Development of a Novel HS-GC/MS Method Using the Total Ion Spectra Combined with Machine Learning for the Intelligent and Automatic Evaluation of Food-Grade Paraffin Wax Odor Level.

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.

Detection and quantification of cocoa powder adulteration using Vis-NIR spectroscopy with chemometrics approach.

The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of adulteration practices aimed at achieving economic benefits. This study aimed to detect and quantify cocoa powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat. The NIRS data could not be resolved using Savitzky-Golay smoothing. Nevertheless, the application of a random forest and support vector machine successfully classified the samples with 100% accuracy. Quantification of adulteration using partial least squares (PLS), Lasso, Ridge, elastic Net, and RF regressions provided R2 higher than 0.96 and root mean square error <2.6. Coupling PLS with the Boruta algorithm produced the most reliable regression model (R2 = 1, RMSE = 0.0000). Finally, an online application was prepared to facilitate the determination of adulterants in the cocoa powder.

Substrate preference of protein kinase B isoforms can vary depending on the cell line.

Many proteins in higher eukaryotes, especially those with crucial functions, have multiple isoforms with redundant roles providing protection against potential functional deficiencies in one isoform. However, these isoforms can also have some unique roles. Protein kinase B, also known as Akt, is one such protein that has three isoforms encoded on different genes. Due to high sequence similarity and the general lack of specific reagents, most studies on Akt generalize their findings and do not distinguish between the isoforms. Using an established chemical genetic strategy and a set of known Akt substrates, this work explores substrate specificity of Akt isoforms under steady state conditions in two commonly used cell lines. This strategy can be applied to study any Akt isoform-specific substrates of interest in any cell line of choice as long as the cell line can be transfected.

Machine learning-based approaches to Vis-NIR data for the automated characterization of petroleum wax blends.

Petroleum waxes are products derived from lubricating oils with a wide spectrum of industrial and consumer applications that depend on their composition. In addition, the intended applications of this product are also subject to the practice of blending petroleum waxes with different chemical characteristics (e.g., paraffin waxes and microwaxes) to achieve the appropriate physicochemical properties. This study introduces a novel method based on visible and near-infrared spectroscopy (Vis-NIR) combined with machine learning (ML) for the characterization of blends of the two types of commonly marketed petroleum waxes (paraffin waxes and microwaxes). With spectroscopic data, Partial Least Squared Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF) Regression-based regression ML models have been developed, obtaining satisfactory results for the characterization of the percentage of blending in petroleum waxes. Moreover, strategies using wrapper variable selection methods like the Boruta algorithm and Genetic Algorithm (GA) have been implemented to assess if fewer predictors enhance model performance. Particularly, the application of wrapper variable selection methods, specifically the Boruta algorithm, has led to an improvement in the performance of the models obtained. Results obtained by the Boruta-PLS model showed the best performance with an RMSE of 2.972 and an R2 of 0.9925 for the test set and an RMSE of 1.814 and an R2 of 0.9977 for the external validation set. Additionally, this model allowed for establishing the relative importance of the variables in the characterization of the waxes mixture, pointing out that the hydrocarbon content ratio is critical in the determination of this value. An interactive web application was developed using the best model developed for easy processing of the data by the users.

Enhancing Anthocyanin Extraction from Wine Lees: A Comprehensive Ultrasound-Assisted Optimization Study.

Wine lees, an important by-product of the wine industry, pose a major environmental problem due to the enormous quantities of solid-liquid waste that are discarded annually without defined applications. In this study, the optimization of a method based on a Box-Behnken design with surface response has been carried out to obtain extracts with high anthocyanin content and potent antioxidant activity. Six variables have been considered: %EtOH, temperature, amplitude, cycle, pH, and ratio. The developed method exhibited important repeatability properties and intermediate precision, with less than 5% CV being achieved. Furthermore, these novel methods were successfully applied to diverse wine lees samples sourced from Cabernet Sauvignon and Syrah varieties (Vitis vinifera), resulting in extracts enriched with significant anthocyanin content and noteworthy antioxidant activity. Additionally, this study evaluated the influence of grape variety, fermentation type (alcoholic or malolactic), and sample treatment on anthocyanin content and antioxidant activity, providing valuable insights for further research and application in various sectors. The potential applications of these high-quality extracts extend beyond the winemaking industry, holding promise for fields like medicine, pharmaceuticals, and nutraceuticals, thus promoting a circular economy and mitigating environmental contamination.

