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This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO), were applied to predict adulteration in orange blossom (OB) and sunflower (SF) honeys. The SVR model achieved R2 values above 0.90 for combined honey types. Treating OB and SF honeys separately resulted in a significant accuracy improvement, with R2 values exceeding 0.99. LASSO proved especially effective when honey types were treated individually. The integration of UF-GC with machine learning not only provides a reliable method for detecting honey adulteration, but also sets a precedent for future research in the application of this technique to other food products, potentially enhancing food authenticity across the industry.

The intensity of the odor in food-grade paraffin waxes is a pivotal quality characteristic, with odor panel ratings currently serving as the primary criterion for its assessment. This study presents an innovative method for assessing odor intensity in food-grade paraffin waxes, employing headspace gas chromatography with mass spectrometry (HS/GC-MS) and integrating total ion spectra with advanced machine learning (ML) algorithms for enhanced detection and quantification. Optimization was conducted using Box–Behnken design and response surface methodology, ensuring precision with coefficients of variance below 9%. Analytical techniques, including hierarchical cluster analysis (HCA) and principal component analysis (PCA), efficiently categorized samples by odor intensity. The Gaussian support vector machine (SVM), random forest, partial least squares regression, and support vector regression (SVR) algorithms were evaluated for their efficacy in odor grade classification and quantification. Gaussian SVM emerged as superior in classification tasks, achieving 100% accuracy, while Gaussian SVR excelled in quantifying odor levels, with a coefficient of determination (R2) of 0.9667 and a root mean square error (RMSE) of 6.789. This approach offers a fast, reliable, robust, objective, and reproducible alternative to the current ASTM sensory panel assessments, leveraging the analytical capabilities of HS-GC/MS and the predictive power of ML for quality control in the petrochemical sector’s food-grade paraffin waxes.

Curricula related to instrumental analysis aim for competency-based education to promote the development of teaching strategies that encourage students to successfully tackle the problem-solving and tasks inherent to their profession. However, this endeavor is constrained by the complexity of equipment and the lack of time in laboratory classes. The objective of this work is to present an alternative high-performance liquid chromatography (HPLC) practice that adapts the desired competencies to a shorter and more effective timeframe. It seeks more active participation from the student and contextualizes chromatographic analysis within a real-world problem that encompasses the entire analytical process, from sample to final result. In this scenario, the student receives a solid sample of spicy paprika from a supermarket and must report the level of spiciness in terms of the total amount of capsaicinoids. To achieve this, they must first apply different experimental conditions for extracting the analytes of interest (varying temperatures and solvents) and selecting the most optimal condition. This practice is designed for short sessions, specifically conducted in two 2.5 h laboratory sessions, and has been implemented in the subject “Advanced Techniques in Instrumental Analysis (ATIA)” in the fourth year of the Degree in Biotechnology at the University of Cadiz. The results obtained demonstrated a significant increase in student motivation and an improvement in the acquisition of skills; 100% of the students achieved a grade higher than seven in the final evaluation of their learning process.

Naringenin is one of the main phenolic compounds found in grapefruit (Citrus × paradisi Macfad.). This compound is known for its therapeutical properties as an antioxidant, antidiabetic, antihyperlipidemic and antineoplastic agent. In order to enable the development of drugs based on this compound, an appropriate extraction method needs to be developed. For this study, enzymatic extraction was chosen, as it is a cheap and green extraction method. Optimal extraction conditions (pH, temperature, agitation, solvent composition, sample-to-solvent ratio and enzyme-to-sample ratio) were determined through a Plackett–Burman and a Box–Behnken design, resulting in pH 6.0, 40 °C, 50 rpm, 20% EtOH, 0.2 g sample per 15 mL solvent and 1000 U/g. Once extraction conditions were determined, a single-factor experiment was performed under optimal conditions to determine extraction time, which resulted in 10 min per extraction. Finally, repeatability and intermediate precision were evaluated through naringenin quantification. Good values were obtained for both parameters (1.80% and 2.05%, respectively). Furthermore, extracts presented significant amount of naringenin (0.18 ± 0.02 mg/g).

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.

Petroleum-derived waxes are used in the food industry as additives to provide texture and as coatings for foodstuffs such as fruits and cheeses. Therefore, food waxes are subject to strict quality controls to comply with regulations. In this research, a combination of visible and near-infrared (Vis-NIR) spectroscopy with machine learning was employed to effectively characterize two commonly marketed petroleum waxes of food interest: macrocrystalline and microcrystalline. The present study employed unsupervised machine learning algorithms like hierarchical cluster analysis (HCA) and principal component analysis (PCA) to differentiate the wax samples based on their chemical composition. Furthermore, nonparametric supervised machine learning algorithms, such as support vector machines (SVMs) and random forest (RF), were applied to the spectroscopic data for precise classification. Results from the HCA and PCA demonstrated a clear trend of grouping the wax samples according to their chemical composition. In combination with five-fold cross-validation (CV), the SVM models accurately classified all samples as either macrocrystalline or microcrystalline wax during the test phase. Similar high-performance outcomes were observed with RF models along with five-fold CV, enabling the identification of specific wavelengths that facilitate discrimination between the wax types, which also made it possible to select the wavelengths that allow discrimination of the samples to build the characteristic spectralprint of each type of petroleum wax. This research underscores the effectiveness of the proposed analytical method in providing fast, environmentally friendly, and cost-effective quality control for waxes. The approach offers a promising alternative to existing techniques, making it a viable option for automated quality assessment of waxes in food industrial applications.

