A comprehensive "quality-quantity-activity" approach based on portable near-infrared spectrometer and membership function analysis to systematically evaluate spice quality: Cinnamomum cassia as an example

Spices have long been popular worldwide. Besides serving as aromatic and flavorful food and cooking ingredients, many spices exhibit notable bioactivity. Quality evaluation methods are essential for ensuring the quality and flavor of spices. However, existing methods typically focus on the content of particular components or certain aspects of bioactivity. For a systematic evaluation of spice quality, we herein propose a comprehensive "quality-quantity-activity" approach based on portable near-infrared spectrometer and membership function analysis. Cinnamomum cassia was used as a representative example to illustrate this approach. Near-infrared spectroscopy and chemometric methods were combined to predict the geographical origin, cinnamaldehyde content, ash content, antioxidant activity, and integrated membership function value. All the optimal prediction models displayed good predictive ability (correlation coefficient of prediction > 0.9, residual predictive deviation > 2.1). The proposed approach can provide a valuable reference for the rapid and comprehensive quality evaluation of spices.

The microalgae's ability to accumulate selected trace elements studied by ICP-MS/MS and chemometric methods

BACKGROUND

Microalgae can be used in different branches of industry, including cosmetology, pharmaceutics and the food industry, information on their ability to accumulate different elements becomes more important. The microalgae biomass grown in the media enriched in elements can increase the accumulation of different ions and give a possibility to control the contents of the various elements.

METHODS

The aim of the study was to determine the total content of metals in microalgae by tandem mass spectrometry with inductively coupled plasma (ICP-MS/MS) and analysis of the contents of particular metals as a function of the type of microalgae and conditions of cultivation. As the adverse effects of metals on the health of humans and animals have been well-documented and the use of microalgae has increased, the knowledge of metal contents in them is of particular importance in control of their quality.

RESULTS

Analysis of results permitted distinction of three main groups of microalgae with similar total metal content levels. Moreover, the results revealed the ways of stimulating more significant accumulation of selected elements (for example, Se concentration in control algae 0.279 µg g-1, in the algae cultivated in enriched medium - 219.7 µg g-1). They indicated the possible correlations between the accumulation of different ions. The result obtained shows a significant effect of metal accumulation and has a considerable impact on the differentiation of Arthrospira platensis grown in the medium enriched in different elements (selenium, zinc, chromium) (p ≤ 0.05).

CONCLUSIONS

Particular impact on the content of selected elements had the conditions of cultivation (type of support) and the microalgae species. Although the one species as the most significant source of selected elements cannot be indicated, it is possible to control the accumulation by the composition of the medium.

UV-Vis absorption spectrophotometry and LC-DAD-MS-ESI(+)-ESI(-) coupled to chemometrics analysis of the monitoring of sulfamethoxazole degradation by chlorination, photodegradation, and chlorination/photodegradation

Sulfamethoxazole (SMX) is one of the most widely used antibiotics worldwide and has been detected at high concentrations in wastewater treatment plant effluents and river waters. In this study, the SMX degradation process combining the simultaneous chlorine oxidation and UV photodegradation is assessed and compared with both photodegradation and chlorine oxidation processes individually. Photodegradation and Chlorine/UV tests were performed using Suntest CPS equipment. Different experimental techniques, including UV-Visible spectrophotometry and liquid chromatography coupled to a diode array detector and positive and negative ionization mass spectrometry (LC-DAD-MS-ESI(+)-ESI(-)), were used to evaluate the degradation reaction of SMX. All the analytical data generated have been processed with the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) method to monitor, resolve, and identify the several transformation products generated during the studied degradation processes. A new data fusion analysis strategy is proposed to examine the three processes simultaneously (with only photodegradation, only chlorination, and simultaneous chlorination+photodegradation). Combined with the analysis of different analytical techniques individually (spectrophotometry, LC-DAD, and LC-MS), the fusion of all generated data improved the description of the degradation processes. Detection using DAD allowed a better correspondence among the species monitored spectrophotometrically (UV-Vis) with those analyzed chromatographically. On the other side, detection using MS in both positive and negative acquisition modes allowed resolving a larger number of chemical compounds (specially SMX degradation subproducts) that could not be detected by UV-Vis spectrometry. The results obtained permitted the comparison of the effects produced by the three different degradation processes.

HPLC-DAD fingerprinting coupled with chemometric analysis can successfully differentiate Indian Berberis species and its plant parts

Identification of the plant parts and genuine species is one of the most important steps for the quality control of raw material used in herbal medicines. In the present study, comparative analysis of TLC, HPLC and FTIR-ATR fingerprinting in conjunction with chemometric method was done to select the most appropriate fingerprinting method for quality control of Berberis aristata and its related species. To achieve this, extracts were first utilized to obtain TLC, HPLC and FT-IR fingerprinting data. The data was analyzed by chemometric methods and similarity analysis. The PCA model obtained with HPLC fingerprinting method could successfully classify the Berberis species as well plant parts viz. root, stem and leaves which was not possible with the PCA model of TLC or FT-IR fingerprints. Similarity analysis based on the mean HPLC fingerprinting chromatogram could also appropriately assess the chemical variability and identification of different Berberis species.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03644-6.

Quality control strategies of medicine food homology materials based on fingerprint profiling and chemometrics: Citri Reticulata Pericarpium as an example

The study aimed to provide a reliable and feasible strategy for the comprehensive quality control of medicine food homology materials (MFHM). The high performance liquid chromatography (HPLC) fingerprints and Fourier transform mid-infrared (FT-MIR) quantized fingerprints were successfully developed to comprehensively evaluate overall quality of Citri Reticulata Pericarpium (CRP) by applying comprehensive quantified fingerprint method (CQFM). All samples were well distinguished and divided into 5 grades. In addition, through principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), the identification ability of HPLC fingerprints and FT-MIR fingerprints on CRP with different storage years was discussed. The results showed that HPLC fingerprints combined with PCA had good discrimination ability, and the PLS-DA model established by the preprocessed FT-MIR fingerprint data could accurately distinguish and predict the storage period of CRP. Finally, based on 1, 1-diphenyl-2-picrylhydrazyl radical (DPPH•) scavenging assay, combined with bivariate correlation analysis, the fingerprint-activity relationship of offline antioxidant activity of CRP samples with the fingerprints peak were studied. In general, the comprehensive strategies provide a reliable and scientific reference scheme for the quality control of MFHM in the future.

