Multi-class Method Research Articles

Development and validation of a multiresidue method for the determination of antibiotic residues in crawfish using LCMS/MS

ABSTRACT This study developed a rapid multi-class method for determining 29 antibiotic residues in crawfish using liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. A quick and affordable extraction method was employed, and sample preparation was performed using a modified QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) technique. The extraction process involved adding acetonitrile to crawfish samples. The sample was homogenised with a solution of sodium citrate buffer and sodium EDTA, followed by a cleanup step. The method was validated according to European Union (EU) guidelines 2002/657/EC. The limit of quantitation (LOQ) for all antibiotics was 5 µg/kg, except for flumequine and trimethoprim, which had LOQs of 2.5 µg/kg. The LOQ for the tetracycline group was 25 µg/kg, and for chloramphenicol, it was 0.015 µg/kg. The average recoveries at different concentration levels varied between 60–120%. The precision, expressed as repeatability and reproducibility, was calculated to be less than 16%. Thirty crawfish samples collected from local Egyptian market were monitored for antibiotic residues. The results revealed contamination with antibiotics belonging to three different groups. Of the thirty samples, 27% were contaminated with ciprofloxacin and enrofloxacin, 17% with chlortetracycline and oxytetracycline, 7% with oxolinic acid, and 3% with sulphamethazine. The applicability of the developed method was proven through successful proficiency testing. The developed method met performance criteria and is suitable for monitoring these antibiotics in crawfish.

Multiclass method for detecting 41 antibiotic residues in bovine liver, muscle, and milk using LC-Q-Orbitrap-HRMS

This study developed and validated an analytical method for the simultaneous identification and quantification of 41 veterinary antibiotic drugs from 9 different classes, including sulfonamides, quinolones, tetracyclines, penicillins, macrolides, nitroimidazoles, cephalosporins, diaminopyrimidines, and amphenicol, in animal-derived food products. The method used modified QuEChERS and liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry (LC-Q-Orbitrap HRMS). The method was validated in accordance with Commission Implementing Regulation (CIR) EU 2021/808 at five different concentrations ranging from 0.075 to 10,000 μg kg−1. The mean recoveries ranged from 58 % to 123 %, while repeatability values were all below 21 %. The calibration curves showed good linearity, with correlation coefficients (R2) ranging from 0.9905 to 0.9999. The limits of detection (LOD) and limits of quantification (LOQ) were in the range of 0.005–43.0 μg kg−1 and 0.008–71.7 μg kg−1, respectively. The decision limit (CCα) and detection capability (CCβ) ranges were 0.37–2347 µg/kg and 0.44–3099 µg/kg, respectively. Out of the 1330 samples collected from Egyptian local markets, 1.20 % had antibiotic residues. The applicability of the developed method was proven through successful four proficiency testing (PT). The proposed method was demonstrated to be reliable for the simultaneous analysis of multiclass veterinary drugs in bovine liver, muscle tissue, and milk.

Development and validation of a multiclass method for the determination of veterinary drug residues in honey

ABSTRACT This study developed and validated an analytical method for simultaneous identification and quantification of 41 veterinary drugs in honey using liquid liquid extraction (LLE) and liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry (LC-Q-Orbitrap HRMS). The method was validated in accordance with Commission Implementing Regulation (CIR) EU 2021/808 at five different concentrations ranging from 0.075 to 50 μg/kg. The mean recoveries ranged from 70 to 105, while repeatability values were all below 17%. The linearity, as correlation coefficients (R2) ranged from 0.994 to 1. The limits of detection (LOD) and limits of quantification (LOQ) were in the range of 0.006–3.92 μg/kg and 0.011–6.54 μg/kg, respectively. The decision limit (CCα) and detection capability (CCβ) ranges were 0.0759–5.33 µg/kg and 0.0804–6.08 µg/kg, respectively. Of the 263 honey samples that were collected from local markets in Egypt, 47.5% had antibiotic residues. The mean concentration (µg/kg) and detection frequency (%) of the five most frequently detected antibiotics in the honey samples were as follows: trimethoprim (143.25 µg/kg and 39.9%), sulfamethoxazole (136.69 µg/kg and 30.7%), sulphadiazine (77.19 µg/kg and 18.6%), tylosin (184.37 µg/kg and 18.2%), and ciprofloxacin (185.33 µg/kg and 7.60%). The applicability of the developed method was proven through successful three proficiency testing (PT). The proposed method was demonstrated to be reliable for the simultaneous analysis of multi-class veterinary drugs in honey.

