Publications by authors named "Jose Raul Belmonte-Sanchez"
H 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 H NMR and chromatographic techniques (gas and liquid chromatography) coupled to mass spectrometry was applied to provide more accurate knowledge about rums.
View Article and Find Full Text PDFAn Innovative Metabolomic Approach for Golden Rum Classification Combining Ultrahigh-Performance Liquid Chromatography-Orbitrap Mass Spectrometry and Chemometric Strategies.
J Agric Food Chem
January 2019
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.
View Article and Find Full Text PDFArticle Synopsis
- Quantitative boron-11 NMR spectroscopy has been successfully introduced as a new technique to measure boric acid content in commercial biocides, with detection limits as low as 0.02% w/w.
- The method shows strong performance metrics, including high linearity, good recovery rates, and low precision and uncertainty values, making it reliable for testing.
- When applied to five different biocides, the results were comparable to those obtained using ICP-MS, confirming its effectiveness and potential for use with various other materials.
Article Synopsis
- A study developed targeted and untargeted analyses using HS-SPME-GC-MS to classify 33 commercial rums, finding that certain compounds correlated with aging but posed challenges across different brands.
- To enhance classification, unsupervised methods like hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used, revealing significant chemical descriptors for rum classification.
- Linear discriminant analysis (LDA) effectively classified rums based on production factors like manufacturing country and aging, achieving high accuracy rates between 91% to 95%.