Assessment of surface water quality during different tides and an anthropogenic impact on coastal water at Gulf of Kachchh, West Coast of India

The port-based activity is often associated with industrial growth in the hinterland and similar phenomenon reported from the Gulf of Kachchh, India. Industrialization exerts pressure on coastal water through the release of waste water or effluents which influence the entire marine ecosystem. The present paper tries to evaluate the variation in the water quality during the high tide and low tide in relation to the anthropogenic or natural influence in Gulf of Kachchh. The tidal variation is important as it reflects the influence of the land-based activity on the coastal waters. To prove this logic, a series of stations were taken along the coastal water and statistical analysis, viz., Pearson correlation, Box plot, hierarchical cluster analysis (HCA), and factor analysis (PCA/FA) were conducted. Pearson correlation and Box plot represent visual impact of parameter variations in respected tides. The chemometric analysis, i.e., HCA and PCA/FA, clearly indicates an anthropogenic impact on coastal water. The results of HCA revealed that major anthropogenic and domestic impacts were found at various stations during the low tide. The HCA points out that an anthropogenic and the tidal activity in the Gulf of Kachchh influence the physical water quality parameters like pH, salinity, dissolved solid, oxygen, turbidity, sulfate, and nutrients in the coastal ecosystem. The PCA/FA further ascertains the finding of HCA analysis that the state of the art of the water quality of coastal ecosystem has direct relevance with the land-based activities and sewage outfall points. Tide-based control on the water quality parameters was evident that the high tide nutrients like phosphates and nitrogen were high, while during the low tide, temperature, salinity, total solids, and sulfate showed higher concentrations. The findings of the paper will be useful for developing effective management strategies for policy makers or stakeholders operating in the coastal area.

A green spectrophotometric method for the simultaneous determination of nasal binary mixture used in respiratory diseases: Applying isosbestic point and chemometric approaches as a resolving tool, greenness evaluation

Nasal drug combination is a very useful therapy for elevating the symptoms of various respiratory diseases as seasonal allergic rhinitis and infectious respiratory illness as pandemic COVID-19. One of best combination is Fluticasone propionate (FLU) and Azelastine (AZE). In this study, different UV spectrophotometric and chemometric methods have been applied for quantitative analysis of FLU and AZE without previous separation in their pure form, laboratory prepared mixture and pharmaceutical dosage form. Absorbance subtraction (AS) and Amplitude modulation (AM) spectrophotometric methods have been applied for the simultaneous determination of the cited drugs. Besides, three well-known chemometric techniques; namely, classical least squares (CLS), partial least square (PLS), and principal component regression (PCR) have been applied for the simultaneous analysis of both drugs by using spectrophotometric data. To be friendly to the environment, the greenness of the proposed methods was taken into consideration and evaluation of the analytical methods' greenness was done using two green analytical chemistry metrics known as, Analytical Greenness Calculator and an eco-scale scoring method. They indicated that the methods were environmentally friendly in relation to numerous approaches like instrument, reagents, and safety of waste. Analyzing laboratory prepared mixtures including different quantities of FLU and AZE, as well as their marketed dose form, was used to assess the selectivity of the applied methods. The validity of the developed methods was investigated by applying the standard addition technique. The resulting data were statistically compared to those obtained by the official or reported HPLC methods for FLU and AZE, which revealed no significant difference in accuracy and precision at p = 0.05.

Quantitative analysis of nutrients for nucleosides, nucleobases and amino acids hidden behind five distinct regions-derived Poria cocos using ultra-performance liquid chromatography coupled with triple-quadrupole linear ion-trap tandem mass spectrometry

Poria cocos is an edible fungus used as a health product and traditional Chinese medicinal preparation. Nevertheless, little is known about its nutrients. In this study, ultra-high performance liquid chromatography coupled with triple-quadrupole linear ion-trap tandem mass spectrometry was conducted to quantify nucleosides, nucleobases, and amino acids in 32 batches of Poria cocos samples collected from Anhui, Sichuan, Hubei, Hunan and Guizhou. Subsequently, the linearity, precision, repeatability, stability, and recovery of our methods were validated. Samples from different regions were clearly separated by partial least squares discriminant analysis and cluster analysis. Our results suggested that Poria cocos samples from different geographical environments differed in nucleosides, nucleobases, and amino acids. The plot of variable importance for projection disclosed differential compositions of L-Leucine, Uridine, L-Asparagine, L-Glutamine, L-phenylalanine, L-Ornithine monohydrochloride, L-Hydroxyproline, Taurine and Inosine in Poria cocos from five regions. We found the highest content of total analytes, total amino acids and total non-essential amino acids in Poria cocos from Anhui, total essential amino acids in the Sichuan samples and total nucleosides in Hunan samples. Overall, we determined the content of Poria cocos-derived nucleosides, nucleobases, and amino acids, providing the foothold for further chemical mining and use of Poria cocos. This article is protected by copyright. All rights reserved.

Metabolomics insight into the influence of environmental factors in responses of freshwater biofilms to the model herbicide diuron

Freshwater biofilms have been increasingly used during the last decade in ecotoxicology due to their ecological relevance to assess the effect(s) of environmental stress at the community level. Despite growing knowledge about the effect of various stressors on the structure and the function of these microbial communities, a strong research effort is still required to better understand their response to chemical stress and the influence of environmental stressors in this response. To tackle this challenge, untargeted metabolomics is an approach of choice because of its capacity to give an integrative picture of the exposure to multiple stress and associated effect as well as identifying the molecular pathways involved in these responses. In this context, the present study aimed to explore the use of an untargeted metabolomics approach to unravel at the molecular/biochemical level the response of the whole biofilm to chemical stress and the influence of various environmental factors in this response. To this end, archived high-resolution mass spectrometry data from previous experiments at our laboratory on the effect of the model photosynthesis inhibitor diuron on freshwater biofilm were investigated by using innovative solutions for OMICs data (e.g., DRomics) and more usual chemometric approaches (multivariate and univariate statistical analyses). The results showed a faster (1 min) and more sensitive response of the metabolome to diuron than usual functional descriptors, including photosynthesis. Also, the metabolomics response to diuron resulted from metabolites following various trends (increasing, decreasing, U/bell shape) along increasing concentration and time. This metabolomics response was influenced by the temperature, photoperiod, and flow. A focus on a plant-specific omega-3 (eicosapentaenoic acid) playing a key role in the trophic chain highlighted the potential relevance of metabolomics approach to establish the link between molecular alteration and ecosystem structure/functioning impairment but also how complex is the response and the influence of all the tested factors on this response at the metabolomics level. Altogether, our results underline that more fundamental researches are needed to decipher the metabolomics response of freshwater biofilm to chemical stress and its link with physiological, structural, and functional responses toward the unraveling of adverse outcome pathways (AOP) for key ecosystem functions (e.g., primary production).

