Traditional Extraction Methods Research Articles

OCNet-Based Water Body Extraction from Remote Sensing Images

Water body extraction techniques from remotely sensed images are crucial in water resources distribution studies, climate change studies and other work. The traditional remote sensing water body extraction has the problems of low accuracy and being time-consuming and laborious, and the water body recognition technique based on deep learning is more efficient and accurate than the traditional threshold method; however, there is the problem that the basic model of semantic segmentation is not well-adapted to complex remote sensing images. Based on this, this study adopts an OCNet feature extraction network to modify the base model of semantic segmentation, and the resulting model achieves excellent performance on water body remote sensing images. Compared with the traditional water body extraction method and the base network, the OCNet modified model has obvious improvement, and is applicable to the extraction of water bodies in true-color remote sensing images such as high-score images and unmanned aerial vehicle remote sensing images. The results show that the model in this study can realize automatic and fast extraction of water bodies from remote sensing images, and the predicted water body image accuracy (ACC) can reach 85%. This study can realize fast and accurate extraction of water bodies, which is of great significance for water resources acquisition and flood disaster prediction.

A novel hydrangea-like magnetic composite Fe3O4@MnO2@ZIF-67 for efficient selective adsorption of Pd(II) from metallurgical wastewater

The selective adsorption of palladium from wastewater is a feasible solution to solving palladium pollution and resource scarcity. Because traditional solvent extraction methods often involve the use of considerable amounts of organic solvents, research is focused on investigating adsorption techniques that can selectively remove palladium from wastewater. In this paper, the magnetic composite Fe3O4@MnO2@ZIF-67 was synthesized and its performance for the adsorption of Pd(II) in acidic water was investigated. Fe3O4@MnO2@ZIF-67 was characterized by various analytical methods such as TEM, SEM, EDS, BET, XRD, FTIR, zeta potential analysis, VSM, and TGA. The effects of palladium ion concentration, contact time, pH, and temperature on adsorption were evaluated. The kinetics were shown to follow the pseudo-second-order kinetic model and Elovich model, and the rate-limiting step was chemisorption. Thermodynamic studies showed that increasing the temperature promoted the adsorption of Pd(II), and the maximum uptake capacity of Fe3O4@MnO2@ZIF-67 for Pd(II) was 531.91 mg g−1. Interestingly, Fe3O4@MnO2@ZIF-67 exhibited superior selectivity for Pd(II) in the presence of Ir(IV), Pt(IV), and Rh(III). The adsorbent can be used repeatedly for selective adsorption of palladium. Even at the fifth cycle, the uptake rate of Pd(II) remained as high as 83.1%, and it showed a favorable adsorption capacity and selectivity for Pd(II) in real metallurgical wastewater. The adsorption mechanism was analyzed by SEM, FTIR, XRD, XPS, and DFT calculations, which indicated that electrostatic interactions and coordination with nitrogen-containing groups were involved. Fe3O4@MnO2@ZIF-67 is a promising adsorbent for the efficient adsorption and selective separation of palladium ions.

A fully automated Lab-on-a-Disc platform integrated a high-speed triggered siphon valve for PBMCs extraction

Human Peripheral Blood Mononuclear Cells (PBMCs) are isolated from peripheral blood and identified as any blood cell with a round nucleus that exhibits immune responses and undergoes immunophenotypic changes upon exposure to various pathophysiological stimuli. Obtaining high-recovery and clinical-grade PBMCs without decreasing cell viability and causing stress is crucial for disease diagnosis and successful immunotherapy. However, traditional manual PBMCs extraction methods rely on manual intervention with less recovery rate and reliability. In this study, we introduced a novel and efficient strategy for the fully automated extraction of PBMCs based on a Lab-on-a-Disk (LoaD) platform. The centrifugal chip used percoll as density gradient media (DGM) for separation and extraction on account of the density difference of cells in whole blood, without labeling and any additional extra cellular filtration or cell lysis steps. Above all, we proposed a high-speed triggered siphon valve, which was closed under the speed of cell sedimentation and subsequently opened by increasing speed to complete the extraction of PBMCs. It can avoid the problem that previous siphon valves rely on unstable hydrophilic surface treatment and prime under low/zero speed conditions. With valves and the clock channel integrated on the chip, users can achieve fully automated collection of PBMCs. Compared with the clinical laboratory results, the recovery rate of extracted PBMCs was 80 %. The experimental results prove that the high-speed triggered siphon valve improves the extraction efficiency of PBMCs. The robust chips, which are not only simple to manufacture and assemble but also stable and reliable to use, have great potential in biomedical and clinical applications.

