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.

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.

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.

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.

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 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.

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.

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.

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.

Development of a Rapid UHPLC-PDA Method for the Simultaneous Quantification of Flavonol Contents in Onions (Allium cepa L.)

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.

Simultaneous determination by UHPLC-PDA of major capsaicinoids and capsinoids contents in peppers

Capsaicinoids and capsinoids compounds have been a focus of special attention for their health benefits. An effective and rapid Ultra-High-Performance Liquid Chromatography (UHPLC-PDA) method has been developed and validated for the simultaneous separation and quantitative determination of the major capsaicinoids and capsinoids present in peppers. The separation of all the compounds of interest was achieved in less than 2 min by means of an ACQUITY UPLC BEH rp-C18 column (100 mm × 2.1 mm i.d., 1.7 µm particle size). The variables that have been optimized are the mobile phase (water as solvent A and acetonitrile as solvent B, both acidified by adding 0.1% acetic acid), separation gradient, column temperature (35-70 °C), flow rate (0.6-0.95 mL min^-1), and injection volume (2.5-3.5 µL). The evaluation of the chromatographic performance revealed excellent resolution, retention factor, and selectivity. The method was satisfactorily validated in terms of linearity, detection and quantification limits, precision, and robustness.

Characterization of Biodegraded Ignitable Liquids by Headspace-Ion Mobility Spectrometry

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

Content of Capsaicinoids and Capsiate in "Filius" Pepper Varieties as Affected by Ripening

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.

Development of a rapid and accurate UHPLC-PDA-FL method for the quantification of phenolic compounds in grapes

Grapes are a great source of phenolic compounds, which have excellent antioxidant properties. Efficient analytical methods are necessary to selectively and precisely determine these compounds content in grapes. In this study, a reverse-phase ultra-high performance liquid chromatography (UHPLC) method with fluorescence and photodiode array detection has been developed to determine and quantify 27 of the main phenolic compounds present in grapes. An ACQUITY UPLC® BEH C18 (50 mm × 2.1 mm i.d., 1.7 mm particle size) column was employed. A gradient method was developed and column temperature (25-55 °C), as well as flow rate (0.6-0.75 mL min^-1), were optimized. The optimum conditions allowed the separation of all the compounds in less than 9 min. The method was validated and demonstrated excellent detection and quantification limits, precision, and selectivity. Finally, several grape varieties were studied in order to demonstrate the applicability of the method to the analysis of real matrix samples.

OTP-PRL: an app for occupational risk prevention in policing activities

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.

Discrimination of Myrtle Ecotypes from Different Geographic Areas According to Their Morphological Characteristics and Anthocyanins Composition

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.

A screening method based on Visible-NIR spectroscopy for the identification and quantification of different adulterants in high-quality honey

According to European Union regulations, honey is a pure product and adding to or removing from it any kind of substance is illegal. Nevertheless, its adulteration by adding inexpensive and artificial adulterants is a common practice. This paper deals with the use of visible and near-infrared spectroscopy (Vis-NIRS) combined with chemometric tools as a screening technique for the identification and quantification of different types of adulterants (inverted sugar, rice syrup, brown cane sugar and fructose syrup) added to high-quality honey (Granada Protected Designation of Origin, Spain) at different levels (5%-50%). A complete discrimination between non-adulterated and adulterated samples was achieved. A general regression model to quantify the adulteration levels was developed as well as specific models for each adulterant. The coefficients of determination were higher than 0.96 for all the models. These results demonstrate the capacity of Vis-NIRS combined with chemometric tools for honey quality control.

A Screening Method Based on Headspace-Ion Mobility Spectrometry to Identify Adulterated Honey

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.