Cost-effective and earth-friendly chemometrics-assisted spectrophotometric methods for simultaneous determination of Acetaminophen and Ascorbic Acid in pharmaceutical formulation

The development of analytical chemistry is omnipresent in all fields, this leads to considerable consumption of organic solvents and hazardous reagents with an increase in the production of waste to be treated. In this work, we developed simple, fast, cost-effective and above all environmentally friendly methods for the analysis of Acetaminophen (ACT) and Ascorbic acid (ASC) in synthetic mixtures and pharmaceutical formulation, using UV spectroscopy. Four chemometric methods were studied, including PLS-1 with full-spectrum (Full-PLS) and PLS-1 using three variable selection methods, namely subset selection through a genetic algorithm (GA), uninformative variable elimination using iterative predictor weighting (IPW), and variable selection by sub-window permutation analysis (SwPA). The accuracy of the developed methods was evaluated through the root mean square error of prediction (RMSEP), the mean absolute percentage error (MAPE) and the recovery values. All methods showed more accurate prediction results in comparison with full-PLS calibration. Furthermore, the results indicate that the GA-PLS models showed the highest prediction accuracy among all other models with RMSEP and MAPE values of (0.0494 and 0.610) and (0.0163 and 0.321) for the estimation of ACT and ASC, respectively. The proposed methods were successfully applied to the determination of ACT and ASC in their combined dosage form. In addition, the results obtained were statistically compared to those of the conventionally used HPLC method and were found to be in good agreement. The main advantages of the developed methods over HPLC during routine analysis are that they are faster, inexpensive, simple to perform, without the need for major pretreatment of samples. Besides, no organic solvents are used, and thus toxicity and pollution are avoided.

Chemometric quality assessment of Paracetamol and Phenylephrine Hydrochloride with Paracetamol impurities; comparative UV-spectrophotometric implementation of four predictive models

Spectrophotometric data analysis using multivariate approaches has many useful applications. One of these applications is the analysis of active ingredients in presence of impurities. Four chemometric-assisted spectrophotometric methods, namely, principal component regression (PCR), partial least-squares (PLS), artificial neural networks (ANN) and multivariate curve resolution-alternating least squares (MCR-ALS) were proposed and validated. The developed chemometric methods were compared to resolve the severely overlapped spectrum of Paracetamol (PAR) and Phenylephrine HCl (PHE) along with PAR impurities namely, P-Aminophenol (PAP), P-Nitrophenol (PNP), Acetanilide (ACT) and P-Chloroacetanilide (CAC). The four multivariate calibration methods succeeded in simultaneous determination of PAR and PHE with further quantification of PAR impurities. So, the proposed methods could be used with no need of any separation step and successfully applied for pharmaceutical formulation analysis. Furthermore, statistical comparison between the results obtained by the proposed chemometric methods and the official ones showed no significant differences.

Writing sequence identification of seals and signatures in documents using ambient mass spectrometry imaging with chemometric methods

Identifying the writing sequence of seals and signatures in documents is often performed and difficult to resolve in forensic determination. Morphological and physical-chemical analysis methods are often limited by the destructive nature of samples, a high signal response strength and specific materials. Mass spectrometry imaging (MSI) has been used as an alternative method because it can generate molecular images from many surfaces and produce rich chemical information. Herein, we developed a sequence identification method by coupling an air flow-assisted desorption electrospray ionization (AFADESI)-MSI system with a chemometric analysis, which can holistically and directly analyse document samples under ambient, moderate and selectable conditions and maintain the original appearance of the paper documents after sampling. By integrating principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA), equivocal point analysis can be objectively performed, where knowing the components of the seal or signature is not necessary to identify the sequence. In total, 28 prepared samples with known sequences and two original blind test samples were analysed. One prepared sample was analysed in negative ionization mode, and other samples were inferred in positive ionization mode. All writing sequences were in accordance with the actual case. The writing sequence of the blind testing of the original samples was correctly identified. This study provided a convenient, objective and quasi-nondestructive method to investigate the sequence differences among equivocal document samples and is promising for providing an alternative method for the sequence identification of seals and signatures in questionable documents.

Simple and efficient spectroscopic-based univariate sequential methods for simultaneous quantitative analysis of vandetanib, dasatinib, and sorafenib in pharmaceutical preparations and biological fluids

Six sequential spectrophotometric-based univariate methods were developed and validated for the simultaneous estimation of three novel anticancer drugs vandetanib (VAN), dasatinib (DAS), and sorafenib (SOR) in a mixture, without the requirement for separation. These methods are novel, simple, precise, and accurate. Different steps including zero crossing, ratio-based, and/or derivative spectra were utilized to develop these analytical methods, namely, ratio difference spectrophotometric method, constant center method, successive derivative ratio method, isoabsorptive method, mean centering of the ratio spectra method, and derivative ratio spectrum-zero crossing method. The calibration curve linearity was ranged from 2 to 9, 2-9, and 3-9 μgmL^-1 for VAN, DAS, and SOR, respectively. These established methods were applied for the quantification of the three selected drugs in different biological fluids (spiked human plasma and urine) and pharmaceutical preparations. The aforementioned methods were established for the concurrent estimation of ternary and binary mixtures to enhance the signal-to-noise ratio. The results did not statistically differ from the other reported methods, indicating no significant difference in accuracy and precision at p = 0.05.