Aqueous two-phase systems coupled with chemometrics-enhanced HPLC-DAD for simultaneous extraction and determination of flavonoids in honey.

In this study, an accurate, rapid, green, and environment friendly method for the extraction and quantitative analysis of flavonoids in honey was established by using the aqueous two-phase extraction combined with the chemometrics-assisted HPLC-DAD. The first purpose of this study was to extract seven flavonoids in five different types of honey using alcohol/salt aqueous two-phase system (ATPS). The system with 2.82mL sodium citrate (30%), 1.58mL water, and 3.10mL isopropanol, showed the highest flavonoids extraction yields in the top phase (87.66-101.50%). Additionally, the three-way array of honey samples based on HPLC-DAD was decomposed mathematically by the alternating trilinear decomposition (ATLD) algorithm to obtain reasonable chromatograms, spectra, and concentration profiles for each analyte. Compared with the traditional solid-phase extraction method, the ATPS-ATLD-based method showed satisfactory spiked recoveries, lower limit of detection, and higher sensitivity, further verifying its accuracy and stability.

A prospective study: Advances in chaotic characteristics of serum Raman spectroscopy in the field of assisted diagnosis of disease

Chaos theory is an important branch of mathematics and its theory has been widely applied in many fields such as physics and medicine. Based on existing spectroscopic techniques, this paper used chaos theory as a research method for nonlinear time series to analyze Raman spectral time domain curves in order to improve the performance of disease diagnostic models and explore a new paradigm of spectroscopic technology for intelligent assisted disease diagnosis. We quantitatively identified the chaotic characteristics of time domain Raman spectra by three methods, extracted chaotic features such as correlation dimension and Kolmogorov entropy, and used the chaotic features as input to Extreme Learning Machine (ELM), Back Propagation Neural Network (BPNN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) to diagnose patients with lung cancer (LC), glioma, renal cell carcinoma (RCC) and esophageal cancer (EC). The Raman spectra were also analyzed by traditional spectral feature extraction & modelling method, and the results of traditional spectral feature extraction & modelling method and chaotic feature modelling method were compared. The experimental results showed that the extraction of effective chaotic features in the full spectral range could achieve comparable diagnostic results with the traditional spectral feature extraction & modelling method. To further validate the effectiveness of chaos theory in Raman spectral data, the full spectrum was divided into three consecutive subsequences of 500–1000, 1000–1500, and 1500–2000 cm−1, and the above experimental steps were repeated respectively, and the results of the traditional spectral feature extraction & modelling method and chaotic feature modelling were compared. The results showed that as the spectral range was split into consecutive subsequences, the diagnostic performance of the chaotic features in each subsequence performed better than that of conventional spectral analysis techniques. In this study, the technique bridges the gap in the application of chaotic signals to Raman spectroscopy techniques, focuses on global features in the time domain profile of Raman spectra, and demonstrates the significant value of chaos theory in artificial intelligence-assisted spectroscopic medical diagnosis.

Tensile Strength and Flexibility Characterization of Biodegradable Plastic from Avocado (Persea Americana) Seed

The increased use of plastics has become a major environmental problem, especially during this pandemic. Plastics have generated many problems, particularly waste disposal, which made the researchers aimed to develop biodegradable plastic through fruit waste material, avocado seeds. From a total of 3 kilograms of the avocado seed, starch was extracted through the traditional starch extraction method. There were three groups with the same ingredients but differing in their concentration; the relationship between the proportion of the ingredients and their respective differences was seen by using ANOVA and Post hoc tests. The samples have undergone a tensile strength test following ASTM D882. From the results, the best experimental group was the group with 40 grams of starch, 20 grams of polyvinyl alcohol, and 10 grams of used cooking oil; however, it is different from the biodegradable plastic bags used as the control group. It could be deduced that it is possible to make biodegradable plastic out of Avocado seed starch that is biodegradable, water-soluble, and non-toxic. However, further analysis may be conducted to provide physical features as commercial plastics.

