Simultaneous Determination of Metal Ions in Zinc Sulfate Solution Using UV-Vis Spectrometry and SPSE-XGBoost Method

A UV-Vis spectroscopic detection method for cobalt ions in zinc sulfate solution based on discrete wavelet transform and extreme gradient boosting

Zinc is a crucial strategic metal resource. The concentration of cobalt ions in zinc refining solution significantly impacts the efficiency of zinc electrolysis production. The traditional method of detecting cobalt ions in zinc solution is time-consuming, labor-intensive and ineffective. However, optical detection offers the advantage of high efficiency and low cost, making it a potential replacement for the traditional method. In this study, the spectral curve of cobalt ions in zinc solution is detected by ultraviolet-visible (UV-Vis) spectrophotometry. Additionally, we propose a model for the concentration-absorbance relationship of cobalt ions in zinc solution based on discrete wavelet transform and extreme gradient boosting (DWT-XGBoost) algorithms. First, the spectral curve's information region is denoised by using Savitzky-Golay (S-G) smoothing. Then, the denoised spectra is utilized to extract features through discrete wavelet transform and principal component analysis. These features are used as inputs to the XGBoost model to establish prediction models for low and high cobalt ions in zinc solution. Bayesian optimization is implemented to adjust the model's hyperparameters, including learning rate, feature sampling ratio, to enhance the prediction performance. Finally, applying the model to zinc solution samples from a zinc smelter and compared with other state-of-the-art algorithms, the DWT-XGBoost algorithm exhibits the lowest RMSE, MAE and MAPE, with values of 0.034 mg/L, 0.025 mg/L, 6.983 % for low cobalt and with values of 0.231 mg/L, 0.067 mg/L and 0.472 % for high cobalt. The experimental results demonstrate that the DWT-XGBoost model exhibits significantly superior prediction performance.

Simultaneous Determination of Estradiol Cypionate and Medroxyprogesterone Acetate Hormones in Injectable Suspension by UV Spectrophotometry Based on Least-Squares Support Vector Machine and Fuzzy Inference System: Comparison with HPLC

BACKGROUND

The combination of estradiol cypionate (ECA) and medroxyprogesterone acetate (MPA) is used to prevent pregnancy in women. The analysis of the ECA and MPA combination reveals a challenge due to the strong overlap of the spectra of these compounds.

OBJECTIVE

Spectrophotometry techniques along with chemometrics methods are simple, fast, precise, and low-cost for the simultaneous determination of ECA and MPA in a combined pharmaceutical dosage form.

METHODS

Two developed approaches, the least-squares support vector machine (LSSVM) and fuzzy inference system (FIS), along with a spectrophotometric method were proposed to solve such a challenging overlap.

RESULTS

Based on the cross-validation method, the regularization parameter (γ) and width of the function (σ) in the LSSVM model were optimized and the root mean square error (RMSE) values were found to be 0.3957 and 0.2839 for ECA and MDA, respectively. The mean recovery values were 99.87 and 99.63% for ECA and MDA, respectively. The FIS coupled with principal component analysis (PCA) showed mean recovery percentages equal to 99.05 and 99.50% for ECA and MDA, respectively. Also, the RMSE of both components was lower than 0.3.

CONCLUSION

The analysis results of a real sample (injection suspension) using the proposed methods were compared with HPLC by a one-way analysis of variance (ANOVA) test, and no significant differences were found in the results.

HIGHLIGHTS

Intelligent methods were proposed for the simultaneous determination of ECA and MPA. The least-squares support vector machine and fuzzy inference system along with spectrophotometry were used. HPLC as a reference method was performed and compared with chemometrics methods. The benefits of the proposed approaches are that they are rapid, simple, low-cost, and accurate.

Recent advances in instrumental techniques for heavy metal quantification

Heavy metals (HMs) are ubiquitous; they are found in soil, water, air, and all biological matrices. The toxicity, bioaccumulation potential, and deleterious effects of most of these metals on humans and the environment have been widely documented. Consequently, the detection and quantification of HMs in various environmental samples have become a pressing issue. The analysis of the concentrations of HMs is a vital component of environmental monitoring; hence, the selection of the most suitable analytical technique for their determination has become a topic of great interest in food, environment, and human health safety. Analytical techniques for the quantification of these metals have evolved. Presently, a broad range of HM analytical techniques are available with each having its outstanding merits as well as limitations. Most analytical scientists, therefore, adopt complementation of more than one method, with the choice influenced by the specific metal of interest, desired limits of detection and quantification, nature of the interference, level of sensitivity, and precision among others. Sequel to the above, this work comprehensively reviews the most recent advances in instrumental techniques for the determination of HMs. It gives a general overview of the concept of HMs, their sources, and why their accurate quantification is pertinent. It highlights various conventional and more advanced techniques for HM determination, and as one of its kind, it also gives special attention to the specific merits and demerits of the analytical techniques. Finally, it presents the most recent studies in this regard.

Comparing Ensemble-Based Machine Learning Classifiers Developed for Distinguishing Hypokinetic Dysarthria from Presbyphonia

It is essential to understand the voice characteristics in the normal aging process to accurately distinguish presbyphonia from neurological voice disorders. This study developed the best ensemble-based machine learning classifier that could distinguish hypokinetic dysarthria from presbyphonia using classification and regression tree (CART), random forest, gradient boosting algorithm (GBM), and XGBoost and compared the prediction performance of models. The subjects of this study were 76 elderly patients diagnosed with hypokinetic dysarthria and 174 patients with presbyopia. This study developed prediction models for distinguishing hypokinetic dysarthria from presbyphonia by using CART, GBM, XGBoost, and random forest and compared the accuracy, sensitivity, and specificity of the development models to identify the prediction performance of them. The results of this study showed that random forest had the best prediction performance when it was tested with the test dataset (accuracy = 0.83, sensitivity = 0.90, and specificity = 0.80, and area under the curve (AUC) = 0.85). The main predictors for detecting hypokinetic dysarthria were Cepstral peak prominence (CPP), jitter, shimmer, L/H ratio, L/H ratio_SD, CPP max (dB), CPP min (dB), and CPPF0 in the order of magnitude. Among them, CPP was the most important predictor for identifying hypokinetic dysarthria.

Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA

Tool wear negatively impacts the quality of workpieces produced by the drilling process. Accurate prediction of tool wear enables the operator to maintain the machine at the required level of performance. This research presents a novel hybrid machine learning approach for predicting the tool wear in a drilling process. The proposed approach is based on optimizing the extreme gradient boosting algorithm’s hyperparameters by a spiral dynamic optimization algorithm (XGBoost-SDA). Simulations were carried out on copper and cast-iron datasets with a high degree of accuracy. Further comparative analyses were performed with support vector machines (SVM) and multilayer perceptron artificial neural networks (MLP-ANN), where XGBoost-SDA showed superior performance with regard to the method. Simulations revealed that XGBoost-SDA results in the accurate prediction of flank wear in the drilling process with mean absolute error (MAE) = 4.67%, MAE = 5.32%, and coefficient of determination R² = 0.9973 for the copper workpiece. Similarly, for the cast iron workpiece, XGBoost-SDA resulted in surface roughness predictions with MAE = 5.25%, root mean square error (RMSE) = 6.49%, and R² = 0.975, which closely agree with the measured values. Performance comparisons between SVM, MLP-ANN, and XGBoost-SDA show that XGBoost-SDA is an effective method that can ensure high predictive accuracy about flank wear values in a drilling process.