Spectroscopic approaches for rapid beer and wine analysis

Feasibility assessment of a low-cost visible spectroscopy-based prototype for monitoring polyphenol extraction in fermenting musts

BACKGROUND

Polyphenols have long been used to evaluate grape and wine quality and it is necessary to measure them throughout various winemaking stages. They are currently assessed predominantly through analytical methods, which are characterized by time-consuming procedures and environmentally harmful practices. Non-destructive spectroscopy-based devices offer an alternative but they tend to be costly and not readily accessible for smaller wineries. This study introduces the initial steps in employing a portable, user-friendly, and cost-effective visible (VIS) spectrophotometer prototype for direct polyphenol measurement during winemaking.

RESULTS

Grapes (cv Syrah, Bobal, and Cabernet Sauvignon) at different maturation stages were fermented with or without stems. Throughout fermentation, parameters such as color intensity, total polyphenol index, total anthocyanins, and tannins were monitored. Concurrently, VIS spectra were acquired using both the prototype and a commercial instrument. Chemometric approaches were then applied to establish correlation models between spectra and destructive analyses. The prototype models demonstrated an acceptable level of confidence for only a few parameters, indicating their lack of complete reliability at this stage.

CONCLUSIONS

Visible spectroscopy is already utilized for polyphenol analysis in winemaking but the aspiration to automate the process in wineries, particularly with low-cost devices, remains unrealized. This study investigates the feasibility of a low-cost and user-friendly spectrophotometer. The results indicate that, in the early stages of prototype utilization, the goal is attainable but requires further development and in-depth assessments. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Phenolics and spectroscopy: challenges and successful stories in the grape and wine industry

Phenolic compounds are considered to have a major role in the quality of grapes and wine. These compounds contribute to the sensory perception of red wine as they are involved in astringency and bitterness as well as in determining the colour intensity of grapes and wine (e.g., anthocyanins content). Several techniques are used to characterise and quantify these compounds in grapes and wine samples such as ultraviolet-visible spectroscopy or high-performance liquid chromatography. More recently, different applications and reports have shown the value of vibrational spectroscopy techniques to monitor and measure phenolic compounds along the grape and wine value chain. This article summarises as well as discusses challenges and successful stories in relation to the utilisation of vibrational spectroscopy techniques to measure phenolic compounds in grapes and wine. Specifically, content presented at the workshop 'Outstanding sensors challenge beverage and food future' organised by the Italian Society of Food Science and Technology and the University of Pisa (Pisa, Italy) is summarised. Although vibrational spectroscopy techniques have been proven to be of importance to measure composition across the grape and wine value chain, the adoption of these technologies has been compromised by the accessibility and price of instruments. Understanding the basic principles of the different vibrational spectroscopy methods (e.g., characteristics, limit of detection) as well as how to effectively use the data generated are still main barriers facing the incorporation of these techniques into the grape and wine industry. Furthermore, is still not clear for many users of these technologies how they will contribute to the sustainability of the wine industry as well as to preserve the identity of the wine making process. © 2023 Society of Chemical Industry.

Monitoring the homemade fermentation of readymade malt extract using the SCiO NIR sensor: A convergence of technology and tradition

Miniaturized near-infrared (NIR) instruments are launched on the market to satisfy the need of both researchers and consumers for quick ways of analysing stuff, especially in the food field. Their portability and ease of operation are at the centre of the "do-it-yourself" idea behind providing virtually anyone with the ability of making analytical measurements on their own. In this study, we highlighted the convergence of technology and tradition in two seemingly disparate domains: spectroscopy and homebrewing. Homebrewing is in fact a leisure activity which conceptually shares the do-it-yourself approach: the consumer becomes the producer of his/her own beer. Hence, in our study, we investigated the analytical possibilities provided by a handheld NIR spectrometer to monitor the homemade fermentation process of a commercial malt extract, over the course of one week. The spectroscopic data, acquired using the SCiO sensor (Consumer Physics), were analysed via Principal Component Analysis (PCA) to investigate temporal trends and relationships with brewing parameters. Our findings reveal that discernible trends, reflective of brewing stage progression and temperature variations, can be captured and unveiled with simple data analysis approaches. At the same time, the detection of scattering effects that can be attributed to bubble formation on the spectrometer's acquisition window confirm the need for robust and reasoned experimental procedures, but also for proper data preprocessing methods. This rather simple and basic analytical approach could provide the homebrewer the possibility to qualitatively monitor the advancement of the brewing process.