(1) Background: A set of relevant police ergonomic parameters (PEP) have been used for the assessment and prevention of occupational risks involved in police physical interventions (PITs). Through this set of PEPs, a comparison has been made between two traditional PITs against two novel OTPs (operational tactical procedures). (2) Method: The data have been collected by means of a motion capture suit fitted with 19 inertial measurement units (IMUs). A specific and powerful software package specific for ergonomic analysis has been used to manage the large amount of data registered and to generate a series of three-dimensional plots. Traditional PITs and newer OTPs have been performed for their analysis. Specifically, the PEPs corresponding to the implementation of four PITs have been analyzed by collecting the measurements provided by a set of IMUs installed on a motion capture suit, and their occupational risk assessments have been compared against those corresponding to newer OTPs. (3) Results: For the four PITs, the PEPs have been analyzed with the measured values from the IMUs, throughout the duration of each technique. The two traditional intervention techniques have scored higher than the new OTPs in the REBA system, the asymmetry angle, L5-pelvis shear and joint contact forces, and the total muscle power measurements. (4) Conclusions: It has been confirmed that the new OTPs minimize the risk of injuries, since their PEPs reached lower values than those corresponding to the traditional PITs, which are still being taught at police academies.

Honey is one of the most adulterated foods, usually through the addition of sweeteners or low-cost honeys. This study presents a method based on visible near infrared spectroscopy (Vis-NIRs), in combination with machine learning (ML) algorithms, for the correct identification and quantification of adulterants in honey. Honey samples from two botanical origins (orange blossom and sunflower) were evaluated and adulterated with low-cost honey in different percentages (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%). The results of the exploratory analysis showed a tendency to group the samples according to botanical origin, as well as the presence of adulteration. A supervised analysis was performed to detect the presence of adulterations. The best performance with 100% accuracy was achieved by support vector machines (SVM) and random forests (RF). A regression study was also carried out to quantify the percentage of adulteration. The best result was obtained by support vector regression (SVR) with a coefficient of determination (R2) of 0.991 and a root mean squared error (RMSE) of 1.894. These results demonstrate the potential of combining ML with spectroscopic data as a method for the automated quality control of honey.

Fruit juices are one of the most widely consumed beverages worldwide, and their production is subject to strict regulations. Therefore, this study presents a methodology based on the use of headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) in combination with machine-learning algorithms for the characterization juices of different raw material (orange, pineapple, or apple and grape). For this purpose, the ion mobility sum spectrum (IMSS) was used. First, an optimization of the most important conditions in generating the HS was carried out using a Box–Behnken design coupled with a response surface methodology. The following factors were studied: temperature, time, and sample volume. The optimum values were 46.3 °C, 5 min, and 750 µL, respectively. Once the conditions were optimized, 76 samples of the different types of juices were analyzed and the IMSS was combined with different machine-learning algorithms for its characterization. The exploratory analysis by hierarchical cluster analysis (HCA) and principal component analysis (PCA) revealed a clear tendency to group the samples according to the type of fruit juice and, to a lesser extent, the commercial brand. The combination of IMSS with supervised classification techniques reported an excellent result with 100% accuracy on the test set for support vector machines (SVM) and random forest (RF) models regarding the specific fruit used. Nevertheless, all the models have proven to be an effective alternative for characterizing and classifying the different types of juices.

The population is now more aware of their diets due to the connection between food and general health. Onions (Allium cepa L.), common vegetables that are minimally processed and grown locally, are known for their health-promoting properties. The organosulfur compounds present in onions have powerful antioxidant properties and may decrease the likelihood of developing certain disorders. It is vital to employ an optimum approach with the best qualities for studying the target compounds to undertake a thorough analysis of these compounds. In this study, the use of a direct thermal desorption–gas chromatography–mass spectrometry method with a Box–Behnken design and multi-response optimization is proposed. Direct thermal desorption is an environmentally friendly technique that eliminates the use of solvents and requires no prior preparation of the sample. To the author’s knowledge, this methodology has not been previously used to study the organosulfur compounds in onions. Likewise, the optimal conditions for pre-extraction and post-analysis of organosulfur compounds were as follows: 46 mg of onion in the tube, a desorption heat of 205 °C for 960 s, and a trap heat of 267 °C for 180 s. The repeatability and intermediate precision of the method were evaluated by conducting 27 tests over three consecutive days. The results obtained for all compounds studied revealed CV values ranging from 1.8% to 9.9%. The major compound reported in onions was 2,4-dimethyl-thiophene, representing 19.4% of the total area of sulfur compounds. The propanethial S-oxide, the principal compound responsible for the tear factor, accounted for 4.5% of the total area.

Moringa oleifera Lam. is known to have significant antioxidant properties. Because of this, the development of an optimal extraction method is crucial to obtain pharmacological products based on the bioactive compounds produced by this tree. Through a Plackett–Burman and a Box–Behnken design, enzymatic extraction conditions (temperature, agitation, solvent pH and composition, sample-to-solvent ratio, enzyme-to-sample ratio and extraction time) have been optimized using normalized areas (UA/g) as response variable and relative mass (mg/g) as quantification variable. Extractions were performed in an incubator, where all the extraction conditions could be digitally controlled. Thus, 58.9 °C, 50 rpm, 4.0 pH, 32.5% EtOH, 0.2 g sample in 15 mL solvent and 106 U/g were established as the optimal extraction conditions for the extraction with a mix of pectinases coming from Aspergillus niger. Under these optimal conditions, two-minute extractions were performed and evaluated through a single factor design. The enzymatic extraction method demonstrated its suitability to produce extracts with good antioxidant power (antioxidant activity 4.664 ± 0.059 mg trolox equivalent/g sample and total phenolic compounds 6.245 ± 0.101 mg gallic acid equivalent/g sample). The method was also confirmed to have good repeatability (1.39%) and intermediate precision (2.37%) levels.

Peppers are a very popular ingredient in many dishes, either as the fruit itself or as derived products. They are not only consumed because of their organoleptic characteristics, but also because of their high content of bioactive compounds. The aim of this work was to determine the optimal time to harvest the Bolilla pepper, based on the capsaicinoid concentration in the pericarp and placenta at two different plant maturities (young and adult), in order to maximize their potential biological properties. In the case of the pericarp, the maximum capsaicinoid concentration was reached at 30 days post-anthesis (dpa) (with values of 1565.98 and 2158.82 µmol/kg, for the young and adult plant, respectively), while in the placenta it was registered at 41 dpa with greater values (5898.12 and 2349.67 µmol/kg). In either case, from that moment on, there was a drastic reduction in the capsaicinoid content. With regard to the capsaicinoid content levels in the pericarp, this was greater in older plants, while the placenta presented greater content in younger plants, which is of considerable importance from an economic point of view. This work allows a better selection of the final product, taking full advantage of its beneficial effects on health and taste, which would be very interesting for analytical laboratories or industries.