On the Valorization of Arbutus unedo L. Pomace: Polyphenol Extraction and Development of Novel Functional Cookies.

The fruits of Arbutus unedo L. have a crimson colour and are enriched with remarkable concentrations of bioactive compounds such as anthocyanins and polyphenols. These fruits are commonly used in the production of a Portuguese Protected Geographical Indication distillate called "Aguardente de Medronho". During this process, a solid pomace is generated and presently discarded without valuable applications. In this work, two strategies have been developed for the valorisation of A. unedo pomace. The first approach considers the extraction of polyphenols from this by-product through the optimization of an ultrasound-assisted method using a Box-Behnken design coupled with response surface methodology. The results indicate that the temperature and the percentage of methanol, along with their interaction, significantly influence the total concentration of polyphenols and the antioxidant activity of the extracts obtained. The optimal conditions identified consider the extraction of 0.5 g of sample with 20 mL of a solvent containing 74% MeOH (aq), at a pH of 4.8, maintained at 70 °C for 15 min. On the other hand, the second valorisation strategy considered the use of A. unedo pomace in the development of functional cookies. The incorporation of 15-20% pomace in the cookie formulation was well-received by consumers. This incorporation results in an intake of ca. 6.55 mg of polyphenols per gram of cookie consumed, accompanied by an antioxidant activity of 4.54 mg Trolox equivalents per gram of cookie consumed. Overall, these results encourage the employment of A. unedo pomace either as a reliable source of extracts enriched in polyphenols or as a nutraceutical active ingredient in functional cookies, thereby positively impacting human health.

Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach.

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.

Ultrasound-Assisted Anthocyanins Extraction from Pigmented Corn: Optimization Using Response Surface Methodology.

This study aimed to determine the optimal UAE conditions for extracting anthocyanins from pigmented corn using the Box-Behnken design (BBD). Six anthocyanins were identified in the samples and were used as response variables to evaluate the effects of the following working variables: extraction solvent pH (2-7), temperature (10-70 °C), solvent composition (0-50% methanol in water), and ultrasound power (20-80%). The extraction time (5-25 min) was evaluated for complete recovery. Response surface methodology suggested optimal conditions, specifically 36% methanol in water with pH 7 at 70 °C using 73% ultrasound power for 10 min. The method was validated with a high level of accuracy (>90% of recovery) and high precision (CV < 5% for both repeatability and intermediate precision). Finally, the proposed analytical extraction method was successfully applied to determine anthocyanins that covered a wide concentration range (36.47-551.92 mg kg-1) in several pigmented corn samples revealing potential varieties providing more health benefits.

Comparison between Ultrasound- and Microwave-Assisted Extraction Methods to Determine Phenolic Compounds in Barley (Hordeum vulgare L.).

Barley (Hordeum vulgare L.) is one of the major cereal crops worldwide. It is grown not only to be used as fodder but also for human consumption. Barley grains are a great source of phenolic compounds, which are particularly interesting for their health-promoting antioxidant properties, among other benefits. Two extraction methods, namely ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE), have been optimized and compared by using Box-Behnken design (BBD) to determine both the antioxidant power and the phenolic compound levels of the extracts. Three variables have been assessed based on these designs: solvent composition (% MeOH in water), temperature (°C), and sample-to-solvent ratio (mg sample mL-1 solvent). The solvent composition used and the interaction between the solvent and the temperature were the most significant variables in terms of recovery of phenolic compounds and antioxidant capacity of the extracts. Short extraction times, a high precision level, and good recoveries have been confirmed for both methods. Moreover, they were successfully applied to several samples. Significant differences regarding the level of phenolic compounds and antioxidant power were revealed when analyzing three different barley varieties. Specifically, the amounts of phenolic compounds ranged from 1.08 to 1.81 mg gallic acid equivalent g-1 barley, while their antioxidant capacity ranged from 1.35 to 2.06 mg Trolox equivalent g-1 barley, depending on the barley variety. Finally, MAE was found to be slightly more efficient than UAE, presenting higher levels of phenolic compounds in the extracts.

