Desarrollo de Técnicas de Extracción y Análisis de Polifenoles y Antocianos en Açaí (Euterpe olearecea)

El açaí es un fruto que presenta unas propiedades nutricionales muy importantes así como una alta cantidad de compuestos de marcado interés biológico, como compuestos antioxidantes y compuestos anti-cancerígenos, entre los que destacan las antocianinas y los compuestos fenólicos. Hay autores que han aplicado técnicas de extracción para la identificación y cuantificación de los compuestos de interés biológico presentes en el açaí, como la extracción asistida por ultrasonidos, pero ningún autor ha realizado un estudio exhaustivo sobre su extracción. Por tal motivo, en este trabajo se ha llevado a cabo el desarrollo de técnicas de extracción de antocianinas y compuestos fenólicos totales en açaí mediante el empleo de la extracción asistida por ultrasonidos (UAE), la extracción mediante fluidos presurizados (PLE) y la extracción asistida por microondas (MAE). Una vez obtenidas las condiciones óptimas de extracción se realizó un estudio sobre el tiempo óptimo de extracción y la repetitividad y reproducibilidad del método. Los tres métodos de extracción desarrollados han presentado una alta repetitividad y reproducibilidad tanto para antocianinas totales como para compuestos fenólicos totales. Una vez desarrollados los tres métodos de extracción se han aplicado para la extracción y análisis de distintos alimentos elaborados a partir de açaí (liofilizados, zumos, mermelada y pastillas).Açaí is the fruit of the açaí palm tree (Euterpe oleracea), native from Northern South America. This tropical fruit is very appreciated because of its nutritional properties such as a great number of compounds of biological interests like anthocyanin’s and phenolic compounds which are antioxidants and anti-carcinogenic compounds. Some authors have already applied extraction techniques in order to identify and quantify biological compounds in açaí, such as ultrasonic assisted extraction. However, no author has made an exhaustive extraction study. For this reason, the aim of this work was the development of extraction techniques for anthocyanins and total phenolic compounds in açaí by using Ultrasonic Assisted Extraction (UAE), Pressurized Liquid Extraction (PLE) and Microwave Assisted Extraction (MAE). Once optimal extraction conditions had been obtained, a study of the optimum extraction time and the repeatability and reproducibility of the method was performed. The three developed extraction methods have presented a high repeatability and reproducibility for both total anthocyanins and total phenolics. Once the three extraction methods were developed, they have been applied for the extraction and analysis of different food made from açaí (freeze-dried, juice, jam, and lozenges).67 página

A Novel Method Based on Headspace-Ion Mobility Spectrometry for the Detection and Discrimination of Different Petroleum Derived Products in Seawater

The objective of the present study is to develop an optimized method where headspace-ion mobility spectrometry is applied for the detection and discrimination between four petroleum-derived products (PDPs) in water. A Box-Behnken design with a response surface methodology was used, and five variables (incubation temperature, incubation time, agitation, sample volume, and injection volume) with influences on the ion mobility spectrometry (IMS) response were optimized. An IMS detector was used as a multiple sensor device, in which, each drift time acts as a specific sensor. In this way, the total intensity at each drift time is equivalent to multiple sensor signals. According to our results, 2.5 mL of sample incubated for 5 min at 31 degrees C, agitated at 750 rpm, and with an injection volume of 0.91 mL were the optimal conditions for successful detection and discrimination of the PDPs. The developed method has exhibited good intermediate precision and repeatability with a coefficient of variation lower than 5%, (RSD (Relative Standard Deviation): 2.35% and 3.09%, respectively). Subsequently, the method was applied in the context of the detection and discrimination of petroleum-derived products added to water samples at low concentration levels (2 mu L center dot L-1). Finally, the new method was applied to determine the presence of petroleum-derived products in seawater samples.This work has been co-financed by the 2014-2020 ERDF Operational Program and by the Department of Economy, Knowledge, Business and University of the Regional Government of Andalusia. Project reference: "FEDER-UCA18-107214"