Determination of sulfonamides in muscle: a metrological tool for food safety

Sulfonamides represent a wide class of synthetic drugs commonly used in veterinary therapy for the treatment of several bacterial and protozoan infections in cattle, swine and poultry. The use of these drugs in farming can lead to the possibility of having their residues in animal products intended for human consumption. Consequently, to ensure high consumer protection, for sulfonamides European Union (EU) set a Maximum Residue Limit (MRL) equal to 100 µg/kg, either as a single molecule or as a sum of all detected compounds within the class). Official laboratories are directly involved in the execution or residue plans by developing, validating and then applying analytical methods for the measurement of drug residues. Accordingly, official laboratories should update their procedures following the evolution of required drugs and MRLs. A multiclass method previously developed and validated for the determination in animal muscle of ten classes of antibiotics was adjusted to comply with the current European requirements which establish the minimum set of sulfonamides to be determined. Therefore, eight new sulfonamides were added assessing method performance characteristics according to European Regulation (EU) 808/2021.

Extention and interlaboratory comparison of an LC-MS/MS multi-class method for the determination of 15 different classes of veterinary drug residues in milk and poultry feed

An HPLC-MS/MS multi-class method for quantitation of 15 different classes of veterinary drug residues (>140 analytes) in milk and poultry feed was developed and validated. Accuracy criteria for routine laboratories were met for the majority of analytes, > 83 % in milk and between 50 and 60 % in chicken feed, with an apparent recovery of 60–140 %. Extraction efficiency criteria were met for >95 % of the analytes for milk and > 80 % for chicken feed. Intermediate precision meets the SANTE criterion of RSD < 20 % for 80–90 % of the analytes in both matrices. For all analytes with an existing MRL in milk, the LOQ was below the related MRL. Twenty-nine samples of commercial milk and chicken feed were analyzed within the interlaboratory comparison. No residues of veterinary drugs were found in the milk samples. However, the feed samples exhibited high levels of nicarbazin, salinomycin, and decoquinate.

Multi-class machine learning classification of PFAS in environmental water samples: a blinded test of performance on unknowns

A multi-class method was developed to identify PFAS origin based on chemical composition, and performance of the method was evaluated in a blinded test against unknowns. The method showed great promise in its ability to recognize sample origin.

Multiclass Method for the Determination of Anthelmintic and Antiprotozoal Drugs in Livestock Products by Ultra-High-Performance Liquid Chromatography-Tandem Mass Spectrometry.

The objective of this study was to establish a multi-residue quantitative method for the analysis of anthelmintic and antiprotozoal drugs in various livestock products (beef, pork, and chicken) using ultra-high-performance liquid chromatography-tandem mass spectrometry. Each compound performed validation at three different levels i.e., 0.5, 1, and 2× the maximum residue limit according to the CODEX guidelines (CAC/GL 71-2009). This study was conducted according to the modified quick, easy, cheap, effective, rugged, and safe procedure. The matrix-matched calibrations gave correlation coefficients >0.98, and the obtained recoveries were in the range of 60.2%-119.9%, with coefficients of variation ≤32.0%. Furthermore, the detection and quantification limits of the method were in the ranges of 0.03-3.2 and 0.1-9.7 μg/kg, respectively. Moreover, a survey of residual anthelmintic and antiprotozoal drugs was also carried out in 30 samples of beef, pork, and chicken collected in Korea. Toltrazuril sulfone was detected in all three samples. Thus, our results indicated that the developed method is suitable for determining the anthelmintic and antiprotozoal drug contents in livestock products.

Determination of quinolones, macrolides, sulfonamides and tetracyclines in honey using QuEChERS sample preparation and UHPLC-MS/MS analysis

Monitoring beehive health and timely prevention of possible infections by bacteria, mould, viruses or parasites is exceptionally important in beekeeping. Antibiotics originating from the environment can be found in honey and due to improper beekeeping practices. Enabling the detection of antibiotic residues in honey and suppressing the development of antibiotic resistance require the development of a sensitive multi-class method for the determination of antibiotics. For the purpose of analysing honey, a screening and confirmatory method for the determination of 36 antibiotics was developed. The QuEChERS procedure and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was selected to achieve high sensitivity and selectivity. Validation according to the new Commission Implementing Regulation (EU) 2021/808 included the following performance characteristics: selectivity, trueness, precision, decision limit (CCα), detection capability (CCβ) and relative matrix effect. The method was validated in the measurement range from 1 to 10 μg/kg, where maximum trueness and acceptable coefficients of variation were achieved. The detection capability (CCβ) equals the lowest concentration level for all analytes, ranging from 0.1 μg/kg to 2.5 μg/kg, as the false compliant rate less than 5% was confirmed at this level of interest. For unauthorised pharmacologically active substances, the CCα was determined and ranged from 0.16 μg/kg for sulfaquinoxaline to 3.67 μg/kg for difloxacin. The high matrix influence of floral and chestnut honey indicated the need for quantitative analysis using the matrix calibration curve.