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning.

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.

Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices.

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.

Determination of Caffeoylquinic Acids Content by UHPLC in Scolymus hispanicus Extracts Obtained through Ultrasound-Assisted Extraction.

Scolymus hispanicus L., also known as golden thistle, Spanish oyster thistle or, more commonly, as tagarnina is a plant that belongs to the Asteraceae family. It is collected from the wild for human consumption in Mediterranean countries. It is a relevant ingredient in Andalusian culinary culture, where the midribs of young plants are harvested for consumption. Scolymus hispanicus L. contains a wide variety of phenolic compounds such as caffeoylquinic acids (CQAs), among others. In the present work, the major phenolic compounds present in tagarnina have been identified, with 5-caffeoylquinic acid (5-CQA) and 3,5-dicaffeoylquinic acid (3,5-diCQA) being the main ones. A method based on ultrasound-assisted extraction (UAE) has been developed for the extraction of these compounds, with the percentage of methanol, sample-to-solvent ratio and the pH being the most influential factors. The developed method has been validated and employed to determine the concentration of 5-CQA and 3,5-diCQA in the midribs of Scolymus hispanicus, collected in six different places in the south of Spain. The antioxidant activity of the samples has also been determined, and a direct correlation with their caffeoylquinic compounds content has been established, showing an antioxidant effect.

Effects of Different Freezing Treatments during the Winemaking of a Varietal White Wine with Regard to Its Phenolic Components.

In white wine production, the technique consisting of freezing whole or crushed grapes usually increases the levels of aroma-related compounds in the final wine products. However, this technique may affect phenolic compounds, among other chemical compounds. Phenolic compounds are crucial to white wines because of their susceptibility to oxidation and their role with regard to color stability. In this study, white wines made from Muscat of Alexandria grapes were subjected to two different freezing techniques: whole-bunch freezing and crushed-grape freezing. In addition, a pre-fermentative maceration was applied to each experiment in order to determine if the effects of freezing were comparable to those of maceration. The phenolic compounds studied were gallic acid, protocatechuic acid, caffeic acid, trans-coutaric acid, and epicatechin, which are the key compounds from the point of view of wine stability. The freezing of crushed grapes enhanced the extraction of phenolic compounds in comparison to the freezing of whole bunches of grapes without pre-fermentative maceration. On the other hand, the effect of pre-fermentative maceration was comparable to that resulting from freezing crushed grapes. This step made the must from whole frozen grapes having even larger levels of phenolic compounds. Without pre-fermentative maceration, freezing whole bunches of grapes only allowed a moderate extraction of phenolic compounds and produced wines with lower individual phenolic contents than those obtained through traditional winemaking procedures.

Application of Direct Thermal Desorption-Gas Chromatography-Mass Spectrometry for Determination of Volatile and Semi-Volatile Organosulfur Compounds in Onions: A Novel Analytical Approach.

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.

Optimizing an Enzymatic Extraction Method for the Flavonoids in Moringa (Moringa oleifera Lam.) Leaves Based on Experimental Designs Methodologies.

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.

Response Surface Methodology Optimization for Analytical Microwave-Assisted Extraction of Resveratrol from Functional Marmalade and Cookies.

