Rapid quality control of spirit drinks and beer using multivariate data analysis of Fourier transform infrared spectra

Advancing ethanol content determination in hydrogels: non-destructive and operational methods for health and criminal inspections

The significance of accurate determination of ethanol content in hydrogel formulations was accentuated during COVID-19 pandemic coinciding with the heightened demand for sanitizing agents. The present article proposes three robust methodologies for this purpose: Fourier Transform Infrared Spectroscopy (FTIR), Raman spectroscopy, and Densitometry with matrix effect correction by Near-Infrared Spectroscopy (NIR). All three methods demonstrated outstanding linearity (R2 ≥ 0.99) and minimal errors (< 1.7%), offering simplicity and operational efficiency. FTIR and Raman, being non-destructive and requiring minimal preparation, enable practical on-site analysis capabilities, underscoring the potential of the spectroscopic methods to expedite health investigations and inspections, empowering on-site ethanol determination, and relieving the burden on official laboratories. Additionally, the densitometry with NIR-based approach showcased superior accuracy and precision compared to spectroscopic methods, meeting validation criteria while offering operational advantages over the costly official distillation-based method. Therefore, it stands as a reliable and reproducible technique for comprehensive health and criminal compliance assessments, making it a compelling alternative for both industry and official laboratories.

The Prediction of Quality Parameters of Craft Beer with FT-MIR and Chemometrics

Beer is one of the oldest and most known alcoholic beverages whose organoleptic characteristics are the attributes that the consumer seeks, which is why it is essential to ensure proper quality control of the final product. Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis can be an alternative to traditional methods to predict quality parameters in craft beer. This study aims to develop prediction models based on FT-MIR spectroscopy to simultaneously quantify quality parameters (color, specific gravity, alcohol volume, bitterness, turbidity, pH, and total acidity) in craft beer. Additionally, principal component analysis (PCA) was applied, and it was possible to classify craft beer samples according to their style. Partial least squares (PLS1) developed the best predictive model by obtaining higher R2c (0.9999) values and lower standard error of calibration (SEC: 0.01-0.11) and standard error of prediction (SEP: 0.01-0.14) values in comparison to the models developed with the other algorithms. Specific gravity could not be predicted due to the low variability in the values. Validation and prediction with external samples confirmed the predictive capacity of the developed model. By making a comparison to traditional techniques, FT-MIR coupled with multivariate analysis has a higher advantage, since it is rapid (approximately 6 min), efficient, cheap, and eco-friendly because it does not require the use of solvents or reagents, representing an alternative to simultaneously analyzing quality parameters in craft beer.

A highly sensitively "off-on-off" fluorescence probe for detection of aluminum ion and water as well as application in Chinese Baijiu

Developing highly sensitive fluorescent probe for Al3+ and H2O detection is highly desirable, due to aluminum toxicity poses a significant threat to public health. On the other hand, the determination of water content holds immense significance in a wide range of fields such as food processing, pharmaceutical manufacturing. In this paper, a novel acylhydrazone-based fluorescent probe P was successfully synthesized and characterized for the sequential detection of Al3+ and water in alcohols. The probe P exhibited a remarkable "turn-on" response towards Al3+ by emitting yellow fluorescence at 567 nm, with high selectivity and large Stokes shift (147 nm). Meanwhile, the in situ formed P-Al3+ complex demonstrated significant solvatofluorochromic characteristic, which could be utilized as a second probe for detecting water via fluorescence quenching with low detection limit in alcohols (0.008%, methanol; 0.013%, ethanol; 0.013%, isopropanol; 0.037%, n-butanol; vol.%) and acetonitrile (0.072%, vol.%). Moreover, the P-Al3+ complex was able to detect the alcoholic strength of Chinese Baijiu without the interference of other alcohols, providing an excellent recovery rate (100.0-107.0%). Different Chinese Baijius, with various alcoholic strength, could be distinguished by simple test strips. Furthermore, the P-Al3+ complex could also analyze the water content in organic solvents .

