Reproducible mass spectrometry data processing and compound annotation in MZmine 3.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants.

Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.

Migration of contaminants from printed masks for children to saliva simulant using liquid chromatography coupled to ion mobility-time of flight-mass spectrometry and gas chromatography-mass spectrometry.

The COVID-19 pandemic has led to children using polymeric FFP2 and polymeric surgical masks on a daily basis. Children often bite and suck on such masks as they wear them closed to their mouths. In this work, the migration of contaminants from printed and unprinted children`s masks to a saliva simulant has been studied. Liquid chromatography coupled to ion-mobility quadrupole time-of-flight mass spectrometry has been used for the detection and identification of non-volatile migrants. An orthogonal projection to latent structures - discriminant analysis (OPLS-DA) was applied to compare the data from the printed masks against the data from the unprinted ones. Headspace solid phase microextraction coupled to gas chromatography mass spectrometry was used to assess the migration of volatile compounds. Thirteen compounds were found in the masks with concentrations ranging from 5 ng/g to 254 ng/g. Toluene, chlorobenzene, irganox 1076 and 2-(2-butoxyethoxy)ethyl acetate were all found to migrate from the masks studied. Moreover, differences between the migrants from printed and unprinted FFP2 masks were found. Octocrylene, 4-(dimethylamine)benzoate, methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propionate and tris(3-methylphenyl)phosphate were found to migrate only from printed masks. Toluene that migrated from all the masks studied and tris(3-methylphenyl)phosphate, that migrated only from printed masks, have been listed as hazardous priority substances.

Ultra-high performance liquid chromatography coupled to ion mobility quadrupole time-of-flight mass spectrometry for the identification of non-volatile compounds migrating from 'natural' dishes.

Although most new biomaterials for food industry applications are labelled '100% natural fabrication' and 'chemical-free', certain compounds may migrate from those materials to the food, compromising the organoleptic characteristics and safety of the product. In this work, the degree of compound migration from dishes made with four different biomaterials: bamboo, palm leaf, wood and wheat pulp was investigated. Migration tests were carried out using three food simulants, 10% ethanol (simulant A), 3% acetic acid (simulant B), and 95% ethanol (simulant D2). Unequivocal identification of non-intentionally added substances (NIAS) is challenging even when using high-resolution mass spectrometry techniques however, a total of 25 different non-volatile compounds from the migration tests were identified and quantified using Ultra-high performance liquid chromatography coupled to ion mobility quadrupole time-of-flight mass spectrometry (UPLC-IMS-MS). In the bamboo samples three oligomers, cyclic diethylene glycol adipate, 3,6,9,16,19,22-hexaoxabicyclo[22.3.1]-octacosa-1(28),24,26-triene-2,10,15,23-tetrone and 1,4,7,14,17,20-hexaoxacyclohexacosane-8,13,21,26-tetrone exceeded the specified limits of migration.

Identification of Nonvolatile Migrates from Food Contact Materials Using Ion Mobility-High-Resolution Mass Spectrometry and in Silico Prediction Tools.

The identification of migrates from food contact materials (FCMs) is challenging due to the complex matrices and limited availability of commercial standards. The use of machine-learning-based prediction tools can help in the identification of such compounds. This study presents a workflow to identify nonvolatile migrates from FCMs based on liquid chromatography-ion mobility-high-resolution mass spectrometry together with in silico retention time (RT) and collision cross section (CCS) prediction tools. The applicability of this workflow was evaluated by screening the chemicals that migrated from polyamide (PA) spatulas. The number of candidate compounds was reduced by approximately 75% and 29% on applying RT and CCS prediction filters, respectively. A total of 95 compounds were identified in the PA spatulas of which 54 compounds were confirmed using reference standards. The development of a database containing predicted RT and CCS values of compounds related to FCMs can aid in the identification of chemicals in FCMs.

Prediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products.