A novel analytical method based on microwave-assisted extraction has been successfully optimized and validated to determine resveratrol from functional marmalade and cookies. The optimization was performed using a Box−Behnken design with three factors: solvent composition (60−100% and 10−70% methanol in water for marmalade and cookies, respectively), microwave power (250−750 W), and solvent-to-solid ratio (20:5−60:5). The main and quadratic effects of solvent composition significantly contributed to the recovery values (p < 0.005) for both kinds of samples. Additionally, the solvent-to-solid ratio and the quadratic effect of microwave power also influenced the resveratrol recovery from functional marmalade. Hence, the optimum condition for resveratrol extraction from marmalade (80% methanol, 500 W, solvent-to-solid ratio 40:5) and cookies (80% methanol, 250 W, solvent-to-solid ratio 20:5) was proposed. The extraction kinetics (5−30 min) was then studied to clarify the complete recovery of resveratrol from the food matrices resulting in 15 min as the optimum extraction time. The developed method showed a satisfactory precision indicated by the coefficient of variation (CV) lower than 5.70% for both repeatability and intermediate precision. To check the matrix effects, the developed MAE procedures were applied to a number of commercial marmalade and cookies. The high-fat and fiber cookies resulted in overestimated values due to the interfering ingredients. As a final point, the methods successfully measured the stability of naturally present resveratrol in grape-derived products while preparing functional marmalade and cookies.

Electrodeposition of Ag/ZIF-8-Modified Membrane for Water Remediation.

Metal-organic framework (MOF)-based membranes have been widely used in gas and liquid separation due to their porous structures and tunable compositions. Depending on the guest components, heterostructured MOFs can exhibit multiple functions. In the present work, we report a facile and rapid preparation of zeolitic imidazolate framework-8 (ZIF-8) and silver nanoparticle incorporated ZIF-8 (Ag/ZIF-8)-based membranes on stainless-steel mesh (SSM) through a "green" electrodeposition method. The SSM was first coated with a Zn-plated layer which contains mainly zinc hydroxide nitrate (Zn5(OH)8(NO3)2·2H2O) with a "leaf-like" morphology, providing anchoring points for the deposition of ZIF-8 and Ag/ZIF-8. It takes only 10 min to prepare a uniform coating of Zn5(OH)8(NO3)2·2H2O in aqueous conditions without the use of a strong base; this is by far the most efficient way of making zinc hydroxide nitrate nanocrystals. Following a similar electrodeposition approach, ZIF-8 and Ag/ZIF-8-coated SSM can be prepared within 20 min by applying a small current. The encapsulation of Ag does not alter the chemical composition nor the crystal structure of ZIF-8. The resulting ZIF-8 and Ag/ZIF-8-coated SSM have been tested for their effectiveness for rhodamine B dye removal in a fast vacuum filtration setting. Additionally, growth of E. coli was significantly inhibited after overnight incubation with Ag/ZIF-8-coated SSM. Overall, we demonstrate a fast synthesis procedure to make ZIF-8 and Ag/ZIF-8-coated SSM membranes for organic dye removal with excellent antimicrobial activity.

The effect of ripening on the capsaicinoids composition of Jeromin pepper (Capsicum annuum L.) at two different stages of plant maturity.

The changes of capsaicinoids in the pericarp and placenta of Jeromin pepper fruits, collected at two different stages of plant's maturity (young and adult), has been studied throughout the ripening process. This variety is used in the production of "Pimentón de La Vera" and recognized under a Protected Designation of Origin, so it is of great importance to determine their optimum harvesting time to get the most of its beneficial health effects. Capsaicinoids reached the maximum concentration on the 30th days post-anthesis (dpa) for the young plant, while in the adult plant it was later, specifically on 40th and 60th dpa for the placenta and pericarp, respectively. From this moment on, a sharp decrease in their content is observed. In addition, higher amounts of total capsaicinoids have been found in the second stage of plant maturity with respect to the first one, both in the placenta and in the pericarp.

Optimization of a Microwave Assisted Extraction Method for Maximum Flavonols and Antioxidant Activity of Onion Extracts.

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.

Essential Mineral Content (Fe, Mg, P, Mn, K, Ca, and Na) in Five Wild Edible Species of Lactarius Mushrooms from Southern Spain and Northern Morocco: Reference to Daily Intake.

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.