Simultaneous determination of heterocyclic aromatic amines and N-nitrosamines in fried bacon cubes and slices using LC-(ESI/APCI)-MS/MS

Bacon manufacturing involves several processing steps including nitrite curing, followed by cooking processes, typically frying. During these processes, harmful processing contaminants such as N-nitrosamines (NAs) and heterocyclic aromatic amines (HAAs) can be formed. Consequently, we developed and validated a multi-class method for quantification of the most frequently reported HAAs and NAs in fried bacon. Satisfactory repeatability and reproducibility with limits of quantification between 0.1 and 0.5 ng g−1 for most of the compounds were achieved. Quantification in pan-fried bacon cubes and slices revealed generally low levels of individual HAAs (≤1.5 ng g−1), except in ready-to-eat bacon (0.9–2.9 ng g−1). Differences in amounts of individual HAAs were observed in cubes and slices, most likely due to meat thickness. Among volatile NAs (VNAs), only N-nitrosopiperidine (NPIP), N-nitrosopyrolidine (NPYR), and N-nitrosodibutylamine (NDBA) were found at generally low concentrations (≤5 ng g−1). In contrast, non-volatile NAs (NVNAs) were present in all tested samples at considerably higher amounts, for example, N-nitroso-thiazolidine-4-carboxylic acid (NTCA) at 12–77 ng g−1. N-nitrosodimethylamine (NDMA), N-nitrosodiethylamine (NDEA), N-nitrosodipropylamine (NDPA) were not detected in any samples. Statistical evaluation and principal component analysis revealed some differences among tested samples. Dietary exposure estimation of the Danish population to HAAs and NAs showed the highest exposure in the teenage group (10–17 years).

Multiclass method to determine emerging pollutants in bats using a non-invasive approach based on guano matrix

Emerging pollutants have been ubiquitously found in environmental compartments, while there is scarce information about these substances and their effects on health status in wild terrestrial mammals. Bat species are very sensitive animals to any changes in the environment and are considered one of the best bioindicators of the quality of the environment to terrestrial wildlife. To acquire a better knowledge of the environmental exposure to these animals, a multiclass method is proposed to determine 20 emerging pollutants (six perfluoroalkyl substances (PFAS), four parabens (PB), four benzophenones (BP), a plasticizer (BPA), and five surfactants (four linear alkylbenzene sulfonates (LAS) and nonylphenol (NP)) in bats using a non-invasive approach based on guano matrix. Sample treatment involved ultrasonic solvent extraction and dispersive solid phase extraction prior to analysis in a single run with liquid chromatography-tandem mass spectrometry. The main variables affecting the extraction and clean-up steps were evaluated using single and multivariate strategies. Under the optimized conditions, satisfactory analytical characteristics in terms of linearity, recoveries (>80 %), precision (RSD < 24 %) and method quantification limits (from 0.01 to 64 ng/g dry weight) were obtained. Furthermore, and as a proof of concept, guano samples from a bat reference population were collected from a colony located in Brenna village, in south Poland. The results confirm the exposure of wild bats to emerging pollutants (LAS, PFAS, and PB compounds were frequently detected in the samples) and the suitability of the bat guano matrix for understanding the environmental exposure in terrestrial mammals.

Classification of chocolate according to its cocoa percentage by using Terahertz time-domain spectroscopy

Feasibility of a non-destructive classification of chocolate based on its cocoa content was examined by using a Terahertz time-domain spectroscopy system combined with a multivariate analysis. For this purpose, the spectra from 0.5 THz to 10 THz of 5 chocolate samples (50%, 60%, 70%, 80% and 90% of cocoa) were examined. The acquired data matrices were analyzed by using a Fourier Transform, obtaining the dielectric function and the absorbance curve. Based on the latter, samples were classified by using 24 models of mathematical classification, achieving differences of around 93% through the model of Gaussian SVM algorithm with a kernel scale of 0.35 and a one-against-one multiclass method. This was reduced by using a Main Component Analysis, obtaining most of the spectral variations with PC1 (63.8%) and PC2 (36.2%). It was concluded that the combined processing and classification of images obtained from Terahertz time-domain spectroscopy, as well as the use of machine learning algorithms, can be used to successfully classify chocolates with different percentages of cocoa.

