Vis-NIR Reflectance Spectroscopy and PLSR to Predict PCB Content in Severely Contaminated Soils: A Perspective Approach  
[PERSON] Leone  
Water Research Institute, National Research Council (IRSA-CNR), 70132 Bari, Italy Department of Biology, University of Bari [PERSON], 70126 Bari, Italy Institute for Mediterranean Agriculture and Forest System, National Research Council (ISAFoM-CNR), 80055 Portici, Italy  
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of determination, which could be useful, especially when there are many soil samples to be analysed [4]. In recent years, reflectance spectroscopy, which is the ratio of the spectral radiant flux reflected from a soil surface to the spectral radiant impinging on it [5] in the vis-NIR domain, has proved to be a useful technique for the prediction of various soil pollutants [6; 7; 8; 9; 10; 11; 12; 13] as well as other soil properties [4; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. The principle on which visible-near infrared reflectance spectroscopy is based is that the characteristics of radiation reflected from a material are a function of its chemical and physical properties; thus, observations of soil reflectance can provide information on its properties [5]. Compared to conventional laboratory analyses, vis-NIR spectroscopy has the advantage of being faster, inexpensive, and non-destructive; in addition, it can be used to evaluate different soil properties simultaneously [17]. So far, many studies pertaining to the use of reflectance spectroscopy for the prediction of soil contaminants have been focused on heavy metals [6; 7; 8; 9; 10; 11; 12; 13]. Several studies have regarded organic contaminants [9]. Only limited research has covered polychlorinated compounds and, in particular, PCBs. Recently, ref. [26] found significant bivariate statistical relationships between soil colour parameters and PCBs content in the ex-MATRA contaminated site. However, it should be noted that soil colour is an expression of the visible portion of the spectra [27]. Therefore, it is not unlikely that further information can be extracted from the near infrared portion of the spectrum. The potential of the whole vis-NIR spectrum in the prediction of PCBs needs to be further evaluated. Due to the overlapping absorption of soil components, soil reflectance spectra in the visible and near-infrared ranges are mainly non-specific. The lack of specificity is further intensified by scattering effects caused by the soil structure or specific components [24]. All these factors produce complex absorption patterns that must be mathematically retrieved from the spectra and related to soil properties. Thus, chemometrics and pedometrics are needed to investigate soil reflectance spectra [28]. The most popular calibration methods for soil applications are based on linear regressions, specifically stepwise multiple linear regression (SMLR), principal component regression (PCR), and partial least squares regression (PLSR) [14; 29]. The fundamental justification for utilising SMLR is the inadequacies of more traditional regression techniques such as multiple linear regression (MLR) and the lack of consciousness among soil scientists of the existence of entire spectrum data compression techniques such as PCR and PLSR. Both PCR and PLSR can analyse data with a large number of predictor variables that are highly collinear. PCR and PLSR are related techniques, and, in most cases, they show similar prediction errors [24]. However, PLSR is often preferred by analysts because it relates the response and predictor variables so that the model explains more of the variance in the response with fewer components; it is more interpretable, and the algorithm is faster in computation. Some relevant theoretical aspects of PLSR are discussed in depth in [4]. The calibration of robust predictive models based on the use of PLSR, as well as other numerical methods, requires the availability of a large number of samples, which will be partly used to calibrate the models, and partly for its validation.