Characterization of Biodegraded Ignitable Liquids by Headspace-Ion Mobility Spectrometry

The detection of ignitable liquids (ILs) can be crucial when it comes to determining arson cases. Such identification of ILs is a challenging task that may be affected by a number of factors. Microbial degradation is considered one of three major processes that can alter the composition of IL residues. Since biodegradation is a time related phenomenon, it should be studied at different stages of development. This article presents a method based on ion mobility spectroscopy (IMS) which has been used as an electronic nose. In particular, ion mobility sum spectrum (IMSS) in combination with chemometric techniques (hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA)) has been applied for the characterization of different biodegraded ILs. This method intends to use IMSS to identify a range of ILs regardless of their degree of biodegradation. Three ILs (diesel, gasoline and kerosene) from three different commercial brands were evaluated after remaining in a soil substrate for several lengths of time (0, 2, 5, 13 and 38 days). The HCA results showed the samples' trend to fall into categories characterized by ILs type and biodegradation time. The LDAs allowed a 99% successful classification of the samples according to the IL type. This is the first time that an HS-IMS technique has been used to detect ILs that have undergone biodegradation processes. The results show that IMS may be a promising alternative to the current standard method based on gas-chromatography for the analysis of biodegraded ILs. Furthermore, no pretreatment of the samples nor the use of a solvent is required

Machine learning approaches over ion mobility spectra for the discrimination of ignitable liquids residues from interfering substrates

In arson fires, ignitable liquids (ILs) are frequently used to start combustion. For this reason, detecting IL residues (ILRs) at the fire scene is a key factor in fire investigation to determine whether a crime has been committed as well as to establish the modus operandi of the perpetrator. In the present study, the application of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) for the detection of ILRs in fire debris from complex matrices in combination with machine learning (ML) tools is proposed as an alternative to the traditional method, based on gas chromatography–mass spectrometry (GC-MS), described by the ASTM E1618 standard method. For this purpose, petroleum-derived substrates (vinyl, nylon, and polyester) and natural substrates (cotton, cork and linoleum) burned alone and with different ILs (gasoline, diesel, ethanol and charcoal starter with kerosene) were used. In addition, samples were taken at different times (0, 1, 6, 12, 24 and 48 h) after the fire was finished. The ion mobility sum spectrum (IMSS) of each sample was obtained and different ML algorithms were applied. The first derivative was performed at the IMSS, as well as a Savitzky-Golay filter. Hierarchical cluster analysis (HCA) revealed a clustering trend as a function of substrate and ILs used, where the studied sampling times did not affect the resulting clusters. The classification models for the detection of the presence of ILRs have high performance with an accuracy of 100% for support vector machines (SVM) and random forest model (RF), followed by linear discriminant analysis (LDA) with an accuracy of 86.67%. When discriminating the type of ILs used, the RF model obtained an accuracy of 100%, followed by the LDA with 97.22% and finally the SVM model with an accuracy of 93.06%. In addition, a simple web application has been developed where the trained models can be used, so any researcher can apply the method to detect ILRs in fire debris

A comparison study between ultrasound-assisted and enzyme-assisted extraction of anthocyanins from blackcurrant (Ribes nigrum L.)

Blackcurrant (Ribes nigrum L.) is a fruit rich in vitamins, fatty acids, minerals, essential oils and phenolic com-pounds, including anthocyanins. In the present work, two anthocyanin extraction methods from blackcurrant samples based on Ultrasound-Assisted Extraction (UAE) and Enzyme-Assisted Extraction (EAE) have been developed. A Plackett-Burman design with seven variables has been preliminary used for both UAE and EAE in order to determine the most influential variables in each methodology. After that, a Box-Behnken design was employed to optimize the extraction methods. The composition of the extraction solvent (% EtOH in water) has been the most influential variable for both UAE and EAE. The optimal extraction times have been 5 min for UAE and 10 min for EAE. No differences have been observed in anthocyanin extraction with both methodologies. Both methods have been applied to blackcurrant-derived products and proven their suitability for quality control analysis.This work has been supported by the project "EQC2018-005135-P" (Equipment for liquid chromatography by means of mass spectrometry and ion chromatography) of the State Sub-program of Research In-frastructures and Technical Scientific Equipment

