1H NMR and multi-technique data fusion as metabolomic tool for the classification of golden rums by multivariate statistical analysis.

1H NMR spectroscopy combined with chemometrics was applied for the first time for golden rum classification based on several factors as fermentation barrel, raw material, distillation method and aging. Principal component analysis (PCA) was used to assess the overall structure, and partial least square discriminant analysis (PLS-DA) was carried out for the analytical discrimination of rums. Additionally, data-fusion of 1H NMR and chromatographic techniques (gas and liquid chromatography) coupled to mass spectrometry was applied to provide more accurate knowledge about rums. This approach provided a classification of samples with lower error rate than the one obtained by the use of a single technique (spectroscopic or chromatographic). The results showed that 1H NMR spectroscopy is an appropriate technique for the suitable classification of >95.5% of the samples. When data fusion methodology of spectroscopic and spectrometric data was performed, the prediction efficiency can reach 100% of the samples.

An Innovative Metabolomic Approach for Golden Rum Classification Combining Ultrahigh-Performance Liquid Chromatography-Orbitrap Mass Spectrometry and Chemometric Strategies.

A comprehensive fingerprinting strategy for golden rum classification considering different categories such as fermentation barrel, raw material, and aging is provided, using a metabolomic fingerprinting approach. A nontarget fingerprinting of 30 different rums using liquid chromatography coupled to high-resolution mass spectrometry (Exactive Orbitrap mass analyzer, LC-HRMS) was applied. Principal component analysis (PCA) was used to assess the overall structure of the data and to identify potential outliers. Different chemometric analyses such as partial least-squares discriminant analysis (PLS-DA) were used. A variable importance in projection (VIP) selection method was applied to identify the most significant markers that allow group separation. Compounds related to aging and fermentation processes such as furfural derivates (e.g., hydroxymethylfurfural) and sugars (e.g., glucose, mannitol) were found as the most discriminant compounds (VIP threshold value >1.5). Suitable separation according to selected categories was achieved, and a classification ability of the models of close to 100% was achieved.

Pushing the frontiers: boron-11 NMR as a method for quantitative boron analysis and its application to determine boric acid in commercial biocides.

Quantitative boron-11 NMR (11B qNMR) spectroscopy has been introduced for the first time as a method to determine boric acid content in commercial biocides. Validation of the method affords a limit of detection of 0.02% w/w and a limit of quantification of 0.04% w/w, which are low enough to determine boric acid in commercial biocides. Other figures of merit such as linearity (R2 > 0.99), recovery (93.6%-106.2%), intra- and inter-day precision (from 0.7 to 2.0%), uncertainty (3.7 to 4.4%) and matrix effects were also evaluated. This method was successfully applied to determine boric acid in five different commercial biocides in a wide range of concentrations (<0.05 to 10% w/w) providing excellent results when they were compared with those obtained using inductively coupled plasma-mass spectrometry (ICP-MS). The suitability of this method for a fast and reliable quantification of boric acid in commercial biocide preparations has been demonstrated. The absence of the matrix effect allows the application of this validated method for the determination of boric acid in other matrices of diverse composition.

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

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.