Inferring lake water chemistry from filtered seston using NIR spectrometry

Near-infrared spectrometry (NIR) is a rapid, inexpensive and reagent-free technique, widely used in industry in areas such as quality control and process management. The technique has great potential for environmental monitoring of aqueous systems. This study assesses relationships, using PLS regression, between NIR spectra of seston collected on glass fibre filters and the following measured lake water parameters: total organic carbon (TOC), total phosphorus (TP), Abs420 and pH. Water samples were collected from 271 oligotrophic lakes during autumn 1995. The predictive model for TOC explained 68% of the variance (SEP=2.1 mg L−1, range 14.9 mg L−1), and that for colour 71% (SEP=0.04 A, range 0.36 A), while the explained variances for pH and TP were 72% (SEP=0.36 μg L−1, range 3.13 μg L−1) and 45% (SEP=4 μg L−1, range 41 μg L−1), respectively. A model correlating NIR spectra and the actual amount of phosphorus in the seston captured on filters explained 86% of the variance (SEP=0.044 μg/filter, range 0.47). Several pretreatments and regression techniques were used in an attempt to enhance modeling performance. However, straightforward PLS on raw data performed best in all cases.