Discrimination of Brazilian lager beer by 1H NMR spectroscopy combined with chemometrics

Enhancing beer authentication, quality, and control assessment using non-invasive spectroscopy through bottle and machine learning modeling

Fraud in alcoholic beverages through counterfeiting and adulteration is rising, significantly impacting companies economically. This study aimed to develop a method using near-infrared (NIR) spectroscopy (1596-2396 nm) through the bottle, along with machine learning (ML) modeling for beer authentication, quality traits, and control assessment. For this study, 25 commercial beers from different brands, styles, and three types of fermentation were used. To obtain the ground-truth data, a quantitative descriptive analysis was conducted with 11 trained panelists to evaluate the intensity of 16 sensory descriptors, and volatile aromatic compounds were analyzed using gas chromatography-mass spectroscopy (GC-MS). The ML models were developed using artificial neural networks with NIR absorbance values as inputs to predict (i) type of fermentation (Model 1), (ii) intensity of 16 sensory descriptors (Model 2), and (iii) peak area of volatile aromatic compounds (Model 3). All models resulted in high overall accuracy (Model 1: 99%; Model 2: R = 0.92; Model 3: R = 0.94), and model deployment for new beer samples showed high performance (Model 1: 95%; Model 2: R = 0.83). This method enables brewers and retailers to analyze beers without opening bottles, preventing quality assurance issues, fraud, and provenance concerns. Further model training with new targets could assess additional quality traits like physicochemical parameters and origin. PRACTICAL APPLICATION: Near-infrared spectroscopy coupled with ML modeling is a novel method for assessing beer quality and authentication through the bottle. It serves as a rapid, accurate tool for predicting sensory and aroma profiles without opening the bottle. Additionally, it monitors quality traits during transport and storage.

Computer vision with artificial intelligence for a fast, low-cost, eco-friendly and accurate prediction of beer styles and brands

Beer is the most consumed alcoholic beverage worldwide and are highly susceptible to fraudulent processes. Traditional sensory analysis can lack precision. With the growth of Industry 4.0, new techniques using artificial intelligence are being developed to address this issue. This scenario makes it appealing to propose low-cost techniques with broad classification capabilities based on sample fingerprints, such as computer vision (CV). CV involves image acquisition, processing, and classification using machine learning. In this work, a computer vision prototype associated with an artificial neural network was developed to classify beer in terms of style and brand. A total of 111 samples were analyzed in triplicate, with the data separated into training and testing sets. Accuracy and precision above 96% were obtained for the training set and 78% for the test set. The computer vision method proved to be a simple, low-cost, eco-friendly, and fast tool for detecting fraud in the brewing industry.

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.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

Texture Feature Extraction from 1H NMR Spectra for the Geographical Origin Traceability of Chinese Yam

Adulteration is widespread in the herbal and food industry and seriously restricts traditional Chinese medicine development. Accurate identification of geo-authentic herbs ensures drug safety and effectiveness. In this study, ¹H NMR combined intelligent "rotation-invariant uniform local binary pattern" identification was implemented for the geographical origin confirmation of geo-authentic Chinese yam (grown in Jiaozuo, Henan province) from Chinese yams grown in other locations. Our results showed that the texture feature of ¹H NMR image extracted with rotation-invariant uniform local binary pattern for identification is far superior compared to the original NMR data. Furthermore, data preprocessing is necessary. Moreover, the model combining a feature extraction algorithm and support vector machine (SVM) classifier demonstrated good robustness. This approach is advantageous, as it is accurate, rapid, simple, and inexpensive. It is also suitable for the geographical origin traceability of other geographical indication agricultural products.