A new on-line SPE LC-HRMS method for simultaneous analysis of selected emerging contaminants in surface waters.

In recent years emerging contaminants (ECs) have received significant attention due to their widespread detection in surface waters and concerns that these compounds can cause adverse ecological and/or human health effects. Therefore, accurate methods for determining and quantifying ECs in surface water are essential for estimating their environmental impact. This work describes the development, validation and application of a sensitive multiclass method for simultaneous determination of 22 per and polyfluorinated alkyl substances (PFASs), 3 pharmaceuticals, 15 pesticides, and 2 bisphenols in surface water using on-line solid phase extraction (SPE) coupled with ultra-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS). The method allows simultaneous sample clean-up from interfering matrices and lower limits of detection (LODs) by injecting a large sample volume into the LC system without compromising chromatographic efficiency and resolution. Linearity of response over several orders of magnitude was demonstrated for all tested compounds (R2 > 0.99), with the LODs ranging from 0.8 and 33.7 pg mL-1, allowing detection of ECs at trace levels in surface water. The method showed acceptable accuracy and precision (CV, % and RE below 20%) for all tested ECs. It also provided recoveries between 60% and 130% for all tested ECs. The validated method was successfully applied for analysis of surface water samples from three rivers (Cam, Ouse and Thames) in England. Several ECs, including perfluorooctanesulfonic acid (PFOS), perfluorobutanesulfonic acid (PFBS), perfluorohexanoic acid (PFHxA), perfluorohexane sulfonic acid (PFHxS), dimethyl-metatoluamide (DEET) and ibuprofen were observed in analysed surface water above the method's limit of quantitation (LOQ), with concentrations ranging between 3.5 and 460 pg mL-1.

Artificial Intelligence-Assisted Classification of Gliomas Using Whole Slide Images.

Glioma is the most common primary brain tumor in adults. The diagnosis and grading of different pathological subtypes of glioma is essential in treatment planning and prognosis. To propose a deep learning-based approach for the automated classification of glioma histopathology images. Two classification methods, the ensemble method based on 2 binary classifiers and the multiclass method using a single multiclass classifier, were implemented to classify glioma images into astrocytoma, oligodendroglioma, and glioblastoma, according to the 5th edition of the World Health Organization classification of central nervous system tumors, published in 2021. We tested 2 different deep neural network architectures (VGG19 and ResNet50) and extensively validated the proposed approach based on The Cancer Genome Atlas data set (n = 700). We also studied the effects of stain normalization and data augmentation on the glioma classification task. With the binary classifiers, our model could distinguish astrocytoma and oligodendroglioma (combined) from glioblastoma with an accuracy of 0.917 (area under the curve [AUC] = 0.976) and astrocytoma from oligodendroglioma (accuracy = 0.821, AUC = 0.865). The multiclass method (accuracy = 0.861, AUC = 0.961) outperformed the ensemble method (accuracy = 0.847, AUC = 0.933) with the best performance displayed by the ResNet50 architecture. With the high performance of our model (>80%), the proposed method can assist pathologists and physicians to support examination and differential diagnosis of glioma histopathology images, with the aim to expedite personalized medical care.

THYROID DISEASE CLASSIFICATION ANALYSIS USING XGBOOST MULTICLASS

ABSTRAK- Sickness is an unusual condition of the body or mind that causes discomfort, malfunction, or suffering to the sick person. One disorder that occurs due to a lack of health concerns is thyroid disease. The thyroid is a butterfly-shaped endocrine gland near the neck's bottom. The diagnosis of thyroid disease is complicated because the symptoms of thyroid disease can fluctuate based on the rise and fall of thyroid hormones, which increase the utilization of oxygen by the body's cells. In this case, a thyroid examination by a doctor and proper interpretation of clinical data is required to identify thyroid disease. However, the limitations of a doctor due to age and time constraints lead to a lack of interpretation of patient clinical data. Therefore, a study was conducted on the analysis of thyroid disease classification to simplify and speed up the process of diagnosing thyroid disease using the Xgboost Multiclass method, which is expected to get an accuracy value above 90%. Keywords: Classification, Thyroid, Xgboost Multiclass, Machine Learning

Validated single urinary assay designed for exposomic multi-class biomarkers of common environmental exposures.

