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.

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.

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.

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.

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.

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.

Optimization through a Box-Behnken Experimental Design of the Microwave-Assisted Extraction of the Psychoactive Compounds in Hallucinogenic Fungi (Psylocibe cubensis).

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.

Exposure to Essential and Toxic Elements via Consumption of Agaricaceae, Amanitaceae, Boletaceae, and Russulaceae Mushrooms from Southern Spain and Northern Morocco.

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.

Extraction of Antioxidant Compounds from Onion Bulb (Allium cepa L.) Using Individual and Simultaneous Microwave-Assisted Extraction Methods.

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.

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data.

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.

Assessment of Volatile Compound Transference through Firefighter Turnout Gear.

There is high concern about the exposure of firefighters to toxic products or carcinogens resulting from combustion during fire interventions. Firefighter turnout gear is designed to protect against immediate fire hazards but not against chemical agents. Additionally, the decontamination of firefighter personal protective equipment remains unresolved. This study evaluated the feasibility of a screening method based on headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) in combination with chemometrics (cluster analysis, principal component analysis, and linear discriminant analysis) for the assessment of the transference of volatile compounds through turnout gear. To achieve this, firefighter turnout gears exposed to two different fire scenes (with different combustion materials) were directly analyzed. We obtained a spectral fingerprint for turnout gears that were both exposed and non-exposed to fire scenes. The results showed that (i): the contamination of the turnout gears is different depending on the type of fire loading; and (ii) it is possible to determine if the turnout gear is free of volatile compounds. Based on the latest results, we concluded that HS-GC-IMS can be applied as a screening technique to assess the quality of turnout gear prior to a new fire intervention.

Development of Optimized Ultrasound-Assisted Extraction Methods for the Recovery of Total Phenolic Compounds and Anthocyanins from Onion Bulbs.

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.

A Novel Ultrasound-Assisted Extraction Method for the Analysis of Anthocyanins in Potatoes (Solanum tuberosum L.).

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.

A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars.

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.

Optimization of an Ultrasound-Assisted Extraction Method for the Analysis of Major Anthocyanin Content in Erica australis Flowers.

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.

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

The objective of the present study is to develop an optimized method where headspace-ion mobility spectrometry is applied for the detection and discrimination between four petroleum-derived products (PDPs) in water. A Box-Behnken design with a response surface methodology was used, and five variables (incubation temperature, incubation time, agitation, sample volume, and injection volume) with influences on the ion mobility spectrometry (IMS) response were optimized. An IMS detector was used as a multiple sensor device, in which, each drift time acts as a specific sensor. In this way, the total intensity at each drift time is equivalent to multiple sensor signals. According to our results, 2.5 mL of sample incubated for 5 min at 31 °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.