Supercritical CO2 Extraction, Quantification and Pharmacological Screening of Steroidal Saponins from Fruits of Momordica charantia L

Momordica charantia L. (MC), a Cucurbitaceae family member, is the most recognized plant for its hypoglycemic activity. Charantin, a steroidal saponin, is the most studied potent phytochemical in MC for diabetes. This research paper aims at the extraction, quantification and pharmacological screening of charantin, from fruits of MC. Extraction was performed by using traditional soxhlet extraction and modified supercritical fluid extraction (SFE) techniques, and compared the results of both techniques in terms of percent extract yield, quantity of charantin and in-vitro antidiabetic activity of both extracts. (Soxhlet extract and SC-CO2 extract). Further, quantitative estimation of charantin in both extracts was done by HPLC-UV method and it was validated as per the ICH guidelines. When compared to soxhlet extract, the SC-CO2 extract displayed high antihyperglycemic activity by blocking α-amylase and α-glucosidase enzymes. Study also indicates that SC-CO2 extract had higher antioxidant activity (0.25 mg/mL) than soxhlet extract (0.33 mg/mL), signifying the SFE technique’s efficiency over the traditional soxhlet extraction method. In-vitro antidiabetic study indicated that the biomolecule charantin extracted from fruits of MC possess potent antidiabetic and high antioxidant activities and, therefore, hold potential for manufacturing innovative natural remedies to treat diabetes and its complications with no side effects.

Essential oil of Citrus hystrix DC.: A mini-review on chemical composition, extraction method, bioactivities, and potential applications in food and pharmaceuticals

Citrus hystrix DC. is a common herb in tropical regions. Its essential oils are now widely researched and applied because of their high economic value and safety for humans and are interesting materials for future trends. This review provides an extensive overview of the biological activities of C. hystrix essential oil, characterized predominantly by citronellal, ?-Pinene, sabinene, limonene, and terpinene-4-ol, which are deciding factors in antimicrobial, antioxidant, insect repellent, anti-tumor, and anti-inflammatory properties. Therefore, it is applied in the fields of food preservation and pharmaceuticals. However, these applications should consider the ratio of these components in the essential oil, which is variable when using materials from different parts of the plant and depending on the original location of the plant, growth stages, traditional or modern extraction methods, and pre-treatment methods.

Droplet-based optical trapping for cell separation in mock forensic samples.

Optical tweezers have a wide range of uses for mechanical manipulation of objects in the microscopic range. This includes both living and static cells in a variety of biomedical and research applications. Single-focus optical tweezers, formed by focusing a laser beam through a high numerical aperture immersion objective, create a significant force, which enables controlled transport of a variety of different cell types and morphologies in three dimensions. Optical tweezers have been previously reported to capture and separate spermatozoa from a reconstituted simulated postcoital sample. We report herein the development of a simplified, more efficient cell transfer protocol that can separate and isolate both spermatozoa as well as leukocytes, with similar efficiencies as those previously reported. The new cell transfer method was used to separate sperm cells from a reconstituted mixture of spermatozoa and vaginal epithelial cells, with complete STR profiles developed from 50 cells with little evidence of contribution from the female contributor to the mixture. This modified protocol was then used to separate 21 samples of enriched leukocytes, with trapped cells ranging from 5 to 22 cells. Complete STR profiles were developed from as few as 10 leukocytes. Thus, with minimal sample preparation and a short trapping time, this method has the potential to provide an alternative to traditional differential extraction methods for separation of sperm:nonsperm mixtures while also providing versatility for separation of cells with differing morphologies.

Enhanced extraction of bioactive compounds from propolis (Apis mellifera L.) using subcritical water

The bioactive compounds and antioxidant activities of propolis extracts were investigated using subcritical water extraction (SWE). SWE was performed by varying temperature (110–200 °C) and time (10–30 min). SWE using only water as solvent successfully to extracted bioactive compounds from propolis using high-purity glass thimbles. The concentrations of galangin (16.37 ± 0.61 mg/g), and chrysin (7.66 ± 0.64 mg/g) were maximal at 200 °C for 20 min, and 170 °C for 20 min, respectively. The antioxidative properties from propolis increased with the increasing extraction temperature and extraction time on SWE. The maximum yields of the total phenolics (226.37 ± 4.37 mg/g), flavonoids (70.28 ± 1.33 mg/g), and antioxidant activities (88.73 ± 0.58%, 98.86 ± 0.69%, and 858.89 ± 11.48 mg/g) were obtained at 200 °C for 20 min. Compared with using ethanol extraction (at 25 °C for 24 h, total phenolics = 176.28 ± 0.35, flavonoids = 56.41 ± 0.65, antioxidant activities = 72.74 ± 0.41%, 95.18 ± 0.11%, 619.51 ± 8.17 mg/g), all yields of SWE extracts obtained at 200 °C for 20 min were higher. SWE is suitable for a much faster and more efficient method extracting bioactive compounds from propolis compared to traditional extraction method.

