Rum classification using fingerprinting analysis of volatile fraction by headspace solid phase microextraction coupled to gas chromatography-mass spectrometry

Abstract

In this study, targeted and untargeted analyses based on headspace solid phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME-GC-MS) method were developed for classifying 33 different commercial rums. Targeted analysis showed correlation of ethyl acetate and ethyl esters of carboxylic acids with aging when rums of the same brand were studied, but presented certain limitations when the comparison was carried out between different brands. To overcome these limitations, untargeted strategies based on unsupervised treatments, such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as supervised methods, such as linear discriminant analysis (LDA) were applied. HCA allowed distinguishing main groups (with and without additives), while the PCA method indicated 40 ions corresponding to 13 discriminant compounds as relevant chemical descriptors for the correct rum classification (PCA variance of 88%). The compounds were confirmed based on the combination of retention indexes and low and high-resolution mass spectrometry (HRMS). Using the obtained results, LDA was carried out for the analytical discrimination of the remaining rums based on manufacturing country, raw material type, distillation method, wood barrel type and aging period and 94%, 91%, 92%, 95% and 94% of rums, respectively, were correctly classified. The proposed methodology has led to a robust analytical strategy for the classification of rums as a function of different parameters depending on the rum production process.

Influence of Prolonged Deep‐Frying Using Various Oils on Volatile Compounds Formation of French Fries Using GC–MS, GC‐O, and Sensory Evaluation

“…To verify the statistical model validation, permutation test (200 permutations) was applied. The recommended values for good fitting of models have been described as a R 2 Y intercept = ~0.3 and Q 2 Y intercept<0.05 (Belmonte‐Sánchez et al, 2018). The result of permutation test showed that the model validation was significant with the intercepts of R 2 Y = (0.0, 0.546) and Q 2 Y = (0.0, −0.668) and represented in Fig.…”

Feasibility of Applying Untargeted Metabolomics with GC-Orbitrap-HRMS and Chemometrics for Authentication of Black Pepper (Piper nigrum L.) and Identification of Geographical and Processing Markers

“…Differential metabolites were putatively identified by matching the experimental mass spectra with those provided by the NIST MS Search 2.2 library (National Institute of Standards and Technology, Gaithersburg, MD), both recorded using EI at 70 eV. A NIST match factor threshold > 750 was considered for reliable identification . The high-resolution filtering (HRF) tool provided by Compound Discoverer software was also considered for matching deconvoluted experimental high-resolution mass spectra and to propose a logical candidate.…”

“…A NIST match factor threshold > 750 was considered for reliable identification. 26 The high-resolution filtering (HRF) tool provided by Compound Discoverer software was also considered for matching deconvoluted experimental high-resolution mass spectra and to propose a logical candidate. Besides, the putative identification was confirmed by comparing the experimental KI calculated for each metabolite 27 with the KI included in the NIST library, considering a maximum KI deviation of ± 20 for reliable putative identification.…”

Varietal Discrimination of Pineapple (<i>Ananas comosus</i> L.) Using Chromatographic Fingerprints and Chemometrics

“…PCA has been implemented in many recent studies for authentication and classification in food [25][26][27][28]. It was also used in a recent study to determine the seasonal and regional variation in food authentication based on chromatographic fingerprinting [29].…”