Nowadays, consumers demand bioactive foods that have the potential to limit the risk of suffering from several medical conditions. Onions present these desirable capabilities owing to its high content in antioxidant bioactive compounds. This work has used a Box–Behnken design with a response surface methodology to determine the best conditions in which to extract the polyphenols that are found in onions. Two extraction methods—one for the extraction of total flavonols and another one intended to obtain extracts with the highest possible antioxidant activity—have been developed and optimized. The following factors have been studied: temperature, %methanol in water, solvent pH, and sample–solvent volumetric ratio. The optimal conditions for the extraction of flavonols were 93.8% methanol in water, pH 2, 50 °C extraction temperature and 0.2:17.9 g:mL sample–solvent ratio. The best antioxidant activity levels were registered when using 74.2% methanol in water, pH 2, 99.9 °C extraction temperature and 0.2:18.2 g:mL sample–solvent ratio. Both optimized methods used short extraction times, and presented good precision levels and successful results when used with an assortment of onion varieties. According to total flavonols and antioxidant activity data, with 7.557 ± 0.3261 and 12.08 ± 0.0379 mg g−1, respectively, the developed methods achieved comparable or even superior results to those obtained by other authors.

Mushroom consumption has increased in recent years due to their beneficial properties to the proper functioning of the body. Within this framework, the high potential of mushrooms as a source of essential elements has been reported. Therefore, the present study aims to determine the mineral content of seven essential metals, Fe, Mg, Mn, P, K, Ca, and Na, in twenty samples of mushrooms of the genus Lactarius collected from various locations in southern Spain and northern Morocco, by FAAS, UV-Vis spectroscopy, and ICP-OES after acid digestion. Statistics showed that K was the macronutrient found at the highest levels in all mushrooms studied. ANOVA showed that there were statistically significant differences among the species for K, P, and Na. The multivariate study suggested that there were differences between the accumulation of the elements according to the geographic location and species. Furthermore, the intake of 300 g of fresh mushrooms of each sample covers a high percentage of the RDI, but does not meet the recommended daily intake (RDI) for any of the metals studied, except for Fe. Even considering these benefits, the consumption of mushrooms should be moderated due to the presence of toxic metals, which may pose health risks.

Abstract Background The rapid progress in information and communication technologies has promoted the development of smartphone applications (apps) for a wide variety of purposes including workplace safety. However, no specific apps for occupational risk prevention in the police domain seemingly exist as yet. In this work, an app running under the iOS and Android operating systems was developed to help police officers become acquainted with policing-related occupational risks and to prevent their damaging consequences. Results The proposed app, which uses an iterative user-centered design to avoid occupational risks in policing activities, was assessed for performance by a group of users and experts through a System Usability Scale (SUS) questionnaire. The mean overall score for the questionnaire was 82.3. The app has backend support to facilitate continual improvement through contributions from users and administrators. A field test revealed increased awareness of policing occupational risks after using the app in many users. Conclusions A novel product that covers the needs of ORP requirements of police officers has been developed.

The consumption of mushrooms has considerably increased in recent years because of their beneficial nutritional properties due to their essential amino acids, proteins, and dietary fiber content. Recent research has shown that they are also rich in polysaccharides and phenolic compounds. These compounds exhibit decisive free radical and ROS scavenging power with potential application to the treatment of neurodegenerative disorders. In addition, they present important properties like antioxidant, antiaging, and immune modulation. In the present research, the optimization for the extraction of total phenolic compounds and the antioxidant activity (DPPH and ABTS), based on ultrasound–assisted techniques has been carried out. Five variables (% MeOH in solvent, extraction temperature, amplitude, cycle, and sample:solvent ratio have been selected; both the total phenolic compounds content as well as the antioxidant activity (DPPH and ABTS)) have been considered as the response variables. The optimal conditions, determined by means of a multiresponse optimization method, were established at 0.2 g of sample extracted with 15.3 mL of solvent (93.6% MeOH) at 60 °C for 5 min and using 16.86% amplitude and 0.71 s−1 cycles. A precision study of the optimized method has been performed with deviations lower than 5%, which proves the repeatability and precision of the extraction method. Finally, the extraction method has been applied to wild and commercial mushrooms from Andalusia and Northern Morocco, which has confirmed its suitability for the extraction of the phenolic compounds from mushroom samples, while ensuring maximum antioxidant activity.

Hallucinogenic fungi, mainly those from the Psilocybe genus, are being increasingly consumed even though there is no control on their culture conditions. Due to the therapeutic potential as antidepressants and anxiolytics of the alkaloids that they produce (psilocin and psilocybin), some form of control on their production would be highly recommended. Prior to identifying their optimal culture condition, a methodology that allows their study is required. Microwave-assisted extraction method (MAE) is a technique that has proven its efficiency to extract different compounds from solid matrices. For this reason, this study intends to optimize a MAE method to extract the alkaloids found in Psylocibe cubensis. A surface-response Box–Behnken design has been employed to optimize such extraction method and significantly reduce time and other resources in the extraction process. Based on the Box–Behnken design, 50 °C temperature, 60% methanol as extraction solvent, 0.6 g:10 mL sample mass:solvent ratio and 5 min extraction time, were established as optimal conditions. These mild conditions, combined with a rapid and efficient UHPLC analysis result in a practical and economical methodology for the extraction of psilocin and psilocybin from Psylocibe cubensis.

The demand and interest in mushrooms, both cultivated and wild, has increased among consumers in recent years due to a better understanding of the benefits of this food. However, the ability of wild edible mushrooms to accumulate essential and toxic elements is well documented. In this study, a total of eight metallic elements and metalloids (chromium (Cr), arsenic (As), cadmium (Cd), mercury (Hg), lead (Pb), copper (Cu), zinc (Zn), and selenium (Se)) were determined by ICP-MS in five wild edible mushroom species (Agaricus silvicola, Amanita caesarea, Boletus aereus, Boletus edulis, and Russula cyanoxantha) collected in southern Spain and northern Morocco. Overall, Zn was found to be the predominant element among the studied species, followed by Cu and Se. The multivariate analysis suggested that considerable differences exist in the uptake of the essential and toxic elements determined, linked to species-intrinsic factors. Furthermore, the highest Estimated Daily Intake of Metals (EDIM) values obtained were observed for Zn. The Health Risk Index (HRI) assessment for all the mushroom species studied showed a Hg-related cause of concern due to the frequent consumption of around 300 g of fresh mushrooms per day during the mushrooming season.

Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices.

Despite the excellent beneficial properties that anthocyanins and total phenolic compounds give to the red onion bulbs, few articles have investigated modern extraction techniques or experimental designs in this field. For this reason, the present study proposes the development and optimization of alternative methods for the extraction of these compounds based on microwave-assisted extraction and the Box-Behnken experiment design. The optimal values for the extraction of total anthocyanins have been established at 62% methanol composition as a solvent, pH 2, 56 °C temperature, and 0.2:13 g:mL sample-solvent ratio. Regarding the extraction of total phenolic compounds, the optimal conditions have been established at 100% pure methanol as a solvent with pH 2, 57 °C temperature, and 0.2:8.8 g:mL sample-solvent ratio. Short extraction times (min), good recoveries (mg of bioactive compound g−1 of dry onion), and high repeatability and intermediate precision (coefficient of variation (%)) have been confirmed for both methods. Regarding total anthocyanins, the following results have been obtained: 2 min, 2.64 ± 0.093 mg of total anthocyanins g−1 of dry onion, and 2.51% and 3.12% for precision. Regarding phenolic compounds, the following results have been obtained: 15 min, 7.95 ± 0.084 mg of total phenolic compound g−1 of dry onion, and 3.62% and 4.56% for precision. Comparing these results with those of other authors and with those obtained in a previous study of ultrasound-assisted extraction, it can be confirmed that microwave-assisted extraction is a quantitative, repeatable, and very promising method for the extraction of phenolic compounds and anthocyanins, which offers similar and even superior results with little solvent expense, time, and costs.

Food chemistry is a branch of chemistry that aims to characterize the chemical composition of food products, both qualitatively and quantitatively [...]

Fruit juices are one of the most adulterated beverages, usually because of the addition of water, sugars, or less expensive fruit juices. This study presents a method based on Fourier transform infrared spectroscopy (FT-IR), in combination with machine learning methods, for the correct identification and quantification of adulterants in juices. Thus, three types of 100% squeezed juices (pineapple, orange, and apple) were evaluated and adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The results of the exploratory data analysis revealed a clear clustering trend of the samples according to the type of juice analyzed. The supervised learning analysis, based on the development of models for the detection of adulteration, obtained significant results for all tested methods (i.e., support-vector machines or SVM), random forest or RF, and linear discriminant analysis or LDA) with an accuracy above 97% on the test set. Regarding quantification, the best results are obtained with the support vector regression and with partial least square regression showing an R2 greater than 0.99 and a root mean square error (RMSE) less than 1.4 for the test set.

Allium cepa L. is one of the most abundant vegetable crops worldwide. In addition to its versatile culinary uses, onion also exhibits quite interesting medicinal uses. Bulbs have a high content of bioactive compounds that are beneficial for human health. This study intends to develop and optimize two appropriate ultrasound-assisted methods for the extraction of the phenolic compounds and anthocyanins present in red onion. A response surface methodology was employed and, specifically, a Box–Behnken design, for the optimization of the methods. The optimal conditions for the extraction of the phenolic compounds were the follows: 53% MeOH as solvent, pH 2.6, 60 °C temperature, 30.1% amplitude, 0.43 s cycle, and 0.2:11 g sample/mL solvent ratio. On the other hand, the optimal conditions for the anthocyanins were as follows: 57% MeOH as solvent, pH 2, 60 °C temperature, 90% amplitude, 0.64 s cycle, and 0.2:15 g sample/mL solvent ratio. Both methods presented high repeatability and intermediate precision, as well as short extraction times with good recovery yields. These results illustrate that the use of ultrasound-assisted extraction, when properly optimized, is suitable for the extraction and quantification of the compounds of interest to determine and improve the quality of the raw material and its subproducts for consumers.

In recent years, there has been increasing interest in the functional components of sweet potato because of its nutritional and medicinal value. The aim of this study is to analyse how much sweet potato phenolic compounds composition (derived from caffeoylquinic acids) varies as a result of cooking. Traditional techniques such as: boiling, oven roasting and more recent processing techniques such as microwave cooking were tested. Three sweet potato varieties were cooked for different periods of time and under different conditions. Ultrasound-assisted extraction (UAE) was used to extract the compounds of interest and then, a chemometric tool such as Box-Behnken design (BBD) was successfully used to evaluate and optimise the most influential factors in the extraction, i.e., temperature, solvent composition and sample-to-solvent ratio. The optimal settings for UAE were: solvent 100% methanol, a temperature of 39.4 °C and a mass/volume ratio of 0.5 g per 10 mL solvent. Oven roasting of sweet potatoes resulted in increased levels of caffeoylquinic acids, whereas prolonged cooking times in water resulted in decreasing levels of the same.

Purple potato is one of the least known and consumed potato varieties. It is as rich in nutrients, amino acids and starches as the rest of the potato varieties, but it also exhibits a high content of anthocyanins, which confer it with some attractive health-related properties, such as antioxidant, pain-relieving, anti-inflammatory and other promising properties regarding the treatment of certain diseases. A novel methodology based on ultrasound-assisted extraction has been optimized to achieve greater yields of anthocyanins. Optimal extraction values have been established at 70 °C using 20 mL of a 60% MeOH:H2O solution, with a pH of 2.90 and a 0.5 s−1 cycle length at 70% of the maximum amplitude for 15 min. The repeatability and intermediate precision of the extraction method have been proven by its relative standard deviation (RSD) below 5%. The method has been tested on Vitelotte, Double Fun, Highland and Violet Queen potatoes and has demonstrated its suitability for the extraction and quantification of the anthocyanins found in these potato varieties, which exhibit notable content differences. Finally, the antioxidant capacity of these potato varieties has been determined by means of 2,2-diphenyl-1-picrylhydrazyl (DDPH) radical scavenging and the values obtained were similar to those previously reported in the literature.