Algorithm and simulation study of oil painting classification based on visual perception and improved embedded learning

This paper presents an in-depth study and analysis of oil painting classification and simulation using an improved embedded learning fusion vision perception algorithm. This paper analyzes and models the image quality evaluation problem by simulating the human visual system and extracting quality perception features as the main entry point to improve the prediction accuracy of the overall algorithm. This paper proposes a multi-classification method of CCNN, which uses the similarity measure based on information first to achieve multi-classification of artwork styles and artists, and this part is the main part of this paper. This paper uses the wiki art repository to construct a dataset of oil paintings, including over 2000 works by 20 artists in 13 styles. CNN achieves an accuracy of 85.75% on the artist classification task, which is far more effective than traditional deep learning networks such as Resnet. Finally, we use the network model of this paper and other network models to train the classification of 3, 4, and 6 categories of art images. The accuracy of art image classification by this paper’s algorithm is higher than that of the current mainstream convolutional neural network models, and the extracted features are more comprehensive and more accurate than traditional art image feature extraction methods, which do not rely on researchers to extract image features. Experiments show that the proposed method can achieve excellent prediction accuracy for both synthetic distorted images and distorted images.

A rapid LC-MS/MS method for multi-class identification and quantification of cyanotoxins

Cyanobacteria can form harmful blooms in specific environmental conditions due to certain species producing toxic metabolites known as cyanotoxins. These toxins pose significant risks to public health and the environment, making it critical to identify and quantify them in food and water sources to avoid contamination. However, current screening methods only focus on a single class of cyanotoxins, limiting their effectiveness. Thus, fast and sensitive liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) method was developed to analyze eighteen cyanotoxins simultaneously. A simplified extraction procedure using lyophilized samples of cyanobacterial biomass was also used, eliminating the need for traditional solid-phase extraction methods. This method uses multiple reaction monitoring and allows accurate determination and quantification of eighteen cyanotoxins, including anatoxin-a, homoanatoxin-a, cylindrospermopsin, deoxy-cylindrospermopsin, nodularin, guanitoxin, seven microcystins (RR, [D-Asp3] RR, LA, LR, LY, LW, and YR), and five saxitoxins (gonyautoxins - GTX-1&4, GTX-2&3, GTX-5), decarbamoylgonyautoxin (dcGTX-2&3), and N-Sulfocarbamoylgonyautoxin (C1&C2), all in a short acquisition time of 8 min. Therefore, this method provides a simple and efficient approach to identify and quantify harmful compounds produced by cyanobacteria. Hence, this represents the first method to detecting guanitoxin among cyanotoxins. By expanding the range of toxins analyzed, this method can help ensure high-quality food and drinking water and protect recreational users from exposure to cyanotoxins.

A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data

Power systems are prone to a variety of events (e.g. line trips and generation loss) and real-time identification of such events is crucial in terms of situational awareness, reliability, and security. Using measurements from multiple synchrophasors, i.e., phasor measurement units (PMUs), we propose to identify events by extracting features based on modal dynamics. We combine such traditional physics-based feature extraction methods with machine learning to distinguish different event types. Including all measurement channels at each PMU allows exploiting diverse features but also requires learning classification models over a high-dimensional space. To address this issue, various feature selection methods are implemented to choose the best subset of features. Using the obtained subset of features, we investigate the performance of two well-known classification models, namely, logistic regression (LR) and support vector machines (SVM) to identify generation loss and line trip events in two datasets. The first dataset is obtained from simulated generation loss and line trip events in the Texas 2000-bus synthetic grid. The second is a proprietary dataset with labeled events obtained from a large utility in the USA involving measurements from nearly 500 PMUs. Our results indicate that the proposed framework is promising for identifying the two types of events.

Study on Extraction and Purification of Acanthopanax senticosus Polyphenols by an Ionic Liquid-Assisted Aqueous Two-Phase System.

