Geographical and varietal origin differentiation of alcoholic beverages through the association between FT-Raman spectroscopy and advanced data processing strategies.

The present work aimed to test the efficiency of FT-Raman spectroscopy for fruit spirits discrimination by developing differentiation models based on two approaches, namely a supervised statistical method (Partial Least Squares Discriminant Analysis), and a Machine Learning technique (Support Vector Machines). For this purpose, a data set comprising 86 Romanian distillate samples was used, which aimed to be differentiated in terms of the raw material used for production (plum, apple, pear and grape) and county of origin (Cluj, Satu Mare and Salaj). Eight distinct preprocessing methods (autoscale, mean center, variance scaling, smoothing, 1st derivative, 2nd derivative, standard normal variate and Pareto) followed by a feature selection step were applied to identify the meaningful input data based on which the most efficient classification models can be constructed. Both types of models led to accuracy scores greater than 90% in differentiating the distillate samples in terms of geographical and botanical origin.

Botanical honey recognition and quantitative mixture detection based on Raman spectroscopy and machine learning.

The development of new approaches for honey recognition, based on spectroscopic techniques, presents a huge market potential especially because of the fast development of portable equipment. As an emerging approach, the association between Raman spectroscopy and Artificial Intelligence (i.e. Machine Learning algorithms) for food and beverages recognition starts to prove its efficiency, becoming an important candidate for the development of a practical application. Through this study, new recognition models for the rapid and efficient botanical differentiation of investigated honey varieties were developed, allowing the correct prediction of each type in a percentage better than 81%. The performances of the constructed models were expressed in terms of precision, sensitivity, and specificity. Moreover, through this approach, the detection of honey mixtures was possible to be made and an estimative percentage of the mixture components was obtained. Thus, the applicative potential of this new approach for honey recognition as well as a qualitative and quantitative estimation of the honey mixture was demonstrated.

Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market.

Food composition issues represent an increasing concern nowadays, in the context of diverse food commodity varieties. The contents and types of fatty acids are a constant preoccupation among consumers because of their reflections of nutrition and health problems. This study aims to find the best tool for the rapid and reliable identification of similarities and differences among several food items from a fatty acid profile perspective. An acknowledged GC-FID method was considered, while, for a better interpretation of the analytical results, machine learning algorithms were used. It was possible to develop a recognition model able to simultaneously differentiate, with an accuracy of 79.3%, nine product types using the bagged tree ensemble model. The low number of samples or some similarities among the classes could be responsible for the wrong assignments that occurred, especially in the biscuit, wafer and instant soup classes. Better accuracies values of 95, 86.1, and 97.8% were obtained when the products were grouped into three categories: (1) sunflower oil, mayonnaise, margarine, and cream cheese; (2) biscuits, cookies, margarine, and wafers; and (3) sunflower oil, chips, and instant soup.

The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools.

The newly developed prediction models, having the aim to classify Romanian honey samples by associating ATR-FTIR spectral data and the statistical method, PLS-DA, led to reliable differentiations among the samples, in terms of botanical and geographical origin and harvesting year. Based on this approach, 105 out of 109 honey samples were correctly attributed, leading to true positive rates of 95% and 97% accuracy for the harvesting differentiation model. For the botanical origin classification, 83% of the investigated samples were correctly predicted, when four honey varieties were simultaneously discriminated. The geographical assessment was achieved in a percentage of 91% for the Transylvanian samples and 85% of those produced in other regions, with overall accuracy of 88% in the cross-validation procedure. The signals, based on which the best classification models were achieved, allowed the identification of the most significant compounds for each performed discrimination.

Combined Electrochemical, Raman Analysis and Machine Learning Assessments of the Inhibitive Properties of an 1,3,4-Oxadiazole-2-Thiol Derivative against Carbon Steel Corrosion in HCl Solution.