Novel method based on ion mobility spectrometry sum spectrum for the characterization of ignitable liquids in fire debris

The destructive nature of fire together with a variety of interfering products from pyrolysis or background compounds among others, still offer a challenge on the proper identification of ignitable liquid residues (ILRs) in fire investigations. Nowadays, analysts use chromatography-mass spectrometry to try and classify ignitable liquids (IL) into one of the classes in the American Standards Testing Material method (ASTM E1618). In this study, an alternative approach is proposed to such analysis of fire debris. The proposed method would be based on ion mobility spectrometry sum spectrum (IMSSS) from headspace analysis, in combination with pattern recognition tools (Linear Discriminant Analysis, LDA). Four different substrates (pinewood, cork, paper, and cotton sheet) were burnt with and without different ILs (gasoline, diesel, ethanol, and paraffin). According to LDA, 100% of fire debris samples were correctly classified for presence/absence and type of IL. A characteristic fingerprint for each ILR was created for quick discrimination. These results demonstrate the potential of using IMSSS for a fast, objective and easy interpretation of fire debris data. In addition, ion mobility spectrometry (IMS) presents some advantages over traditional techniques such as its real-time monitoring capability and its capacity to work at atmospheric pressure, which allow the development of portable devices that would perform the analysis at the fire scene.

Development of New Analytical Microwave-Assisted Extraction Methods for Bioactive Compounds from Myrtle (Myrtus communis L.)

The phenolic compounds and anthocyanins present in myrtle berries are responsible for its beneficial health properties. In the present study, a new, microwave-assisted extraction for the analysis of both phenolic compounds and anthocyanins from myrtle pulp has been developed. Different extraction variables, including methanol composition, pH, temperature, and sample–solvent ratio were optimized by applying a Box–Behnken design and response surface methodology. Methanol composition and pH were the most influential variables for the total phenolic compounds (58.20% of the solvent in water at pH 2), and methanol composition and temperature for anthocyanins (50.4% of solvent at 50 °C). The methods developed showed high repeatability and intermediate precision (RSD < 5%). Both methods were applied to myrtle berries collected in two different areas of the province of Cadiz (Spain). Hierarchical clustering analysis results show that the concentration of bioactive compounds in myrtle is related to their geographical origin.

Interpreting the near infrared region of explosives

The NIR spectra from 1000 to 2500 nm of 18 different explosives, propellant powders and energetic salts were collected and interpreted. NIR spectroscopy is known to provide information about the combination bands and overtones of highly anharmonic vibrations as those occurring in XH bonds (CH, NH and OH). Particularly intense and complex were the bands corresponding to the first combination region (2500-1900 nm) and first overtone stretching mode (2ν) of CH and NH bonds (1750-1450 nm). Inorganic oxidizing salts including sodium/potassium nitrate, sodium/potassium chlorate, and sodium/potassium perchlorate displayed low intense or no NIR bands.

FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines

During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV-visible (UV-Vis) spectroscopic techniques combined with multivariate analysis were used to obtain regression models to correlate both spectroscopic data and chromatic parameters with the ageing level of high quality Sherry wines. Three spectral ranges were obtained that contain the highest variance: two different fingerprint ranges in FT-IR (1100-2000 cm^-1 and 2300-2999 cm^-1) and one range in the visible region (380-450 nm). The regression model has enabled full differentiation between the seven levels of ageing in the wine explored. A good linear regression fit (R² above 0.95) was obtained regardless of the ranges used. The results demonstrate that both spectroscopic techniques can be used to optimize the ageing process in a simple and fast way.

Effects of Fire Suppression Agents and Weathering in the Analysis of Fire Debris by HS-MS eNose

In arson attacks the detection of ignitable liquid residues (ILRs) at fire scenes provides key evidence since ignitable liquids, such as gasoline, are commonly used to initiate the fire. In most forensic laboratories gas chromatography-mass spectrometry is employed for the analysis of ILRs. When a fire occurs, suppression agents are used to extinguish the fire and, before the scene is investigated, the samples at the scene are subjected to a variety of processes such as weathering, which can significantly modify the chemical composition and thus lead to erroneous conclusions. In order to avoid this possibility, the application of chemometric tools that help the analyst to extract useful information from data is very advantageous. The study described here concerned the application of a headspace-mass spectrometry electronic nose (HS-MS eNose) combined with chemometric tools to determine the presence/absence of gasoline in weathered fire debris samples. The effect of applying two suppression agents (Cafoam Aquafoam AF-6 and Pyro-chem PK-80 Powder) and delays in the sampling time (from 0 to 48 h) were studied. It was found that, although the suppression systems affect the mass spectra, the HS-MS eNose in combination with suitable pattern recognition chemometric tools, such as linear discriminant analysis, is able to identify the presence of gasoline in any of the studied situations (100% correct classification).

