Rapid identification of Radix Astragali origin by using fluorescence probe combined with chemometrics

Fluorescent probes for metal ion recognition can be divided into selective probes, weakly selective probes, and non-selective probes roughly. Weakly selective probes are not often used for quantitative analysis of metal ions due to their overlapping spectra resulting from simultaneous interactions with multiple metal ions. Conversely, the different metal ions contained in herbal medicine extracts from different geographical origins will produce corresponding fluorescence fingerprint profiles after interaction with weakly selective fluorescence probes. The performance can be used in the study of origin tracing of food or Chinese herbal medicine. Weakly selective fluorescent probes of benzimidazole derivatives have been synthesized and attempted to be used in the origin tracing of Radix Astragali in this work. Radix Astragali from different origins will produce different fluorescence fingerprint spectra due to the difference of metal ions and content in combination with the probe. Excitation-emission matrix (EEM) fluorescence spectroscopy in conjunction with N-way partial least squares discriminant analysis (N-PLS-DA), and unfolded partial least squares discriminant analysis (U-PLS-DA) were used to identify the origin of 150 Radix Astragali samples from five geographical origins. The prediction results showed that the correct recognition rates of the U-PLS-DA model and N-PLS-DA model are 95.92% and 93.88%, respectively. In comparison, the results of U-PLS-DA are slightly better than those of N-PLS-DA. These findings indicate that EEM fluorescence spectroscopy based on weakly selective fluorescent probes combined with multi-way chemometrics provides a good idea for the origin tracing of traditional Chinese medicine.

Quantum dots-based "chemical tongue" for the discrimination of short-length Aβ peptides

A "chemical tongue" is proposed based on thiomalic acid-capped quantum dots (QDs) with signal enrichment provided by excitation-emission matrix (EEM) fluorescence spectroscopy for the determination of close structural analogs-short-length amyloid β (Aβ) peptides related to Alzheimer's disease. Excellent discrimination is obtained by principal component analysis (PCA) for seven derivatives: Aβ1-16, Aβ4-16, Aβ4-9, Aβ5-16, Aβ5-12, Aβ5-9, Aβ12-16. Detection of Aβ4-16, Aβ4-16, and Aβ5-9 in binary and ternary mixtures performed by QDs-based chemical tongue using partial least squares-discriminant analysis (PLS-DA) provided perfect 100% accuracy for the two studied peptides (Aβ4-16 and Aβ4-16), while for the third one (Aβ5-9) it was slightly lower (97.9%). Successful detection of Aβ4-16 at 1 pmol/mL (1.6 ng/mL) suggests that the detection limit of the proposed method for short-length Aβ peptides can span nanomolar concentrations. This result is highly promising for the development of simple and efficient methods for sequence recognition in short-length peptides and better understanding of mechanisms at the QD-analyte interface.

Characterizing the spatiotemporal distribution of dissolved organic matter (DOM) in the Yongding River Basin: Insights from flow regulation

Artificial flow regulation is an important measure to alleviate water shortages and improve the ecological quality of river basins. Dissolved organic matter (DOM) plays a crucial role in the carbon cycle and regulates biogeochemical and ecological processes in aquatic systems. Among the numerous studies on the effects of anthropogenic activities on the quality and quantity of river DOM, few studies have focused on the influence of different artificially regulated flow on the composition, source, and fate of fluvial DOM. This study aims to elucidate the impact of different artificial regulation modes of river flows on the source, migration, and transformation of DOM. The optical properties of DOM were used to explore the temporal and spatial distribution characteristics of DOM in the Yongding River Basin, where artificial regulation of river flows by cross-basin and inner-basin water transfers were implemented. Excitation-emission matrix fluorescence spectroscopy coupled with parallel factor analysis revealed four fluorescent substances of DOM in the water: one microbial humic-like (C1), one terrestrial humic-like (C2), one non-point source pollution humic-like (C4), and one tryptophan-like (C3) substance. Due to cross-basin water transfer from the Yellow River, the flow is the highest (21.79 m³/s) during spring, which was the reason that the signal of C2 was stronger during spring (71.45 QSU) compared to summer (57.12 QSU) and autumn (51.78 QSU). Due to inner-basin water transfer from upstream reservoirs, C3 derived from autochthonous sources were higher during autumn (130.81 QSU) than during spring (77.17 QSU) and summer (93.16 QSU). With no water transfer, more C1 were present at higher temperatures during summer (141.51 QSU) than during spring (126.73 QSU) and autumn (128.8 QSU). Moreover, C4 originating from urban and/or agricultural non-point source runoff increased during summer (57.07 QSU) than during spring (33.29 QSU) and autumn (52.27 QSU) because of increased rainfall. The different modes of artificial regulation of river flows changed the hydrological characteristics of the basin, which in turn altered the temporal and spatial distribution characteristics of the quantity and quality of DOM. The finding of this study can help promote the development of appropriate management strategies for artificial regulation of river flows in the basin. Furthermore, this study provides a basis for investigating the effects of different artificial flow regulations on the carbon cycles and ecological risks of rivers in the basin.