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.

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

Food authenticity and the interactions with human health and climate change

Food authenticity and fraud, as well as the interest in food traceability have become a topic of increasing interest not only for consumers but also for the research community and the food manufacturing industry. Food authenticity and fraud are becoming prevalent in both the food supply and value chains since ancient times where different issues (e.g., food spoilage during shipment and storage, mixing decay foods with fresh products) has resulted in foods that influence consumers health. The effect of climate change on the quality of food ingredients and products could also have the potential to influence food authenticity. However, this issue has not been considered. This article focused on the interactions between consumer health and the potential effects of climate change on food authenticity and fraud. The role of technology and development of risk management tools to mitigate these issues are also discussed. Where applicable papers that underline the links between the interactions of climate change, human health and food fraud were referenced.

Quantitative analysis of key components in Qingke beer brewing process by multispectral analysis combined with chemometrics

In order to monitor the Qingke beer brewing process in real time, this paper presents an analytical method for predicting the content of key components in the wort during the mashing and boiling stages using multi-spectroscopy combined with chemometrics. The results showed that the Neural Networks (NN) model based on Raman spectroscopy (RPD = 3.9727) and the NN model based on NIR spectroscopy (RPD = 5.1952) had the best prediction performance for the reducing sugar content in the mashing and boiling stages; The partial least Squares (PLS) model based on Raman spectroscopy (RPD = 2.7301) and the NN model based on Raman spectroscopy (RPD = 4.3892) predicted the content of free amino nitrogen best; The PLS model based on UV-Vis spectroscopy (RPD = 4.0412) and the NN model based on Raman spectroscopy (RPD = 4.0540) are most suitable for the quantitative analysis of total phenols. The results can be used as a guide for real-time control of wort quality in industrial production.

Rapid screening of bacteriostatic and bactericidal antimicrobial agents against Escherichia coli by combining machine learning (artificial intelligence) and UV-VIS spectroscopy

Antibiotics are compounds that have a particular mode of action upon the microorganism they are targeting. However, discovering and developing new antibiotics is a challenging and timely process. Antibiotic development process can take up to 10-15 years and over $1billion to develop a single new therapeutic product. Rapid screening tools to understand the mode of action of the new antimicrobial agent are considered one of the main bottle necks in the antimicrobial agent development process. Classical approaches require multifarious microbiological methods and they do not capture important biochemical and organism therapeutic-interaction mechanisms. This work aims to provide a rapid antibiotic-antimicrobial biochemical diagnostic tool to reduce the timeframes of therapeutic development, while also generating new biochemical insight into an antimicrobial-therapeutic screening assay in a complex matrix. The work evaluates the effect of antimicrobial action through "traditional" microbiological analysis techniques with a high-throughput rapid analysis method using UV-VIS spectroscopy and chemometrics. Bacteriostatic activity from tetracycline and bactericidal activity from amoxicillin were evaluated on a system using non-resistant Escherichia coli O157:H7 by confocal laser scanning microscopy (CLSM), scanning electron microscopy (SEM), and UV-VIS spectroscopy (high-throughput analysis). The data were analysed using principal component analysis (PCA) and support vector machine (SVM) classification. The rapid diagnostic technique could easily identify differences between bacteriostatic and bactericidal mechanisms and was considerably quicker than the "traditional" methods tested.

Review of technology advances to assess rice quality traits and consumer perception

The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.

Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality

Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.

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.

