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In this contribution, surface-enhanced Raman spectroscopy (SERS) coupled with an advanced chemometric method-multiplicative effects model (MEM SERS ) has been applied to quantitative analysis of ametryn in water samples of the Xiangjiang River (Changsha, China). The adoption of MEM SERS calibration model was to eliminate the detrimental effects caused by variations in the physical properties of enhancing substrates, the intensity and alignment/focusing of laser excitation source. Experimental results showed that the combination of SERS with MEM SERS can provide quite precise concentration predictions for ametryn in water samples of the Xiangjiang River with an average relative prediction error of about 4.8%. The combination of SERS with MEM SERS can compete with LC-MS/MS in terms of precision and accuracy of quantitative results. The limit of quantification was about 0.09 μM. More importantly, no laborious reference methods (e.g., HPLC) were needed to build the MEM SERS calibration model, since the MEM SERS calibration model built on the calibration samples prepared with ultrapure water could provide satisfactory quantification results for the test samples prepared with water collected from the Xiangjiang River. Therefore, it is reasonable to expect that SERS in combination with MEM SERS model would become a competitive alternative in routine quantitative analysis of ametryn in environmental water samples.

Surface-enhanced Raman spectroscopy (SERS) is a leading non-destructive technique with single-molecule sensitivity and has great potential for application in various fields. However, quantitative analysis of analytes using SERS is still quite challenging, since SERS signals are significantly affected by the physical properties of SERS enhancing substrates. Here, we report the detection of rhodamine 6G (R6G) and thiram (a dithiocarbamate fungicide) by SERS technique through the combination of a recently proposed multiplicative effects model (MEM) and magnetic nanoparticles (i.e. Fe 3 O 4 @Au and Fe 3 O 4 @Ag). Experimental results showed that R6G and thiram can be accurately determined with average relative prediction error of 3.2% and 7.5%, respectively. It looks promising that SERS technique based on the combination of multiplicative effects model and magnetic nanoparticles can be applied to the quantitative assay of many other analytes.

In this contribution, the combination of polydimethylsiloxane microfluidics with a recently developed multiplicative effects model for surface-enhanced Raman spectroscopy (MEMSERS) has been proposed to improve the accuracy and precision of quantitative SERS assays based on silver nanocolloids. The performance of the proposed method has been tested on two proof-of-concept systems and another real system (i.e., quantification of Rhodamine 6G by both internal standard addition and internal standard tagging detection modes, quantification of malachite green in fishpond water by internal standard addition detection mode). The average relative prediction error values of the proposed method for the test samples of the above three systems were 6.0%, 8.6% and 8.4% respectively. Conservatively speaking, these results demonstrated that accurate quantitative SERS analysis with an average relative prediction error less than 10% can be expected through the combination of microfluidics with the MEMSERS calibration model.

The presence of scatterers in turbid media could severely distort the Raman measurements and thereby prevent accurate determination of analytes in turbid media. To address this issue, in this contribution, an advanced model, multiplicative effects model (MEM), has been derived to explicitly model the effects of scatterers on Raman measurements. Preliminary experimental results for a proof of concept system with varying turbidity levels demonstrated that MEM could effectively account for the detrimental multiplicative effects of scatterers and ultimately achieved accurate quantitative analysis of analyte of interest in turbid media with a relative prediction error of about 4.3%. The enhanced levels of accuracy obtained with MEM open up an avenue for prospective prediction studies in turbid media such as biological tissues by Raman spectrometry.

Particle size distribution and compactness have significant confounding effects on Raman signals of powder mixtures, which cannot be effectively modeled or corrected by traditional multivariate linear calibration methods such as partial least-squares (PLS), and therefore greatly deteriorate the predictive abilities of Raman calibration models for powder mixtures. The ability to obtain directly quantitative information from Raman signals of powder mixtures with varying particle size distribution and compactness is, therefore, of considerable interest. In this study, an advanced quantitative Raman calibration model was developed to explicitly account for the confounding effects of particle size distribution and compactness on Raman signals of powder mixtures. Under the theoretical guidance of the proposed Raman calibration model, an advanced dual calibration strategy was adopted to separate the Raman contributions caused by the changes in mass fractions of the constituents in powder mixtures from those induced by the variations in the physical properties of samples, and hence achieve accurate quantitative determination for powder mixture samples. The proposed Raman calibration model was applied to the quantitative analysis of backscatter Raman measurements of a proof-of-concept model system of powder mixtures consisting of barium nitrate and potassium chromate. The average relative prediction error of prediction obtained by the proposed Raman calibration model was less than one-third of the corresponding value of the best performing PLS model for mass fractions of barium nitrate in powder mixtures with variations in particle size distribution, as well as compactness.