Tracing the origin of beer samples by NMR and chemometrics: Trappist beers as a case study

A comparative evaluation of low-field and high-field NMR untargeted analysis: Authentication of virgin coconut oil adulterated with refined coconut oil as a case study

Despite the advances in low-field nuclear magnetic resonance (NMR), there are limited spectroscopic applications for untargeted analysis and metabolomics. To evaluate its potential, we combined high-field and low-field NMR with chemometrics for the differentiation between virgin and refined coconut oil and for the detection of adulteration in blended samples. Although low-field NMR has less spectral resolution and sensitivity compared to high-field NMR, it was still able to achieve a differentiation between virgin and refined coconut oils, as well as between virgin coconut oil and blends, using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and random forest techniques. These techniques were not able to distinguish between blends with different levels of adulteration; however, partial least squares regression (PLSR) enabled the quantification of adulteration levels for both NMR approaches. Given the significant benefits of low-field NMR, including economic and user-friendly analysis and fitting in an industrial environment, this study establishes the proof of concept for its utilization in the challenging scenario of coconut oil authentication. Also, this method has the potential to be used for other similar applications that involve untargeted analysis.

Profiling of contemporary beer styles using liquid chromatography quadrupole time-of-flight mass spectrometry, multivariate analysis, and machine learning techniques

Although all beer is brewed using the same four classes of ingredients, contemporary beer styles show wide variation in flavor and color, suggesting differences in their chemical profiles. A selection of 32 beers covering five styles (India pale ale, blonde, stout, wheat, and sour) were investigated to determine chemical features, which discriminate between popular beer styles. The beers were analyzed in an untargeted fashion using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). The separation and detection method were tuned to include compounds from important beer components, namely iso-α-acids and phenolic compounds. Due to the sheer number of unknown compounds in beer, multivariate analysis and machine learning techniques were used to pinpoint some of the compounds most influential in distinguishing beer styles. It was determined that while many phenols and iso-α-acids were present in the beers, they were not the compounds most responsible for the variations between styles. However, it was possible to discriminate each beer style using multivariate analysis. Principal component analysis (PCA) was able to separate and cluster the individual beer samples by style. A combination of statistical tools were used to predict formulas for some of the most influential metabolites from each style. Machine learning models accurately classified patterns in the five beer styles, indicating that they can be precisely distinguished by their nonvolatile chemical profile.

Impact of Dealcoholization by Osmotic Distillation on Metabolic Profile, Phenolic Content, and Antioxidant Capacity of Low Alcoholic Craft Beers with Different Malt Compositions

Beer antioxidants originate mainly from malts, classified as colored, caramel, and roasted, according to the malting process. This study aimed to characterize, in terms of phenolic antioxidants, three types of Pale Ale craft beers brewed using increasing percentage of dark malt (0, 5, and 15% Caraamber malt, called PA100, PA95, PA85, respectively) and to evaluate the impact of dealcoholization by osmotic distillation (OD) on the same antioxidants. All the alcoholic (PA, 6.2-6.8 vol %) and low alcoholic (LA-PA, 1 vol %) beers were analyzed by HPLC-ESI-MS/MS, total phenolic content (TPC), and antioxidant activity (AA): similar phenolic profiles were evidenced and 43 compounds identified or tentatively identified. Some differences were found among PA100, PA95, and PA85: PA85 was richer in free phenolic compounds (10.55 mg/L) and had a higher TPC (463.7 GAE mg/L) and AA (852.1 TE mg/L). LA-PA beers showed the same phenolic profile and similar TPC and AA compared to PA beers; however, there were some differences regarding LA-PA85 (5.91 mg/L). Dealcoholization by OD seemed to weakly affect the phenolic fraction. ESI-MS/MS infusion experiments evidenced oligosaccharides, small organic acids, and amino acids, whose presence was confirmed and quantitated by NMR: besides ethanol and other alcohols, weak to strong loss of low-molecular-weight metabolites was evidenced in LA-PA beers.

Application of NMR and Chemometrics for the Profiling and Classification of Ale and Lager American Craft Beer

In this paper, Nuclear Magnetic Resonance spectroscopy (NMR)-based metabolomics were applied for the discrimination of ale and lager craft American beers. A modified pulse sequence that allows the efficient suppression of the water and ethanol peaks was used to achieve high-quality spectra with minimal sample preparation. The initial chemometrics analysis generated models of low predictive power, indicating the high variability in the groups. Due to this variability, we tested the effect of various data pretreatment and chemometrics approaches to improve the model’s performance. Spectral alignment was found to improve the classification significantly, while the type of normalization also played an important role. NMR combined with statistical and machine-learning techniques such as orthogonal projection to latent structures discriminant analysis (OPLS-DA) and random forest was able to discriminate between ale and lager beers, thus providing an important tool for the quality control and analysis of these products.

