European Journal of Soil Science, 2010 doi: 10.1111/j.1365-2389.2010.01301.x Soil properties prediction of western Mediterranean islands with similar climatic environments by means of mid-infrared diffuse reflectance spectroscopy L. P. D’Acqui a , A. Pucci a & L. J. Janik b a Istituto per lo Studio degli Ecosistemi, Consiglio Nazionale delle Ricerche - Via Madonna del Piano 10, 50019 Sesto Fiorentino (Firenze), Italy, and b Commonwealth Scientific and Industrial Research Organisation - Land & Water, PMB No 2, Glen Osmond, SA 5064, Australia Summary This study investigated the suitability of mid-infrared diffuse reflectance Fourier transform (MIR-DRIFT) spectroscopy, with partial least squares (PLS) regression, for the determination of variations in soil properties typical of Italian Mediterranean off-shore environments. Pianosa, Elba and Sardinia are typical of islands from this environment, but developed on different geological substrates. Principal components analysis (PCA) showed that spectra could be grouped according to the soil composition of the islands. PLS full cross-validation of soil property predictions was assessed by the coefficient of determination (R 2 ), the root mean square error of cross-validation and prediction (RMSECV and RMSEP), the standard error (SECV for cross-validation and SEP for prediction), and the residual predictive deviation (RPD). Although full cross-validation appeared to be the most accurate (R 2 = 0.95 for organic carbon (OC), 0.96 for inorganic carbon (IC), 0.87 for CEC, 0.72 for pH and 0.74 for clay; RPD = 4.4, 6.0, 2.7, 1.9 and 2.0, respectively), the prediction errors were considered to be optimistic and so alternative calibrations considered to be more similar to ‘true’ predictions were tested. Predictions using individual calibrations from each island were the least efficient, while predictions using calibration selection based on a Euclidian distance ranking method, using as few as 10 samples selected from each island, were almost as accurate as full cross-validation for OC and IC (R 2 = 0.93 for OC and 0.96 for IC; RPD = 3.9 and 4.7, respectively). Prediction accuracy for CEC, pH and clay was less accurate than expected, especially for clay (R 2 = 0.73 for CEC, 0.50 for pH and 0.41 for clay; RPD = 1.8, 1.5 and 1.4, respectively). This study confirmed that the DRIFT PLS method was suitable for characterizing important properties for soils typical of islands in a Mediterranean environment and capable of discriminating between the variations in soil properties from different parent materials. Introduction A detailed study of the role of many soil physical, chemical and biological properties in terrestrial ecosystems demands less expen- sive and faster data acquisition techniques to quantify the different soil properties. This is especially true for precision agriculture and other applications where precise spatial resolution of soil prop- erties is required. Recent studies have shown the possibility of using infrared reflectance spectroscopy and multivariate chemo- metrics in order to obtain qualitative and quantitative assessment of some chemical and physical parameters of the soil rapidly and at low cost (Janik & Skjemstad, 1995; Reeves et al., 2001; Viscarra Rossel et al., 2006; Minasny et al., 2008; Du & Jianmin, 2009). Correspondence: L. P. D’Acqui. E-mail: dacqui@ise.cnr.it Received 15 September 2009; revised version accepted 17 August 2010 Infrared spectroscopy is associated with the vibrations of molec- ular functional groups, enabling the identification of specific soil chemistry. Samples for infrared soil analysis are usually scanned as dry powdered samples. The intensity loss of diffusely reflected infrared radiation at specific frequencies from the sample surface is measured and the resulting spectra correlated with soil proper- ties using a multivariate regression method such as partial least squares (PLS) regression modelling. While sample dispersion in a non-absorbing inert matrix, such as powdered potassium bromide, is often required to give spectral absorbances closely related to soil component concentrations, the use of neat, powdered sam- ples for Diffuse Reflectance Fourier Transform Infrared (DRIFT) analysis reduces sample preparation time and weighing errors and thus speeds up analyses. Unfortunately, however, DRIFT intensi- ties for powdered samples, such as soils, can be very non-linear © 2010 The Authors Journal compilation © 2010 British Society of Soil Science 1