Sherry wine vinegar is a Spanish gourmet product under Protected Designation of Origin (PDO). Before a vinegar can be labeled as Sherry vinegar, the product must meet certain requirements as established by its PDO, which, in this case, means that it has been produced following the traditional solera and criadera ageing system. The quality of the vinegar is determined by many factors such as the raw material, the acetification process or the aging system. For this reason, mainly producers, but also consumers, would benefit from the employment of effective analytical tools that allow precisely determining the origin and quality of vinegar. In the present study, a total of 48 Sherry vinegar samples manufactured from three different starting wines (Palomino Fino, Moscatel, and Pedro Ximénez wine) were analyzed by Fourier-transform infrared (FT-IR) spectroscopy. The spectroscopic data were combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as other nonparametric supervised techniques, namely, support vector machine (SVM) and random forest (RF), for the characterization of the samples. The HCA and PCA results present a clear grouping trend of the vinegar samples according to their raw materials. SVM in combination with leave-one-out cross-validation (LOOCV) successfully classified 100% of the samples, according to the type of wine used for their production. The RF method allowed selecting the most important variables to develop the characteristic fingerprint (“spectralprint”) of the vinegar samples according to their starting wine. Furthermore, the RF model reached 100% accuracy for both LOOCV and out-of-bag (OOB) sets.

Erica australis plants have been used in infusions and folk medicine for years for its diuretic and antiseptic properties and even for the treatment of infections. In addition, a recently published thorough study on this species has demonstrated its antioxidant, antibiotic, anti-inflammatory, anticarcinogenic and even antitumoral activities. These properties have been associated with the high content of anthocyanins in E. australis leaves and flowers. The aim of the present research is to optimize an ultrasound-assisted extraction methodology for the recovery of the anthocyanins present in E. australis flowers. For that purpose, a Box Behnken design with response surface methodology was employed, and the influence of four variables at different values was determined: namely, the composition of the extraction solvents (0–50% MeOH in water), the pH level of those solvents (3–7), the extraction temperature (10–70 °C), and the sample:solvent ratio (0.5 g:10 mL–0.5 g:20 mL). UHPLC-UV-vis has been employed to quantify the two major anthocyanins detected in the samples. The extraction optimum conditions for 0.5 g samples were: 20 mL of solvent (50% MeOH:H2O) at 5 pH, with a 15 min extraction time at 70 °C. A precision study was performed and the intra-day and inter-day relative standard deviations (RSDs) obtained were 3.31% and 3.52%, respectively. The developed methodology has been successfully applied to other Erica species to validate the suitability of the method for anthocyanin extraction.

Betacyanins have been reported to provide prominent health-promoting effects, in addition to contributing to the exotic color and pleasant palate of red dragon fruit that has been attracting universal interest for over a decade. An accurate determination of betacyanins in dragon fruit is important if we are to benefit from the nutraceutical features of these compounds. Seven betacyanin pigments have been identified and extracted by means of ultrasound-assisted techniques from red dragon fruit. Chemometric tools such as Box–Behnken Design (BBD) in conjunction with response surface methodology (RSM) have been successfully used to evaluate and optimize the relevant extraction variables i.e., temperature (A: 10, 35, 60 °C), solvent composition (B: 20, 50, 80% methanol in water), sample to solvent ratio (C: 0.1:10, 0.2:10, 0.3:10 g/mL), power (D: 20, 45, 70%), and cycle (E: 0.3, 0.5, 0.7 s−1). The results obtained from the analysis of variance (ANOVA) suggest that the solvent composition (p 0.0063), sample to solvent ratio (p 0.0126), and cycle (p 0.0302) are the most influential variables in betacyanin extraction. The optimal variable settings for ultrasound-assisted extraction (UAE) were a 54.6 °C extraction temperature, 0.3 s−1 cycle, 20% ultrasound power, 21.4% methanol in water solvent composition, 0.2:10 sample to solvent ratio, and a 5 min extraction time. The validation of the method for repeatability and intermediate precision provided excellent results at 1.56% and 2.94% respectively. Furthermore, the novel UAE method was successfully used for the determination of betacyanins content in red dragon fruit from different geographic origins.

Onion, one of the most consumed vegetables in the world, is also known to contain high levels of antioxidant compounds, with protective effects against different degenerative pathologies. Specifically, onion is rich in flavonols, mainly quercetin derivatives, which are compounds with high antioxidant and free radical scavenging power. For this reason, it is of the utmost importance to count on optimal analytical methods that allow for the determination and quantification of these compounds of interest. A rapid ultra-high performance liquid chromatography (UHPLC)-photo-diode array (PDA) method for the separation of the major flavonols in onions was developed using a Box–Behnken design in conjunction with multiresponse optimization on the basis of the desirability function. The conditions that provided a successful separation were 9.9% and 53.2% of phase B at the beginning and at the end of the gradient, respectively; 55 °C column working temperature; and 0.6 mL min−1 flow rate. The complete separation was achieved in less than 2.7 min with excellent chromatographic characteristics. The method was validated, and its high precision, low detection and quantification limits, good linearity, and robustness were confirmed. The correct applicability of the method improves the analysis of the raw material, increasing the quality of onions and its subproducts in terms of bioactive compounds and functional characteristics for consumers.

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 °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 µL·L−1). Finally, the new method was applied to determine the presence of petroleum-derived products in seawater samples.