Seasonal variation of water-soluble brown carbon in Qingdao, China: Impacts from marine and terrestrial emissions

Brown carbon (BrC) has been attracting more and more attention owing to its significant effects on climate. However, the limited knowledge on its chemical composition and sources limits the precision of aerosol radiative forcing estimated by climate models. In this study, the chemical components of PM_2.5 and optical properties of water-soluble BrC (WS-BrC) were investigated from atmospheric particles collected in summer and winter in Qingdao, China. On the whole, though there were slight diurnal variations, seasonal differences were more obvious. Due to the influence of emission sources and meteorological conditions, the heavier pollution of carbonaceous aerosols occurred in winter. By comparison, the absorption Ångström exponent (AAE) and mass absorption efficiency of WS-BrC at 365 nm (MAE₃₆₅) showed that WS-BrC in winter had stronger wavelength dependence and light absorption capacity, which might be associated with biomass burning source contributions. This was further confirmed by a strong correlation between the light absorption coefficient at 365 nm (Abs₃₆₅) and non-sea salt K⁺, an indicator for biomass burning emissions. Four fluorescent components (C1∼C4) with high unsaturation in water-soluble organic carbon (WSOC) were identified by excitation-emission matrix fluorescence spectroscopy combined with parallel factor analysis method, which showed that WSOC in Qingdao was mainly related to humic-like chromophores. It is worth noting that C1 was similar to the water-soluble chromophore of simulated marine aerosols, which proved that marine emissions do have a certain impact on atmospheric particulate matter in coastal areas. In addition, the results of source analysis showed that WS-BrC originated from different terrestrial sources in different seasons. The current results may help to improve the knowledge of optical properties of WS-BrC in coastal cities, optimize the global climate model and formulate air management policies.

Probabilistic latent semantic analysis of composite excitation-emission matrix fluorescence spectra of multicomponent system

In the present work, a simple and fast analytical procedure involving minimum user intervention was developed by combining the excitation-emission matrix fluorescence (EEMF) spectroscopy with Probabilistic latent semantic analysis (pLSA) technique. Akaike Information Criterion (AIC) was used to enable the user to automatically select the optimum model for analysing the mixtures of fluorescent components. The utility of the present work was successfully evaluated by analysing the dilute aqueous mixtures of certain fluorescent molecule such as Catechol, Hydroquinone, Indole, Tryptophan and Tyrosine of biological relevance. The developed AIC assisted pLSA model of five components explained >90% variance of spectral data sets. The identity between the pLSA retrieved spectral profiles was established using similarity index (SI) parameter in automatic manner. The SI values were found to be close to unit values for each of the five analyzed molecules. The regression parameter between the actual and pLSA predicted concentrations were found to be well within acceptable limits. Both root mean square of calibration and predictions for each of the five fluorescent molecules were found to be <1%, whereas, the square of the correlation coefficient (R²) value was found to be >0.98 suggesting the developed pLSA model was quite precise in analysing both calibration and validation set samples. The uniqueness of the developed pLSA model for EEMF spectroscopic data was successfully tested using the sequential quadratic programming (SQP) algorithm. The differences between the upper and lower bands in SQP were found to be ≤0.005. In summary, the proposed approach serve as swift and simple analytical tool for the analysis of fluorescent mixtures without involving pre-separation step.

