Combining surface-enhanced Raman spectroscopy and paper spray mass spectrometry for the identification and confirmation of psychotropic substances in alcoholic beverages.

Criminal practices in which an individual becomes vulnerable and prone to sexual assault after ingesting drinks spiked with doping substances have become a social concern globally. As forensic protocols require a multi-tiered strategy for chemical evidentiary analysis, the backlog of evidence has become a significant problem in the community. Herein, a fast, sensible, and complementary dual analytical methodology was developed using a single commercial paper substrate for surface-enhanced Raman spectroscopy (SERS) and paper spray mass spectrometry (PS-MS) analysis to identify psychotropic substances added to alcoholic beverages irrefutably. To study and investigate this criminal practice, pharmaceutical formulations containing distinct psychotropic substances (zolpidem, clonazepam, diazepam, and ketamine) were added to drinks typically consumed at parties and festivals (Pilsen beer, açaí Catuaba®, gin tonic, and vodka mixed with Coca-Cola Zero®). A simple liquid-liquid extraction with a low-temperature partitioning (LLE-LTP) procedure was applied to the drinks and effectively minimized matrix effects. As a preliminary analysis, SERS spectra combined with Hierarchical Clustering Analysis (HCA) provided sufficient information to investigate the samples further. The presence of the protonated species for the psychotropic substances in the spiked drinks was readily verified in the mass spectra and confirmed by tandem mass spectrometry. Finally, the results demonstrate the potential of this methodology to be easily implemented into the routine of forensic laboratories and to be further employed at harm reduction tends at parties and festivals to detect contaminated beverages promptly and irrefutably as an efficient tool to prevent such crimes.

Sandpaper: Efficient Medium for Analysis of Polishable Solid Samples by Ambient Mass Spectrometry.

In this paper, we employed sandpaper as an efficient medium to analyze polishable solid samples. As proof of principle, the surfaces of coffee beans were sanded with pieces of sandpaper cut into a triangular shape. The triangle was placed in front of the mass spectrometer inlet, and methanol was dropped on its surface. After a high-voltage application, the fingerprints of the coffee beans (n = 100) were achieved in the positive and negative ion modes in an identical procedure employed for paper spray mass spectrometry (PS-MS) analysis. The new methodology, named sandpaper spray mass spectrometry (SPS-MS), allowed for identifying many compounds in coffee beans, mainly caffeine, sugars, and carboxylic acids, among other molecules. The new technique shows advantages over PS-MS in analyzing polishable solid samples. Furthermore, compared to the direct analysis of tissues such as leaves, grains, and seeds, in which the pieces must be cut in a triangular shape (a difficult task depending on the samples' hardness), the SPS-MS technique is much more straightforward. Finally, SPS-MS can be potentially applied to analyze other hard surfaces such as wood, plastic, and assorted crop grains.

Raman spectroscopy: A new strategy for monitoring the quality of green coffee beans during storage.

Raman spectroscopy was used to identify chemical changes associated sensory quality of coffee beans, for natural and pulped natural coffee stored in different packaging. The green beans of natural coffee and pulped natural coffee were stored in three types of packaging materials in a commercial warehouse. Sensory analyses were performed, and Raman spectra were collected after 0, 3, 6, 9, 12, and 18 storage months. Raman spectra were used to construct multivariate control charts. The charts, which were constructed using principal component analysis, can only be used to identify chemical changes in the green beans from pulped natural coffee stored in different packaging materials. Raman spectroscopy is more sensitive than sensory analysis for detecting chemical changes in stored pulped natural coffee. The measured changes ultimately affect the quality of the beverage because samples stored for six months in paper packaging were determined to no longer meet the quality control requirements.

Raman spectroscopy for the differentiation of Arabic coffee genotypes.

The objective of this study was to evaluate the ability of Raman spectroscopy to identify the genotype of green coffee beans. Four genotypes of Arabic coffee: one Mundo Novo line (G1) and three Bourbon lines (G2, G3, and G4). The harvest was selected using a wet processing method. Raman spectra of the samples were obtained using a FT-Raman RFS/100 spectrometer in the spectral range of 3500-400 cm-1. The data were treated using chemometric unsupervised classification tools and supervised analysis. Using the unsupervised analysis (PCA), the apparent tendency of agglomeration between samples G1 and G3 was verified. These differences were present in the spectral bands that are characteristic of fatty acids and kahweol. Based on this information, a classification model to discriminate (PLS-DA) the Mundo Novo and Bourbon samples was utilized. Raman spectroscopy allowed the building of an adequate model to differentiate between coffee genotypes.

Discrimination between conventional and omega-3 fatty acids enriched eggs by FT-Raman spectroscopy and chemometric tools.