The onion is one of the most cultivated and consumed vegetables, and is a very valuable source of antioxidant substances. Every onion variety is rich in flavonols—mainly quercetin derivatives which makes onions the main dietary source of these compounds. This study intends to develop an ultrasound-assisted extraction method (UAE), an economical, green, and efficient technique, that allows us to determine the quality of onion extracts in terms of flavonol composition and antioxidant activity. For this purpose, an ultrasound-assisted extraction method has been optimized to obtain extracts with a high amount of flavonols, as well as with a high antioxidant activity, not only separately using a Box–Behnken design, but also simultaneously, based on multi-response optimization. Multiple response optimization has not only demonstrated an efficacy level similar to that of the individual ones, but it also represents a considerable reduction in terms of cost, time and work. The optimal conditions for simultaneous extractions were determined as follows: 76.8% methanol as an extraction solvent at pH 2 and 58.5 °C temperature, 85% amplitude, 0.9 s cycle, and 0.2:13 g:mL sample mass/solvent volume ratio. Furthermore, the developed method exhibited a high precision level and great recoveries in a rather short extraction time (2 min). These results, together with the development of a fast and simple UHPLC analysis method, lead us to consider the developed UAE method as a suitable technique for the extraction of bioactive compounds from onion matrices.

Petroleum remains the principal raw material for the manufacture of a wide variety of products with multiple uses. Within this framework, it is possible to find petroleum-derived products (PDPs), such as waxes, with applications in the agri-food industry. Due to the above-mentioned use, the adaptation of the quality control of these products to the requirements of this industry is a factor to be considered by its producers to comply with food safety standards. The present study is focused on establishing the starting conditions for the development and optimization of an analytical methodology based on TD-GC-MS (Thermal Desorption-Gas Chromatography-Mass Spectrometry) that allows for the determination and identification of volatile organic compounds (VOCs) that are undesirable not only because of their smell but also because they may pose a health risk due to prolonged exposure. The desorption temperature was studied, and 80 °C was selected as the initial sample heating temperature to guarantee the integrity of VOCs. A total of 10 compounds, relevant from a food safety point of view, were identified. The n-alkanes were the most represented chemical family in the studied samples.

Interpretation of data from fire debris is considered as one of the most challenging steps in fire investigation. Forensic analysts are tasked to identify the presence or absence of ignitable liquid residues (ILRs) which may indicate whether a fire was started deliberately. So far, data analysis is subjected to human interpretation following the American Society for Testing and Materials’ guidelines (ASTM E1618) based on gas chromatography–mass spectrometry data. However, different factors such as interfering pyrolysis compounds may hinder the interpretation of data. Some substrates release compounds that are in the range of common ignitable liquids, which interferes with accurate determination of ILRs. The aim of the current research is to investigate whether headspace–mass spectroscopy electronic nose (HS-MS eNose) combined with pattern recognition can be used to classify different ILRs from fire debris samples that contain a complex matrix (petroleum-based substrates or synthetic fibers carpet) that can strongly interfere with their identification. Six different substrates—four petroleum-derived substrates (vinyl, linoleum, polyester, and polyamide carpet), as well as two different materials for comparison purposes (cotton and cork) were used to investigate background interferences. Gasoline, diesel, ethanol, and charcoal starter with kerosene were used as ignitable liquids. In addition, fire debris samples were taken after different elapsed times. A total of 360 fire debris samples were analyzed. The obtained total ion mass spectrum was combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) as well as supervised linear discriminant analysis (LDA). The results from HCA show a strong tendency to group the samples according to the ILs and substrate used, and LDA allowed for a full identification and discrimination of every ILR regardless of the substrate.

In recent years, consumers’ preference for fruits such as blueberry has increased noticeably. This fact is probably related to their bioactive components such as anthocyanins, phenolic compounds, vitamins, minerals, and tannins that have been found in blueberries by the latest research studies. Both total anthocyanins (TA) and total phenolic compounds (TPC) are known for their multiple beneficial effects on our health, due to their anti-inflammatory, anti-oxidant, and anti-cancer properties. This is the reason why the development of new methodologies for the quality control analysis of raw materials or derived products from blueberry has a great relevance. Two ultrasound-assisted extraction methods (UAE) have been optimized for the quantification of TA and TPC in blueberry samples. The six variables to be optimized were: solvent composition, temperature, amplitude, cycle, extraction solvent pH, and sample/solvent ratio using response surface methodology. The optimized methods have proven to be suitable for the extraction of the TPC and TA with good precision (repeatability and intermediate precision) (coefficient of variation (CV) < 5%) and potentially for application in commercial samples. This fact, together with the multiple advantages of UAE, makes these methods a good alternative to be used in quality control analysis by both industries and laboratories.

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.

Interest in the consumption of the fruits of pepper (Capsicum spp.) is not only due to its organoleptic characteristics, but also due to its bioactive compounds content, which are reported to provide essential benefits to human health. However, the amount and diversity of these compounds in each fruit specimen depend on its genotype and on a number of environmental factors. This work describes the quantitative ultra-high-performance liquid chromatography coupled to photodiode-array (UHPLC-PDA) analysis of the capsinoids content in four varieties of pepper (‘Habanero’, ‘Habanero Roxo’, ‘Bode’, and ‘Malagueta’) grown until different development stages in a greenhouse under controlled conditions. In all the varieties analyzed, capsiate was the only capsinoid found. The accumulation of capsiate, in all the pepper varieties, started from the 10th to the 20th day post-anthesis (dpa), and increased during the first days (between the 20th and the 27th dpa). From that moment a drastic reduction took place until the end of the ripening stage, except for ‘Bode’ peppers, where the capsiate content increased from the first harvest point on the 20th dpa up to the 76th dpa. The capsiate accumulation patterns over the development of the fruit has been related to the capsaicionoids accumulation patterns in the same samples of the four varieties of pepper. According to our results, the content evolution of both families of compounds will vary depending on each fruit’s genotype, as well as on environmental conditions. No clear trends have been established and, therefore, an in-depth analysis under controlled conditions should be carried out.

Peppers are fruits with wide genetic variability and multiple ways of being consumed that hold a relevant position in the human diet. Nowadays, consumers are interested in new gastronomic experiences provided by pepper cultivars that present new shapes, colors, and flavors while preserving their bioactive compounds, such as their capsaicinoids and capsinoids. However, numerous changes take place during their development that may alter their biological properties. Therefore, this work evaluates the capsaicinoid and capsiate contents in two traditional varieties of ornamental peppers (“Filius Blue” and “Filius Green’”) during fruit maturation. The aim is to determine the ideal harvesting moment depending on the farmer’s objective (e.g., achieving a specific color, shape, or flavor; achieving the maximum concentrations of bioactive compounds). The capsaicinoid contents followed different patterns in the two varieties analyzed. The “Filius Blue” variety exhibited increasing concentrations of capsaicinoids up to the 41st day post-anthesis (dpa), from which point on this trend was reversed. The concentrations in the “Filius Green” variety increased and decreased several times, reaching maximum concentrations on the 69th dpa. Regarding capsiate contents, both varieties varied in the same way, reaching maximum concentrations on the 34th dpa and then decreasing.

