Real-time P$$_2$$O$$_5$$ quantification in industrial phosphoric acid using laser-induced breakdown spectroscopy: a paradigm shift in process analytical chemistry

Industrial chemical manufacturing relies heavily on process analytical technology for quality assurance and efficiency. However, many critical assays remain constrained by slow, off-line laboratory methods, creating significant latency in process control. Here we demonstrate that Laser-Induced Breakdown Spectroscopy (LIBS), a form of atomic emission spectroscopy, can overcome these limitations for direct in-line analysis of industrial liquids. Using LIBS as the measurement platform, we develop and validate a comprehensive chemometric framework for quantifying phosphorus pentoxide (P$$_2$$O$$_5$$) equivalence in concentrated phosphoric acid, a key parameter in fertilizer and chemical production. The LIBS analysis requires less than ten seconds, representing a greater than 180 fold decrease in analysis time compared to classical ICP-OES analysis or chromatographic procedures. The core of this work lies in the systematic evaluation of data processing strategies: twelve regression algorithms spanning linear, regularized, ensemble, and non-linear paradigms were benchmarked across four preprocessing conditions (raw data, variable-selected, SNV-corrected, and SNV/Savitzky–Golay smoothed spectra), providing a rigorous and reproducible comparison of chemometric approaches for LIBS-based liquid analysis. A strategic variable selection strategy, targeting chemically significant emission wavelengths combined with variable importance in projection (VIP) analysis. Using a carefully designed experimental setup with laser characteristics that were customized ($$\lambda = 1064\,\textrm{nm}$$, pulse energy around $$25\,\textrm{mJ}$$), the optimized partial least squares (PLS) regression model, built on SNV + Savitzky–Golay preprocessed spectra in the 200–900 nm range, achieves a cross-validated coefficient of determination $$R^2 = 0.96$$ and a root mean square error of prediction (RMSEP) of 1.81 % P$$_2$$O$$_5$$. This methodology makes in situ, real-time feedback for closed-loop process controls possible, which can translate to dramatically increased product consistency, resource utilization, and sustainability in production. Beyond the specific application to phosphoric acid, this work establishes a transferable methodological template, integrating spectral preprocessing, data-driven feature selection, and systematic model benchmarking, that can be extended to various liquid process streams in chemical production.