Coupling of spectrometric, chromatographic, and chemometric analysis in the investigation of the photodegradation of sulfamethoxazole

A workflow is proposed for the study of the photodegradation process of the sulfamethoxazole (SMX) based on the combination of different experimental techniques, including liquid chromatography, mass spectrometry, UV-Visible spectrophotometry, and the treatment of all the analytical data with advanced chemometric methods. SMX, which is one of the most widely used antibiotics worldwide and has been found at remarkable concentrations in various rivers and effluents over all Europe, was degraded in the laboratory under a controlled source of UV radiation, which simulates the environmental solar radiation (Suntest). Kinetic monitoring of the photodegradation process was performed using UV-Visible spectrophotometric measurements and by further Liquid Chromatography with Diode Array Detector and Mass Spectrometry analysis (LC-DAD-MS). Additionally, the acid-base properties were also investigated to see how the pH can affect the speciation of this substance during the photodegradation process. Based on the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) application, the proposed chemometric method coped with the large amounts of data generated by the different analytical techniques used to monitor the evolution of the photodegradation process. Their simultaneous analysis involved applying a data fusion strategy and an advanced MCR-ALS constrained analysis, which allowed and improved the description of the complete degradation process, detecting the different species of the reaction, and identifying the possible transformation products formed. A total number of six species were resolved in the degradation process of SMX. In addition to the initial SMX, a second species corresponded to a conformational isomer, and the other four species represented different photoproducts, which have also been identified. Furthermore, three different acid-base species of SMX were obtained, and their pK_a values were estimated.

Spectrum-effect relationship between UPLC fingerprints and antidiabetic and antioxidant activities of Rosa rugosa

In this study, the antidiabetic and antioxidant properties of the chemical constituents of Rosa rugosa Thunb. (R. rugosa) was evaluated through analysis of spectrum-effect relationship. The ultra-performance liquid chromatography (UPLC) fingerprints of 21 batches of R. rugosa were evaluated by similarity analysis (SA) and hierarchical clustering analysis (HCA). The 28 common components were identified by ultra-high-performance liquid chromatography coupled to quadrupole-orbitrap high resolution mass spectrometry (UHPLC-Q-orbitrap-HRMS/MS). Meanwhile, the antidiabetic activities and antioxidant activities of 21 batches of R. rugosa were estimated in vitro. Besides, four chemometrics named principal component analysis (PCA), grey correlation analysis (GRA), partial least squares regression (PLSR) and the bivariate correlations analysis (BCA) were applied to construct spectrum-effect relationship between the UPLC fingerprints and biological activities of R. rugosa. The spectrum-effect relationship study revealed that di-O-galloyl-HHDP-glucoside, galloyl-HHDP-glucoside and avicularin were more relevant to antidiabetic activity. Di-O-galloyl-HHDP-glucoside, galloyl-HHDP-glucoside and ellagic acid were the main antioxidant components of R. rugosa. The current bioassay and spectrum-effect relationships are proper for associating sample quality with the active ingredient, and our finding would provide foundation and further understanding of the quality evaluation and quality control of R. rugosa.

Rapid and practical qualitative and quantitative evaluation of non-fumigated ginger and sulfur-fumigated ginger via Fourier-transform infrared spectroscopy and chemometric methods

A strategy was developed to distinguish and quantitate nonfumigated ginger (NS-ginger) and sulfur-fumigated ginger (S-ginger), based on Fourier transform near infrared spectroscopy (FT-NIR) and chemometrics. FT-NIR provided a reliable method to qualitatively assess ginger samples and batches of S-ginger (41) and NS-ginger (39) were discriminated using principal component analysis and orthogonal partial least squares discriminant analysis of FT-NIR data. To generate quantitative methods based on partial least squares (PLS) and counter propagation artificial neural network (CP-ANN) from the FT-NIR, major gingerols were quantified using high performance liquid chromatography (HPLC) and the data used as a reference. Finally, PLS and CP-ANN were deployed to predict concentrations of target compounds in S- and NS-ginger. The results indicated that FT-NIR can provide an alternative to HPLC for prediction of active components in ginger samples and was able to work directly on solid samples.

Prediction of tumor size in patients with invasive ductal carcinoma using FT-IR spectroscopy combined with chemometrics: a preliminary study

Precise detection of tumor size is essential for early diagnosis, treatment, and evaluation of the prognosis of breast cancer. However, there are some errors between the tumor size of breast cancer measured by conventional imaging methods and the pathological tumor size. Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer. In this study, serum Fourier transform infrared spectroscopy (FT-IR) combined with chemometric methods was used to predict the maximum diameter and maximum vertical diameter of tumors in IDC patients. Three models were evaluated based on the pathological tumor size measured after surgery and included grid search support vector machine regression (GS-SVR), back propagation neural network optimized by genetic algorithm (GA-BP-ANN), and back propagation neural network optimized by particle swarm optimization (PSO-BP-ANN). The results show that three models can accurately predict tumor size. The GA-BP-ANN model provided the best fitting quality of the largest tumor diameter with the determination coefficients of 0.984 in test set. And the GS-SVR model provided the best fitting quality of the largest vertical tumor diameter with the determination coefficients of 0.982 in test set. The GS-SVR model had the highest prediction efficiency and the lowest time complexity of the models. The results indicate that serum FT-IR spectroscopy combined with chemometric methods can predict tumor size in IDC patients. In addition, compared with traditional imaging methods, we found that the experimental results of the three models are better than traditional imaging methods in terms of correlation and fitting degree. And the average fitting error of PSO-BP-ANN and GA-BP-ANN models was less than 0.3 mm. The minimally invasive detection method is expected to be developed into a new clinical diagnostic method for tumor size estimation to reduce the diagnostic trauma of patients and provide new diagnostic experience for patients. Graphical Abstract.

The main effects of elevated CO2 and soil-water deficiency on 1H NMR-based metabolic fingerprints of Coffea arabica beans by factorial and mixture design

The metabolic response of Coffea arabica trees in the face of the rising atmospheric concentration of carbon dioxide (CO₂) combined with the reduction in soil-water availability is complex due to the various (bio)chemical feedbacks. Modern analytical tools and the experimental advance of agronomic science tend to advance in the understanding of the metabolic complexity of plants. In this work, Coffea arabica trees were grown in a Free-Air Carbon Dioxide Enrichment dispositive under factorial design (2²) conditions considering two CO₂ levels and two soil-water availabilities. The ¹H NMR mixture design-fingerprinting effects of CO₂ and soil-water levels on beans were strategically investigated using the principal component analysis (PCA), analysis of variance (ANOVA) - simultaneous component analysis (ASCA) and partial least squares-discriminant analysis (PLS-DA). From the ASCA, the CO₂ factor had a significant effect on changing the ¹H NMR profile of fingerprints. The soil-water factor and interaction (CO₂ × soil-water) were not significant. ¹H NMR fingerprints with PCA, ASCA and PLS-DA analysis determined spectral profiles for fatty acids, caffeine, trigonelline and glucose increases in beans from current CO₂, while quinic acid/chlorogenic acids, malic acid and kahweol/cafestol increased in coffee beans from elevated CO₂. PLS-DA results revealed a good classification performance between the significant effect of the atmospheric CO₂ levels on the fingerprints, regardless of the soil-water availabilities. Finally, the PLS-DA model showed good prediction ability, successfully classifying validation data-set of coffee beans collected over the vertical profile of the plants and included several fingerprints of different extracting solvents. The results of this investigation suggest that the association of experimental design, mixture design, PCA, ASCA and PLS-DA can provide accurate information on a series of metabolic changes provoked by climate changes in products of commercial importance, in addition to minimizing the extra work necessary in classic analytical approaches, encouraging the development of similar strategies.