The use of ion mobility separation (IMS) in conjunction with high-resolution mass spectrometry has proved to be a reliable and useful technique for the characterization of small molecules from plastic products. Collision cross-section (CCS) values derived from IMS can be used as a structural descriptor to aid compound identification. One limitation of the application of IMS to the identification of chemicals from plastics is the lack of published empirical CCS values. As such, machine learning techniques can provide an alternative approach by generating predicted CCS values. Herein, experimental CCS values for over a thousand chemicals associated with plastics were collected from the literature and used to develop an accurate CCS prediction model for extractables and leachables from plastic products. The effect of different molecular descriptors and machine learning algorithms on the model performance were assessed. A support vector machine (SVM) model, based on Chemistry Development Kit (CDK) descriptors, provided the most accurate prediction with 93.3% of CCS values for [M + H]+ adducts and 95.0% of CCS values for [M + Na]+ adducts in testing sets predicted with <5% error. Median relative errors for the CCS values of the [M + H]+ and [M + Na]+ adducts were 1.42 and 1.76%, respectively. Subsequently, CCS values for the compounds in the Chemicals associated with Plastic Packaging Database and the Food Contact Chemicals Database were predicted using the SVM model developed herein. These values were integrated in our structural elucidation workflow and applied to the identification of plastic-related chemicals in river water. False positives were reduced, and the identification confidence level was improved by the incorporation of predicted CCS values in the suspect screening workflow.

A Collision Cross Section Database for Extractables and Leachables from Food Contact Materials.

The chemicals in food contact materials (FCMs) can migrate into food and endanger human health. In this study, we developed a database of traveling wave collision cross section in nitrogen (TWCCSN2) values for extractables and leachables from FCMs. The database contains a total of 1038 TWCCSN2 values from 675 standards including those commonly used additives and nonintentionally added substances in FCMs. The TWCCSN2 values in the database were compared to previously published values, and 85.7, 87.7, and 64.9% [M + H]+, [M + Na]+, and [M - H]- adducts showed deviations <2%, with the presence of protomers, post-ion mobility spectrometry dissociation of noncovalent clusters and inconsistent calibration are possible sources of CCS deviations. Our experimental TWCCSN2 values were also compared to CCS values from three prediction tools. Of the three, CCSondemand gave the most accurate predictions. The TWCCSN2 database developed will aid the identification and differentiation of chemicals from FCMs in targeted and untargeted analysis.

Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials.

The synthetic chemicals in food contact materials can migrate into food and endanger human health. In this study, the traveling wave collision cross section in nitrogen values of more than 400 chemicals in food contact materials were experimentally derived by traveling wave ion mobility spectrometry. A support vector machine-based collision cross section (CCS) prediction model was developed based on CCS values of food contact chemicals and a series of molecular descriptors. More than 92% of protonated and 81% of sodiated adducts showed a relative deviation below 5%. Median relative errors for protonated and sodiated molecules were 1.50 and 1.82%, respectively. The model was then applied to the structural annotation of oligomers migrating from polyamide adhesives. The identification confidence of 11 oligomers was improved by the direct comparison of the experimental data with the predicted CCS values. Finally, the challenges and opportunities of current machine-learning models on CCS prediction were also discussed.

The migration of NIAS from ethylene-vinyl acetate corks and their identification using gas chromatography mass spectrometry and liquid chromatography ion mobility quadrupole time-of-flight mass spectrometry.

An exhaustive migration study of eight corks, made of ethylene-vinyl acetate, was carried out to identify any non-volatile and volatile compounds using an untargeted approach. The challenge associated with the structural elucidation of unknowns was undertaken using both ultra-high-performance liquid chromatography coupled to an ion-mobility separation quadrupole-time of flight mass spectrometer and gas chromatography mass spectrometry. A total of fifty compounds were observed to migrate from the corks, and among these additives such as antioxidants (Butyl 4-hydroxybenzoate, Irganox 1010, Irganox 1075, Irgafos 168 and BHT) or lubricants (EBO and octadecanamide, N,N'-1,2-ethanediylbis-) were identified. A high proportion (84%) of the detected compounds was non-intentionally added substances (NIAS), and included several cyclic oligomers with different chain sequences. NIAS, such as 2,6-bis(1,1-dimethylethyl)-4-ethyl and 7,9-ditert-butyl-1-oxaspiro[4.5]deca-6,9-diene-2,8-dione, break-down products, including hexa-, hepta- and nonadecanamide, N,N'-1,2-ethanediylbis-, and oxidation products were also identified. One cork was found to be unsuitable for use as a food contact material.

The detection and elucidation of oligomers migrating from biodegradable multilayer teacups using liquid chromatography coupled to ion mobility time-of-flight mass spectrometry and gas chromatography-mass spectrometry.

Biodegradable materials are increasingly being used in manufacturing processes due to their environmental benefits. In this work, a study has been performed to assess the migration of compounds from biodegradable multilayer teacups to a tea solution. Liquid chromatography in conjunction with ion-mobility quadrupole time-of-flight mass spectrometry has been used for the elucidation of non-volatile compounds. An orthogonal projection to latent structures-discriminant analysis has been carried out to compare the tea after migration against untreated tea used as blank. Headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry has been optimised to analyse the migration of volatile compounds. Eight migrants were identified in the tea, six of which were non-intentionally added oligomers. The degree of migration for hot tea ranged from 0.05 and 4.68 mg/kg, exceeding the specific migration limit. Nevertheless, the migration to cold tea was an order of magnitude lower (between 0.003 and 0.56 mg/kg).