Simultaneous analysis of the oxidation of solvent-extracted and cold-pressed green coffee oil during accelerated storage using 1H NMR and 13C NMR spectroscopy

Green coffee oil (GCO) extracted from green coffee beans, is known for its antioxidant and anticancer properties, and has been increasingly utilised in cosmetic and other consumer products. However, lipid oxidation of GCO fatty acid components during storage may be harmful to human health, and there remains a need to understand the evolution of GCO chemical component oxidation. In this study, proton nuclear magnetic resonance (¹H and ¹³C NMR) spectroscopy was used to investigate the oxidation status of solvent-extracted and cold-pressed GCO under accelerated storage conditions. Results show that the signal intensity of oxidation products gradually increased with increasing oxidation time, while unsaturated fatty acid signals gradually weakened. Five different types of GCO extracts were clustered according to their properties, except for minor overlapping in the two-dimensional plane of the principal component analysis. Partial least squares-least analysis results demonstrate that oxidation products (δ = 7.8-10.3 ppm), unsaturated fatty acids (δ = 5.28-5.42 ppm), and linoleic acid (δ = 2.70-2.85 ppm) in ¹H NMR can be used as characteristic indicators of GCO oxidation levels. Furthermore, the kinetics curves of unsaturated fatty acids, linoleic, and linolenic acyl groups all fit an exponential equation with high coefficients of GCO for 36 days under accelerated storage conditions. Our results show that the current NMR system is a fast, easy-operated and convenient tool for the oxidation process monitoring and quality control of GCO.

NMR-Based Approaches in the Study of Foods

In this review, the three different NMR-based approaches usually used to study foodstuffs are described, reporting specific examples. The first approach starts with the food of interest that can be investigated using different complementary NMR methodologies to obtain a comprehensive picture of food composition and structure; another approach starts with the specific problem related to a given food (frauds, safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc.) that can be addressed by choosing the most suitable NMR methodology; finally, it is possible to start from a single NMR methodology, developing a broad range of applications to tackle common food-related challenges and different aspects related to foods.

An Overview of the Application of Multivariate Analysis to the Evaluation of Beer Sensory Quality and Shelf-Life Stability

Achieving beer quality and stability remains the main challenge for the brewing industry. Despite all the technologies available, to obtain a high-quality product, it is important to know and control every step of the beer production process. Since the process has an impact on the quality and stability of the final product, it is important to create mechanisms that help manage and monitor the beer production and aging processes. Multivariate statistical techniques (chemometrics) can be a very useful tool for this purpose, as they facilitate the extraction and interpretation of information from brewing datasets by managing the connections between different types of data with multiple variables. In addition, chemometrics could help to better understand the process and the quality of the product during its shelf life. This review discusses the basis of beer quality and stability and focuses on how chemometrics can be used to monitor and manage the beer quality parameters during the beer production and aging processes.

Rapid evaporative ionization mass spectrometry (REIMS) combined with chemometrics for real-time beer analysis

The beer industry plays an important role in the economy since this is the third most consumed beverage worldwide. Efficient analytical methods must be developed to ensure the quality of the product. Rapid evaporative ionization mass spectrometry (REIMS) can provide molecular-level information, while enabling fast analysis. REIMS requires minimal sample preparation and it is ideal for the analysis of homogeneous liquid samples, such as beers, within only five seconds. In this article, 32 different beers were analyzed by REIMS in positive and negative ionization modes using a hybrid quadrupole time-of-flight mass spectrometer. The positive and negative MS spectrum blocks were augmented for data fusion. A predictive model by partial least squares discriminant analysis (PLS-DA) was used to discriminate the samples (i) by their brands and (ii) by the beer type (Premium and Standard American lagers). The results showed that REIMS provided a rich fingerprint of beers, which was successfully used to discriminate the brands and types with 96.9% and 97.9% accuracy, respectively. We believe that this proof-of-concept has great potential to be applied on a larger scale for industrial purposes due to its high-throughput.

