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Portable and benchtop Raman spectrometers coupled to cluster analysis to identify quinine sulfate polymorphs in solid dosage forms and antimalarial drug quantification in solution by AuNPs-SERS with MCR-ALS
摘要： This paper proposes for the first time: (a) a qualitative analytical method based on portable and benchtop backscattering Raman spectrometers coupled to hierarchical cluster analysis (HCA) and multivariate curve resolution – alternating least-squares (MCR-ALS) to identify two polymorphs of antimalarial quinine sulfate in commercial pharmaceutical tablets in their intact forms and (b) a quantitative analytical method based on gold nanoparticles (AuNPs) as active substrates for surface-enhanced Raman scattering (SERS) in combination with MCR-ALS to quantify quinine sulfate in commercial pharmaceutical tablets in solution. The pure concentration and spectral profiles recovered by MCR-ALS proved that both formulation present different polymorphs. These results also were confirmed by two clusters observed in HCA model, according to their similarities within and among the samples that provided useful information about homogeneity of different pharmaceutical manufacturing processes. AuNPs-SERS coupled to MCR-ALS was able to quantify quinine sulfate in the calibration range from 150.00 to 200.00 ng mL-1 even with strong overlapping spectral profile of background SERS signal, proving that is a powerful ultrahigh sensitivity analytical method. This reduced linearity was validated through a large calibration range from 25.00 to 175.00 μg mL-1 used in a reference analytical method based on high performance liquid chromatography with diode array detector (HPLC-DAD) coupled to MCR-ALS for analytical validation purposes even in the presence of coeluted compound. The analytical methods herein developed are fast, because second-order chromatographic data and first-order SERS spectroscopic data where obtained in less than 6 and 2 min, respectively. Concentrations of quinine sulfate were estimated with a low root mean square error of prediction (RMSEP) values and a low relative error of prediction (REP%) in the range 1.8-6.1%.
关键词： Quinine sulfate pharmaceutical tablets,Raman spectrometer,polymorphs,HCA and MCR-ALS,AuNPs-SERS
更新于2025-09-23 15:19:57
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Synchrotron infrared spectral regions as signatures for foodborne bacterial typing
摘要： Fourier-transform infrared (FTIR) spectroscopy has emerged as a viable alternative to biochemical and molecular biology techniques for bacterial typing with advantages such as short analysis time, low cost and laboratorial simplicity. In this study, synchrotron radiation-based FTIR (SR-FTIR) spectroscopy with higher spectral quality was successfully applied to type 16 foodborne pathogenic bacterial strains. Combined with principal component analysis (PCA) and hierarchical cluster analysis (HCA), we found that the specific spectral region 1300–1000 cm-1, which reflects the information of phosphate compounds and polysaccharides, can be used as the signature region to cluster the strains into groups similar with genetic taxonomic method. These findings demonstrated that FTIR spectra combined with HCA have a great potential in quickly typing bacteria depending on their biochemical signatures.
关键词： FTIR,PCA,Bacterial typing,Spectral signature,HCA,Synchrotron radiation
更新于2025-09-19 17:15:36
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Potato hierarchical clustering and doneness degree determination by near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy
摘要： Near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy were used to identify potato varieties and detect potato doneness degree. The varieties of potato tubers can be successfully classified by hierarchical cluster analysis (HCA). The partial least squares regression (PLSR) model exhibited good prediction result for the doneness degree evaluation. Principal component and first-derivative iteration algorithm (PCFIA) was introduced to select feature variables instead of using the full wavelength spectra for modelling. Based on two sets of feature variables selected from NIR and MIR regions, both NIR–PCFIA–HCA and MIR–PCFIA–HCA showed higher performances of hierarchical clustering. Moreover, NIR–PCFIA–PLSR and MIR–PCFIA–PLSR models were effectively used to predict tuber doneness degree, yielding the RP as high as 0.935 and the RMSEP as low as of 0.503. It is concluded that the PCFIA is an effective approach for feature variable selection, and both NIR and MIR spectroscopic techniques are capable of classifying potato varieties and determining potato doneness degree.
关键词： HCA,ATR-MIR,Potato,Variable selection,PLSR,NIR
更新于2025-09-19 17:15:36