## 4 Conclusions  
The primary qualitative factor of pig iron, which specifies its class and features required by the steel plant, is its chemical composition. In particular, one of the most important indicators of the technical level and the quality of steel production is the Si content achieved in the intermediate pig iron produced in the blast furnace. This study demonstrates that broadband LIBS analysis performed by an \(h\)LIBS instrument associated with PLS treatment of spectral data is able to identify and quantify different elements, specifically C, Mn, P, Si, and Ti, in pig iron samples. Thus, the capacity of an \(h\)LIBS instrument to perform spatially resolved, multi-element, in-situ, online analysis of element concentrations in open air without any sample pre-treatment provides an obvious advantage with respect to the use of a traditional laboratory bench-top apparatus.  
Although \(h\)LIBS cannot be considered yet totally satisfactory in terms of accuracy, the time reduction from sampling to result delivery and the limited sample preparation no doubt contribute to a more rapid and efficient control of the steel-making process. The use of the \(h\)LIBS instrument is thus  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Calibration** & **Calibration** & **Calibration** & **Validation** \\ \hline Element & R & RMSE & LOD (\%) & MAPE (\%) \\ Si (Univariate) & 0.95 & 237.05 & 0.195 & 18.04 \\ Ti (Univariate) & 0.86 & 126.10 & 0.017 & 19.40 \\ Mn (Univariate) & 0.12 & null & null & Null \\ P (Univariate) & 0.26 & null & null & Null \\ C (Univariate) & 0.19 & null & null & Null \\ Si (SNV + PLS) & 0.99 & 0.151 & 0.06 & 20.49 \\ Ti (SNV + PLS) & 0.98 & 0.012 & 0.006 & 22.08 \\ Mn (SNV + PLS) & 0.54 & null & null & Null \\ P (SNV + PLS) & 0.85 & 0.08 & 0.03 & 7.87 \\ C (SNV + PLS) & 0.96 & 0.1 & 0.63 & 1.89 \\ Si (SG + SNV + PLS) & 0.99 & 0.067 & 0.06 & 14.07 \\ Ti (SG + SNV + PLS) & 0.99 & 0.0026 & 0.0026 & 11.61 \\ Mn (SG + SNV + PLS) & 0.93 & 0.03 & 0.031 & 15.42 \\ P (SG + SNV + PLS) & 0.96 & 0.016 & 0.016 & 7.63 \\ C (SG + SNV + PLS) & 0.97 & 0.58 & 0.58 & 1.3 \\ \hline \end{tabular} The best SG were with seven points and order three polynomium. R: Pearson’s correlation coefficient; RMSE: root mean square error; LOD: limit of detection; MAPE: mean absolute prediction error.  
\end{table}
Table 1: Results achieved by applying univariate, SNV + PLS and SNV + SG + PLS models.  
Figure 8: Calibration (**a**,**c**,**e**,**g**,**i**) and validation (**b**,**d**,**f**,**h**,**j**) curves for the elements C, Mn, P, Si, and Ti constructed by partial least square (PLS) treatment of data versus XRF certified concentrations.  
expected to streamline operations in the blast furnace, gain real-time information of sample quality, and allow a rapid decision of which samples to consider for further laboratory analyses, thus saving time and costs and minimizing transportation to the laboratory. Further, testing of industrial materials by portable instruments is often performed in field harsh conditions, thus easy portability, high robustness, and fast data acquisition are required for these instruments to incorporate easily the analytical process into the production and the processing workflows. Finally, a variety of industrial applications of _h_LIBS systems can be expected by the progress of spectral data evaluation tools.  
Conceptualization, G.S.S.; Data curation, B.S.M.; Formal analysis, G.S.S.; Investigation, G.S.S. and B.S.M.; Methodology, G.S.S. and B.S.M.; Resources, A.B.; Supervision, G.S.S.; Validation, B.S.M.; Writing--original draft, G.S.S.; Writing--review & editing, G.S.S., O.D.P. and B.S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors acknowledge [PERSON] from MADAtec s.r.l. and B&W Tek for providing the _NanoLIBS_ handheld instrument used in this work.  
The authors declare no conflict of interest.