Simultaneous quantification of Aroclor mixtures in soil samples by gas chromatography/mass spectrometry with solid phase microextraction using partial least-squares regression

Source apportionment of polychlorinated biphenyls (PCBs) using different receptor models: A case study on sediment from the Portland Harbor Superfund Site (PHSS), Oregon, USA

Multivariate modelling techniques are used by a wide variety of investigations in environmental chemistry. It is surprisingly rare for studies to show a detailed understanding of uncertainties created by modelling or how uncertainties in chemical analysis impact model outputs. It is common to use untrained multivariate models for receptor modelling. These models produce a slightly different output each time they are run. The fact that a single model can provide different results is rarely acknowledged. In this manuscript, we attempt to address this by investigating differences that can be generated using four different receptor models (NMF, ALS, PMF & PVA) to perform source apportionment of polychlorinated biphenyls (PCBs) in surface sediments from Portland Harbor. Results showed that models generally had a strong agreement and identified the same main signatures that represented commercial PCB mixtures, however, subtle differences were identified by; different models, same models but with a different number of end members (EM), and the same model with the same number of end members. As well as identifying different Aroclor-like signatures, the relative proportion of these sources also varied. Depending on which method is selected it may have a significant impact on conclusions of a scientific report or litigation case and ultimately, allocation on who is responsible for paying for remediation. Therefore, care must be taken to understand these uncertainties to select a method that produces consistent results with end members that can be chemically explained. We also investigated a novel approach to use our multivariate models to identify inadvertent sources of PCBs. By using a residual plot produced from one of our models (NMF) we were able to suggest the presence of approximately 30 different potentially inadvertently produced PCBs which account for 6.6 % of the total PCBs in Portland Harbor sediments.

Application of hydride generation - microwave plasma - atomic emission spectrometry and partial least squares regression for the determination of antimony directly in water and in PET after alkaline methanolysis

Antimony is present in different types of plastics as a catalyzer residue and/or as a synergistic fire retardant; relatively high concentrations of this element reported in polyethylene terephthalate (PET) bottles and wrappers as well as its migration to the edible products or to different environment compartments are of concern. In this work, Sb determination is such products had been undertaken using hydride generation - microwave plasma - atomic emission spectrometry. To avoid harsh conditions typically reported for the digestion of PET, alkaline methanolysis was introduced whereas water samples were analyzed directly. Another original approach was to perform quantification by partial least squares regression (PLS1), taking spectral data from 2-nm range that comprised two emission lines (217.581 nm and less intense 217.919 nm). For PET, the calibration solutions contained Sb-free digest and covered the Sb concentration range 80-230 μg L^-1. For the analysis of water, the calibration range was 0.5-10 μg L^-1 and aqueous standard solutions were used. PLS1 provided reliable prediction, eliminating spectral interferences detected in the presence of PET digests and compensating for the spectral changes observed at low Sb concentrations. After standard addition to the real-world samples, the percentage recoveries were in the range 93.8-99.3% and 68-102% for PET and for bottled water, respectively. The method quantification limit for PET was 10 mg kg^-1 and for water it corresponded to 0.20 μg L^-1. The concentrations of Sb found in the analyzed samples were: 154-279 mg kg^-1 for PET bottles and <0.5-5.30 μg L^-1 for water.

Determination of copper and lead in tequila by conventional matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and partial least squares regression

RATIONALE

Quantification of small molecules by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) is challenging yet attractive, due to micro-scale procedural simplicity, high throughput and lack of memory effects. Since these features are important while analyzing trace elements in quality control schemes, MALDI-TOFMS was used for the determination of copper (Cu) and lead (Pb) in tequila with quantification carried out by partial least squares regression (PLS2) and by univariate calibration (UC).

METHODS

In the proposed procedure, Bi(III) was added as internal standard (IS), diethyldithiocarbamate complexes were formed (pH 7.4) and extracted into chloroform; after solvent evaporation and re-constitution in acetonitrile, the sample was co-crystallized with α-cyano-4-hydroxycinnamic acid on a steel target. From the acquired mass spectra, UC was performed using IS-normalized signals of the monoisotopic ions of analytes, and the m/z range 350-513 was used for PLS2. Accuracy was tested by recovery experiments and by inductively coupled plasma (ICP)-MS analysis.

