**4. Conclusions**

208 Infrared Spectroscopy – Life and Biomedical Sciences

with *R*2 of 0.69 and RPD of 1.83, whereas PLSR model for Vis-NIR-DRIFT spectra calibrated with 5 latent variables performed better than Vis-NIR– ATR with higher prediction accuracy (*R*2 of 0.70 and RPD of 1.94). These models for combinational spectra performed slightly better than those for Vis-NIR spectra (Table 4). However, these models did not produce better performance than those for DRIFT spectra (Table 6) and ATR spectra (Table 7) with

**3.6 Comparison of PLSR model performance among Vis-NIR, ATR-FTIR, DRIFT and** 

As shown in Tables 4, 6 and 7, model performance is not only a function of wavelength ranges used during PLS regression analysis, but also a function of spectral pre-processing techniques. Overall, for TN, TC and OC, PLSR models calibrated for DRIFT spectra outperformed those for Vis-NIR spectra and ATR-FTIR spectra. For IC, both ATR-FTIR and DRIFT models outperformed Vis-NIR models no matter what spectral pre-processing techniques were applied. However, if coupled with appropriate spectral pre-processing techniques, Vis-NIR models for TN and OC can produce competitive prediction performance (R2>0.90 and RPD>3.0) with less number of latent variables (3 or 4) as compared to best ATR-PLSR models calibrated with 6 latent variables. For TC, ATR-FTIR models performed slightly better than Vis-NIR models. The lower accuracy for the calibration of IC compared to TN, TC and OC may be attributed to errors in the reference method for IC determination, since IC is calculated by difference between TC and OC.

Researchers have reported that the particle size distribution within the soil sample population and also within each sample of the calibration set affects the accuracy of calibration for TC and OC both in Mid-IR and Vis-NIR (Madari*, et al.*, 2006; Mouazen*, et al.*, 2005, Yang*, et al.*, 2011b). However, Vis-NIR proved to be more sensitive to particle size effects than the Mid-IR range (Madari*, et al.*, 2006). Vis-NIR spectroscopy performed very well for a very homogenous sample population, even slightly better than Mid-IR, but with increasing heterogeneity among and within the soil samples the accuracy decreased drastically. By contrast, the particle size distribution had less effect in the Mid-IR range. For the very homogeneous sample population, the accuracy was slightly lower than Vis-NIR, but with the increase in the heterogeneity of the sample population the accuracy did not diminish drastically and was higher than using with Vis-NIR (Madari*, et al.*, 2006). Thus Mid-IR spectroscopy coupled with appropriate

Soil N content is often highly correlated with C (Martin*, et al.*, 2002). For example, Chang, *et al.* (2001) reported *r* of 0.95 between TC and TN. In this study, the mean (±s.d.) values of TC/TN and OC/TN are 10.6(±0.59) and 9.95(±0.52), respectively (Table 2). It is an interesting point to explore whether there is an independent spectral basis for the determination of N in soil by infrared (IR) spectroscopy or whether N is predicted through high correlation with C. In the work by Chang and Laird (2002), in which C and N were added to a soil resulting in a wide range of C-to-N ratios, N was proved to be predicted in soil independently of C. Although the N absorbers are present in the soil spectra, their absorbance is not as strong as that of C bonds, as the mass of C in soil is generally an order of magnitude higher than that of N (about 10 times in our case). Thus, Martin *et al*. (2002) explained that N is predicted best

chemometrics can be considered to be more robust than Vis-NIR.

**3.7 Fundamentals of predicting N in soil** 

only exception of Vis-NIR-ATR models for OC with *R*2 of 0.91 and RPD of 3.42.

**combinational spectra** 

The Mid-IR spectroscopy, including ATR and DRIFT, and Vis-NIR spectroscopy were implemented for the prediction of soil TN, TC, OC and IC. Results proved that both Vis-NIR and Mid-IR when combined with chemometric methods have great potential to quantify soil N and C at the field scale. It was also shown that DRIFT is more robust than Vis-NIR or ATR in terms of prediction accuracy. Although the Mid-IR spectra holds more information and usually easier to interpret as compared to Vis-NIR spectra with overtones and combinations features, until recently the MIR instruments are less portable and born to easier damage of optical materials. In contrast, the Vis-NIR has some advantages related to portability, mobile (on-line) measurement, remote sensing and others. This study suggests that Vis-NIR spectroscopy, if coupled with proper spectral pre-processing techniques, has the potential for successful prediction of soil N and C, although the combination and overtone peaks in the Vis-NIR spectral range are usually weak.