This work developed an analytical method to differentiate conventional and omega-3 fat acids enriched eggs by Raman spectroscopy and multivariate supervised classification with Partial Least Squares Discriminant Analysis (PLS-DA). Forty samples of enriched eggs and forty samples of different types of common eggs from different batches were used to build the model. Firstly, gas chromatography was employed to analyze fatty acid profiles in egg samples. Raman spectra of the yolk extracts were recorded in the range from 3100 to 990 cm-1. PLS-DA model was able to correctly classify samples with nearly 100% success rate. This model was validated estimating appropriate figures of merit. Predictions uncertainties were also estimated by bootstrap resampling. The most discriminant Raman modes were identified based on VIP (variables importance in projection) scores. This method has potential to assist food industries and regulatory agencies for food quality control, allowing detecting frauds and enabling faster and reliable analyzes.

Technological aspects of lactose-hydrolyzed milk powder.

Few reports describe the effect of lactose hydrolysis on the properties of milk powder during production and storage. Hence, the aim of this study was to evaluate the effects of five different levels of enzymatic lactose hydrolysis during the production and storage of milk powder. As the lactose hydrolysis rate increased, adhesion to the drying chamber also increased, due to higher levels of particle agglomeration. Additionally, more brown powder was obtained when the lactose hydrolysis rate was increased, which in turn negatively affected rehydration ability. Using Raman spectroscopy, crystallization of the lactose residues in various samples was assessed over 6weeks of accelerated aging at a room temperature environment with 75.5% of air moisture. Products with 25% or greater lactose hydrolysis showed no signs of crystallization, in contrast to the non-hydrolyzed sample.

Raman hyperspectral imaging in conjunction with independent component analysis as a forensic tool for explosive analysis: The case of an ATM explosion.

In this work, Raman hyperspectral imaging, in conjunction with independent component analysis, was employed as an analytical methodology to detect an ammonium nitrate fuel oil (ANFO) explosive in banknotes after an ATM explosion experiment. The proposed methodology allows for the identification of the ANFO explosive without sample preparation or destroying the sample, at quantities as small as 70µgcm-2. The explosive was identified following ICA data decomposition by the characteristic nitrate band at 1044cm-1. The use of Raman hyperspectral imaging and independent component analysis shows great potential for identifying forensic samples by providing chemical and spatial information.

Heart fossilization is possible and informs the evolution of cardiac outflow tract in vertebrates.

Elucidating cardiac evolution has been frustrated by lack of fossils. One celebrated enigma in cardiac evolution involves the transition from a cardiac outflow tract dominated by a multi-valved conus arteriosus in basal actinopterygians, to an outflow tract commanded by the non-valved, elastic, bulbus arteriosus in higher actinopterygians. We demonstrate that cardiac preservation is possible in the extinct fish Rhacolepis buccalis from the Brazilian Cretaceous. Using X-ray synchrotron microtomography, we show that Rhacolepis fossils display hearts with a conus arteriosus containing at least five valve rows. This represents a transitional morphology between the primitive, multivalvar, conal condition and the derived, monovalvar, bulbar state of the outflow tract in modern actinopterygians. Our data rescue a long-lost cardiac phenotype (119-113 Ma) and suggest that outflow tract simplification in actinopterygians is compatible with a gradual, rather than a drastic saltation event. Overall, our results demonstrate the feasibility of studying cardiac evolution in fossils.

Analysis of spreadable cheese by Raman spectroscopy and chemometric tools.

In this work, FT-Raman spectroscopy was explored to evaluate spreadable cheese samples. A partial least squares discriminant analysis was employed to identify the spreadable cheese samples containing starch. To build the models, two types of samples were used: commercial samples and samples manufactured in local industries. The method of supervised classification PLS-DA was employed to classify the samples as adulterated or without starch. Multivariate regression was performed using the partial least squares method to quantify the starch in the spreadable cheese. The limit of detection obtained for the model was 0.34% (w/w) and the limit of quantification was 1.14% (w/w). The reliability of the models was evaluated by determining the confidence interval, which was calculated using the bootstrap re-sampling technique. The results show that the classification models can be used to complement classical analysis and as screening methods.

Classification of frankfurters by FT-Raman spectroscopy and chemometric methods.

Frankfurters are widely consumed all over the world, and the production requires a wide range of meat and non-meat ingredients. Due to these characteristics, frankfurters are products that can be easily adulterated with lower value meats, and the presence of undeclared species. Adulterations are often still difficult to detect, due the fact that the adulterant components are usually very similar to the authentic product. In this work, FT-Raman spectroscopy was employed as a rapid technique for assessing the quality of frankfurters. Based on information provided by the Raman spectra, a multivariate classification model was developed to identify the frankfurter type. The aim was to study three types of frankfurters (chicken, turkey and mixed meat) according to their Raman spectra, based on the fatty vibrational bands. Classification model was built using partial least square discriminant analysis (PLS-DA) and the performance model was evaluated in terms of sensitivity, specificity, accuracy, efficiency and Matthews's correlation coefficient. The PLS-DA models give sensitivity and specificity values on the test set in the ranges of 88%-100%, showing good performance of the classification models. The work shows the Raman spectroscopy with chemometric tools can be used as an analytical tool in quality control of frankfurters.