This study aimed to extract and purify polyphenols from Acanthopanax senticosus. A new green method was developed, in which ionic liquids (ILs) were used as aqueous two-phase (ATP) adjuvants to extract the polyphenols from A. senticosus. An ionic liquid-assisted aqueous two-phase system (IL-ATPS) was established. The purification of the polyphenols from the extraction fluid by AB-8 macroporous resin was conducted, and the kinetic mechanisms were studied. The reuse of ionic liquids was executed. The results showed that an [OMIM]Br-assisted ethanol/NaH2PO4 system (IL-ATPS) was the best extraction solvent. In this study, the following optimal extraction conditions were determined: 32 wt.% ethanol, 25 wt.% NaH2PO4, 9 wt.% additional ionic liquid, a solid-liquid ratio of 1:40 g/mL, an extraction temperature of 50 °C, a pH of 4.0, an extraction time of 50 min, and an extraction rate of the polyphenols at 15.90 mg/g. The optimum adsorption parameters of the macroporous resin AB-8 were as follows: a flow rate of 3.5 BV·h-1, a sample volume of 40 mL, an elution flow rate of 3.5 BV·h-1, an eluent volume of 80 mL, and an eluant that was constituted by an 85% volume fraction of ethanol. The decolorization effect of 4% activated carbon was better than the other amounts; in addition, a decolorization rate of 76.81% and an ionic liquid recovery rate of 81.12% were found to be the most optimal. Compared with the traditional extraction methods, IL-ATPS has the advantages of requiring simple operation, saving time, and high efficiency. In addition, it can be used for the extraction of the polyphenolic compounds.

Microwave-assisted extraction in closed vessel in food analysis.

Microwave-assisted extraction (MAE) is an important technique in analytical chemistry. It offers several advantages over traditional extraction methods, such as improved extraction efficiency, shorter extraction times, reduced solvent consumption, and enhanced analyte recovery. Using microwaves, heat is directly applied to the sample, leading to rapid and efficient extraction of target compounds by enhancing the solubility and diffusion of the target compounds, thus requiring lower solvent volume. Therefore, MAE can be considered a more environmentally friendly and cost-effective option facilitating the transition toward greener and more sustainable analytical chemistry workflows. This contribution systematically reviews the application of MAE to a selection of target compounds/compounds classes of relevance for food quality and safety assessment. As inclusion criteria, MAE active temperature control and molecularly-resolved characterization of the extracts were considered. Contents include a brief introduction of the principles of operation, available systems characteristics, and key parameters influencing extraction efficiency and selectivity. The application section covers functional food components (e.g., phenols, diterpenes, and carotenoids), lipids, contaminants (e.g., polycyclic aromatic hydrocarbons and mineral oil hydrocarbons), pesticides, veterinary drug residues, and a selection of process contaminants and xenobiotics of relevance for food safety.

Fourier‐transform infrared spectroscopy for soil organic matter analysis

AbstractThe study of soil organic matter (SOM) can benefit from the use of Fourier‐transform infrared spectroscopy, an analytical method that complements traditional fractionation and extraction methods. This review provides guidance on the use of diffuse reflectance infrared Fourier transform (DRIFT) in the mid‐infrared region (MIR: 4000–400 cm−1). Two distinct applications of DRIFT spectroscopy are reviewed: soil organic matter (1) characterization and (2) quantification. Characterization of SOM involves the qualitative to semi‐quantitative measurement of functional groups that constitute organic matter, and quantification employs chemometrics to predict fractions of SOM. Guidance on decision‐making in how methods are conducted based on sample type and research question, and on interpretation of results are provided.

Recent Advances on Key Enzymes of Microbial Origin in the Lycopene Biosynthesis Pathway.

Lycopene is a common carotenoid found mainly in ripe red fruits and vegetables that is widely used in the food industry due to its characteristic color and health benefits. Microbial synthesis of lycopene is gradually replacing the traditional methods of plant extraction and chemical synthesis as a more economical and productive manufacturing strategy. The biosynthesis of lycopene is a typical multienzyme cascade reaction, and it is important to understand the characteristics of each key enzyme involved and how they are regulated. In this paper, the catalytic characteristics of the key enzymes involved in the lycopene biosynthesis pathway and related studies are first discussed in detail. Then, the strategies applied to the key enzymes of lycopene synthesis, including fusion proteins, enzyme screening, combinatorial engineering, CRISPR/Cas9-based gene editing, DNA assembly, and scaffolding technologies are purposefully illustrated and compared in terms of both traditional and emerging multienzyme regulatory strategies. Finally, future developments and regulatory options for multienzyme synthesis of lycopene and similar secondary metabolites are also discussed.

