Mineral characterization using tunable external cavity quantum cascade lasers for mid-infrared reflectance spectroscopy is presented. Reflection spectra taken on common minerals are shown along with their spectral variations. Measurements at different incident angles were also obtained to mimic in the field applications. Finally, spectra were obtained on mineral mixtures and their analysis assesses the quality and usability of the technique for mineral characterization.

Bioactive glasses have been used for many years as bone graft substitutes in orthopedic and dental applications as well as an additive in toothpastes, cosmetics, and cosmeceutical products. The interest of using bioactive glass comes from its ability to dissolve and release dissolution products that stimulate bone regeneration. Porous bioactive glass scaffolds that can provide structural support while bone is growing into the structure have generated interest. However, little data is available in the literature on the effect of environmental conditions or sterilization treatments on the structure and properties of these materials. This study presents the evolution of the structure and microstructure of bioactive foams exposed to different accelerated and real‐time aging conditions and sterilization treatments. The results indicate that the material is relatively stable. For example, different sterilization methods (steam, ethylene oxide, hydrogen peroxide, gamma‐rays) have limited effect on the structure and properties of the foams. However, carbonate species may form on the surface of the material when exposed to CO₂ and humidity. Some carbonates dissolve rapidly in water and may impact the pH of the solution. Adequate packaging should limit the reaction of the bioactive glass with CO₂ and humidity and the formation of carbonate.

Rapid and accurate assessment of nutritional quality, particularly crude protein content and essential nutrient concentrations, remains a major challenge in the food and feed industries. In this study, laser-induced breakdown spectroscopy (LIBS) was combined with advanced chemometric modeling to predict the levels of crude protein and key macro- and micronutrients (Ca, Mg, K, Na, Fe, Mn, P, Zn) in 61 barley forage samples composed of whole aerial plant parts ground prior to analysis. LIBS offers a compelling alternative to traditional analytical methods by enabling real-time analysis with minimal sample preparation. To minimize interference from atmospheric nitrogen, nitrogen spectral lines were excluded from the protein calibration model in favor of spectral lines from elements biochemically associated with proteins. We compared the performance of Partial Least Squares (PLSR) regression and Extreme Learning Machine (ELM) using fivefold cross-validation. ELM outperformed PLS in terms of prediction, achieving a coefficient of determination (R²) close to 1 and a ratio of performance to deviation (RPD) exceeding 2.5 for proteins and several nutrients. These results underscore the potential of LIBS-ELM integration as a robust, non-destructive, and in situ tool for rapid forage quality assessment, particularly in complex and heterogeneous plant matrices.

This study presents a comparative evaluation of multiple-approach optical spectroscopic sensor—Laser-Induced Breakdown Spectroscopy (LIBS), Mid-Infrared Spectroscopic sensing (MIRS), and Hyperspectral Imaging (HSI)-based sensors operating in the Visible–Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) ranges—for the quantitative detection of calcium carbonate (CaCO₃) in pelletized CaCO₃-CaO mixtures. The objective was to assess and compare the sensing performance of these optical sensor platforms for carbonate quantification. Each spectroscopic sensor dataset was processed using chemometric calibration methods, including Partial Least Squares Regression (PLSR), to ensure robust and reproducible quantitative predictions. Although the samples consisted of binary CaCO₃-CaO mixtures, the sensing task focused exclusively on CaCO₃ content. Results indicate that LIBS, MIRS, and HSI-SWIR-based sensing approaches achieved comparable quantitative performance, with LIBS providing the highest prediction accuracy. In contrast, the HSI-VNIR sensor configuration demonstrated lower predictive capability relative to the other optical sensing modalities. These findings highlight the potential and limitations of different optical sensor technologies for carbonate detection in heterogeneous mineral systems.

Mid-infrared reflectance spectroscopy and laser-induced breakdown spectroscopy are combined to provide the quantitative mineral composition of rocks. Measurement results for kaolinite are presented and a relative error of kaolinite abundance of 0.8 % is obtained.

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.