Emerging trends and applications of metabolomics in food science and nutrition

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

Development of a smart spectral analysis method for the determination of mulberry (Morus alba var. nigra L.) juice quality parameters using FT-IR spectroscopy

Recently, the application of Fourier transform infrared (FT-IR) spectroscopy as a noninvasive technique combined with chemometric methods has been widely noted for quality evaluation of agricultural products. Mulberry (Morus alba var. nigra L.) is a native fruit of Iran and there is limited information about its quality characteristics. The present study aims at assessing a nondestructive optical method for determining the internal quality of mulberry juice. To do so, first, FT-IR spectra were acquired in the spectral range 1000-8333 nm. Then, the principal component analysis (PCA) was used to extract the principal components (PCs) which were given as inputs to three predictive models (support vector regression (SVR), partial least square (PLS), and artificial neural network (ANN)) to predict the internal parameters of the mulberry juice. The performance of predictive models showed that SVR got better results for the prediction of ascorbic acid (R 2 = .84, RMSE = 0.29), acidity (R 2 = .71, RMSE = 0.0004), phenol (R 2 = .35, RMSE = 0.19), total anthocyanin (R 2 = .93, RMSE = 5.85), and browning (R 2 = .89, RMSE = 0.062) compared to PLS and ANN. However, the ANN predicted the parameters TSS (R 2 = .98, RMSE = 0.003) and pH (R 2 = .99, RMSE = 0.0009) better than the other two models. The results indicated that a good prediction performance was obtained using the FT-IR technique along with SVR and this method could be easily adapted to detect the quality parameters of mulberry juice.

FT-IR combined with chemometrics in the quality evaluation of Nongxiangxing baijiu

Recently, there has been an increasing demand for developing a reliable method to assess the quality of liquor in the baijiu industry quickly and accurately. The present study sought to establish a strategy for rapid quantitative analysis of the primary flavor components in Nongxiangxing baijiu. Under the experimental conditions, 7 of the 10 major flavor components in Nongxiangxing baijiu could be quantified effectively, such as ethyl butyrate (R²p = 0.9942), ethyl lactate (R²p = 0.9438), n-butanol (R²p = 0.9048), isobutanol (R²p = 0.9696), acetic acid (R²p = 0.9600), butyric acid (R²p = 0.8448), caproic acid (R²p = 0.9971). This result indicates that FT-IR combined with quantitative chemometric modeling could be a potential approach for rapid quality assessment of Nongxiangxing baijiu. Overall, this study provides a theoretical basis for subsequent related studies on Nongxiangxing baijiu.

Defining No and Low (NoLo) Alcohol Products

Reducing the alcoholic strength in beverages as a strategy to reduce harmful alcohol use has been proposed by multilateral institutions such as the World Health Organization and governments worldwide. Different industrial and artisanal techniques are used to achieve low-alcohol content beverages. Therefore, regulations regarding the content of alcohol in beverages and strategies to monitor compliance are important, because they are the main reason for classification of the beverages and are central to their categorization and market labelling. Furthermore, analytical techniques with adequate sensitivity as low as 0.04% vol are necessary to determine the alcohol ranges necessary for classification. In this narrative review, the definitions of no and low (NoLo) alcohol products are described and the differences in the legal definitions of these products in several regions of the world are highlighted. Currently, there is clearly confusion regarding the terminology of "no", "free", "zero", "low", "light", or "reduced" alcohol products. There is an urgent need for global harmonization (e.g., at the Codex Alimentarius level) of the definitions from a commercial perspective and also to have common nomenclature for science and for consumer information.

Pattern Recognition Approach for the Screening of Potential Adulteration of Traditional and Bourbon Barrel-Aged Maple Syrups by Spectral Fingerprinting and Classical Methods

This study aims to generate predictive models based on mid-infrared and Raman spectral fingerprints to characterize unique compositional traits of traditional and bourbon barrel (BBL)-aged maple syrups, allowing for fast product authentication and detection of potential ingredient tampering. Traditional (n = 23) and BBL-aged (n = 17) maple syrup samples were provided by a local maple syrup farm, purchased from local grocery stores in Columbus, Ohio, and an online vendor. A portable FT-IR spectrometer with a triple-reflection diamond ATR and a compact benchtop Raman system (1064 nm laser) were used for spectra collection. Samples were characterized by chromatography (HPLC and GC-MS), refractometry, and Folin-Ciocalteu methods. We found the incidence of adulteration in 15% (6 out of 40) of samples that exhibited unusual sugar and/or volatile profiles. The unique spectral patterns combined with soft independent modeling of class analogy (SIMCA) identified all adulterated samples, providing a non-destructive and fast authentication of BBL and regular maple syrups and discriminated potential maple syrup adulterants. Both systems, combined with partial least squares regression (PLSR), showed good predictions for the total ˚Brix and sucrose contents of all samples.

