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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.

The quantitative determination of analytes in plasma by dry film-based near infrared spectroscopy (NIR) is significantly affected by the thickness variations of dry films. Internal standards are generally used to enhance the reproducibility of quantitative results. However, it is difficult to select an appropriate internal standard with general applicability, and the introduction of an internal standard into plasma samples also complicates experimental procedures. In this work, a quantitative NIR transmission spectroscopy model was proposed to explicitly model the effects of the variations in thickness and light scattering characteristics of dry plasma films on NIR transmission measurements, and an advance dual calibration strategy based on support vector regression (DCS SVR ) was derived from the proposed quantitative model to realize accurate determination of analytes (e.g. glucose) in dried plasma samples without the use of internal standards. By using the dry film method coupled with the proposed technique, glucose in plasma could be determined over a concentration range of 0.4–20 mmol L − 1 with satisfactory accuracy (average relative predictive error less than 7.0%) while avoiding any use of internal standards. It can reasonably be expected that the DCS SVR strategy might be of major benefit for the quantitative NIR spectroscopic analysis of analytes of clinical significance in dried biofluid samples.

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.

The presence of practically unavoidable scatterers and background absorbers in turbid media such as biological tissue or cell suspensions can significantly distort the shape and intensity of fluorescence spectra of fluorophores and, hence, greatly hinder the in situ quantitative determination of fluorophores in turbid media. In this contribution, a quantitative fluorescence model (QFM) was proposed to explicitly model the effects of the scattering and absorption on fluorescence measurements. On the basis of the proposed model, a calibration strategy was developed to remove the detrimental effects of scattering and absorption and, hence, realize accurate quantitative analysis of fluorophores in turbid media. A proof-of-concept model system, the determination of free Ca(2+) in turbid media using Fura-2, was utilized to evaluate the performance of the proposed method. Experimental results showed that QFM can provide quite precise concentration predictions for free Ca(2+) in turbid media with an average relative error of about 7%, probably the best results ever achieved for turbid media without the use of advanced optical technologies. QFM has not only good performance but also simplicity of implementation. It does not require characterization of the light scattering properties of turbid media, provided that the light scattering and absorption properties of the test samples are reasonably close to those of the calibration samples. QFM can be developed and extended in many application areas such as ratiometric fluorescent sensors for quantitative live cell imaging.

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.

Liquid chromatography-mass spectrometry assays suffer from signal instability caused by the gradual fouling of the ion source, vacuum instability, aging of the ion multiplier, etc. To address this issue, in this contribution, an internal standard was added into the mobile phase. The internal standard was therefore ionized and detected together with the analytes of interest by the mass spectrometer to ensure that variations in measurement conditions and/or instrument have similar effects on the signal contributions of both the analytes of interest and the internal standard. Subsequently, based on the unique strategy of adding internal standard in mobile phase, a multiplicative effects model was developed for quantitative LC-MS assays and tested on a proof of concept model system: the determination of amino acids in water by LC-MS. The experimental results demonstrated that the proposed method could efficiently mitigate the detrimental effects of continuous signal variation, and achieved quantitative results with average relative predictive error values in the range of 8.0-15.0%, which were much more accurate than the corresponding results of conventional internal standard method based on the peak height ratio and partial least squares method (their average relative predictive error values were as high as 66.3% and 64.8%, respectively). Therefore, it is expected that the proposed method can be developed and extended in quantitative LC-MS analysis of more complex systems.

Spectral measurements of complex heterogeneous types of mixture samples are often affected by significant multiplicative effects resulting from light scattering, due to physical variations (e.g., particle size and shape, sample packing, and sample surface, etc.) inherent within the individual samples. Therefore, the separation of the spectral contributions due to variations in chemical compositions from those caused by physical variations is crucial to accurate quantitative spectroscopic analysis of heterogeneous samples. In this work, an improved strategy has been proposed to estimate the multiplicative parameters accounting for multiplicative effects in each measured spectrum and, hence, mitigate the detrimental influence of multiplicative effects on the quantitative spectroscopic analysis of heterogeneous samples. The basic assumption of the proposed method is that light scattering due to physical variations has the same effects on the spectral contributions of each of the spectroscopically active chemical components in the same sample mixture. On the basis of this underlying assumption, the proposed method realizes the efficient estimation of the multiplicative parameters by solving a simple quadratic programming problem. The performance of the proposed method has been tested on two publicly available benchmark data sets (i.e., near-infrared total diffuse transmittance spectra of four-component suspension samples and near-infrared spectral data of meat samples) and compared with some empirical approaches designed for the same purpose. It was found that the proposed method provided appreciable improvement in quantitative spectroscopic analysis of heterogeneous mixture samples. The study indicates that accurate quantitative spectroscopic analysis of heterogeneous mixture samples can be achieved through the combination of spectroscopic techniques with smart modeling methodology.