Quantification of whisky congeners by 1H NMR spectroscopy

Quantitative NMR analysis of sugars in natural sweeteners: Profiling in honey, jaggery, date syrup, and coconut sugar

Due to the high demand for natural sweeteners and their perceived health benefits, it is crucial to use analytical techniques for accurately profiling natural sweeteners. The present study describes a simple and fast approach for the analysis of sweeteners using 1D - 1H NMR spectroscopy. This method is based on the direct detection of protons in sugar molecules with an internal standard, without the need for complex derivatization steps. The presented approach offers a faster and more convenient way of quantifying mono-saccharides mainly glucose and fructose and di-saccharides like sucrose in various selected sweeteners. These includes honey, jaggery, coconut sugar, and date syrup. The direct 1D - 1H NMR method with an internal standard yields accurate and precise quantification results with good reproducibility and minute analysis times. This information is of increasing importance to both consumers and the food industry, as it provides a reliable and accurate method for characterizing and verifying natural sweeteners. Overall, 1D - 1H NMR spectroscopy can be a valuable tool for the rapid and easy analysis of sugar content in food products, and it may have potential applications in the food industry.

A QSPR Model for Henry's Law Constants of Organic Compounds in Water and Ethanol for Distilled Spirits

Henry's law describes the vapor-liquid equilibrium for dilute gases dissolved in a liquid solvent phase. Descriptions of vapor-liquid equilibrium allow the design of improved separations in the food and beverage industry. The consumer experience of taste and odor are greatly affected by the liquid and vapor phase behavior of organic compounds. This study presents a machine learning (ML) based model that allows quick, accurate predictions of Henry's law constants (kH) for many common organic compounds. Users input only a Simplified Molecular-Input Line-Entry System (SMILES) string or a common English name, and the model returns Henry's law estimates for compounds in water and ethanol. Training was performed on 5,690 compounds. Training data were gathered from an existing database and were supplemented with quantum mechanical (QM) calculations. An extra trees regression model was generated that predicts kH with a mean absolute error of 1.3 in log space and an R2 of 0.98. The model is applied to common flavor and odor compounds in bourbon whiskey as a test case for food and beverage applications.

Food for Thought: Optical Sensor Arrays and Machine Learning for the Food and Beverage Industry

Arrays of cross-reactive sensors, combined with statistical or machine learning analysis of their multivariate outputs, have enabled the holistic analysis of complex samples in biomedicine, environmental science, and consumer products. Comparisons are frequently made to the mammalian nose or tongue and this perspective examines the role of sensing arrays in analyzing food and beverages for quality, veracity, and safety. I focus on optical sensor arrays as low-cost, easy-to-measure tools for use in the field, on the factory floor, or even by the consumer. Novel materials and approaches are highlighted and challenges in the research field are discussed, including sample processing/handling and access to significant sample sets to train and test arrays to tackle real issues in the industry. Finally, I examine whether the comparison of sensing arrays to noses and tongues is helpful in an industry defined by human taste.

Recent advances in whiskey analysis for authentication, discrimination, and quality control

In order to safeguard authentic whiskey products from fraudulent or counterfeit practices, high throughput solutions that provide robust, rapid, and reliable solutions are required. The implementation of some analytical strategies is quite challenging or costly in routine analysis. Qualitative screening of whiskey products has been explored, but due to the nonspecificity of the chemical compounds, a more quantitative confirmatory technique is required to validate the result of the whiskey analysis. Hence, combining analytical and chemometric methods has been fundamental in whiskey sample differentiation and classification. A comprehensive update on the most relevant and current analytical techniques, including spectroscopic, chromatographic, and novel technologies employed within the last 5 years in whiskey analysis for authentication, discrimination, and quality control, are presented. Furthermore, the technical challenges in employing these analytical techniques, future trends, and perspectives are emphasized.