Temperature, turbidity, and the inner filter effect correction methodology for analyzing fluorescent dissolved organic matter in urban sewage

Dissolved organic matter (DOM) will be increasingly monitored by means of in situ fluorescence spectroscopy devices in order to supervise wastewater treatment plant efficiency, due to their ease of implementation and high-frequency measurement capacity. However, fluorescence spectroscopy measurements are reported to be sensitive to the sample matrix effects of temperature, the inner filter effect (IFE), and turbidity. Matrix effect estimation tests and signal correction have been developed for DOM (tyrosine-like, tryptophan-like, and humic substances-like fluorescent compounds) fluorescence measurements in unfiltered urban sewage samples. All such tests are conducted in temperature, absorbance, and turbidity ranges representative of urban sewage. For all fluorophores studied, an average of 1% fluorescence intensity decrease per degree (°C) of temperature increase could be observed. Protein-like fluorescent compound signals were found to be significantly affected by turbidity (0 to 210 NTU) and IFE (absorbance 254 nm > 0.200). Only temperature needs to be corrected for humic substances-like fluorescent compounds since other effects were not observed over the studied ranges of absorbance and turbidity. The fluorescence intensity correction method was applied first to each matrix effect separately and then combined by using a sequential mathematical correction methodology. An efficient methodology for determining the matrix effect correction equations for DOM fluorescence analysis into unfiltered urban sewage samples has been highlighted and could be used for in situ fluorescence measurement devices.

Introducing Principal Coordinate Analysis (PCoA) Assisted EEMF Spectroscopic Based Novel Analytical Approach for the Discrimination of Commercial Gasoline Fuels

In the present work, a novel analytical procedure by integrating principal coordinate analysis (PcoA) with excitation-emission matrix fluorescence (EEMF) spectroscopy was introduced for discriminating the commercial gasoline fuels. The PcoA technique involved analysis of the distance matrices containing the dissimilarity information and it can serve as an efficient tool for capturing the major as well as subtle compositional differences among the analyzed commercial gasoline samples. The utility of the proposed PcoA assisted EEMF analytical procedure was successfully tested by discriminating gasoline fuel samples belonging to five different industrial brands. The obtained results clearly showed that combination of PcoA and EEMF could provide a simple, sensitive and economical analytical procedure to carry out the rapid analyses of the gasoline samples belonging to different brands.

Diagnosis of the unexpected fluorescent contaminants in quantifying dissolved organic matter using excitation-emission matrix fluorescence spectroscopy

Dissolved organic matter (DOM) is widely present in aqueous environments and plays a significant role in pollutant mitigation and transformation. So far, excitation-emission matrix (EEM) fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC) has been widely applied to quantify fluorescent DOM. However, this approach fails to provide accurate concentration of DOM when fluorescent contaminants exist. In this work, a new method, prior linear decomposition (PLD), is developed to solve this problem by introducing prior information, i.e., EEMs of DOM, into data decomposition. First, EEM of humic acid (HA) with different numbers of random Gaussian peaks are tested to confirm the robustness of PLD. The percentages for the relative errors within 5% are found to be 97.7% and 69% using PLD and PARAFAC, respectively. Then, the determination of mixture of HA with several contaminants is performed, validating the feasibility of DOM quantification and capability of contaminant diagnosis using PLD for synthetic water samples. Finally, DOM-containing natural water samples collected from a polluted lake, river and wastewater treatment plant (WWTP) are measured. The testing results confirm that PLD provides an accurate result with less evaluated error than PARAFAC and the EEMs of the contaminants can be inferred precisely. This work clearly demonstrates that PLD offers a robust approach for quantifying fluorescent DOM, which is of great significance in both natural and engineered aqueous environments.

Introducing 'Simple Variable Selection (SVS) Approach' for Improving the Quantitative Accuracy of Chemometric Assisted Fluorimetric Estimations of Dilute Aqueous Mixtures

Excitation emission matrix fluorescence (EEMF) spectroscopy is a multiparametric fluorescence technique where the fluorescence intensity of a fluorophore is a function of excitation wavelength, emission wavelength and its concentration. The manual analysis of large volume of highly correlated EEMF data sets towards developing a calibration model for quantifying each fluorophores present in multifluorophoric mixtures is a difficult and time-consuming task. Over the years, Partial least square (PLS) algorithm has found its application towards providing swift and efficient analyses of large volumes of highly correlated spectral data sets. The PLS assisted EEMF spectroscopy has been successfully used towards quantifying the fluorophores in multifluorophoric mixtures without involving any pre-separation. However, the accuracy and robustness of developed calibration model can be significantly improved provided PLS analysis is carried out on the analytically relevant EEMF spectral variables. In the present work, a variable selection method baptized as simple variable selection (SVS) approach is introduced that provides a simple and computationally economical means of identifying the useful spectral variables for subsequent PLS analysis. The proposed SVS approach is successfully validated by analyzing the complex EEMF data sets of multifluorophoric mixtures of consisting of multifluorophoric mixtures of biological relevance. The proposed approach is found to provide a simple, swift and efficient means for developing a robust PLS assisted EEMF spectroscopy based calibration model for simultaneous quantification of various fluorophores present in multifluorophoric mixtures.