Molecular mechanism underlying the anti-inflammatory effects of volatile components of Ligularia fischeri (Ledeb) Turcz based on network pharmacology

Background

Ligularia fischeri (Ledeb) Turcz (LFT) is a well-known expectorant and active anti-inflammatory agent in Chinese traditional medicine. LFT’s expectorant effect is closely related to its anti-inflammatory effects. This study aimed to evaluate the differential composition and anti-inflammatory mechanisms of the volatile components in LFT from different production areas.

Method

Headspace solid-phase microextraction-gas chromatography-mass spectrometry analysis of volatile components, as well as chemometric methods, including similarity analysis, hierarchical clustering analysis, and principal component analysis, were performed to identify LFT produced in different areas. The molecular mechanism underlying the anti-inflammatory effects of these components was determined by network pharmacology analysis.

Results

We observed significant differences in the chemical constituents and percentage contents in samples with different origins. Eighteen volatile components were identified in four different producing areas, among which the highest content of olefinic components was the main component of the aroma of LFT. The mechanisms of these pharmacological effects involved multiple targets and pathways. Twenty-seven potential target proteins and 65 signaling pathways were screened, and a “component-target-disease” interaction network map was constructed. The volatile components of the LFT function mainly by inhibiting the production of inflammatory factors.

Conclusion

This study provides a theoretical framework for further development and application of LFT used in traditional Chinese medicine.

Graphical abstract

Evaluation of the impact of buffered peptone water composition on the discrimination between Salmonella enterica and Escherichia coli by Raman spectroscopy

The detection of Salmonella spp. in food samples is regulated by the ISO 6579:2002 standard, which requires that precise procedures are followed to ensure the reliability of the detection process. This standard requires buffered peptone water as a rich medium for the enrichment of bacteria. However, the effects of different brands of buffered peptone water on the identification of microorganisms by Raman spectroscopy are unknown. In this regard, our study evaluated the discrimination between two bacterial species, Salmonella enterica and Escherichia coli, inoculated and analyzed with six of the most commonly used buffered peptone water brands. The results showed that bacterial cells behaved differently according to the brand used in terms of biomass production and the spectral fingerprint. The identification accuracy of the analyzed strains was between 85% and 100% depending on the given brand. Several batches of two brands were studied to evaluate the classification rates between the analyzed bacterial species. The chemical analysis performed on these brands showed that the nutrient content was slightly different and probably explained the observed effects. On the basis of these results, Raman spectroscopy operators are encouraged to select an adequate culture medium and continue its use throughout the identification process to guarantee optimal recognition of the microorganism of interest.

Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy

Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C-H, C-H₂, C-H₃), alkene group (-CH=CH-), hydroxyl group (O-H) and Amides I and II vibrational modes.

Comparing the predictability of different chemometric models over UV-spectral data of isoxsuprine and its toxic photothermal degradation products

Isoxsuprine (ISX) is widely used for cerebral and peripheral vascular diseases. A comparative study was held among different multivariate calibration models for selective determination of a complex mixture of Isoxsuprine and four of its toxic photothermal degradation products that impair kidney and liver functions. The Partial Least Squares (PLS) and Artificial Neural Network (ANN) models were applied on the specific spectrum and on selected wavelengths using genetic algorithm (GA) technique as an efficient variable selection tool. The effect of GA on the model construction and performance was evaluated. The multilevel multifactor experimental design was adopted for the construction of the calibration set. Optimized parameters were used for the development of the different models. The performances of the developed models were assessed by predicting the concentration of eight different mixtures composing the validation set. Results were compared to one another and to the official method using one-way ANOVA statistical test to assure the validity of the constructed models. The lower chance of overfitting offered by ANN minimized the RMSEP relative to the PLS. On the other hand, the application of GA prior to model implementation affected the number of latent variables the prediction ability of both PLS and ANN models. The validated models were successfully applied as stability indicating assay methods for the selective determination of ISX and its photothermal degradation products in ISX raw material and market formulations.

Assessment of ecotoxicity and total volatile organic compound (TVOC) emissions from food and children's toy products

The development of new methods for identifying a broad spectrum of analytes, as well as highly selective tools to provide the most accurate information regarding the processes and relationships in the world, has been an area of interest for researchers for many years. The information obtained with these tools provides valuable data to complement existing knowledge but, above all, to identify and determine previously unknown hazards. Recently, attention has been paid to the migration of xenobiotics from the surfaces of various everyday objects and the resulting impacts on human health. Since children are among those most vulnerable to health consequences, one of the main subjects of interest is the migration of low-molecular-weight compounds from toys and products intended for children. This migration has become a stimulus for research aimed at determining the degree of release of compounds from popular commercially available chocolate/toy sets. One of main objectives of this research was to determine the impact of time on the ecotoxicity (with Vibrio fischeri bioluminescent bacteria) of extracts of products intended for children and to assess the correlation with total volatile organic compound emissions using basic chemometric methods. The studies on endocrine potential (with XenoScreen YES/YAS) of the extracts and showed that compounds released from the studied objects (including packaging foils, plastic capsules storing toys, most of toys studied and all chocolate samples) exhibit mostly androgenic antagonistic behavior while using artificial saliva as extraction medium increased the impact observed. The impact of time in most cases was positive one and increased with prolonging extraction time. The small-scale stationary environmental test chambers - μ-CTE™ 250 system was employed to perform the studies aimed at determining the profile of total volatile organic compounds (TVOCs) emissions. Due to this it was possible to state that objects from which the greatest amounts of contaminants are released are plastic containers (with emission rate falling down from 3273 to 2280 ng/g of material at 6 h of conditioning in elevated temperature).

