ArticleScientific reports2026
Real-time P[Formula: see text]O[Formula: see text] quantification in industrial phosphoric acid using laser-induced breakdown spectroscopy: a paradigm shift in process analytical chemistry.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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[Formula: see text]O[Formula: see text]) 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 ([Formula: see text], pulse energy around [Formula: see text]), 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 [Formula: see text] and a root mean square error of prediction (RMSEP) of 1.81 % P[Formula: see text]O[Formula: see text]. 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.
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