Discovery and Characterization of Phenolic Compounds in Bearberry (Arctostaphylos uva-ursi) Leaves Using Liquid Chromatography-Ion Mobility-High-Resolution Mass Spectrometry.

The characterization and quantification of phenolic compounds in bearberry leaves were performed using hyphenated ion mobility spectroscopy (IMS) and a quadrupole time-of-flight mass spectrometer. A higher identification confidence level was obtained by comparing the measured collision cross section (TWCCSN2) with predicted values using a machine learning algorithm. A total of 88 compounds were identified, including 14 arbutin derivatives, 33 hydrolyzable tannins, 6 flavanols, 26 flavonols, 9 saccharide derivatives, and glycosidic compounds. Those most reliably reproduced in all samples were quantified against respective standards. Arbutin (47-107 mg/g), 1,2,3,4,6-pentagalloylglucose (6.6-12.9 mg/g), and quercetin 3-galactoside/quercetin 3-glucoside (2.7-5.7 mg/g) were the most abundant phenolic components in the leaves. Quinic acid and ellagic acid were also detected at relatively high concentrations. The antioxidant activity of the most abundant compounds was evaluated. A critical view of the advantages and limitations of traveling wave IMS and CCS for the discovery of natural products is given.

The application of ion mobility time of flight mass spectrometry to elucidate neo-formed compounds derived from polyurethane adhesives used in champagne cork stoppers.

Polyurethane adhesives are used to bond agglomerated cork and natural disk cork to produce cork stoppers that are used in champagne bottles. These adhesives are manufactured by reacting polyols with an excess of diisocyanates. Isocyanates are highly reactive compounds that have a propensity to form non-intentionally added substances (NIAS) in the end product. In this work, ion mobility-time of flight-mass spectrometry was used to elucidate such NIAS, through the comparison of accurate mass spectra with the fragmentation patterns of proposed candidates. Twelve neo-formed compounds, including amines, amides and urethanes, resulting from the reaction of isocyanates with acetic acid and ethanol used as food simulants, were identified. Additionally, markers from champagne vs. champagne after its exposure to the adhesive were investigated using the supervised multivariate analysis method of Orthogonal Projection to Latent Structures - Discriminant Analysis. Four neo-formed compounds, resulting from the reaction of diisocyanates with malic acid or tartaric acid contained in the champagne, were identified for the first time in this work. All of the compounds identified were subsequently quantified using ultra-high pressure liquid chromatography coupled to a triple quadrupole mass spectrometer. Limits of detection were below 5 µg/kg in the food simulants and below 30 µg/kg in champagne samples. Migration levels ranged from 70 to 721 µg/kg, with most of them exceeding the specific migration limit established for Cramer class III compound (90 µg/kg).

Critical evaluation of ambient mass spectrometry coupled with chemometrics for the early detection of adulteration scenarios in Origanum vulgare L.

Nowadays, most of the screening methods in food manufacturing are based on spectroscopic techniques. Ambient Mass Spectrometry is a relatively new field of analytical chemistry which has proven to offer similar speed and ease-of-use when compared to other fingerprinting techniques, alongside the advantages of good selectivity, sensitivity and chemical information. Numerous applications have been explored in food authenticity, based either on the target detection of adulteration markers or, less frequently, on the development of multivariate classification models. The aim of the present work was to evaluate and compare the capabilities of Direct Analysis in Real Time (DART) and Atmospheric Solid Analysis Probe (ASAP) Mass Spectrometry (MS) for the high-throughput authenticity screening of commercial herbs and spices products. The gross addition of bulking material to dried Mediterranean oregano was taken as case study. First, a pilot sample set, constituted by authentic dried oregano, olive leaves (a frequently reported adulterant) and mixtures thereof at different levels (i.e. 10, 20, 30 and 50% w/w) was used. Each sample was fingerprinted by both ambient-MS techniques. After appropriate pre-processing, the whole mass spectra were used for the subsequent multivariate data analysis. Soft Independent Modelling of Class Analogy was adopted as classification algorithm and the model was challenged with both new authentic oregano and in-house prepared blends. To the best of our knowledge, this is the first report of DART-MS and ASAP-MS used in full scan mode and coupled to chemometric modelling as rapid fingerprinting approach for food authentication. Although both the techniques provided satisfactory results, ASAP-MS clearly showed greater potential, leading to reproducible, diagnostic feature-rich mass spectra. For this reason, ASAP-MS was further tested under a more convoluted scenario, where the training and validation sets were enlarged with additional authentic oregano samples and a wider range of adulterant species, respectively. Overall good results were achieved, with 93% model predictive accuracy, and screening detection capability estimated between 5-20% (w/w) addition, depending on the adulterant considered with the only exception of majorana. Investigation of Q residuals could highlight the statistically-relevant chemical markers which could be tentatively annotated by coupling the ASAP probe with a high resolution mass analyser. The results from the validation study confirmed the great potential of ASAP-MS in combination with chemometrics as fast MS-based screening solution and demonstrated its feasibility for classification model building.