Methanol Mitigation during Manufacturing of Fruit Spirits with Special Consideration of Novel Coffee Cherry Spirits

Methanol is a natural ingredient with major occurrence in fruit spirits, such as apple, pear, plum or cherry spirits, but also in spirits made from coffee pulp. The compound is formed during fermentation and the following mash storage by enzymatic hydrolysis of naturally present pectins. Methanol is toxic above certain threshold levels and legal limits have been set in most jurisdictions. Therefore, the methanol content needs to be mitigated and its level must be controlled. This article will review the several factors that influence the methanol content including the pH value of the mash, the addition of various yeast and enzyme preparations, fermentation temperature, mash storage, and most importantly the raw material quality and hygiene. From all these mitigation possibilities, lowering the pH value and the use of cultured yeasts when mashing fruit substances is already common as best practice today. Also a controlled yeast fermentation at acidic pH facilitates not only reduced methanol formation, but ultimately also leads to quality benefits of the distillate. Special care has to be observed in the case of spirits made from coffee by-products which are prone to spoilage with very high methanol contents reported in past studies.

Network analysis on Fourier-transform infrared (FTIR) spectroscopic data sets in an Eigen space layout: Introducing a novel approach for analysing wine samples

In the present work, Eigen-directed network analysis for Fourier-transform infrared (FTIR) spectroscopic data sets of wine samples was introduced. A network can generally be viewed as a collection of nodes connected to each other through links, often also called edges. Herein, each node in the network represents a sample and the dissimilarity weight associated with the difference between the two connected nodes is described by the edge. The utility of the approach was tested by analysing a collection of 148 wine samples. The networking on FTIR data sets of these samples in the Eigen space layout was found to impart required aesthetic values as well as the chemical significance to the nodes positioning. The proposed approach successfully captured the compositional differences among the analysed wine samples and classified them in two groups. The Eigen-directed network analysis also allowed a swift assessment regarding inter- and intra-group homogeneity. Homogeneous groups were found to contain nodes with high degree of adjacency and edges with smaller lengths. In comparative study, the proposed approach was found to outperform the network analysis in force-directed layout and principal component analysis. In summary, the proposed Eigen-directed network analysis provided a simplified illustration of highly correlated spectral data sets enabling a swift and intuitive interpretation.

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.

Methanol in Grape Derived, Fruit and Honey Spirits: A Critical Review on Source, Quality Control, and Legal Limits

Spirits are alcoholic beverages commonly consumed in European countries. Their raw materials are diverse and include fruits, cereals, honey, sugar cane, or grape pomace. The main aim of this work is to present and discuss the source, quality control, and legal limits of methanol in spirits produced using fruit and honey spirits. The impact of the raw material, alcoholic fermentation, and the distillation process and aging process on the characteristics and quality of the final distilled beverage are discussed. In addition, a critical view of the legal aspects related to the volatile composition of these distillates, the origin and presence of methanol, and the techniques used for quantification are also described. The methanol levels found in the different types of spirits are those expected based on the specific raw materials of each and, almost in all studies, respect the legal limits.

Application of 1H Nuclear Magnetic Resonance Spectroscopy as Spirit Drinks Screener for Quality and Authenticity Control

Due to legal regulations, the rise of globalised (online) commerce and the need for public health protection, the analysis of spirit drinks (alcoholic beverages >15% vol) is a task with growing importance for governmental and commercial laboratories. In this article a newly developed method using nuclear magnetic resonance (NMR) spectroscopy for the simultaneous determination of 15 substances relevant to assessing the quality and authenticity of spirit drinks is described. The new method starts with a simple and rapid sample preparation and does not need an internal standard. For each sample, a group of 1H-NMR spectra is recorded, among them a two-dimensional spectrum for analyte identification and one-dimensional spectra with suppression of solvent signals for quantification. Using the Pulse Length Based Concentration Determination (PULCON) method, concentrations are calculated from curve fits of the characteristic signals for each analyte. The optimisation of the spectra, their evaluation and the transfer of the results are done fully automatically. Glucose, fructose, sucrose, acetic acid, citric acid, formic acid, ethyl acetate, ethyl lactate, acetaldehyde, methanol, n-propanol, isobutanol, isopentanol, 2-phenylethanol and 5-(hydroxymethyl)furfural (HMF) can be quantified with an overall accuracy better than 8%. This new NMR-based targeted quantification method enables the simultaneous and efficient quantification of relevant spirit drinks ingredients in their typical concentration ranges in one process with good accuracy. It has proven to be a reliable method for all kinds of spirit drinks in routine food control.

