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

Abstract

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.

Multivariate Statistical Analysis of Metabolites in Anisodus tanguticus (Maxim.) Pascher to Determine Geographical Origins and Network Pharmacology

“…Previous studies have indicated that compounds are associated with season, altitude, or some climate factors (Sun et al, 2016 ; Lu et al, 2017 ; Wu et al, 2019 ). In addition, permutation tests ( n = 200) were performed to evaluate whether the discriminant models were overfitting the data (Belmonte-Sánchez et al, 2019 ).…”

Chemical Analysis Combined with Multivariate Statistical Methods to Determine the Geographical Origin of Milk from Four Regions in China

“…Interception is a measure of over-fitting. Desirable values of y-intercepts should be less than 0.40 for R 2 intercept and less than 0.05 for Q 2 intercept, respectively [ 44 ], indicating that the model is effective and there is no over-fitting. The model test results are included in each table ( Table 2 , Table 3 , Table 4 , Table 5 and Table 6 ).…”

Comparison of Potent Odorants in Traditional and Modern Types of Chinese Xiaoqu Liquor (Baijiu) Based on Odor Activity Values and Multivariate Analyses

“…The explained variation (R 2 Y) and the predictive capability (Q 2 Y) for the model were 0.993 and 0.978, respectively, which indicated that the model was excellent because Q 2 Y was close to 1. In addition, permutation tests (n = 200) were performed to evaluate whether the discriminant models were overfitting the data [32]. The permutation tests randomly rearranged the experiments by changing the sort order of the classification variables (Y) and randomly assigned Q 2 Y up to 200 times.…”