Insights into chemical and sensorial aspects to understand and manage beer aging using chemometrics

Beer chemical instability remains, at present, the main challenge in maintaining beer quality. Although not fully understood, after decades of research, significant progress has been made in identifying "aging compounds," their origin, and formation pathways. However, as the nature of aging relies on beer manufacturing aspects such as raw materials, process variables, and storage conditions, the chemical profile differs among beers. Current research points to the impact of nonoxidative reactions on beer quality. The effect of Maillard and Maillard intermediates on the final beer quality has become the focus of beer aging research, as prevention of oxidation can only sustain beer quality to some extent. On the other hand, few studies have focused on tracing a profile of whose compound is sensory relevant to specific types of beer. In this matter, the incorporation of "chemometrics," a class of multivariate statistic procedures, has helped brewing scientists achieve specific correlations between the sensory profile and chemical data. The use of chemometrics as exploratory data analysis, discrimination techniques, and multivariate calibration techniques has made the qualitatively and quantitatively translation of sensory perception of aging into manageable chemical and analytical parameters. However, despite their vast potential, these techniques are rarely employed in beer aging studies. This review discusses the chemical and sensorial bases of beer aging. It focuses on how chemometrics can be used to their full potential, with future perspectives and research to be incorporated in the field, enabling a deeper and more specific understanding of the beer aging picture.

Chemometric Strategies for Spectroscopy-Based Food Authentication

In the last decades, spectroscopic techniques have played an increasingly crucial role in analytical chemistry, due to the numerous advantages they offer. Several of these techniques (e.g., Near-InfraRed—NIR—or Fourier Transform InfraRed—FT-IR—spectroscopy) are considered particularly valuable because, by means of suitable equipment, they enable a fast and non-destructive sample characterization. This aspect, together with the possibility of easily developing devices for on- and in-line applications, has recently favored the diffusion of such approaches especially in the context of foodstuff quality control. Nevertheless, the complex nature of the signal yielded by spectroscopy instrumentation (regardless of the spectral range investigated) inevitably calls for the use of multivariate chemometric strategies for its accurate assessment and interpretation. This review aims at providing a comprehensive overview of some of the chemometric tools most commonly exploited for spectroscopy-based foodstuff analysis and authentication. More in detail, three different scenarios will be surveyed here: data exploration, calibration and classification. The main methodologies suited to addressing each one of these different tasks will be outlined and examples illustrating their use will be provided alongside their description.

Metabolomic profiling of beers: Combining 1H NMR spectroscopy and chemometric approaches to discriminate craft and industrial products

The authentication and traceability of craft beers is an important issue for both beer consumers and producers. Reliable analytical methods able to identify and discriminate products are needed to protect the craft brew market against fraud and counterfeit. Here, ¹H NMR analysis of 31 beer samples, differing for beer style and brewing method (craft or industrial) was combined with multivariate statistical analysis, following both an untargeted and a targeted approach. NMR-based analysis of beer samples was sped developing a specific protocol enabling the automatic identification and quantification of metabolites in approximately thirty seconds per spectrum. A clear discrimination was achieved by exploiting ¹H NMR analysis and multivariate chemometric methods and the targeted approach identified the metabolites responsible for the segregation. Overall, this study reports an analytical approach addressing beer traceability and is the starting point for the development of a standardized protocol for the discrimination of industrial and craft beers.

Discrimination of geographical origin of hop (Humulus lupulus L.) using geochemical elements combined with statistical analysis

Beer is a popular alcoholic beverage worldwide, traditionally made from water, barley and hop (Humulus lupulus L.) strobili. The strobili contain lupulin glands whose components (mostly bitter acids and polyphenols) confer unique and locally different flavours to beer types. It is therefore relevant for brewers and consumers to precisely know the geographical origin of hop plants used for high-quality beer. Hop plants belonging to the variety Hallertau Perle, grown in two locations, Cavalese and Imèr, of the Trentino Region (Italy) were analysed to establish a direct relationship between the chemical elements detected in soil and in plant parts. Chemical elements were determined by X-ray fluorescence and inductively coupled plasma mass spectrometry in soil, leaf and strobili samples from Cavalese and Imèr. The data from the two areas were compared by a nonparametric test (Mann-Whitney) and multivariate statistics (principal component analysis and partial least squares discriminant analysis). The geochemical characterization and the statistical analyses showed different concentrations of major and trace elements in soil and plant parts from the two areas. A reliable correlation could be established between some elements in soil and strobili samples, that is Nb, Fe, Rb and Zr for Cavalese and Mg, Ni, Zn and Zr for Imèr. These elements could therefore be used as geochemical fingerprints to identify the geographical origin of strobili from the two study areas, an approach useful to verify the origin of hop plants for the production of high-quality beer.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.