Uncovering the Counterfeit: A study of whiskey authenticity through volatile organic compound fingerprinting, aroma and color sensory analysis.

This study presents a method employing gas chromatography coupled with mass spectrometry and headspace solid-phase microextraction (HS-SPME-GC-MS), supplemented with chemometrics (Soft independent modelling of class analogies - SIMCA), to analyze volatile organic compound (VOCs) profiles in suspect whiskey samples. Furthermore, a sensory analysis of aroma and color was conducted with a panel of 52 non-trained volunteers to evaluate their ability to discriminate and preference for counterfeit whiskeys. The HS-SPME-GC-MS method successfully distinguished 41 seized samples from authentic beverages. Interestingly, sensory analysis revealed that panelists could differentiate between counterfeit and authentic samples with a reference standard but did not consistently show a preference for aroma. In some cases, there was even a preference for the color of counterfeit whiskeys. The findings suggest that sensorial tests alone may not effectively distinguish counterfeit from authentic whiskeys, especially for non-expert consumers, highlighting the need for analytical instrumentation methods in fraud detection.

From inspection to analysis: A combined approach to identifying counterfeit whiskeys using HS-GC-FID and bottle integrity.

Counterfeiting of alcoholic beverages, particularly high-value spirits such as whiskey, presents significant challenges for regulators, manufacturers, and consumers. In this study, we introduce and validate a novel application of headspace extraction (HS) followed by gas chromatography with flame ionization detection (GC-FID) for the quantitative determination of ethanol content in 42 suspected counterfeit brazilian samples of whiskeys. This method, in conjunction with visual inspection of material inconsistencies, offers a combined approach to identify potential cases of fraud. The HS-GC-FID findings revealed that only 19% of the analyzed samples had ethanol content in the limits declared on the label, emphasizing the role of ethanol content as a chemical marker for suspected beverage fraud.

Hybrid volatilomics in cancer diagnosis by HS-GC-FID fingerprinting.

Assessing volatile organic compounds (VOCs) as cancer signatures is one of the most promising techniques toward developing non-invasive, simple, and affordable diagnosis. Here, we have evaluated the feasibility of employing static headspace extraction (HS) followed by gas chromatography with flame ionization detector (GC-FID) as a screening tool to discriminate between cancer patients (head and neck-HNC,n= 15; and gastrointestinal cancer-GIC,n= 19) and healthy controls (n= 37) on the basis of a non-target (fingerprinting) analysis of oral fluid and urine. We evaluated the discrimination considering a single bodily fluid and adopting the hybrid approach, in which the oral fluid and urinary VOCs profiles were combined through data fusion. We used supervised orthogonal partial least squares discriminant analysis for classification, and we assessed the prediction power of the models by analyzing the values of goodness of prediction (Q2Y), area under the curve (AUC), sensitivity, and specificity. The individual models HNC urine, HNC oral fluid, and GIC oral fluid successfully discriminated between healthy controls and positive samples (Q2Y = 0.560, 0.525, and 0.559; AUC = 0.814, 0.850, and 0.926; sensitivity = 84.8, 70.2, and 78.6%; and specificity = 82.3; 81.5; 87.5%, respectively), whereas GIC urine was not adequate (Q2Y = 0.292, AUC = 0.694, sensitivity = 66.1%, and specificity = 77.0%). Compared to the respective individual models, Q2Y for the hybrid models increased (0.623 for hybrid HNC and 0.562 for hybrid GIC). However, sensitivity was higher for HNC urine and GIC oral fluid than for hybrid HNC (75.6%) and hybrid GIC (69.8%), respectively. These results suggested that HS-GC-FID fingerprinting is suitable and holds great potential for cancer screening. Additionally, the hybrid approach tends to increase the predictive power if the individual models present suitable quality parameter values. Otherwise, it is more advantageous to use a single body fluid for analysis.