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Laser-induced breakdown spectroscopy (LIBS) is a cutting-edge technology which offers appealing features for its application in the field of the cultural heritage. It is a proven technology for the fast and simultaneous detection of major and trace elements with minimal destructiveness, using easily compactable instrumentation into movable platforms for the in situ and standoff chemical analysis of objects in real time. In the present work, a standoff LIBS sensor has been used for surveying the Cathedral of Málaga. The spectroscopic measurements were gathered in situ although from an averaged distance of 35 m. A comprehensive characterization of the materials composing the main façade as well as identification of the noticeable pollutants at their surfaces has been performed. The standoff LIBS results have fitted neatly with the mineralogical analysis of all the stones assayed. The large emissions of Si, Al, Ca and Mg have confirmed that the structure was almost entirely built using sandstone. In turn, the sensitivity to carbonate chemistry has demonstrated the capability of standoff LIBS for coherently classifying different marbles, thus allowing the identification of their origins.Standoff LIBS has also allowed the detection of pollutants such as Si, Ca, Mg, Fe, Al, Ba and Sr, originating from natural sources such as the transport of re-suspended dust and atmospheric particulate matter related to marine aerosols. In addition, trace elements such as Ti, Pb and Mn from exhausts of gasoline and diesel engines are also involved in the pollution triggering of materials. To obtain all these findings, scaffolding or other intrusive facilities have not been required.

The distance between the sensor and the target is a particularly critical factor for an issue as crucial as explosive residues recognition when a laser-assisted spectroscopic technique operates in a standoff configuration. Particularly for laser ablation, variations in operational range influence the induced plasmas as well as the sensitivity of their ensuing optical emissions, thereby confining the attributes used in sorting methods. Though efficient classification models based on optical emissions gathered under specific conditions have been developed, their successful performance on any variable information is limited. Hence, to test new information by a designed model, data must be acquired under operational conditions totally matching those used during modeling. Otherwise, the new expected scenario needs to be previously modeled. To facing both this restriction and this time-consuming mission, a novel strategy is proposed in this work. On the basis of machine learning methods, the strategy stems from a decision boundary function designed for a defined set of experimental conditions. Next, particular semisupervised models to the envisaged conditions are obtained adaptively on the basis of changes in laser fluence and light emission with variation of the sensor-to-target distance. Hence, the strategy requires only a little prior information, therefore ruling out the tedious and time-consuming process of modeling all the expected distant scenes. Residues of ordinary materials (olive oil, fuel oil, motor oils, gasoline, car wax and hand cream) hardly cause confusion in alerting the presence of an explosive (DNT, TNT, RDX, or PETN) when tested within a range from 30 to 50 m with varying laser irradiance between 8.2 and 1.3 GW cm(-2). With error rates of around 5%, the experimental assessments confirm that this semisupervised model suitably addresses the recognition of organic residues on aluminum surfaces under different operational conditions.

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