i.e., soil pH in water, soil organic carbon, total nitrogen, cation exchange capacity and Olsen phosphorus) was assessed, and the results showed a general tendency in improving the correlation coefficient (R²) as the number of scans increased from 10 to 80. In contrast, the cross-validation error RMSECV decreased with increasing scan number, reflecting an improvement of the predictive quality of the calibration models with an increasing number of scans.

Figure 1

Scheme of the workflow for building the database used for the optimization of the number of scans/replica in FTIR analysis of the twelve reference soil samples.

Figure 2

Schematic diagram for MDI calculation for spectral reflectance curve (the number of points between LP and RP pivots can vary depending on the spectral resolution and the width of the selected range).

Figure 3

FTIR spectra of the twelve reference soil samples (50 replicates/sample) in the Mid-infrared range (600 to 4000 cm⁻¹).

Figure 4

Calculated standardized moment distance index (SMDI) for all spectra of the twelve soils samples resulting from different scan numbers. The points’ (SMDI) rapprochement indicates the improvement of the spectra’s similarity.

Figure 5

Variation of the correlation coefficients according to the number of scans fixed during the acquisition of the FTIR spectra of the forty soil samples.

Figure 6

Variation of the root mean squared error of cross validation (RMSECV) for five selected soil properties according to the selected scan number setup of forty soil spectra acquisition.

Figure 7

PLSR models of the five properties of interest, e.g., soil pH, TOC, TN, CEC and Olsen P depending on the scan number when recording the forty FTIR spectra.