Detection and monitoring of drip irrigation clogging using absorbance spectroscopy coupled with multivariate analysis

Clogging is one of the major factors affecting the performance of drip irrigation systems. It can be of a physical, chemical or biological nature but currently no method offers non-destructive field measurements of clogging. The aim is to propose a new method for monitoring and discriminating physical and chemical clogging. It is based on near infrared (NIR) spectroscopy combined with multivariate analysis methods. Kaolinite (physical clogging) and calcium carbonate (chemical clogging) were monitored in a transparent millifluidic cellflow. The evolution of clogging throughout the experiment was monitored by applying a multivariate curve regression – alternating least-squares (MCR-ALS) on absorption spectra. Comparison of the MCR-ALS results with the reference thickness measured by optical coherence tomography (OCT) indicated that absorbance spectroscopy coupled with MCR-ALS allows for accurate monitoring of the clogging thickness of kaolinite and calcium carbonate deposits. The detection of clogging and the monitoring of its thickness in the channel could be performed during both experiments. In addition, based on the spectra, a partial least squares-discriminant analysis (PLS-DA) model was developed to distinguish between the two types of clogging materials. Discrimination between both materials using PLS-DA model was achieved with accuracy greater than 97%. The proposed strategy highlights the potential of NIR spectroscopy coupled with MCR-ALS and PLS-DA methods to monitor the evolution of thickness and nature of clogging in a millifluidic cellflow. These results thus indicate a new methodology for detecting, monitoring and discriminating in situ clogging in the field. •Accurate spectral method for in situ monitoring and discrimination of clogging.•MCR-ALS method allows for clogging quantification with precision around 100 μm.•PLS-DA allows for clogging classification with accuracy greater than 97%.