Prunus spinosa L. is a shrub that produces berries—sloes—with a high content of phenolic compounds and anthocyanins. Numerous consumer products are made from sloes, such as “Pacharán” liquors or jams. For these reasons, it is crucial to design accurate analytical methods for the extraction of these bioactive compounds in order to promote their consumption and to improve our health. In this study, an ultrasound-assisted extraction method was developed to extract phenolic compounds and anthocyanins from sloes. Several process variables (methanol composition, pH, temperature, cycle, amplitude, and sample–solvent ratio) were optimized based on a Box–Behnken design. The most influential parameters for the extraction of total phenolic compounds were the cycle, the percentage of methanol, and pH of the extraction solvent, with 0.2 s, 67%, and pH 7 as their optimal values, respectively. For total anthocyanins, all parameters were influential, with 44% methanol at pH 2, 48 °C, 0.2 s cycles, 30% amplitude, and 1.5 g/20 mL ratio as the optimal values for their corresponding variables. Both methods showed high recoveries, short time use, and high precision (RSD < 5%). In addition, the sloe liqueur “Pacharán” and sloe jams were analyzed to demonstrate the applicability of these methods for the extraction of the real matrix.

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.

“Naga Jolokia” (Capsicum chinense Jacq.) is a hot pepper variety native to India which has received the attention of the global scientific community due to its high capsaicinoid concentration. The present study evaluated the influence of fruit ripening on the total and individual capsaicinoids, as well as capsiate content. The aim was to determine the optimal moment to harvest the peppers depending on their pungent properties. Ultrasound-assisted extraction (UAE) using methanol as the extraction solvent and reverse-phase ultra-high-performance liquid chromatography (UHPLC-photodiode array (PDA)) were employed. Capsaicinoids gradually accumulated in the peppers from the moment they started growing until they reached a maximum concentration (7.99 ± 0.11 mg g−1 of fresh weight (FW)) at 33 days postanthesis (dpa). For this reason, based on its content of pungent compounds, as it is one of the main attributes of this variety, the optimal time for collection would be on day 33. From then on, there was a sharp decrease (96.35% of the total concentration) due to the peroxidase enzymes. The evolution of the principal capsaicinoids in “Naga Jolokia” peppers had a different behavior with respect to literature reports. After this investigation, these changes in content can be attributed to each pepper genotype. Capsiate content reached it maximum value at 19 dpa (0.27 ± 0.01 mg g−1 of FW). Then, there was a gradual drop due to the activities of different peroxidases. Given the important biological activity of capsaicinoids and capsinoids, the information described here allows for determining the ideal time to harvest “Naga Jolokia” peppers.

Two optimized methods for ultrasound-assisted extraction were evaluated for the extraction of two types of acai bioactive compounds: Total anthocyanins (TAs) and total phenolic compounds (TPCs). For the extraction optimization, a Box Behnken factorial design of different variables in the following intervals was used: Methanol-water (25%−75%) for solvent composition, temperatures between 10 and 70 °C, amplitude in the range between 30% and 70% of the maximum amplitude −200 W), extraction solvent pH (2−7), the ratio for sample-solvent (0.5 g:10 mL−0.5 g:20 mL), and cycle between 0.2 and 0.7 s. The extraction kinetics were studied using different periods between 5 and 30 min. TA and TPC were analyzed by UHPLC and the Folin−Ciocalteu method, respectively. Optimized conditions for TA were: 51% MeOH in water, 31 °C temperature, pH 6.38, cycle 0.7 s, 65% amplitude, and 0.5 g:10 mL of sample-solvent ratio. Optimized conditions for the TPC were: 49% MeOH in water, 41 °C temperature, pH 6.98, cycle 0.2 s, 30% amplitude, and 0.5 g:10 mL of sample-solvent ratio. Both methods presented a relative standard deviation below 5% in the precision study. The suitability of the methods was tested in real samples. It was confirmed that these methods are feasible for the extraction of the studied bioactive compounds from different açai matrices.

Currently, açai is one of the most important fruits present in the world. Several studies have demonstrated its high content in phenolic compounds and anthocyanins. Both of them are responsible of interesting properties of the fruit such as anti-inflammatory, antioxidant or anticancer. In the present study, two optimized pressurized liquid extraction (PLE) methods have been developed for the extraction of anthocyanins and total phenolic compounds from açai. A full factorial design (Box−Behnken design) with six variables (solvent composition (25−75% methanol-in-water), temperature (50−100 °C), pressure (100−200 atm), purge time (30−90 s), pH (2−7) and flushing (50−150%)) were employed. The percentage of methanol in the extraction solvent was proven to be the most significant variable for the extraction of anthocyanins. In the case of total phenolic compounds, the extraction temperature was the most influential variable. The developed methods showed high precision, with relative standard deviations (RSD) of less than 5%. The applicability of the methods was successfully evaluated in real samples. In conclusion, two rapid and reliable PLE extraction methods to be used for laboratories and industries to determine anthocyanins and total phenolic compounds in açai and its derived products were developed in this work.

In this work, two methods based on microwave-assisted extraction techniques for the extraction of both anthocyanins and total phenolic compounds from açai 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 °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 °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 °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 açai-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.

