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

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

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

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

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

CHIR99021 causes inactivation of Tyrosine Hydroxylase and depletion of dopamine in rat brain striatum

Dopamina; Estriat; Tirosina hidroxilasaDopamina; Estriado; Tirosina hidroxilasaDopamine; Striatum; Tyrosine hydroxylaseCHIR99021, also known as laduviglusib or CT99021, is a Glycogen-synthase kinase 3β (GSK3β) inhibitor, which has been reported as a promising drug for cardiomyocyte regeneration or treatment of sensorial hearing loss. Since the activation of dopamine (DA) receptors regulates dopamine synthesis and they can signal through the β-arrestin pathway and GSK3β, we decided to check the effect of GSK3β inhibitors (CHIR99021, SB216763 and lithium ion) on the control of DA synthesis. Using ex vivo experiments with minces from rat brain striatum, we observed that CHIR99021, but not SB216763 or lithium, causes complete abrogation of both DA synthesis and accumulation, pointing to off-target effects of CHIR99021. This decrease can be attributed to tyrosine hydroxylase (TH) inhibition since the accumulation of l-DOPA in the presence of a DOPA decarboxylase inhibitor was similarly decreased. On the other hand, CHIR99021 caused a dramatic increase in the DOPAC/DA ratio, an indicator of DA metabolization, and hindered DA incorporation into striatum tissue. Tetrabenazine, an inhibitor of DA vesicular transport, also caused DA depletion and DOPAC/DA ratio increase to the same extent as CHIR99021. In addition, both CHIR99021 or SB216763, but not lithium, decreased TH phosphorylation in Ser19, but not in Ser31 or Ser40. These results demonstrate that CHIR99021 can lead to TH inactivation and DA depletion in brain striatum, opening the possibility of its use in DA-related disorders, and shows effects to be considered in future clinical trials. More work is needed to find the mechanism exerted by CHIR99021 on DA accumulation.This work was supported by Spanish Government grant SAF2017-87199-R. S.H. received a predoctoral fellowship from the Universitat Autònoma de Barcelona

Comparison of different processing approaches by SVM and RF on HS-MS eNose and NIR Spectrometry data for the discrimination of gasoline samples

In the quality control of flammable and combustible liquids, such as gasoline, both rapid analysis and automated data processing are of great importance from an economical viewpoint for the petroleum industry. The present work aims to evaluate the chemometric tools to be applied on the Headspace Mass Spectrometry (HS-MS eNose) and Near-Infrared Spectroscopy (NIRS) results to discriminate gasoline according to their Research Octane Number (RON). For this purpose, data from a total of 50 gasoline samples of two types of RON-95 and 98-analyzed by the two above-mentioned techniques were studied. The HS-MS eNose and NIRS data were com-bined with non-supervised exploratory techniques, such as Hierarchical Cluster Analysis (HCA), as well as other supervised classification techniques, namely Support Vector Machine (SVM) and Random Forest (RF). For su-pervised classification, the low-level data fusion was additionally applied to evaluate if the combined use of the data increases the scope of relevant information. The HCA results showed a clear clustering trend of the gasoline samples according to their RON with HS-MS eNose data. SVM in combination with 5-Fold Cross-Validation successfully classified 100% of the samples with the HS-MS eNose data set. The RF algorithm in combination with 5-Fold Cross-Validation achieved the best accuracy rate for the test set with the low-level data fusion system. Furthermore, it allowed us to identify the most important features that could define the differences between RON 95 and RON 98 gasoline. On the other hand, using the HS-MS eNose and NIRS low-level data fusion reached better results than those obtained using NIRS data individually, with accuracy rates of 100% in both SVM and RF performances with the test set. In general, the performance of the SVM and RF algorithms was found to be similar

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

Toxic elements and trace elements in Macrolepiota procera mushrooms from southern Spain and northern Morocco

Anthropogenic activities, such as mining and fossil fuel combustion, produce large amounts of pollutants that affect environmental homeostasis. Wild edible mushrooms fructify exposed to environmental conditions, proving to be efficient accumulators of trace elements and toxic and potentially toxic elements. Due to the increasing consumption of mushrooms worldwide, this is of public health concern. In this work, the total content of chromium (Cr), arsenic (As), cadmium (Cd), mercury (Hg), lead (Pb), copper (Cu), zinc (Zn), and selenium (Se) was determined by ICP-MS in the caps and stipes of the high valued wild edible mushroom Macrolepiota procera collected in several locations of the South of Spain and the North of Morocco. The results obtained have indicated that the cap of M. procera contains a broad spectrum of both toxic elements and trace elements, occurring in higher contents in this part of the fruiting body with respect to the stipe. Moreover, Cu was the predominant element found in the samples studied, followed by Zn in most of the cases. The one-way ANOVA/Kruskal-Wallis test indicated that there were no significant differences in metal and metalloid content between the geographical areas studied. In addition, the results obtained through Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) support the conclusions drawn through univariate statistical studies, indicating that there is no obvious clustering trend for the M. procera cap samples based on the sampling area. The health risk assessment for M. procera caps showed a cause for concern related to Cr, Cd, As, and Hg due to frequent consumption of around 300 g of fresh caps per day during the mushrooming season. © 2022 The Author(s

Ultrasound-Assisted Extraction of Total Phenolic Compounds and Antioxidant Activity in Mushrooms

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

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