The inhibiting properties of 5-(4-pyridyl)-1,3,4-oxadiazole-2-thiol (PyODT) on the corrosion of carbon steel in 1.0 M HCl solution were investigated by potentiodynamic polarization, electrochemical impedance spectroscopy, Raman spectroscopy, and SEM-EDX analysis. An approach based on machine learning algorithms and Raman data was also applied to follow the carbon steel degradation in different experimental conditions. The electrochemical measurements revealed that PyODT behaves as a mixed-type corrosion inhibitor, reaching an efficiency of about 93.1% at a concentration of 5 mM, after 1 h exposure to 1.0 M HCl solution. Due to the molecular adsorption and structural organization of PyODT molecules on the C-steel surface, higher inhibitive effectiveness of about 97% was obtained at 24 h immersion. The surface analysis showed a significantly reduced degradation state of the carbon steel surface in the presence of PyODT due to the inhibitor adsorption revealed by Raman spectroscopy and the presence of N and S atoms in the EDX spectra. The combination of Raman spectroscopy and machine learning algorithms was proved to be a facile and reliable tool for an incipient identification of the corrosion sites on a metallic surface exposed to corrosive environments.

Eco-Friendly Nitrogen-Doped Graphene Preparation and Design for the Oxygen Reduction Reaction.

Four N-doped graphene materials with a nitrogen content ranging from 8.34 to 13.1 wt.% are prepared by the ball milling method. This method represents an eco-friendly mechanochemical process that can be easily adapted for industrial-scale productivity and allows both the exfoliation of graphite and the synthesis of large quantities of functionalized graphene. These materials are characterized by transmission and scanning electron microscopy, thermogravimetry measurements, X-ray powder diffraction, X-ray photoelectron and Raman spectroscopy, and then, are tested towards the oxygen reduction reaction by cyclic voltammetry and rotating disk electrode methods. Their responses towards ORR are analysed in correlation with their properties and use for the best ORR catalyst identification. However, even though the mechanochemical procedure and the characterization techniques are clean and green methods (i.e., water is the only solvent used for these syntheses and investigations), they are time consuming and, generally, a low number of materials can be prepared, characterized and tested. In order to eliminate some of these limitations, the use of regression learner and reverse engineering methods are proposed for facilitating the optimization of the synthesis conditions and the materials' design. Thus, the machine learning algorithms are applied to data containing the synthesis parameters, the results obtained from different characterization techniques and the materials response towards ORR to quickly provide predictions that allow the best synthesis conditions or the best electrocatalysts' identification.

Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination.

Through this pilot study, the association between Raman spectroscopy and Machine Learning algorithms were used for the first time with the purpose of distillates differentiation with respect to trademark, geographical and botanical origin. Two spectral Raman ranges (region I-200-600 cm-1 and region II-1200-1400 cm-1) appeared to have the higher discrimination potential for the investigated distillates. The proposed approach proved to be a very effective one for trademark fingerprint differentiation, a model accuracy of 95.5% being obtained (only one sample was misclassified). A comparable model accuracy (90.9%) was achieved for the geographical discrimination of the fruit spirits which can be considered as a very good one taking into account that this classification was made inside Transylvania region, among neighbouring areas. Because the trademark fingerprint is the prevailing one, the successfully distillate type differentiation, with respect to the fruit variety, was possible to be made only inside of each producing entity.

The Spin of Electrons and the Proof for the Action of Homeopathic Remedies.

In the last 200 years, the action of the highly diluted homeopathic remedies has been proved by their curative effect on the human organism. In this work, a hypothesis concerning the mystifying question about this action is proposed. The hypothesis suggests that any pathology, either functional or structural, can be detected in the change of the overall energy of the human body. Such energy is constituted by fields of force according to quantum physics. More precisely, every disturbance of the human organism affects the spin on electrons of different elements within the human body, and their reset could take place with an agent similar to the electromagnetic force that created the problem. This statement has been proved by the correct homeopathic treatments, as it can be seen in many published cases. The hypothesis is based on two approaches, the idea of the spin of electrons and the vital force, and their scientific relevance.

Raman spectroscopy and machine-learning for edible oils evaluation.