VinegarScan: A Computer Tool Based on Ultraviolet Spectroscopy for a Rapid Authentication of Wine Vinegars

Ultraviolet-visible (UV-vis) spectroscopy has shown successful results in the last few years to characterize and classify wine vinegar according to its quality, particularly those with a protected designation of origin (PDO). Due to these promising results, together with the simplicity, price, speed, portability of this technique and its ability to create robust hierarchical classification models, the objective of this work was the development of a computer tool or software, named VinegarScan, which uses the UV-vis spectra to be able to perform quality control and authentication of wine vinegar in a quick and user-friendly way. This software was based on the open-source GUI created in C++ using several data mining algorithms (e.g., decision trees, classification algorithms) on UV-vis spectra. This software achieved satisfactory prediction results with the available analytical UV-vis data. The future idea of utility is to combine the VinegarScan tool with a portable UV-vis device that could be used by control bodies of the wine vinegar industry to achieve a clear differentiation from their competitors to avoid fraud.

A Review of Wine Authentication Using Spectroscopic Approaches in Combination with Chemometrics

In a global context where trading of wines involves considerable economic value, the requirement to guarantee wine authenticity can never be underestimated. With the ever-increasing advancements in analytical platforms, research into spectroscopic methods is thriving as they offer a powerful tool for rapid wine authentication. In particular, spectroscopic techniques have been identified as a user-friendly and economical alternative to traditional analyses involving more complex instrumentation that may not readily be deployable in an industry setting. Chemometrics plays an indispensable role in the interpretation and modelling of spectral data and is frequently used in conjunction with spectroscopy for sample classification. Considering the variety of available techniques under the banner of spectroscopy, this review aims to provide an update on the most popular spectroscopic approaches and chemometric data analysis procedures that are applicable to wine authentication.

Review of the Effects of Grapevine Smoke Exposure and Technologies to Assess Smoke Contamination and Taint in Grapes and Wine

Grapevine smoke exposure and the subsequent development of smoke taint in wine has resulted in significant financial losses for grape growers and winemakers throughout the world. Smoke taint is characterized by objectional smoky aromas such as “ashy”, “burning rubber”, and “smoked meats”, resulting in wine that is unpalatable and hence unprofitable. Unfortunately, current climate change models predict a broadening of the window in which bushfires may occur and a rise in bushfire occurrences and severity in major wine growing regions such as Australia, Mediterranean Europe, North and South America, and South Africa. As such, grapevine smoke exposure and smoke taint in wine are increasing problems for growers and winemakers worldwide. Current recommendations for growers concerned that their grapevines have been exposed to smoke are to conduct pre-harvest mini-ferments for sensory assessment and send samples to a commercial laboratory to quantify levels of smoke-derived volatiles in the wine. Significant novel research is being conducted using spectroscopic techniques coupled with machine learning modeling to assess grapevine smoke contamination and taint in grapes and wine, offering growers and winemakers additional tools to monitor grapevine smoke exposure and taint rapidly and non-destructively in grapes and wine.

A high-throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV-visible spectrophotometry with principal component analysis

UV-visible spectroscopy (UV-Vis) is routinely used in microbiology as a tool to check the optical density (OD) pertaining to the growth stages of microbial cultures at the single wavelength of 600 nm, better known as the OD₆₀₀ . Typically, modern UV-Vis spectrophotometers can scan in the region of approximately 200-1000 nm in the electromagnetic spectrum, where users do not extend the use of the instrument's full capability in a laboratory. In this study, the full potential of UV-Vis spectrophotometry (multiwavelength collection) was used to examine bacterial growth phases when treated with antibiotics showcasing the ability to understand the point of resistance when an antibiotic is introduced into the media and therefore understand the biochemical changes of the infectious pathogens. A multiplate reader demonstrated a high throughput experiment (96 samples) to understand the growth of Escherichia coli when varied concentrations of the antibiotic tetracycline was added into the well plates. Principal component analysis (PCA) and partial least squares discriminant analysis were then used as the data mining techniques to interpret the UV-Vis spectral data and generate machine learning "proof of principle" for the UV-Vis spectrophotometer plate reader. Results from this study showed that the PCA analysis provides an accurate yet simple visual classification and the recognition of E. coli samples belonging to each treatment. These data show significant advantages when compared to the traditional OD₆₀₀ method where we can now understand biochemical changes in the system rather than a mere optical density measurement. Due to the unique experimental setup and procedure that involves indirect use of antibiotics, the same test could be used for obtaining practical information on the type, resistance, and dose of antibiotic necessary to establish the optimum diagnosis, treatment, and decontamination strategies for pathogenic and antibiotic resistant species.