A method for the concentration and cleaning of red grape extracts prior to the determination of anthocyanins by UPLC-DAD has been developed. This method is of special interest in the determination of phenolic maturity as it allows the analysis of the anthocyanins present in grapes. Several different SPE cartridges were assessed, including both C-18- and vinylbenzene-based cartridges. C-18-based cartridges presented a very low retention for the glucosylated anthocyanidins while vinylbenzene-based cartridges showed excellent retention for these compounds. The optimized method involves the initial conditioning of the cartridge using 10 mL of methanol and 10 mL of water, followed by loading of up to 100 mL of red grape extract. Ten mL of water was used in the washing step and anthocyanins were subsequently eluted using 1.5 mL of acidified methanol at pH 2. This method simplifies the determination of individual anthocyanins as, on the one hand, it cleans the sample of interference and, on the other hand, it increases the concentration to up to 25:1.5. The developed method has been validated with a range of different grapes and it has also been tested as a means of determining the different anthocyanins in grapes with different levels of maturity.

Berries are considered functional food because of their potential health benefits to consumers due to their high concentrations in bioactive compounds. The extraction process of their antioxidant compounds is a crucial step. In this work, ultrasound (UAE) and microwave (MAE) assisted extraction have been evaluated and compared for the recovery of total phenolic compounds (TPC) and total anthocyanins (TA) from blackberry. Since several variables have an influence on the extraction processes efficiency, a response surface method using a Box−Behnken design (BBD) was chosen for the optimization of UAE and MAE variables. Six variables (solvent, temperature, amplitude, cycle, pH, and sample:solvent ratio) were optimized for UAE while the optimization for MAE was performed on four variables (solvent, temperature, pH, and sample:solvent ratio). It has been proven that solvent and temperature have a significant influence on the extraction of both TA and TPC. Only 10 and 5 min were necessary to complete the UAE and MAE procedures, respectively. A precision study was also carried out, and coefficient of variation lower than 5% was determined. Non-significant differences were obtained when using UAE and MAE at their respective optimum conditions. Thus, the results demonstrated a successful potential use of both techniques for the extraction of TA and TPC from blackberry. In conclusion, this work shows interesting perspectives for quality control analytical laboratories for the development of rapid extraction techniques to quantify these antioxidant compounds in blackberries.

Capsinoids are very similar antioxidant compounds to capsaicinoids, but less irritating, non-pungent and more palatable, and can thus be used in greater concentrations for food applications. To date, three capsinoids (capsiate, dihydrocapsiate, and nordihydrocapsiate) have been isolated from the pepper fruits. Due to its substantial commercial importance, it would be convenient to determine which pepper varieties have a richer content. Ultrasound- (UAE) and microwave- (MAE) assisted extraction have been implemented and analyzed using multivariate statistical methods. Firstly, different solvents were tested individually. The three best solvents were used in a set mixture design, where 42% methanol and 58% ethyl acetate were determined as the optimum combination for UAE, and 100% methanol for MAE. Subsequently, a Box−Behnken experimental design with four variables for both UAE and MAE (time, temperature, pH and sample mass:solvent volume “ratio”) was performed. The sample mass:solvent volume was the most influential variable in UAE; while for MAE no variable was any more influential than the others. Finally, both optimized extraction methods were successfully applied to different varieties of peppers. Besides, to demonstrate the efficiency of both extraction methods, a recovery study was performed. The results prove the potential of both techniques as highly adequate methods for the extraction of capsinoids from peppers.

Myrtus communis L. is an evergreen shrub that produces berries with a high content in antioxidant compounds. Since these compounds have demonstrated a positive effect on human health, the interest on berries and their usages has increased. However, environmental conditions may affect the productivity of these species and consequently the quality of wild myrtle. Ecotypes from diverse geographical origins may result in significant variations in terms of bioactive compounds content as well as in chemical traits. For this reason, in this work ecotypes from two different localizations have been studied to determine if their differences in morphological and anthocyanins traits can be attributed to their origin and the environmental characteristics of these locations. For this, chemometric analyses such as Hierarchical Cluster Analysis and Principal Component Analysis, were employed. The results showed differences between the ecotypes depending on their location. In particular, myrtle berries from maritime zones present greater fruit size and amount of bioactive compounds, which means an improvement in the quality of the final product based on this raw material. It can be concluded that both morphological and anthocyanins traits are related to the location of the ecotype and allow selecting the best ecotype for the required applications.

Chokeberry (Aronia melanocarpa L.) contains a large number of bioactive compounds, which make of it a highly nutritional and antioxidant food with anti-cancer and anti-inflammatory properties. It has been recently seen its consumption increased because of its interesting composition and properties as a functional food. The ultrasound-assisted extraction method has been optimized, since it is a simple and fast technique to extract phenolic compounds, as well as anthocyanins, not only separately but also simultaneously. Multiple response has been optimized and demonstrated an effectiveness level similar to the individual ones. This represents a considerable reduction in costs, time and work. The optimal conditions for simultaneous extractions were: 54% methanol as extraction solvent at pH 2.72 and 69.4 °C temperature, 70% amplitude, 0.7 s cycle, and 0.5:18.2 g:mL sample mass/solvent volume ratio. The developed methods showed a high precision level with coefficients of variation lower than 5%. The methods were successfully applied to commercial samples. Additionally, the extraction of other compounds of biological interest, such as organic acids and sugars, was also studied.

Nowadays, adulteration of honey is a frequent fraud that is sometimes motivated by the high price of this product in comparison with other sweeteners. Food adulteration is considered a deception to consumers that may have an important impact on people’s health. For this reason, it is important to develop fast, cheap, reliable and easy to use analytical methods for food control. In the present research, a novel method based on headspace-ion mobility spectrometry (HS-IMS) for the detection of adulterated honey by adding high fructose corn syrup (HFCS) has been developed. A Box–Behnken design combined with a response surface method have been used to optimize a procedure to detect adulterated honey. Intermediate precision and repeatability studies have been carried out and coefficients of variance of 4.90% and 4.27%, respectively, have been obtained. The developed method was then tested to detect adulterated honey. For that purpose, pure honey samples were adulterated with HFCS at different percentages (10–50%). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed a tendency of the honey samples to be classified according to the level of adulteration. Nevertheless, a perfect classification was not achieved. On the contrary, a full classification (100%) of all the honey samples was performed by linear discriminant analysis (LDA). This is the first time the technique of HS-IMS has been applied for the determination of adulterated honey with HFCS in an automatic way.