Because of the important advantages as rapidity, cost effectiveness and no sample preparation necessity, encountered in most of the cases, Raman spectroscopy gained more and more attention during the last years with regard to its application in food and beverages authenticity. Vegetable cold-pressed oils obtained from: sesame, hemp, walnut, linseed, pumpkin and sea buckthorn have gained increased attention in consumer interest due to their high nutrient value and health benefits. The high commercial value of these, brought the temptation from some unfair producers and sellers to gain an illegal profit by replacing the raw material of these oils by cheaper ones (i.e. sunflower). Here a new approach based on the rapid processing of Raman spectra using Machine Learning algorithms, for edible oil authentication was developed and successfully tested. Through this approach, not only the adulteration detection was achieved but also an initial estimation of its magnitude.

An optimized green preparation method for the successful application of Raman spectroscopy in honey studies.

Raman spectroscopy represents an emerging technique for food authentication being a fast, reliable analytical method, requiring a minimum sample preparation step. Anyway, as in the case of any analytical techniques, there are some limitations which need to be properly assessed before applying this method in honey authentication. In this regard, the aim of this study consisted in the development of a simple working protocol, for honey sample preparation, which can simultaneously overcome the main limitations of Raman spectroscopy in honey studies, such as crystallization and fluorescence. Thus, in this work, a new green sample preparation method is proposed, discussed and its robustness is tested. It has been demonstrated that through honey dilution, in distilled water, reliable and reproductible spectra could be obtained, allowing the investigation of different types of honey. The main advantage of the method consists in the simultaneously overcoming of the most significant limitations of Raman spectroscopy employment in honey studies, such as crystallization and fluorescence, by a simple 1:1 w/v dilution of honey in distilled water.

Electrochemical and spectroscopic studies of ssDNA damage induced by hydrogen peroxide using graphene based nanomaterials.

The oxidative damage of deoxyribonucleic acid (DNA) has been intensively studied due to its role in the occurrence of some diseases. The hydrogen peroxide (H2O2) is one of the reactive oxygen species (ROS). It can induce oxidation of DNA bases, sugar lesions or DNA strand breaks. The Pt/Gr-Au-3 modified electrode was employed for the analysis of four ssDNA samples: single-stranded DNA (ssDNA), ssDNA pre-treated with hydrogen peroxide (ssDNA-H2O2), ssDNA pre-treated with graphene-gold nanoparticles (ssDNA-Gr-Au) and ssDNA-Gr-Au complex pre-treated with hydrogen peroxide (ssDNA-Gr-Au-H2O2). By monitoring the changes of the purine oxidation peaks currents, we obtained valuable information about the damage induced by the hydrogen peroxide onto the un-treated or graphene pre-treated ssDNA and also about the interaction between ssDNA and graphene-based nanomaterial. The FTIR analysis has been also used to obtain information about the ssDNA damage. These findings allowed us to prove the utility of graphene-based nanomaterials (mainly Gr-Au-3) not only for the investigation of the oxidative damage induced by a non-radical oxidant, but also for the determination of the type of interaction between ssDNA and graphene surface. The stability of the ssDNA-Gr-Au-3 complex against the damage induced by H2O2, in the absence of reduced transition metals, was also established.

L-Leucine for gold nanoparticles synthesis and their cytotoxic effects evaluation.

This work reports the preparation of water-soluble leucine capped gold nanoparticles by two single-step synthesis methods. The first procedure involves a citrate reduction approach where the citrate is used as reducing agent and leucine as capping/stabilizing agent. Different sizes of gold nanoparticles, citrate reduced and stabilized by leucine, Leu-AuNPs-C, with the mean diameters in the range of 21-56 nm, were obtained by varying the macroscopic parameters such as: concentration of the gold precursor solution, Au (III):citrate molar ratio and leucine pH. In the second procedure, leucine acts both as reducing and stabilizing agent, allowing us to obtain spherical gold nanoparticles, Leu-AuNPs, with a majority of 80 % (with the mean diameter of 63 nm). This proves that leucine is an appropriate reductant for the formation of water-soluble and stable gold nanoparticles colloids. The characterization of the leucine coated gold nanoparticles was carried out by TEM, UV-Vis and FT-IR analysis. The cytotoxic effect of Leu-AuNPs-C and Leu-AuNPs was also evaluated.