Epidemiological studies often call for analytical methods that use a small biospecimen volume to quantify trace level exposures to environmental chemical mixtures. Currently, as many as 150 polar metabolites of environmental chemicals have been found in urine. Therefore, we developed a multi-class method for quantitation of biomarkers in urine. A single sample preparation followed by three LC injections was optimized in a proof-of-approach for a multi-class method. The assay was validated to quantify 50 biomarkers of exposure in urine, belonging to 7 chemical classes and 16 sub-classes. The classes represent metabolites of 12 personal care and consumer product chemicals (PCPs), 5 polycyclic aromatic hydrocarbons (PAHs), 5 organophosphate flame retardants (OPFRs), 18 pesticides, 5 volatile organic compounds (VOCs), 4 tobacco alkaloids, and 1 drug of abuse. Human urine (0.2 mL) was spiked with isotope-labeled internal standards, enzymatically deconjugated, extracted by solid-phase extraction, and analyzed using high-performance liquid chromatography-tandem mass spectrometry. The methanol eluate from the cleanup was split in half and the first half analyzed for PCPs, PAH, and OPFR on a Betasil C18 column; and pesticides and VOC on a Hypersil Gold AQ column. The second half was analyzed for tobacco smoke metabolites and a drug of abuse on a Synergi Polar RP column. Limits of detection ranged from 0.01 to 1.0 ng/mL of urine, with the majority ≤0.5 ng/mL (42/50). Analytical precision, estimated as relative standard deviation of intra- and inter-batch uncertainty, variabilities, was <20%. Extraction recoveries ranged from 83 to 109%. Results from the optimized multi-class method were qualified in formal international proficiency testing programs. Further method customization options were explored and method expansion was demonstrated by inclusion of up to 101 analytes of endo- and exogenous chemicals. This exposome-scale assay is being used for population studies with savings of assay costs and biospecimens, providing both quantitative results and the discovery of unexpected exposures.

Direct multiclass desorption electrospray ionization-tandem mass spectrometry method for the analysis of sleep inducers and ototoxic drugs in dried blood spots.

An unconventional and innovative approach for the quantitative determination of 11 ototoxic and narcoleptic drugs in whole blood is described. The multiclass method allows the inclusion of the most widespread drugs on the market (antihistamines, antidepressants, antihypertensives, anxiolytics, opioids, Z-drugs) responsible for 10% of occupational accidents. The developed procedure involved the use of the desorption electrospray ionization (DESI) interface for the direct analysis of dried blood spots (DBS). All the issues strictly connected to the chemical-physical characteristics of DBS and DESI (sample inhomogeneity, DBS support, DESI geometry and solvent) were carefully evaluated and innovative strategies were applied. Haematocrit was managed using a small and measured volume of blood (2μL) with analysis of the entire DBS. The proposed method was fully validated in terms of limits of detection, limits of quantitation (LOQs; between 60 pg/mm2 and 1.6ng/mm2 ), linearity (one order of magnitude starting from LOQs) and inter- and intra-day precision (on three levels, with relative standard deviation values not exceeding 17%). Accuracy was calculated by comparison with an ultrahigh-performance liquid chromatography-tandem mass spectrometry method (suitable also as a confirmatory method). Results showed a surprising sensitivity, demonstrating that this procedure could be suitable for applications in various fields, e.g. forensic analysis. Moreover, as a collateral benefit, it was discovered that the method is able to analyse very light traces left on plastic and glass surfaces by detached dried blood.

Development and Validation of Multi-Residue Method for Drugs Analysis in Human Feces by Liquid Chromatography-Tandem Mass Spectrometry.

The use of veterinary drugs in animal production is a common practice to secure animal and human health. However, residues of administrated drugs could be present in animal food products. Levels of drugs in food of animal origin are regulated within the European Union. In recent years, residues have been detected not only in food, but also in the environmental elements such as water or soil, meaning that humans are involuntarily exposed to these substances. This article presents a multiclass method for the analysis of various therapeutic groups of pharmaceuticals in human feces. Pharmaceuticals are extracted from feces with an acid extraction solvent, and after filtration the extract was analyzed by HPLC–MS/MS. A limit of detection of 10 ng/g was achieved for 9 pharmaceuticals, with linearity over 0.99 and repeatability and reproducibility lower than 20%. The method was satisfactorily applied in 25 feces samples of individuals that had declared not to be under medical treatment for the last two months. Results indicate the presence of six different compounds at concentration between 10 and 456 ng/g. This preliminary study showed the involuntary exposure of human gut microbiota to active substances such as pharmaceuticals