Ionic liquid-based ultrasonic-assisted extraction coupled with HPLC to analyze isoquercitrin, trifolin and afzelin in Amygdalus persica L. flowers

This study aimed to establish a method for the simultaneous determination of isoquercitrin, trifolin and afzelin in A. persica flowers by high performance liquid chromatography (HPLC) with ionic liquid as extractant and ultrasonic-assisted extraction. The effects of ionic liquid concentration, solid–liquid ratio, number of crushing mesh, ultrasonic time, extraction temperature, and ultrasonic power on the extraction yield of three target compounds were investigated using the extraction yield of target analytes as the index. According to the results of single factor experiment, the Box-Behnken design-response surface methodology (BBD) was used to optimize the extraction method and compared with the traditional extraction method. The results showed that, calibration curves had excellent linearity (R2 > 0.9990) within the test ranges. In combination with other validation data, this method demonstrated good reliability and sensitivity, and can be conveniently used for the quantification of isoquercitrin, trifolin and afzelinin A. persica flowers. And the contents of isoquercitrin, trifolin and afzelin were 64.08, 20.55 and 75.63 μg/g, respectively. The optimal process obtained by BBD was as follows: ionic liquid concentration was 1.0 mol/L, solid–liquid ratio was 1:40 g/ml, mesh sieve was 50 mesh, ultrasonic time was 40 min, extraction temperature was 50 °C, and ultrasonic power was 400 W. Under the optimal conditions, the theoretical predicted total extraction yield of the three target compounds was 159.77 μg/g, which was close to the actual extraction value (160.26 μg/g, n = 3), this result indicating that the optimal process parameters obtained by response surface methodology analysis were accurate and reliable. The method was simple, accurate and rapid for determination the contents of three active ingredients in A. persica flowers.

The Efficient Extraction Method of Collagen from Deteriorated Leather Artifacts.

Collagen is the most crucial component of leather artifacts and analyzing collagen can provide vital information for studying and conserving such artifacts. However, collagen in leather artifacts often faces challenges such as degradation, denaturation, and contamination, which make it difficult to achieve an ideal protein extract using traditional extraction methods. This study aimed to find an efficient collagen extraction strategy for aging leather by comparing and improving commonly used methods. The results of comparing different extraction methods indicated that a NaOH solution was highly effective in extracting collagen from aged leather. To determine the optimal conditions for collagen extraction from the NaOH solution, we conducted orthogonal experiments. The results revealed that a NaOH concentration of 0.05 mol/L, a dissolution temperature of 80 °C, and a dissolution time of 12 h were the most favorable conditions. To validate the effectiveness of this method, we performed SDS-PAGE and biological mass spectrometry tests on collagen extracts from leather samples with varying degrees of aging. All collagen extracts exhibited distinct bands in the gel, and the molecular weight of collagen in each sample exceeded 20 kDa. Furthermore, even with a reduced sample mass of 1 mg (micro-destructive sampling), biological mass spectrometry identified 124 peptides in the protein extract. Notably, four of these peptides were unique to cattle hide collagen and were not present in the collagen of pig, sheep, horse, deer, or human skins. These experimental findings confirm the efficacy of the NaOH solution for extracting collagen from aging leather, suggesting that it can serve as a significant method for collagen identification and analysis in leather artifacts.

Evaluation of the QuEChERS extraction approach for the analysis of active compounds of pharmaceuticals in olive tree portions

BackgroundA modified version of the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) method has been tested for quantifying six active compounds of pharmaceuticals (PhACs), i.e., two different antibiotics, two anti-inflammatories, one antifungal, and one anti-depressant, extracted from roots, leaves and stems, pulp, pits, and oil obtained from olive trees. The different matrices have been polluted with all contaminants at 25, 50, and 250 µg L−1 and the recoveries were determined by liquid chromatography tandem–mass spectrometry. The validation of the method has been carried out by determining linearity, recovery, precision, limits of detection (LODs), and limits of quantification (LOQs) values. A matrix-matched calibration for each matrix has been adopted in order to avoid the matrix effect at the aforementioned levels of fortification.ResultsThe recoveries of PhACs from the different matrices were always above 70% and the relative standard deviation (RSD) always ≤ 20%, conditions required for the validation of the method. The LOD and LOQ values were always lower than 25 µg L−1, i.e., always lower than the minimum concentration used in the experiment; therefore, the method can be validated at 25, 50, and 250 µg L−1.ConclusionsThis method can represent a valid alternative to the traditional extraction methods to quantify pharmaceuticals extracted also from fatty matrices.Graphical