A single screen-printed electrode in tandem with chemometric tools for the forensic differentiation of Brazilian beers

In the present study a single screen-printed carbon electrode (SPCE) and chemometric techniques were utilized for forensic differentiation of Brazilian American lager beers. To differentiate Brazilian beers at the manufacturer and brand level, the classification techniques: soft independent modeling of class analogy (SIMCA), partial least squares regression discriminant analysis (PLS-DA), and support vector machines discriminant analysis (SVM-DA) were tested. PLS-DA model presented an inconclusive assignment ratio of 20%. On the other hand, SIMCA models had a 0 inconclusive rate but an sensitivity close to 85%. While the non-linear technique (SVM-DA) showed an accuracy of 98%, with 95% sensitivity and 98% specificity. The SPCE-SVM-DA technique was then used to distinguish at brand level two highly frauded beers. The SPCE coupled with SVM-DA performed with an accuracy of 97% for the classification of both brands. Therefore, the proposed electrochemicalsensor configuration has been deemed an appropriate tool for discrimination of American lager beers according to their producer and brands.

Comprehensive characterization of industrial wastewaters using EEM fluorescence, FT-IR and 1H NMR techniques

The organic matter present in six industrial wastewaters (pulp and paper mill, brewery, textile, dairy, slaughterhouse effluents and a municipal landfill leachate) has been studied in this work using three analytical techniques: excitation-emission matrix fluorescence (EEMF), proton nuclear magnetic resonance spectroscopy (¹H NMR) and Fourier transform infrared spectroscopy (FTIR). The pulp and paper mill effluent shows characteristic signals of the presence of lignins, carbohydrates and carboxylic acids, as well as sulfate, carbonate and sulfonates (coming from surfactants used in the cleaning of tanks). The main constituents of the brewery effluent are peptides and proteins coming mainly from spent yeast and diatomite filters (the presence of the latter was confirmed by SiO bands in the FTIR spectrum). The municipal landfill leachate is characterized by the majority presence of humic substances (typical of an old landfill) and a residual presence of small peptides, amino acids and carboxylic acids. Additionally, several inorganic compounds were identified by FTIR, such as nitrate, sulfate, phosphate and cyanide ions. The textile effluent from a cotton-based industry contains carbohydrates, carboxylic acids and sulfonates, which can act as auxochromes in the textile industry. The dairy effluent comprises amino acids and small peptides coming from the biodegradation of milk and whey in addition to carbohydrates (lactose) and carboxylic acids (mainly lactic acid). The presence of tyrosine-like peaks B in the EEMF spectrum of the slaughterhouse effluent indicates the existence of small peptides and amino acids coming from the biodegradation of blood proteins. Additionally, residual glucose, fatty acids, phosphate and sulfate were also identified in this effluent.

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.

A Metabolomic Approach to Beer Characterization

The consumers’ interest towards beer consumption has been on the rise during the past decade: new approaches and ingredients get tested, expanding the traditional recipe for brewing beer. As a consequence, the field of “beeromics” has also been constantly growing, as well as the demand for quick and exhaustive analytical methods. In this study, we propose a combination of nuclear magnetic resonance (NMR) spectroscopy and chemometrics to characterize beer. ¹H-NMR spectra were collected and then analyzed using chemometric tools. An interval-based approach was applied to extract chemical features from the spectra to build a dataset of resolved relative concentrations. One aim of this work was to compare the results obtained using the full spectrum and the resolved approach: with a reasonable amount of time needed to obtain the resolved dataset, we show that the resolved information is comparable with the full spectrum information, but interpretability is greatly improved.

Excitation-emission matrix fluorescence spectroscopy coupled with multi-way chemometric techniques for characterization and classification of Chinese lager beers

This paper proposed excitation-emission matrix fluorescence spectroscopy coupled with multi-way chemometric techniques for characterization and classification of Chinese pale lager beers produced by different manufacturers. The undiluted and diluted beer samples presented different fluorescence fingerprints. Three-way and four-way parallel factor analysis (PARAFAC) were used to decompose the skillfully constructed three-way and four-way data arrays, respectively, to further achieve beer characterization and feature extraction. Based on the features extracted in different ways, four strategies for beer classification were proposed. In each strategy, three supervised classification methods including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA) and k-nearest neighbor (kNN) were used to build discriminant models. By comparison, PARAFAC-data fusion-kNN method in strategy 3 and four-way PARAFAC-kNN method in strategy 4 obtained the best classification results. The classification strategy based on four-way sample-excitation-emission-dilution level data array was proposed to solve the problem of beer classification for the first time.

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.