Rapid Protein and Mineral Characterization of Pea Flour Using QCL-MIR and LIBS Combined with UVE–PLS–ELM Machine Learning Framework

Rapid quantitative characterization of protein-rich agricultural materials remains challenging due to the molecular and elemental complexity of plant-derived matrices. In this study, external-cavity quantum cascade laser-based mid-infrared (QCL-MIR) spectroscopy and laser-induced breakdown spectroscopy (LIBS) were investigated as complementary approaches for predicting protein and elemental composition in pea flour (Pisum sativum L.) samples. QCL-MIR spectroscopy primarily probed molecular vibrational information, whereas LIBS directly monitored elemental composition through plasma-generated atomic and ionic emission signatures. To address the high dimensionality and nonlinear relationships present in both spectroscopic datasets, advanced chemometric strategies combining Uninformative Variable Elimination (UVE), Partial Least Squares (PLS) regression, and Extreme Learning Machine (ELM) modeling were implemented. Given the limited dataset size (N = 20), predictive performance was assessed using a concentration-stratified five-fold prediction strategy. The optimized UVE–PLS–ELM approach improved predictive performance compared with conventional linear PLS models for both techniques. QCL-MIR spectroscopy showed strong predictive performance within the present dataset for total protein content (R2 = 0.97) and also provided promising predictions of phosphorus and potassium through indirect correlations with sample composition. LIBS also demonstrated strong calibration and predictive capability for total protein content (R2 = 0.93) and good predictive performance for Na, Mg, Ca, Mn, and Fe. However, LIBS performance decreased at concentration extremes, particularly for high-concentration elements (K and P) and trace elements such as Zn and Cu. These results demonstrate the complementary strengths of QCL-MIR and LIBS and highlight the potential of nonlinear chemometric approaches for accurate compositional analysis of protein-rich agricultural materials within the present limited dataset.