Application of ATR-FT-MIR for Tracing the Geographical Origin of Honey Produced in the Maltese Islands

Maltese honey has been produced, marketed, and sold as an exclusive local gourmet food product for countless years. Yet, thus far, no study has evaluated the individuality of this local food product. The evaluation of the parameters and properties which characterise the provenance and floral source of honey have been the subject of various studies worldwide, owing to the price and potential beneficial properties of this food product. Models analysing the potential of attenuated total reflection mid-infrared (ATR-FT-MIR) spectroscopy in discriminating and classifying local honey from that of foreign origin were investigated using 21 Maltese honey samples and 49 honey samples collected from abroad (Sicily, Greece, Sweden, Italy, France, Estonia and other samples of mixed geographical origin). Through a combination of spectroscopic techniques, spectral transformations, variable selection and partial least squares discriminant analysis (PLS-DA), chemometric models which successfully classified the provenance of local and non-local honey were developed. The results of these models were also corroborated with other classification and pattern recognition techniques, such as linear discriminate analysis (LDA), support vector machines (SVM) and feed-forward artificial neural networks (FF-ANN).

Analysis of chemical compounds in beverages - Guidance for establishing a compositional analysis

In recent years, much analysis has been carried out regarding the chemical compositions of nonalcoholic and alcoholic beverages. However, no study has ever considered the compositional pitfalls inherent in such studies, which may lead to arbitrary results. Two approaches were compared in this paper, compositional data analysis (CoDa) and classical statistical analyses, to demonstrate (1) how aging affects beer and (2) how the results vary depending on the applied approach. Exemplarily, chemical compounds of 43 beer samples were analyzed. Our work has led us to obtain different results when the correlation analysis and principal component analysis were conducted on the unmodified-, log-transformed-, or closed-data in contrary to the analyses of log-ratio coordinates. The outcome of the latter provided better separation between aged and fresh beer samples and also easier interpretability. These results lead us to advise the usage of CoDa methods for the analyses of chemical compositions of beverages.

Direct Methanol Catalytic Fuel Cell, for Measuring Ethanol Contents in Pharmaceutical Tinctures

Background:

In order to test real direct applicability for analytical purposes, a small and simple direct methanol (or ethanol) catalytic, enzymatic or non-enzymatic fuel cell (DMFC) was used for the analysis of ethanol-based pharmaceutical tinctures; a detailed experimental study was conducted on five different pharmaceutical tinctures available at drugstores.

Results:

The results obtained using both enzymatic and non-enzymatic devices were compared with those obtained by analyzing the same pharmaceutical samples with a conventional catalase biosensor. Finally, the results were compared with the nominal values provided by manufacturing firms.

Conclusion:

The correlations between the different experimental and nominal values considered were good in general or satisfactory and the applied statistical tests (f-test and t-test) were also very comforting. At the end of the study, the use of enzymatic DMFC proved to be better than non- enzymatic DMFC devices, because it requires shorter analysis times.

The Brewing Industry and the Opportunities for Real-Time Quality Analysis Using Infrared Spectroscopy

Brewing is an ancient process which started in the middle east over 10,000 years ago. The style of beer varies across the globe but modern brewing is very much the same regardless of the style. While there are thousands of compounds in beer, current methods of analysis rely mostly on the content of only several important processing parameters such as gravity, bitterness, or alcohol. Near infrared and mid infrared spectroscopy offer opportunities to predict dozens to hundreds of compounds simultaneously at different stages of the brewing process. Importantly, this is an opportunity to move deeper into quality through measuring wort and beer composition, rather than just content. This includes measuring individual sugars and amino acids prior to fermentation, rather than total °Plato or free amino acids content. Portable devices and in-line probes, coupled with more complex algorithms can provide real time measurements, allowing brewers more control of the process, resulting in more consistent quality, reduced production costs and greater confidence for the future.

The Parallel Factor Analysis of Beer Fluorescence

Fluorescence excitation-emission matrices were measured for 111 samples of different types of beer and studied by the parallel factor analysis (PARAFAC). The 5-component PARAFAC model was found to suitably describes the beer fluorescence, accounting for 99.4% of the fluorescence variance in the measured set of samples, and providing the completely resolved excitation and emission spectra of each component. The model was chosen based on a model's core consistency and split-half analysis. It is shown that beer fluorescence is the sum of fluorescence of aromatic amino acids (tryptophan, tyrosine, and phenylalanine), different forms of vitamin B, and phenolic compounds. Obtained PARAFAC model of beer fluorescence demonstrated the potential for the quantification and quality analysis of beer fluorophores and classification of different beer types.