Geochemical signatures and isotopic systematics constraining dynamics of fluoride contamination in groundwater across Jamui district, Indo-Gangetic alluvial plains, India

A data set of 76 water samples are obtained from surface and sub-surface water bodies to investigate chemical parameters and stable isotopic signatures in order to drive factors leading to fluoride (F^-) contamination in groundwater of parts of Jamui district, India. Hydrochemical facies reveals that F^- concentration is lower in Ca²⁺-HCO₃^- facies representative of recharge area, while discharge area has a tendency towards Na⁺-HCO₃^- facies with elevated F^- concentration. The ionic ratios Na⁺/Ca²⁺>1, Na⁺/Cl^->1, (Ca²⁺+Mg²⁺)/HCO₃^-<1, Na⁺+K⁺ = 0.5TZ⁺ and Ca²⁺+Mg²⁺ = TZ⁺ witness silicate weathering by water-rock interaction coupled with ion exchange and prolonged residence time, are the principle factors for fluoride contamination (3.6 mg/L to 5.8 mg/L) in 67% of deeper bore wells. Geochemical modelling testifies excess of alkalinity due to the dominance of bicarbonate ion leading to calcite precipitation and dissolution of fluoride in solution contributing to fluoride contamination. The chemometric analysis reveals that the water chemistry of the study area is controlled by both anthropogenic and natural sources, and enrichment of fluoride in groundwater is possibly from geogenic source (fractured granite gneiss). The stable isotope plot shows that most of the samples fall along local meteoric water line indicating that the groundwater is originated from local precipitation with a possibility of evaporative enrichment. Groundwater enriched in δ¹⁸O is positively correlated with F^- suggesting evaporation and longer residence time of water. Spatially elevated F^- prevails in the eastern bank of Kiul River and along the groundwater flow direction, which is attributed to control of dynamics of hydrogeological conditions.

Rapid detection of adulteration in Anoectochilus roxburghii by near-infrared spectroscopy coupled with chemometric methods

To determine the authenticity of Anoectochilus roxburghii, this study presents an application of near-infrared spectroscopy and chemometric methods for evaluating adulteration of A. roxburghii with two cheaper adulterants, i.e. C. Goodyera schlechtendaliana and Ludisia discolor. Partial least squares discriminant analysis models were built for the accurate classification of authentic A. roxburghii and A. roxburghii adulterated at 5-100% (w/w) levels. Partial least squares regression models were used to predict the level of adulteration in the A. roxburghii. After by compared different spectral pretreatment methods, and using interval PLS and synergy interval PLS for variable selection, optimum models were developed. These results show that the NIR spectroscopy combined with chemometric methods offers a simple, fast, and reliable method for classifying and quantifying the adulteration of A. roxburghii.

Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques

Visible and near infrared spectrometry (vis-NIRS) coupled with data mining techniques can offer fast and cost-effective quantitative measurement of total petroleum hydrocarbons (TPH) in contaminated soils. Literature showed however significant differences in the performance on the vis-NIRS between linear and non-linear calibration methods. This study compared the performance of linear partial least squares regression (PLSR) with a nonlinear random forest (RF) regression for the calibration of vis-NIRS when analysing TPH in soils. 88 soil samples (3 uncontaminated and 85 contaminated) collected from three sites located in the Niger Delta were scanned using an analytical spectral device (ASD) spectrophotometer (350-2500nm) in diffuse reflectance mode. Sequential ultrasonic solvent extraction-gas chromatography (SUSE-GC) was used as reference quantification method for TPH which equal to the sum of aliphatic and aromatic fractions ranging between C₁₀ and C₃₅. Prior to model development, spectra were subjected to pre-processing including noise cut, maximum normalization, first derivative and smoothing. Then 65 samples were selected as calibration set and the remaining 20 samples as validation set. Both vis-NIR spectrometry and gas chromatography profiles of the 85 soil samples were subjected to RF and PLSR with leave-one-out cross-validation (LOOCV) for the calibration models. Results showed that RF calibration model with a coefficient of determination (R²) of 0.85, a root means square error of prediction (RMSEP) 68.43mgkg^-1, and a residual prediction deviation (RPD) of 2.61 outperformed PLSR (R²=0.63, RMSEP=107.54mgkg^-1 and RDP=2.55) in cross-validation. These results indicate that RF modelling approach is accounting for the nonlinearity of the soil spectral responses hence, providing significantly higher prediction accuracy compared to the linear PLSR. It is recommended to adopt the vis-NIRS coupled with RF modelling approach as a portable and cost effective method for the rapid quantification of TPH in soils.

Dynamic surface-enhanced Raman spectroscopy and Chemometric methods for fast detection and intelligent identification of methamphetamine and 3, 4-Methylenedioxy methamphetamine in human urine

Conventional Surface-Enhanced Raman Spectroscopy (SERS) for fast detection of drugs in urine on the portable Raman spectrometer remains challenges because of low sensitivity and unreliable Raman signal, and spectra process with manual intervention. Here, we develop a novel detection method of drugs in urine using chemometric methods and dynamic SERS (D-SERS) with mPEG-SH coated gold nanorods (GNRs). D-SERS combined with the uniform GNRs can obtain giant enhancement, and the signal is also of high reproducibility. On the basis of the above advantages, we obtained the spectra of urine, urine with methamphetamine (MAMP), urine with 3, 4-Methylenedioxy Methamphetamine (MDMA) using D-SERS. Simultaneously, some chemometric methods were introduced for the intelligent and automatic analysis of spectra. Firstly, the spectra at the critical state were selected through using K-means. Then, the spectra were proposed by random forest (RF) with feature selection and principal component analysis (PCA) to develop the recognition model. And the identification accuracy of model were 100%, 98.7% and 96.7%, respectively. To validate the effect in practical issue further, the drug abusers'urine samples with 0.4, 3, 30ppm MAMP were detected using D-SERS and identified by the classification model. The high recognition accuracy of >92.0% can meet the demand of practical application. Additionally, the parameter optimization of RF classification model was simple. Compared with the general laboratory method, the detection process of urine's spectra using D-SERS only need 2 mins and 2μL samples volume, and the identification of spectra based on chemometric methods can be finish in seconds. It is verified that the proposed approach can provide the accurate, convenient and rapid detection of drugs in urine.