Wood Waste from Fruit Trees: Biomolecules and Their Applications in Agri-Food Industry

In the European Union (EU), a total of 11,301,345 hectares are dedicated to the cultivation of fruit trees, mainly olive orchards, grapevines, nut trees (almond, walnut, chestnut, hazelnut, and pistachio), apple and pear trees, stone fruit trees (peach, nectarine, apricot, cherry, and plum), and citrus fruit trees (orange, clementine, satsuma, mandarin, lemon, grapefruit, and pomelo). Pruning these trees, together with plantation removal to a lesser extent, produces a huge amount of wood waste. A theoretical calculation of the wood waste in the European Union estimates approximately 2 and 25 million tons from wood plantation removal and pruning, respectively, per year. This wood waste is usually destroyed by in-field burning or crushing into the soil, which result in no direct economic benefits. However, wood from tree pruning, which is enriched in high added-value mol-ecules, offers a wide spectrum of possibilities for its valorization. This review focuses on the contri-bution of wood waste to both sustainability and the circular economy, considering its use not only as biomass but also as a potential source of bioactive compounds. The main bioactive compounds found in wood are polyphenols, terpenes, polysaccharides, organic compounds, fatty acids, and alkaloids. Polyphenols are the most ubiquitous compounds in wood. Large amounts of hydroxyty-rosol (up to 25 g/kg dw), resveratrol (up to 66 g/kg dw), protocatechuic acid (up to 16.4 g/kg), and proanthocyanins (8.5 g/kg dw) have been found in the wood from olive trees, grapevines, almond trees and plum trees, respectively. The bioactivity of these compounds has been demonstrated at lower concentrations, mainly in vitro studies. Bioactive compounds present antioxidant, antimicro-bial, antifungal, biostimulant, anti-inflammatory, cardioprotective, and anticarcinogenic properties, among others. Therefore, wood extracts might have several applications in agriculture, medicine, and the food, pharmaceutical, nutraceutical, and cosmetics industries. For example, olive tree wood extract reduced thrombin-induced platelet aggregation in vitro; grapevine tree wood extract acts a preservative in wine, replacing SO2; chestnut tree wood extract has antifungal properties on post-harvest pathogens in vitro; and stone tree wood extracts are used for aging both wines and brandies. Moreover, the use of wood waste contributes to the move towards both a more sustainable development and a circular economy.Ministerio de Economía, Industria y Competitividad RTA2015-00005-C02-0

Characterization of Arabica and Robusta Coffees by Ion Mobility Sum Spectrum

Aroma is one of the main characteristics of coffee specimens. Different mixtures of Arabica and Robusta coffees are usually found in the market to offer specific aroma or flavor profiles to consumers. However, the mixed samples or their proportions are not always identified in the product labels. Since the price of Arabica is much higher than that of Robusta, this lack of information is not only an economical issue but a possible fraud to consumers, besides the potential allergic reaction that these mixtures may trigger in some individuals. In this paper, two sample preparation techniques were compared before the analysis of the total volatile organic compounds (VOCs) found in Robusta, Arabica, and in the mixture from both coffee types. The comparison of the signals obtained from the analyses showed that the VOCs concentration levels obtained from the headspace (HS) analyses were clearly higher than those obtained from the pre-concentration step where an adsorbent, an active charcoal strip (ACS + HS), was used. In the second part of this study, the possibility of using the headspace gas-chromatography ion mobility spectrometry (HS-GC-IMS) for the discrimination between Arabica, Robusta, and mixed coffee samples (n = 30) was evaluated. The ion mobility sum spectrum (IMSS) obtained from the analysis of the HS was used in combination with pattern recognition techniques, namely linear discrimination analysis (LDA), as an electronic nose. The identification of individual compounds was not carried out since chromatographic information was not used. This novel approach allowed the correct discrimination (100%) of all of the samples. A characteristic fingerprint for each type of coffee for a fast and easy identification was also developed. In addition, the developed method is ecofriendly, so it is a good alternative to traditional approaches