The use of ion mobility time-of-flight mass spectrometry to assess the migration of polyamide 6 and polyamide 66 oligomers from kitchenware utensils to food.

Oligomers, are, in general, unknown components of the polymer. These oligomers can migrate from the polymer into the food and become a non-intentionally added substance to the food. In this work, ion mobility time-of-flight mass spectrometry has been used to identify oligomers migrating from kitchenware. The structure elucidation of oligomers from polyamide 6 and polyamide 66 was achieved through the analysis of accurate m/z values of adducts and collision cross section values of precursor ions together with high-energy fragmentation patterns. Additionally, a method to extract oligomers from sunflower oil, cooked beans, soup and whole milk has been developed. Extraction recoveries ranged from 87 to 102% and limits of detection were from 0.03 to 0.11 mg/kg. It was observed that the migration from kitchenware to real food was below the specified migration limit of 5 mg/kg. However, this limit was exceeded for food simulants, which therefore overestimated the oligomer migration.

Travelling Wave Ion Mobility-Derived Collision Cross Section for Mycotoxins: Investigating Interlaboratory and Interplatform Reproducibility.

Parent and modified mycotoxin analysis remains a challenge because of their chemical diversity, the presence of isomeric forms, and the lack of analytical standards. The creation and application of a collision cross section (CCS) database for mycotoxins may bring new opportunities to overcome these analytical challenges. However, it is still an open question whether common CCS databases can be used independently from the instrument type and ion mobility mass spectrometry (IM-MS) technologies, which utilize different methodologies for determining the gas-phase mobility. Here, we demonstrated the reproducibility of CCS measurements for mycotoxins in an interlaboratory study (average RSD 0.14% ± 0.079) and across different traveling wave IM-MS (TWIMS) systems commercially available (ΔCCS% < 2). The separation in the drift time dimension of critical pairs of isomers for modified mycotoxins was also achieved. In addition, the comparison of measured and predicted CCS values, including regulated and emerging mycotoxins, was addressed.

Interlaboratory and Interplatform Study of Steroids Collision Cross Section by Traveling Wave Ion Mobility Spectrometry.

Collision cross section (CCS) databases based on single-laboratory measurements must be cross-validated to extend their use in peak annotation. This work addresses the validation of the first comprehensive TWCCSN2 database for steroids. First, its long-term robustness was evaluated (i.e., a year and a half after database generation; Synapt G2-S instrument; bias within ±1.0% for 157 ions, 95.7% of the total ions). It was further cross-validated by three external laboratories, including two different TWIMS platforms (i.e., Synapt G2-Si and two Vion IMS QToF; bias within the threshold of ±2.0% for 98.8, 79.9, and 94.0% of the total ions detected by each instrument, respectively). Finally, a cross-laboratory TWCCSN2 database was built for 87 steroids (142 ions). The cross-laboratory database consists of average TWCCSN2 values obtained by the four TWIMS instruments in triplicate measurements. In general, lower deviations were observed between TWCCSN2 measurements and reference values when the cross-laboratory database was applied as a reference instead of the single-laboratory database. Relative standard deviations below 1.5% were observed for interlaboratory measurements (<1.0% for 85.2% of ions) and bias between average values and TWCCSN2 measurements was within the range of ±1.5% for 96.8% of all cases. In the context of this interlaboratory study, this threshold was also suitable for TWCCSN2 measurements of steroid metabolites in calf urine. Greater deviations were observed for steroid sulfates in complex urine samples of adult bovines, showing a slight matrix effect. The implementation of a scoring system for the application of the CCS descriptor in peak annotation is also discussed.