Combining excitation-emission matrix fluorescence spectroscopy, parallel factor analysis, cyclodextrin-modified micellar electrokinetic chromatography and partial least squares class-modelling for green tea characterization

In this study, an alternative analytical approach for analyzing and characterizing green tea (GT) samples is proposed, based on the combination of excitation-emission matrix (EEM) fluorescence spectroscopy and multivariate chemometric techniques. The three-dimensional spectra of 63 GT samples were recorded using a Perkin-Elmer LS55 luminescence spectrometer; emission spectra were recorded between 295 and 800 nm at excitation wavelength ranging from 200 to 290 nm, with excitation and emission slits both set at 10 nm. The excitation and emission profiles of two factors were obtained using Parallel Factor Analysis (PARAFAC) as a 3-way decomposition method. In this way, for the first time, the spectra of two main fluorophores in green teas have been found. Moreover, a cyclodextrin-modified micellar electrokinetic chromatography method was employed to quantify the most represented catechins and methylxanthines in a subset of 24 GT samples in order to obtain complementary information on the geographical origin of tea. The discrimination ability between the two types of tea has been shown by a Partial Least Squares Class-Modelling performed on the electrokinetic chromatography data, being the sensitivity and specificity of the class model built for the Japanese GT samples 98.70% and 98.68%, respectively. This comprehensive work demonstrates the capability of the combination of EEM fluorescence spectroscopy and PARAFAC model for characterizing, differentiating and analyzing GT samples.

Potential contributions of dissolved organic matter to monomethylmercury distributions in temperate reservoirs as revealed by fluorescence spectroscopy

The monomethylmercury (MMHg) concentrations, water quality parameters (e.g., pH, suspended particles, total phosphorus, sulfate, and chlorophyll-a), and compositions of dissolved organic matter (DOM) were analyzed to understand how the quality of DOM is related to the MMHg distributions in the surface waters of 14 reservoirs. The excitation-emission matrix (EEM) fluorescence spectroscopy identified six fluorescence peaks, and a parallel factor analysis (PARAFAC) of EEM spectra identified three components of DOM: microbial humic-like (C1), terrestrial humic-like (C2), and protein-like DOM (C3). Using the observation data, the principal component analysis (PCA) were carried out to understand the relative importance of the fluorescence properties of DOM, representing DOM quality, on the MMHg distribution. The loading plot of PCA showed a strong positive correlation between the MMHg and protein regions of the EEM spectra and no correlation between MMHg and the terrestrial humic regions of the EEM, suggesting that autochthonous DOM production is a key factor in increasing MMHg concentration in reservoir water. The preliminary mass flux estimation, which was carried out to identify the major sources of MMHg in Okjeong reservoir, revealed that the major sources are sediment diffusion and water column methylation. Because the studied reservoirs are located remotely from a large-sized river and industrial region, most MMHg in reservoir water is likely diffused from the surface sediment or produced in the water column, and these sources tend to increase in reservoirs enriched with autochthonous DOM. It is suggested that EEM fluorescence can improve our ability to trace the major sources of MMHg in diverse reservoirs.

The in situ bacterial production of fluorescent organic matter; an investigation at a species level

Aquatic dissolved organic matter (DOM) plays an essential role in biogeochemical cycling and transport of organic matter throughout the hydrological continuum. To characterise microbially-derived organic matter (OM) from common environmental microorganisms (Escherichia coli, Bacillus subtilis and Pseudomonas aeruginosa), excitation-emission matrix (EEM) fluorescence spectroscopy was employed. This work shows that bacterial organisms can produce fluorescent organic matter (FOM) in situ and, furthermore, that the production of FOM differs at a bacterial species level. This production can be attributed to structural biological compounds, specific functional proteins (e.g. pyoverdine production by P. aeruginosa), and/or metabolic by-products. Bacterial growth curve data demonstrates that the production of FOM is fundamentally related to microbial metabolism. For example, the majority of Peak T fluorescence (> 75%) is shown to be intracellular in origin, as a result of the building of proteins for growth and metabolism. This underpins the use of Peak T as a measure of microbial activity, as opposed to bacterial enumeration as has been previously suggested. This study shows that different bacterial species produce a range of FOM that has historically been attributed to high molecular weight allochthonous material or the degradation of terrestrial FOM. We provide definitive evidence that, in fact, it can be produced by microbes within a model system (autochthonous), providing new insights into the possible origin of allochthonous and autochthonous organic material present in aquatic systems.