RESULTS

When compared with direct analyte signal measurements, application of IS yielded enhanced analytical performance using either UC or PLS2; the method quantification limits were: 11.1 μg L^-1 , 23.4 μg L^-1 for Cu and 89.8 μg L^-1 , 97.1 μg L^-1 for Pb, respectively. In tequila, MALDI-TOFMS and ICP-MS provided consistent results for Cu (165-2599 μg L^-1 ); Pb was not detected in any sample by MALDI-TOFMS, yet recoveries obtained after standard addition were indicative of acceptable accuracy (400 μg L^-1 Pb added; recoveries: 91.2-108% for UC and 98.8-120% for PLS2).

CONCLUSIONS

New experimental evidence has been provided supporting the inclusion of trace metals quantification within a range of MALDI-TOFMS applications. Slightly better results were obtained for UC as compared with PLS2 yet both methods can be recommended for testing the compliance of Cu and Pb levels with Official Mexican Norm. Of note, while using PLS2, there is no need for signal integration nor for IS normalization.

Assessment of temporal and spatial water quality in international Gomishan Lagoon, Iran, using multivariate analysis

Coastal lagoon ecosystems are vulnerable to eutrophication, which leads to the accumulation of nutrients from the surrounding watershed over the long term. However, there is a lack of information about methods that could accurate quantify this problem in rapidly developed countries. Therefore, various statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least square (PLS), principal component regression (PCR), and ordinary least squares regression (OLS) were used in this study to estimate total organic matter content in sediments (TOM) using other parameters such as temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), nitrite (NO₂), nitrate (NO₃), biological oxygen demand (BOD), phosphate (PO₄), total phosphorus (TP), salinity, and water depth along a 3-km transect in the Gomishan Lagoon (Iran). Results indicated that nutrient concentration and the dissolved oxygen gradient were the most significant parameters in the lagoon water quality heterogeneity. Additionally, anoxia at the bottom of the lagoon in sediments and re-suspension of the sediments were the main factors affecting internal nutrient loading. To validate the models, R², RMSECV, and RPDCV were used. The PLS model was stronger than the other models. Also, classification analysis of the Gomishan Lagoon identified two hydrological zones: (i) a North Zone characterized by higher water exchange, higher dissolved oxygen and lower salinity and nutrients, and (ii) a Central and South Zone with high residence time, higher nutrient concentrations, lower dissolved oxygen, and higher salinity. A recommendation for the management of coastal lagoons, specifically the Gomishan Lagoon, to decrease or eliminate nutrient loadings is discussed and should be transferred to policy makers, the scientific community, and local inhabitants.

A Computational Tool for Accelerated Analysis of Oligomeric Proanthocyanidins in Plants

A computational tool was developed to facilitate proanthocyanidin analysis using data collected by ultra-high-performance liquid chromatography-diode array detection-high resolution accurate mass-mass spectrometry (UHPLC-DAD-HRAM-MS). Both identification and semi-quantitation of proanthocyanidins can be achieved by the developed computational tool. It can extract proanthocyanidin chromatographic peaks, deconvolute the isotopic patterns of A-type, B-type, and multi-charged proanthocyanidins ions, and predict proanthocyanidin structures. Proanthocyanidins were quantified by an external calibration curve of catechin and molar relative response factors (MRRFs) of proanthocyanidins. Quantitation results including concentrations of total proanthocyanidins, individual proanthocyanidins, and proanthocyanidins with different degrees of polymerization and different types of linkage were calculated by the program and exported into an Excel spreadsheet automatically. The program was applied to the analysis of seven plant materials including apple, cranberry, dark chocolate, grape seed extract, jujube, litchi, and mangosteen. The identification results were compared with the results obtained by manual processing. The program can greatly save the time needed for the data analysis of proanthocyanidins.