Ion mobility quadrupole time-of-flight high resolution mass spectrometry coupled to ultra-high pressure liquid chromatography for identification of non-intentionally added substances migrating from food cans.

Sealants, incorporated in the lids of food cans to ensure the can is hermetically sealed, are formulated from a wide variety of compounds. These compounds and associated non-intentionally added substances (NIAS) could migrate to the food contained in the can. In this work, ion mobility quadrupole time-of-flight mass spectrometry coupled to ultra-high performance liquid chromatography (UHPLC-IM-QTOF-MS) has been used to obtain ion mobility filtered extracted ion chromatograms. Subsequently, accurate mass precursor ions and their fragments have been used to identify the compounds migrating from the sealant to the content of the cans. Moreover, the correlation between the collision cross-section (CCS) values and m/z of the compounds was used to increase the level of confidence of the identification. Seven compounds were found to have migrated to the food simulants. The compounds bis(2-hydroxy-3-tert-butyl-5-methylphenyl)dicyclopentane,1-tetradecanesulfonic acid, 1-pentadecanesulfonic acid, 1-hexadecanesulfonic acid and naphthalene-2-sulfonic acid (whose migration was over the specific migration limit established by the European Regulation 10/2011/EU) were identified as NIAS in the food simulants studied.

Determination of volatile non intentionally added substances coming from a starch-based biopolymer intended for food contact by different gas chromatography-mass spectrometry approaches.

The rapid growth of polymer technology in the field of food contact materials (FCMs) needs to be supported by continuous improvement in material testing, in order to ensure the safety of foodstuff. In this work, a range of different starch-based biopolymer samples, in the shape of pellets and retail samples (cups and dishes) were studied. The optimized extraction process was performed on three different pellet shapes: pellets with no modification (spherical), pellets shattered under high pressure (lentils), and pellets cryogenically ground (powder). The analysis of unknown volatile and semi-volatile compounds was carried out by gas chromatography-mass spectrometry, using both electron ionization with a single quadrupole mass analyzer (GC-EI-MS), and atmospheric pressure gas chromatography with a quadrupole/time of flight mass analyzer (APGC-Q/ToF). The identification process was implemented using the latest advances in the understanding of APGC ionization pathways. Chemical migration was also assessed on prototype samples using the food simulants: ethanol 10% v/v, acetic acid 3% w/V, ethanol 95% v/v, isooctane, and vegetable oil. Each migration test was performed three consecutive times, as recommended for materials intended for repeated use.

Determination of non-volatile components of a biodegradable food packaging material based on polyester and polylactic acid (PLA) and its migration to food simulants.

Bioplastic materials are increasingly used due to its benefits for the environment preservation. Among them, food packaging materials based on polylactic acid (PLA) are among the most employed. In this work, a sample treatment methodology based on dissolution/precipitation has been optimized, selecting finally dichloromethane/ethanol as solvent/antisolvent system. The extracts obtained were analysed by UPLC-MS(QTOF), that allowed the identification of the main PLA non-volatile components. The recovery results were between 100.9 to 114.0%. The methodology was applied to the analysis of pellets and films of a PLA-polyester blend sample. A total of 37 different compounds were detected, where the four compounds with the highest intensity in pellet samples were cyclic oligomers coming from the polyester part of the blend and composed by adipic acid (AA), phthalic acid (PA) and butanediol (BD). Migration experiments to 3 food simulants were also performed: ethanol 95% (v/v), ethanol 10% (v/v) and acetic acid 3% (w/v). The results showed that in addition to those compounds previously detected in the film, new compounds coming from the reaction of PLA components with food simulants were present in migration solutions.

Development and validation of a LC-MS/MS method for the analysis of bisphenol a in polyethylene terephthalate.

Bisphenol A (BPA) is widely recognized being an endocrine disrupter and it is employed in many food packaging applications. Although it is not intended to take part in the manufacture of polyethylene terephthalate (PET) food grade, the presence of BPA in recycled PET should not be neglected. To satisfy the increasing need to ensure "BPA-free" articles, a liquid chromatography-tandem mass spectrometry method was developed. The crucial step in the sample preparation was the total dissolution/reprecipitation of the polymer. The repeatability of the method (RSD%, n = 6) was lower than 7.6%, while HorRat values ranged between 0.3 and 0.5. Limits of detection and quantitation were 1.0 and 3.3 ng g-1, respectively. Recovery ranged from 89 to 107%. The method was applied to 23 samples of virgin and recycled pellets, preforms and bottles. Migration tests were also carried out. Results shown significantly higher levels of BPA in recycled PET.