Many studies have shown that glutathione (GSH) and cysteine (Cys)/homocysteine (Hcy) levels are interrelated in biological systems. To unravel the complicated biomedical mechanisms by which GSH and Cys/Hcy are involved in various disease states, probes that display distinct signals in response to GSH and Cys/Hcy are highly desirable. In this work, we report a rhodol thioester (1) that responds to GSH and Cys/Hcy with distinct fluorescence emissions in neutral media. Probe 1 reacts with Cys/Hcy to form the corresponding deconjugated spirolactam via a tandem native chemical ligation (NCL) reaction. This intramolecular spirocyclization leads to the “quinone–phenol” transduction of rhodol dyes, and an excited-state intramolecular proton transfer (ESIPT) process between the phenolic hydroxyl proton and the aromatic nitrogen in the benzothiazole unit occurs upon photoexcitation, thus affording 2-(2′-hydroxyphenyl) benzothiazole (HBT) emission (454 nm). In the case of the tripeptide GSH, only transthioesterification takes place removing the intramolecular photo-induced electron transfer (PET) process caused by the electron deficient 4-nitrobenzene moiety giving rise to a large fluorescence enhancement at the rhodol emission band (587 nm). The simultaneous detection of GSH and Cys/Hcy is attributed to the significantly different rates of intramolecular S,N-acyl shift of their corresponding thioester adducts derived from 1. The utility of probe 1 has been demonstrated in various biological systems including serum and cells.

Laser-induced breakdown spectroscopy combined with the random forest (RF) algorithm was proposed for the classification of ten iron ore samples.

The laser induced breakdown spectroscopy (LIBS) technique coupled with a support vector machine (SVM) and partial least square (PLS) methods was proposed to perform quantitative and classification analysis of 20 slag samples. The characteristic lines (Ca, Si, Al, Mg and Ti) of LIBS spectra for slag samples can be identified based on the NIST database. At first, quantitative analysis of the major components (Fe₂O₃, CaO, SiO₂, Al₂O₃, MgO and TiO₂) in slag samples was completed by SVM with the full spectra as the input variable, and two parameters (kernel parameter of RBF-γ and σ²) of SVM were optimized by a grid search (GS) approach based on 5-fold cross-validation (CV). The performance of the SVM calibration model was investigated by 5-fold CV, and the prediction accuracy and root mean square error (RMSE) of SVM and PLS were employed to validate the predictive ability of the multivariate SVM calibration model in slag. The SVM model can eliminate the influence of nonlinear factors due to self-absorption in the plasma and provide a better predictive result. And then, two type of slag samples (open-hearth furnace slag and high titanium slag) were identified and classified by a partial least squares-discrimination analysis (PLS-DA) method with different input variables. Sensitivity, specificity and accuracy were calculated to evaluate the classification performance of the PLS-DA model for slag samples. It has been confirmed that the LIBS technique coupled with SVM and PLS methods is a promising approach to achieve the online analysis and process control of slag and even in the metallurgy field.