A comprehensive approach to actual polychlorinated biphenyls environmental contamination

Worldwide polychlorinated biphenyls (PCBs) pollution is due to complex mixtures with high number of congeners, making the determination of total PCBs in the environment an open challenge. Because the bulk of PCBs production was made of Aroclor mixtures, this analysis is usually faced by the empirical mixture identification via visual inspection of the chromatogram. However, the identification reliability is questionable, as patterns in real samples are strongly affected by the frequent occurrence of more than one mixture. Our approach is based on the determination of a limited number of congeners chosen to enable objective criteria for Aroclor identification, summing up the advantages of congener-specific analysis with the ones of total PCBs determination. A quantitative relationship is established between congeners and any single mixture, or mixtures combination, leading to the identification of the actual contamination composition. The approach, due to its generality, allows the use of different sets of congeners and any technical mixture, including the non-Aroclor ones. The results confirm that PCB environmental pollution in northern Italy is based on Aroclor. Our methodology represents an important tool to understand the source and fate of the PCBs contamination.

Field Analysis of Polychlorinated Biphenyls (PCBs) in Soil Using Solid-Phase Microextraction (SPME) and a Portable Gas Chromatography-Mass Spectrometry System

An expedited field analysis method was developed for the determination of polychlorinated biphenyls (PCBs) in soil matrices using a portable gas chromatography-mass spectrometry (GC-MS) instrument. Soil samples of approximately 0.5 g were measured with a portable scale and PCBs were extracted by headspace solid-phase microextraction (SPME) with a 100 µm polydimethylsiloxane (PDMS) fiber. Two milliliters of 0.2 M potassium permanganate and 0.5 mL of 6 M sulfuric acid solution were added to the soil matrices to facilitate the extraction of PCBs. The extraction was performed for 30 min at 100 ℃ in a portable heating block that was powered by a portable generator. The portable GC-MS instrument took less than 6 min per analysis and ran off an internal battery and helium cylinder. Six commercial PCB mixtures, Aroclor 1016, 1221, 1232, 1242, 1248, 1254, and 1260, could be classified based on the GC chromatograms and mass spectra. The detection limit of this method for Aroclor 1260 in soil matrices is approximately 10 ppm, which is sufficient for guiding remediation efforts in contaminated sites. This method was applicable to the on-site analysis of PCBs with a total analysis time of 37 min per sample. However, the total analysis time could be improved to less than 7 min per sample by conducting the rate-limiting extraction step for different samples in parallel.

Use of fuzzy chromatography mass spectrometric (FCMS) fingerprinting and chemometric analysis for differentiation of whole-grain and refined wheat (T. aestivum) flour

A fuzzy chromatography mass spectrometric (FCMS) fingerprinting method combined with chemometric analysis has been established for rapid discrimination of whole-grain flour (WF) from refined wheat flour (RF). Bran, germ, endosperm, and WF from three local cultivars or purchased from a grocery store were studied. The state of refinement (whole vs. refined) of wheat flour was differentiated successfully by use of principal-components analysis (PCA) and soft independent modeling of class analogy (SIMCA), despite potential confounding introduced by wheat class (red vs. white; hard vs. soft) or resources (different brands). Twelve discriminatory variables were putatively identified. Among these, dihexoside, trihexoside, apigenin glycosides, and citric acid had the highest peak intensity for germ. Variable line plots indicated phospholipids were more abundant in endosperm. Samples of RF and WF from three cultivars (Hard Red, Hard White, and Soft White) were physically mixed to furnish 20, 40, 60, and 80 % WF of each cultivar. SIMCA was able to discriminate between 100 %, 80 %, 60 %, 40 %, and 20 % WF and 100 % RF. Partial least-squares (PLS) regression was used for prediction of RF-to-WF ratios in the mixed samples. When PLS models were used the relative prediction errors for RF-to-WF ratios were less than 6 %. Graphical Abstract Workflow of targeting discriminatory compounds by use of FCMS and chemometric analysis.