Investigation of Solid Phase Microextraction Gas Chromatography–Mass Spectrometry, Fourier Transform Infrared Spectroscopy and 1H qNMR Spectroscopy as Potential Methods for the Authentication of Baijiu Spirits

The baijiu spirit is often the focus of fraudulent activity due to the widely varying prices of the products. In this work, Solid Phase Microextraction Gas Chromatography (SPME GCMS), Fourier Transform Infrared (FTIR) Spectroscopy and 1H qNMR spectroscopy were evaluated as potential methods to authenticate baijiu samples. Data were collected for 30 baijiu samples produced by seven different distilleries. The data from the SPME GCMS and FTIR methods were treated by a Principal Component Analysis to identify clusters that would suggest chemical differences in the products from different distilleries. The results suggest that SPME GCMS has the potential to be a fully portable method for baijiu authentication. FTIR did not appear suitable for authentication but can be used to find the %ABV range of the sample. 1H quantitative NMR (1H qNMR) was utilized to quantify the ethanol concentrations and calculate the observable congener chemistry comprising ester, ethanol, methanol, fusel alcohol, and organic acids. Discrepancies in ethanol content were observed in three samples, and a lack of major congeners in two samples indicates the possible presence of a counterfeit product. Detailed and quantitative congener chemistry is obtainable by NMR and provides a possible fingerprint analysis for the authentication and quality control of baijiu style, producer, and the length of the ageing process.

Growth of Plasmonic Nanoparticles for Aging Cask-Matured Whisky

The maturation of spirit in wooden casks is key to the production of whisky, a hugely popular and valuable product, with the transfer and reaction of molecules from the wooden cask with the alcoholic spirit imparting color and flavor. However, time in the cask adds significant cost to the final product, requiring expensive barrels and decades of careful storage. Thus, many producers are concerned with what "age" means in terms of the chemistry and flavor profiles of whisky. We demonstrate here a colorimetric test for spirit "agedness" based on the formation of gold nanoparticles (NPs) by whisky. Gold salts were reduced by barrel-aged spirit and produce colored gold NPs with distinct optical properties. Information from an extinction profile, such as peak position, growth rate, or profile shape, was analyzed, and our assay output was correlated with measurements of the whisky sample makeup, assays for key functional groups, and spiking experiments to explore the mechanism in more detail. We conclude that age is not just a number, that the chemical fingerprint of key flavor compounds is a useful marker for determining whisky "age", and that our simple reduction assay could assist in defining the aged character of a whisky and become a useful future tool on the warehouse floor.

A Monte Carlo Method for Analyzing Systematic and Random Uncertainty in Quantitative Nuclear Magnetic Resonance Measurements

Quantitative nuclear magnetic resonance (qNMR) is a powerful analytical technology that is capable of quantifying the concentration of any analyte with exquisite accuracy and precision so long as it contains at least one nonlabile nuclear magnetic resonance (NMR)-active nucleus. Unlike with traditional analytical technologies, the concentrations of analytes do not directly influence the uncertainty in the quantification of NMR signals because an ideal NMR response depends only on the nature and amount of the nucleus being observed. Rather, in the absence of spectral artifacts and under favorable experimental conditions, the measurement uncertainty may be influenced by the following factors: (1) spectroscopic parameters such as the spectral width, number of time domain points, and acquisition time; (2) postacquisition data processing, such as apodization and zero-filling; (3) the signal-to-noise ratios (SNRs) and lineshapes of the two signals being used in a qNMR measurement; and (4) the method of signal quantification employed, such as numerical integration or lineshape fitting (LF). Here, a general Monte Carlo (MC) method that considers these factors is presented, with which the random and systematic contributions to qNMR measurement uncertainty may be calculated. Autocorrelation analysis of synthetic and experimental noise is used in a fingerprint-like approach to demonstrate the validity of the simulations. The MC method allows for a general quantitative assessment of measurement uncertainty without the need to acquire spectral replicates and without reference to the molecular structures and concentrations of analytes. Representative examples of qNMR measurement uncertainty simulations are provided in which the metrological performances of integration and LF are contrasted for signal pairs obtained using various acquisition and processing schemes in the low-SNR regime-an area where application of the proposed MC method may prove to be particularly salient.