查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Neural Netw orks is the subject of a report. According to news reporting originating in Zaga zig,Egypt,by NewsRx journalists,research stated,"In this study,five earth-f riendly spectrophotometric methods using multivariate techniques were developed to analyze levofloxacin,linezolid,and meropenem,which are utilized in critica l care units as combination therapies. These techniques were used to determine t he mentioned medications in laboratory-prepared mixtures,pharmaceutical product s and spiked human plasma that had not been separated before handling." The news reporters obtained a quote from the research from Zagazig University," These methods were named classical least squares (CLS),principal component regr ession (PCR),partial least squares (PLS),genetic algorithm partial least squar es (GA-PLS),and artificial neural network (ANN). The methods used a five-level,three-factor experimental design to make different concentrations of the antibi otics mentioned (based on how much of them are found in the plasma of critical c are patients and their linearity ranges). The approaches used for levofloxacin,linezolid,and meropenem were in the ranges of 3-15,8-20,and 5-25 g/mL,respec tively. Several analytical tools were used to test the proposed methods' perform ance. These included the root mean square error of prediction,the root mean squ are error of cross-validation,percentage recoveries,standard deviations,and c orrelation coefficients. The outcome was highly satisfactory. The study found th at the root mean square errors of prediction for levofloxacin were 0.090,0.079,0.065,0.027,and 0.001 for the CLS,PCR,PLS,GA-PLS,and ANN models,respecti vely. The corresponding values for linezolid were 0.127,0.122,0.108,0.05,and 0.114,respectively. For meropenem,the values were 0.230,0.222,0.179,0.097,and 0.099 for the same models,respectively. These results indicate that the de veloped models were highly accurate and precise. This study compared the efficie ncy of artificial neural networks and classical chemometric models in enhancing spectral data selectivity for quickly identifying three antimicrobials. The resu lts from these five models were subjected to statistical analysis and compared w ith each other and with the previously published ones. Finally,the whiteness of the methods was assessed by the recently published white analytical chemistry ( WAC) RGB 12,and the greenness of the proposed methods was assessed using AGREE,GAPI,NEMI,Raynie and Driver,and eco-scale,which showed that the suggested a pproaches had the least negative environmental impact."
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