Study of the Weathering Process of Gasoline by eNose

In a fire investigation the rapid detection of the presence of ignitable liquids like gasoline is of great importance as it allows appropriate treatment of the remains, the identification of prevention methods and detects the possible presence of an arsonist. In some cases, analysts cannot access the fire scene in the first few hours due to the dangers involved in the situation and, as a consequence, phenomena such as weathering start. Ignitable liquid weathering is an evaporation process that results in an increase in the abundance of non-volatile compounds relative to volatile compounds, and this process changes the chemical composition. In the present work, the weathering of samples of gasoline at different times (from 0 h to a month) has been studied using an electronic nose (eNose). The influence of the volume used (40 µL and 80 µL) and the type of support (cork, wood, paper and cotton sheet) has been studied. Chemometric tools have been used with the aim of ascertaining the weathering time for which the developed method is capable of detecting the presence of gasoline. The eNose was able to discriminate samples of weathered gasoline. The support used for the samples did not seem to have an influence on the detection and the system.

Characterization and Differentiation of Petroleum-Derived Products by E-Nose Fingerprints

Characterization of petroleum-derived products is an area of continuing importance in environmental science, mainly related to fuel spills. In this study, a non-separative analytical method based on E-Nose (Electronic Nose) is presented as a rapid alternative for the characterization of several different petroleum-derived products including gasoline, diesel, aromatic solvents, and ethanol samples, which were poured onto different surfaces (wood, cork, and cotton). The working conditions about the headspace generation were 145 °C and 10 min. Mass spectroscopic data (45–200 m/z) combined with chemometric tools such as hierarchical cluster analysis (HCA), later principal component analysis (PCA), and finally linear discriminant analysis (LDA) allowed for a full discrimination of the samples. A characteristic fingerprint for each product can be used for discrimination or identification. The E-Nose can be considered as a green technique, and it is rapid and easy to use in routine analysis, thus providing a good alternative to currently used methods.

Fast analysis of capsaicinoids in Naga Jolokia extracts (Capsicum chinense) by high-performance liquid chromatography using fused core columns

A rapid high-performance liquid chromatography method with a C18 reverse-phase fused-core column has been developed for the determination and quantification of the main capsaicinoids (nornordihydrocapsaicin, nordihydrocapsaicin, capsaicin, dihydrocapsaicin, homocapsaicin and homodihydrocapsaicin) present in Naga Jolokia peppers. A fused-core Kinetex™ C18 column (50×2.1mm i.d.; 2.6μm) was used for the analysis. The chromatographic separation was obtained with a gradient method in which the mobile phase was water (0.1% acetic acid) as solvent A and acetonitrile (0.1% acetic acid) as solvent B. The separation of all compounds was achieved in less than 3min with a total analysis time (sample-to-sample) of 10min. The robustness of the method was evaluated. The method showed excellent repeatability and intermediate precision expressed as coefficient of variance of less than 2%. The developed method was employed for the quantification of the major capsaicinoids present in different peppers and commercial products containing chilli peppers.

Tryptophan Levels during Grape Ripening: Effects of Cultural Practices

Some cultural practices that are carried out during the grape ripening period are associated with vine stress, including leaf removal, grape bunch removal, and vegetable cover crops. Additionally, several nitrogen and sulfur supplements have also been used directly on leaves during the last stage of the ripening period. In the work described here, five different cultural practices and the reference were applied in three replicates in the same vineyard. The evolution of tryptophan levels was evaluated from just after grape veraison until the harvest date. In some cases, certain specific treatments were also evaluated after the regular harvest date. The cultural techniques that involved the application of nitrogen led to higher levels of tryptophan at the harvest day when compared to other cultural techniques. It was also found that the application of nitrogen without sulfur had a faster effect on the level of tryptophan. It was established that a period of around 20 days is needed for the grapes to show clear differences in tryptophan levels after the application of nitrogen.