Total ion chromatogram and total ion mass spectrum as alternative tools for detection and discrimination (A review)

Gas chromatography (GC) and mass spectrometry (MS) are widely used techniques in the analysis of complex mixtures due to their various advantages, such as high selectivity, reproducibility, precision, and sensitivity. However, the data processing is often complex and time-consuming and requires a great deal of experience, which might be a serious drawback in certain areas, such as quality control, or regarding research in the field of medicine or forensic sciences, where time plays a crucial role. For these reasons, some authors have proposed the use of alternative data processing approaches, such as the total ion chromatogram or total mass spectrum, allowing these techniques to be treated as sensors where each retention time or ratio m/z acts as a sensor collecting total intensities. In this way, the main advantages associated with both techniques are maintained, but the outcomes from the analysis can be reached in a faster, simpler, and an almost automated way. In this review, the main features of the GC- and MS-based analysis methodologies and the ways in which to apply them are highlighted. Moreover, their implementation in different fields, such as agri-food, forensics, environmental sciences, or medicine is discussed, highlighting important advantages as well as limitations.info:eu-repo/semantics/publishedVersio

Novel method based on ion mobility spectroscopy for the quantification of adulterants in honeys

According to European Union Regulation, honey is a food product whose composition cannot be modified. However, high-quality honey is often adulterated by adding sweeteners of other sugar compounds. This paper studies the suitability of Ion Mobility Spectra from generated headspace as a method for the detection and discrimination of honey adulterated by different substances. A Box-Behnken design in conjunction with a re sponse surface methodology was employed to optimize five different variables related to headspace generation (incubation temperature, incubation time, injection volume, weight of the samples and pre-heating time). The resulting model showed a regression coefficient of R2 = 88.07%, it is therefore suitable for a reliable selection of the experimental variables. Repeatability and intermediate precision were also evaluated, and coefficients of variation below 5% were obtained (CV of 4.6% and 4.2% respectively). The developed method has been applied to different samples resulting for the mixture of honey and other sweeteners at different percentages (5%–50%) in an attempt to mimic the adulterated products that are more commonly found in the market. A thorough and exact classification (100%) with regards to the presence/absence of adulterant as well as the type of adulterant used has been achieved. A Partial Least Squares regression model was completed in order to determine the percentage of the different adulterants. The prediction error was below 4% in all the cases. These results de monstrate the applicability of the developed method for the detection and quantification of adulterated honey with different adulterant contents

Extraction of Anthocyanins and Total Phenolic Compounds from Açai (Euterpe oleracea Mart.) Using an Experimental Design Methodology. Part 3: Microwave-Assisted Extraction

In this work, two methods based on microwave-assisted extraction techniques for the extraction of both anthocyanins and total phenolic compounds from acai have been developed. For that, a full factorial design (Box-Behnken design) has been used to optimize the following four variables: solvent composition (25-75% methanol in water), temperature (50-100 degrees C), pH (2-7), and sample/solvent ratio (0.5 g: 10 mL-0.5 g: 20 mL). The anthocyanins and total phenolic compounds content have been determined by ultra high-pressure liquid chromatography and Folin-Ciocalteu method, respectively. The optimum conditions for the extraction of anthocyanins were 38% MeOH in water, 99.63 degrees C, pH 3.00, at 0.5 g: 10 mL of ratio, while for the extraction of total phenolic compounds they were 74.16% MeOH in water, 99.14 degrees C, pH 5.46, at 0.5 g: 20 mL of ratio. Both methods have shown a high repeatability and intermediate precision with a relative standard deviation lower than 5%. Furthermore, an extraction kinetics study was carried out using extraction periods ranging from 2 min until 25 min. The optimized methods have been applied to acai-containing real samples. The results with such real samples have confirmed that both methods are suitable for a rapid and reliable extraction of anthocyanins and total phenolic compounds