Rapid classification of heavy metal-exposed freshwater bacteria by infrared spectroscopy coupled with chemometrics using supervised method

Rapid, cost-effective, sensitive and accurate methodologies to classify bacteria are still in the process of development. The major drawbacks of standard microbiological, molecular and immunological techniques call for the possible usage of infrared (IR) spectroscopy based supervised chemometric techniques. Previous applications of IR based chemometric methods have demonstrated outstanding findings in the classification of bacteria. Therefore, we have exploited an IR spectroscopy based chemometrics using supervised method namely Soft Independent Modeling of Class Analogy (SIMCA) technique for the first time to classify heavy metal-exposed bacteria to be used in the selection of suitable bacteria to evaluate their potential for environmental cleanup applications. Herein, we present the powerful differentiation and classification of laboratory strains (Escherichia coli and Staphylococcus aureus) and environmental isolates (Gordonia sp. and Microbacterium oxydans) of bacteria exposed to growth inhibitory concentrations of silver (Ag), cadmium (Cd) and lead (Pb). Our results demonstrated that SIMCA was able to differentiate all heavy metal-exposed and control groups from each other with 95% confidence level. Correct identification of randomly chosen test samples in their corresponding groups and high model distances between the classes were also achieved. We report, for the first time, the success of IR spectroscopy coupled with supervised chemometric technique SIMCA in classification of different bacteria under a given treatment.

Effect of molecular weight of chitosan on the shelf life and other quality parameters of three different cultivars of Actinidia kolomikta (kiwifruit)

The kiwi fruit, Actinidia kolomikta, has valuable properties such as high antioxidant activity, high vitamin C, polyphenols, chlorophylls and organic acids content, but the species are hardly commercialized due to their short shelf life (less than two days). In this study three different cultivars of A. kolomikta (Anykšta, Sentiabrskaya and VIR2) were coated with low, medium and high molecular weight chitosan bio-polymer with the aim to extend the shelf life. The changes in fruit firmness, mass, phenolic compound content, vitamin C content and subjective criteria (withering level, decoloration level and aesthetic appearance) were monitored. It was observed that high molecular weight chitosan had higher positive effect on the shelf life of Sentiabrskaya and Anykšta cultivars than VIR2. Low molecular weight chitosan was found effective on VIR2.

Vibrational Micro-Spectroscopy of Human Tissues Analysis: Review

Vibrational spectroscopy (Infrared (IR) and Raman) and, in particular, micro-spectroscopy and micro-spectroscopic imaging have been used to characterize developmental changes in tissues, to monitor these changes in cell cultures and to detect disease and drug-induced modifications. The conventional methods for biochemical and histophatological tissue characterization necessitate complex and "time-consuming" sample manipulations and the results are rarely quantifiable. The spectroscopy of molecular vibrations using mid-IR or Raman techniques has been applied to samples of human tissue. This article reviews the application of these vibrational spectroscopic techniques for analysis of biological tissue published between 2005 and 2015.

Simultaneous quantitative determination of paracetamol and tramadol in tablet formulation using UV spectrophotometry and chemometric methods

The UV spectrophotometric methods for simultaneous quantitative determination of paracetamol and tramadol in paracetamol-tramadol tablets were developed. The spectrophotometric data obtained were processed by means of partial least squares (PLS) and genetic algorithm coupled with PLS (GA-PLS) methods in order to determine the content of active substances in the tablets. The results gained by chemometric processing of the spectroscopic data were statistically compared with those obtained by means of validated ultra-high performance liquid chromatographic (UHPLC) method. The accuracy and precision of data obtained by the developed chemometric models were verified by analysing the synthetic mixture of drugs, and by calculating recovery as well as relative standard error (RSE). A statistically good agreement was found between the amounts of paracetamol determined using PLS and GA-PLS algorithms, and that obtained by UHPLC analysis, whereas for tramadol GA-PLS results were proven to be more reliable compared to those of PLS. The simplest and the most accurate and precise models were constructed by using the PLS method for paracetamol (mean recovery 99.5%, RSE 0.89%) and the GA-PLS method for tramadol (mean recovery 99.4%, RSE 1.69%).

Chemometrics resolution and quantification power evaluation: Application on pharmaceutical quaternary mixture of Paracetamol, Guaifenesin, Phenylephrine and p-aminophenol

Three advanced chemmometric-assisted spectrophotometric methods namely; Concentration Residuals Augmented Classical Least Squares (CRACLS), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis-Artificial Neural Networks (PCA-ANN) were developed, validated and benchmarked to PLS calibration; to resolve the severely overlapped spectra and simultaneously determine; Paracetamol (PAR), Guaifenesin (GUA) and Phenylephrine (PHE) in their ternary mixture and in presence of p-aminophenol (AP) the main degradation product and synthesis impurity of Paracetamol. The analytical performance of the proposed methods was described by percentage recoveries, root mean square error of calibration and standard error of prediction. The four multivariate calibration methods could be directly used without any preliminary separation step and successfully applied for pharmaceutical formulation analysis, showing no excipients' interference.

Decoding 2-D Maps by Autocovariance Function

This chapter describes a mathematical approach based on the study of the 2-D autocovariance function (2-D ACVF) useful for decoding the complex signals resulting from the separation of protein mixtures. The method allows to obtain fundamental analytical information hidden in 2-D PAGE maps by spot overlapping, such as the number of proteins present in the sample and the mean standard deviation of the spots, describing the separation performance. In addition, it is possible to identify ordered patterns potentially present in spot positions, which can be related to the chemical composition of the protein mixture, such as post-translational modifications.The procedure was validated on computer-simulated maps and successfully applied to reference maps obtained from literature sources.

Fluorescence, electrophoretic and chromatographic fingerprints of herbal medicines and their comparative chemometric analysis

The aim of the present study was to compare the polyphenolic compositions of 47 medicinal herbs (HM) and four herbal tea mixtures from Central Estonia by rapid, reliable and sensitive Spectral Fluorescence Signature (SFS) method in a front face mode. The SFS method was validated for the main identified HM representatives including detection limits (0.037mgL(-1) for catechin, 0.052mgL(-1) for protocatechuic acid, 0.136mgL(-1) for chlorogenic acid, 0.058mgL(-1) for syringic acid and 0.256mgL(-1) for ferulic acid), linearity (up to 5.0-15mgL(-1)), intra-day precision (RSDs=6.6-10.6%), inter-day precision (RSDs=6.4-13.8%), matrix effect (-15.8 to +5.5) and recovery (85-107%). The phytochemical fingerprints were differentiated by parallel factor analysis (PARAFAC) combined with hierarchical cluster analysis (CA) and principal component analysis (PCA). HM were clustered into four main clusters (catechin-like, hydroxycinnamic acid-like, dihydrobenzoic acid-like derivatives containing HM and HM with low/very low content of fluorescent constituents) and 14 subclusters (rich, medium, low/very low contents). The average accuracy and precision of CA for validation HM set were 97.4% (within 85.2-100%) and 89.6%, (within 66.7-100%), respectively. PARAFAC-PCA/CA has improved the analysis of HM by the SFS method. The results were verified by two separation methods CE-DAD and HPLC-DAD-MS also combined with PARAFAC-PCA/CA. The SFS-PARAFAC-PCA/CA method has potential as a rapid and reliable tool for investigating the fingerprints and predicting the composition of HM or evaluating the quality and authenticity of different standardised formulas. Moreover, SFS-PARAFAC-PCA/CA can be implemented as a laboratory and/or an onsite method.

