Analysis of Barrel-Aged Kentucky Bourbon Whiskey by Ultrahigh Resolution Mass Spectrometry

Multiple and Optimal Screening Subset: a method selecting global characteristic congeners for robust foodomics analysis

Metabolomics and foodomics shed light on the molecular processes within living organisms and the complex food composition by leveraging sophisticated analytical techniques to systematically analyze the vast array of molecular features. The traditional feature-picking method often results in arbitrary selections of the model, feature ranking, and cut-off, which may lead to suboptimal results. Thus, a Multiple and Optimal Screening Subset (MOSS) approach was developed in this study to achieve a balance between a minimal number of predictors and high predictive accuracy during statistical model setup. The MOSS approach compares five commonly used models in the context of food matrix analysis, specifically bourbons. These models include Student's t-test, receiver operating characteristic curve, partial least squares-discriminant analysis (PLS-DA), random forests, and support vector machines. The approach employs cross-validation to identify promising subset feature candidates that contribute to food characteristic classification. It then determines the optimal subset size by comparing it to the corresponding top-ranked features. Finally, it selects the optimal feature subset by traversing all possible feature candidate combinations. By utilizing MOSS approach to analyze 1406 mass spectral features from a collection of 122 bourbon samples, we were able to generate a subset of features for bourbon age prediction with 88% accuracy. Additionally, MOSS increased the area under the curve performance of sweetness prediction to 0.898 with only four predictors compared with the top-ranked four features at 0.681 based on the PLS-DA model. Overall, we demonstrated that MOSS provides an efficient and effective approach for selecting optimal features compared with other frequently utilized methods.

Characterizing bourbon whiskey via the combination of LC-MS and GC-MS based molecular fingerprinting

To discover molecular fingerprints that vary by bourbon's age or color, 121 commercial bourbon samples were analyzed using both gas chromatography and liquid chromatography combined with mass spectrometry. In total, 81 non-volatile compounds and 102 volatile compounds were identified. A subset of samples containing age information was divided into training (n = 70) and testing (n = 12) groups to build and validate the Partial Least Square - Discriminant Analysis models for age-based classification. After internal and external validation, our models showed that 8 molecular markers unitedly provided up to 100% accuracy of prediction in distinguishing unaged (0 years), younger (0-4 years), and older (>4 years) bourbons. Additional efforts were also made to detect molecular signatures that contributed to the color differences in bourbon. Overall, our study provided a powerful strategy for the confident identification of high-quality products and robust evaluation of their commercial values.

DLLμE/GC-MS as a Powerful Analytical Approach to Establish the Volatilomic Composition of Different Whiskeys

The volatilomic fingerprint of nine different whiskeys was established using a rapid and sensitive analytical approach based on dispersive liquid–liquid microextraction (DLLμE) followed by gas chromatography mass spectrometry detection (GC-MS) and gas chromatography with flame ionization detection (GC-FID). The influence of the extractor solvent on the extraction efficiency of volatile compounds (VOCs) was evaluated by DLLμE/GC-MS. The highest amounts of VOCs were obtained using 5 mL of sample, dichloromethane as the extractor solvent, and acetone as the disperser solvent. The proposed method showed no matrix effect, good linearity (R2 ≥ 0.993) in the assessed concentration range, recovery (ranging from 70 to 99%, precision (RSD ≤ 15%) and sensitivity (low limits of detection and quantification). A total of 37 VOCs belonging to different biosynthetic pathways including alcohols, esters, acids, carbonyl compounds, furanic compounds and volatile phenols were identified and quantified using DLLμE/GC-MS and DLLμE/GC-FID, respectively. Alcohols (3-methylbutan-1-ol, propan-1-ol), esters (ethyl decanoate, ethyl octanoate, ethyl hexanoate), and acids (decanoic acid, octanoic acid, hexanoic acid) were the most abundant chemical families. The multivariate statistical analysis allowed for the discrimination of whiskeys based on their volatilomic fingerprint, namely octanoic acid, 2-furfural, ethyl octanoate, ethyl hexanoate, acetic acid, ethyl dodecanoate, butan-1-ol, and ethyl decanoate.