Chemometric-assisted surface-enhanced Raman spectroscopy for metformin determination using gold nanoparticles as substrate

Optimisation of a Microwave Synthesis of Silver Nanoparticles by a Quality by Design Approach to Improve SERS Analytical Performances

A major limitation preventing the use of surface-enhanced Raman scattering (SERS) in routine analyses is the signal variability due to the heterogeneity of metallic nanoparticles used as SERS substrates. This study aimed to robustly optimise a synthesis process of silver nanoparticles to improve the measured SERS signal repeatability and the protocol synthesis repeatability. The process is inspired by a chemical reduction method associated with microwave irradiation to guarantee better controlled and uniform heating. The innovative Quality by Design strategy was implemented to optimise the different parameters of the process. A preliminary investigation design was firstly carried out to evaluate the influence of four parameters selected by means of an Ishikawa diagram. The critical quality attributes were to maximise the intensity of the SERS response and minimise its variance. The reaction time, temperature and stirring speed are critical process parameters. These were optimised using an I-optimal design. A robust operating zone covering the optimal reaction conditions (3.36 min-130 °C-600 rpm) associated with a probability of success was modelled. Validation of this point confirmed the prediction with intra- and inter-batch variabilities of less than 15%. In conclusion, this study successfully optimised silver nanoparticles by a rapid, low cost and simple technique enhancing the quantitative perspectives of SERS.

Single versus Double Coffee-Ring Effect Patterns in Thin-Layer Chromatography Coupled with Surface-Enhanced Raman Spectroscopic Analysis of Anti-Diabetic Drugs Adulterated in Herbal Products

In thin-layer chromatography coupled with surface-enhanced Raman spectroscopy (TLC-SERS), the coffee ring effect (CRE) describes the formation of a ring-shape spot (blank in the middle and darker on the edge) caused by the aggregation of silver nanoparticles (Ag NPs), alone (single CRE) or with the analytes (double CRE). In this work, the SCRE and DCRE were investigated in two anti-diabetic drugs, hydrophobic glibenclamide (GLB) and more hydrophilic metformin (MET). The SCRE occurred in GLB analysis, as opposed to the DCRE that occurred in MET. It was proven that for optimization of the TLC-SERS analytical procedure, it is necessary to distinguish the CRE patterns of analytes. Additionally, MET and GLB were analyzed with the developed TLC-SERS method and confirmed by another validated method using high-performance liquid chromatography. Four herbal products collected on the market were found to be adulterated with GLB or/and MET; among those, one product was adulterated with both MET and GLB, and two products were adulterated with GLB at a higher concentration than the usual GLB prescription dose. The TLC-SERS method provided a useful tool for the simultaneous detection of adulterated anti-diabetic herbal products, and the comparison of the SCRE and DCRE provided more evidence to predict CRE patterns in TLC-SERS.

Determination of benzo(a)pyrene in peanut oil based on Raman spectroscopy and machine learning methods

Benzo(a)pyrene (BaP) generated in the production process of oil is harmful to human severely as a kind of carcinogenic substance. In this study, the qualitative and quantitative detection of BaP concentration in peanut oil was investigated based on Raman spectroscopy combined with machine learning methods. The glass substrates and magnetron sputtered gold substrates for the Raman spectra were compared and the data preprocessing methods of principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were used to process Raman signal. Back propagation neural network (BPNN), partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) algorithms were developed to obtain the qualitative and quantitative detection model of BaP concentration in peanut oil. The results showed that the Raman spectra with the glass substrate was more suitable for the BaP detection than magnetron sputtered gold substrates. RF combined with t-SNE could achieve an accuracy of 97.5% in the qualitative detection of BaP concentration levels in model validation experiment, and the correlation coefficient of the prediction set (R_p) in the quantitative detection was 0.9932, the root mean square error (RMSEP) was 0.8323 μg/kg and the bias was 0.1316 μg/kg. It can be concluded that Raman spectroscopy combined with machine learning methods could provide an effective method for the rapid determination of BaP concentration in peanut oil.