Artificial neural network assisted kinetic spectrophotometric technique for simultaneous determination of paracetamol and p-aminophenol in pharmaceutical samples using localized surface plasmon resonance band of silver nanoparticles

Spectrophotometric analysis method based on the combination of the principal component analysis (PCA) with the feed-forward neural network (FFNN) and the radial basis function network (RBFN) was proposed for the simultaneous determination of paracetamol (PAC) and p-aminophenol (PAP). This technique relies on the difference between the kinetic rates of the reactions between analytes and silver nitrate as the oxidizing agent in the presence of polyvinylpyrrolidone (PVP) which is the stabilizer. The reactions are monitored at the analytical wavelength of 420nm of the localized surface plasmon resonance (LSPR) band of the formed silver nanoparticles (Ag-NPs). Under the optimized conditions, the linear calibration graphs were obtained in the concentration range of 0.122-2.425μgmL(-1) for PAC and 0.021-5.245μgmL(-1) for PAP. The limit of detection in terms of standard approach (LODSA) and upper limit approach (LODULA) were calculated to be 0.027 and 0.032μgmL(-1) for PAC and 0.006 and 0.009μgmL(-1) for PAP. The important parameters were optimized for the artificial neural network (ANN) models. Statistical parameters indicated that the ability of the both methods is comparable. The proposed method was successfully applied to the simultaneous determination of PAC and PAP in pharmaceutical preparations.

Simultaneous spectrophotometric determination of celecoxib and diacerein in bulk and capsule by absorption correction method and chemometric methods

Two methods, absorption correction and multivariate spectrophotometric methods were developed for simultaneous estimation of Celecoxib (CEL) and Diacerein (DIA) in combined dosage form. Absorption correction method involves direct estimation of DIA at wavelength 341 nm in which CEL has zero absorbance and shows no interference. For estimation of CEL, corrected absorbance was calculated at 253 nm due to the interference of DIA at this wavelength. Linearity was observed in the range of 6-22 μg mL(-1) for CEL and 3-11 μg mL(-1) for DIA. The method was validated as per ICH guidelines. Chemometric methods including classical least square (CLS), inverse least square (ILS), principal component regression (PCR) and partial least square (PLS) were studied for simultaneous determination of CEL and DIA in capsule using spectrophotometry. A set of 25 standard mixtures containing both drugs were prepared in range of 5-25 μg mL(-1) for CEL and 3-15 μg mL(-1) for DIA. Analytical figure of merit (FOM), such as sensitivity, selectivity, analytical sensitivity, limit of detection and limit of quantitation were determined for chemometric methods. The proposed methods were applied for determination of two components from combined dosage form.

Chemical characteristics combined with bioactivity for comprehensive evaluation of Panax ginseng C.A. Meyer in different ages and seasons based on HPLC-DAD and chemometric methods

Panax ginseng C.A. Meyer has been known as a valuable traditional Chinese medicines for thousands years of history. Ginsenosides, the main active constituents, exhibit prominent immunoregulation effect. The present study first describes a holistic method based on chemical characteristic and lymphocyte proliferative capacity to evaluate systematically the quality of P. ginseng in thirty samples from different seasons during 2-6 years. The HPLC fingerprints were evaluated using principle component analysis (PCA) and hierarchical clustering analysis (HCA). The spectrum-efficacy model between HPLC fingerprints and T-lymphocyte proliferative activities was investigated by principal component regression (PCR) and partial least squares (PLS). The results indicated that the growth of the ginsenosides could be grouped into three periods and from August of the fifth year, P. ginseng appeared significant lymphocyte proliferative capacity. Close correlation existed between the spectrum-efficacy relationship and ginsenosides Rb1, Ro, Rc, Rb2 and Re were the main contributive components to the lymphocyte proliferative capacity. This comprehensive strategy, providing reliable and adequate scientific evidence, could be applied to other TCMs to ameliorate their quality control.

Inorganic salts in atmospheric particulate matter: Raman spectroscopy as an analytical tool

Atmospheric particulate matter is composed of inorganic and organic components of natural and anthropogenic origin. Wind-transport is probably the most important process responsible for the emission of solid particulate matter into the troposphere, but there are also important contributions from chemical reactions due to the interaction of different atmospheric components in presence of water and solar radiation. Sulfate, nitrate and carbonate salts can be both reactants and products in this complex dynamic system, and there is no doubt about their important impact on the climate. Both simple and mixed salts can be produced in atmosphere by dissolution-crystallization processes. The Raman spectra of 45 representative salts of the atmospheric environment were recorded and the bands assigned. The chemometric analysis of the spectroscopic data of these 45 salts demonstrates the suitability of Raman spectroscopy to classify and identify sulfate, nitrate and carbonate salts of atmospheric importance. Salts were classified into three groups: "sulfates", "nitrates or carbonates" and "sulfate-nitrates or sulfate-carbonate". This kind of information is relevant in atmospheric studies because specific characteristics of the salts can provide valuable information about the origin of the salts, the atmospheric chemistry and climate forcing, thus contributing to the evaluation of environmental impacts.

Determination of diosmin in pharmaceutical formulations using Fourier transform infrared spectrophotometry

A Fourier transform infrared (FT-IR) spectrometric method was developed for the rapid, direct measurement of diosmin in different pharmaceutical drugs. Conventional KBr-spectra were compared for best determination of active substance in commercial preparations. The Beer-Lambert law and two chemometric approaches, partial least squares (PLS) and principal component regression (PCR+) methods, were tried in data processing.