Multiclass and multi-residue screening of mycotoxins, pharmacologically active substances, and pesticides in infant milk formulas through ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry analysis

Infant milk formulas are designed to substitute human milk when breastfeeding is unavailable. In addition to human milk and milk-derived products, these formulas can be a vehicle of contaminants. In this work, a multiclass method based on the QuEChERS (quick, easy, cheap, effective, rugged, and safe) approach was developed for the simultaneous determination of contaminants (n = 45), including mycotoxins and veterinary drug residues, occurring in infant milk formulas. By using an ultra-high-performance liquid chromatography quadrupole-Orbitrap coupled with high-resolution mass spectrometry analysis (UHPLC-Q-Orbitrap HRMS; Thermo Fisher Scientific), further retrospective analysis of 337 contaminants, including pesticides, was achieved. The method was validated in accordance with European regulations and applied for the analysis of 54 infant milk samples. Risk assessment was also performed. Dexamethasone was detected in 16.6% of samples (range: 0.905-1.131 ng/mL), and procaine benzyl penicillin in 1 sample at a concentration of 0.295 ng/mL. Zearalenone was found in 55.5% of samples (range: 0.133-0.638 ng/mL) and α-zearalenol in 16.6% of samples (range: 1.534-10.408 ng/mL). Up to 49 pesticides, 11 veterinary drug residues, and 5 mycotoxins were tentatively identified via retrospective analysis based on the mass spectral library. These findings highlight the necessity of careful evaluation of contaminants in infant formulas, considering that they are intended for a vulnerable part of the population.

Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

Human gait recognition (HGR) has received a lot of attention in the last decade as an alternative biometric technique. The main challenges in gait recognition are the change in in-person view angle and covariant factors. The major covariant factors are walking while carrying a bag and walking while wearing a coat. Deep learning is a new machine learning technique that is gaining popularity. Many techniques for HGR based on deep learning are presented in the literature. The requirement of an efficient framework is always required for correct and quick gait recognition. We proposed a fully automated deep learning and improved ant colony optimization (IACO) framework for HGR using video sequences in this work. The proposed framework consists of four primary steps. In the first step, the database is normalized in a video frame. In the second step, two pre-trained models named ResNet101 and InceptionV3 are selected and modified according to the dataset's nature. After that, we trained both modified models using transfer learning and extracted the features. The IACO algorithm is used to improve the extracted features. IACO is used to select the best features, which are then passed to the Cubic SVM for final classification. The cubic SVM employs a multiclass method. The experiment was carried out on three angles (0, 18, and 180) of the CASIA B dataset, and the accuracy was 95.2, 93.9, and 98.2 percent, respectively. A comparison with existing techniques is also performed, and the proposed method outperforms in terms of accuracy and computational time.

A simple and fast multiclass method for determination of steroid hormones in berry fruits, root and leafy vegetables

This study is focused on the development of a multiclass analytical method for sensitive determination of steroid hormones (progestins, oestrogens, androgens and glucocorticoids) in fruits and vegetables. The herein considered analytes present a wide logP range, thus they tend to accumulate differently in edible parts. Therefore, a berry fruit (strawberry), a root vegetable (carrot), and a leafy vegetable (spinach) were chosen as probes to develop the extraction procedure. This, optimized by a chemometric approach (23 experimental design) on lyophilized samples (0.25 g, spike 100 ng g − 1), entails a rapid ultrasonic extraction (3 × 1 min cycles) with low consumption (3 × 2 mL) of MeOH, selected as extraction solvent by a preliminary screening. The clean-up step, necessary due to the complexity of the matrices, was performed by a simple solid-phase extraction procedure on the Supelclean™ LC-NH2 sorbent afore the selective quantification by HPLC–ESI-MS/MS (MRM mode). The final analytical method was successfully applied to multianalyte extraction at lower concentrations (10–50 ng g−1) with good recoveries and repeatability, and it was successfully extended to raspberry, radish and arugula, further supporting applicability to this kind of samples. The results from the analysis of fruits and vegetables purchased from local markets, where some steroids have been quantified at the low ng g−1 levels, are consistent with literature data about concentration of these emerging pollutants in edible plants, also according to their LogP values.