Ontogenetic Variation of Individual and Total Capsaicinoids in Malagueta Peppers (Capsicum frutescens) during Fruit Maturation

The ontogenetic variation of total and individual capsaicinoids (nordihydrocapsaicin (n-DHC), capsaicin (C), dihydrocapsaicin (DHC), homocapsaicin (h-C) and homodihydrocapsaicin (h-DHC)) present in Malagueta pepper (Capsicum frutescens) during fruit ripening has been studied. Malagueta peppers were grown in a greenhouse under controlled temperature and humidity conditions. Capsaicinoids were extracted using ultrasound-assisted extraction (UAE) and the extracts were analyzed by ultra-performance liquid chromatography (UHPLC) with fluorescence detection. A significant increase in the total content of capsaicinoids was observed in the early days (between 12 and 33). Between day 33 and 40 there was a slight reduction in the total capsaicinoid content (3.3% decrease). C was the major capsaicinoid, followed by DHC, n-DHC, h-C and h-DHC. By considering the evolution of standardized values of the capsaicinoids it was verified that n-DHC, DHC and h-DHC (dihydrocapsaicin-like capsaicinoids) present a similar behavior pattern, while h-C and C (capsaicin-like capsaicinoids) show different evolution patterns.

Authentication of virgin olive oil by a novel curve resolution approach combined with visible spectroscopy

Adulteration of olive oil is not only a major economic fraud but can also have major health implications for consumers. In this study, a combination of visible spectroscopy with a novel multivariate curve resolution method (CR), principal component analysis (PCA) and linear discriminant analysis (LDA) is proposed for the authentication of virgin olive oil (VOO) samples. VOOs are well-known products with the typical properties of a two-component system due to the two main groups of compounds that contribute to the visible spectra (chlorophylls and carotenoids). Application of the proposed CR method to VOO samples provided the two pure-component spectra for the aforementioned families of compounds. A correlation study of the real spectra and the resolved component spectra was carried out for different types of oil samples (n=118). LDA using the correlation coefficients as variables to discriminate samples allowed the authentication of 95% of virgin olive oil samples.

Application of an HS-MS for the detection of ignitable liquids from fire debris

In arson attacks, accelerants such as ignitable liquids are commonly used to initiate or accelerate a fire. The detection of ignitable liquid residues at fire scenes is therefore a key step in fire investigations. The most widely used analytical technique for the analysis of accelerants is GC-MS. However, pre-concentration of the ignitable liquid residues is required prior to the chromatographic analysis. The standard method, ASTM E1412, involves passive headspace concentration with activated charcoal strips as a method to isolate the ignitable liquid residues from fire debris and these residues are subsequently desorbed from the carbon strip with solvents such as carbon disulfide. In the work described here, an alternative analytical technique based on an HS-MS (headspace mass spectrometry) has been developed for the thermal desorption of the carbon strips and analysis of different ignitable liquid residues in fire debris. The working conditions for the HS-MS analytical procedure were optimized using different types of fire debris (pine wood burned with gasoline and diesel). The optimized variables were desorption temperature and desorption time. The optimal conditions were 145°C and 15 min. The optimized method was applied to a set of fire debris samples. In order to simulate post burn samples several accelerants (gasoline, diesel, citronella, kerosene, paraffin, and alcohol) were used to ignite different substrates (wood, cotton, cork, paper, and paperboard). chemometric methods (cluster analysis and discriminant analysis) were applied to the total ion spectrum obtained from the MS (45-200 m/z) to discriminate between the burned samples according to the accelerant used. The method was validated by analyzing all samples by GC-MS according to the standard methods ASTM E1412 and ASTM E1618. The results obtained on using the method developed in this study were comparable to those obtained with the reference method. However, the newly developed HS-MS method is faster, safer, and more environmental friendly than the standard method.

A new solid phase extraction for the determination of anthocyanins in grapes

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.