Computer Vision Method in Beer Quality Evaluation—A Review

Beers are differentiated mainly according to their visual appearance and their fermentation process. The main quality characteristics of beer are appearance, aroma, flavor, and mouthfeel. Important visual attributes of beer are foam appearance (volume and persistence), as well as the color and clarity. To replace manual inspection, automatic, objective, rapid and repeatable external quality inspection systems, such as computer vision, are becoming very important and necessary. Computer vision is a non-contact optical technique, suitable for the non-destructive evaluation of the food product quality. Currently, the main application of computer vision occurs in automated inspection and measurement, allowing manufacturers to keep control of product quality. This paper presents an overview of the applications and the latest achievements of the computer vision methods in determining the external quality attributes of beer.

Chemometric assisted Fourier Transform Infrared (FTIR) Spectroscopic analysis of fruit wine samples: Optimizing the initialization and convergence criteria in the non-negative factor analysis algorithm for developing a robust classification model

The present work proposes certain optimization in the non-negative factor analysis (NNFA) algorithm to ensure an efficient analysis of the Fourier transformation infrared (FTIR) spectral data sets of the fruit wine samples. The first optimization deals with initialization of the variables in a controlled fashion that would ensure a reasonably good quality initial estimate to implement NNFA algorithm. It prevents NNFA algorithm from itinerating with random numbers that essentially have no chemical relevance. The second implemented optimization involves eliminating the alternate least square of convergence and allowing the algorithm to iterate until the iteration limit is reached. This criterion avoids the algorithm to have premature convergence and ensures that model provide the solutions which corresponds to the global minima. The application of NNFA with suggested optimizations are found to capture the subtle differences in the spectral profiles and classify the fruit wine samples that are essentially complex mixtures of several chemicals in unknown proportions. The proposed approach is also found to perform better than principal component analysis on practical grounds. In summary, the current work provides a simple, sensitive and cost-effective approach using optimized NNFA and FTIR spectroscopy for classifying the fruit wine samples.

Direct Methanol (or Ethanol) Fuel Cell as Enzymatic or Non-Enzymatic Device, Used to Check Ethanol in Several Pharmaceutical and Forensic Samples

It was already demonstrated by our research group that a direct catalytic methanol (or ethanol) fuel cell (DMFC) device can be used also for analytical purposes, such as the determination of ethanol content in beverages. In the present research we extended the application to the analysis of several ethanol-based pharmaceutical products, i.e., pharmaceutical tinctures (dyes) and disinfectants. In recent work we have also shown that the use of alcohol dehydrogenase enzyme as a component of the anodic section of a direct catalytic methanol (or ethanol) fuel cell significantly improves the performance of a simple DMFC device, making it more suitable to measure ethanol (or methanol) in real samples by this cell. At the same time, we have also shown that DMFC can respond to certain organic compounds that are more complex than methanol and ethanol and having R(R')CH-OH group in the molecule. Firstly, pharmaceutical dyes were analyzed for their ethanol content using the simple catalytic DMFC device, with good accuracy and precision. The results are illustrated in the present paper. Additionally, a detailed investigation carried out on commercial denatured alcoholic samples evidenced several interferences due to the contained additives. Secondly, we hypothesized that by using the enzymatic fuel cell it would be possible to improve the determination, for instance, of certain antibiotics, such as imipenem, or else carry out determinations of ethanol content in saliva and serum (simulating forensic tests, correlated to drivers "breath test"); even if this has already been hypothesized in previous papers, the present study is the first to perform them experimentally, obtaining satisfactory results. In practice, all of the goals which we proposed were reached, confirming the remarkable opportunities of the enzymatic (or non-enzymatic) DMFC device.

Rapid authentication of edible bird's nest by FTIR spectroscopy combined with chemometrics

BACKGROUND

Edible bird's nests (EBNs) have been traditionally regarded as a kind of medicinal and healthy food in China. For economic reasons, they are frequently subjected to adulteration with some cheaper substitutes, such as Tremella fungus, agar, fried pigskin, and egg white. As a kind of precious and functional product, it is necessary to establish a robust method for the rapid authentication of EBNs with small amounts of samples by simple processes. In this study, the Fourier transform infrared spectroscopy (FTIR) system was utilized and its feasibility for identification of EBNs was verified.

RESULTS

FTIR spectra data of authentic and adulterated EBNs were analyzed by chemometrics analyses including principal component analysis, linear discriminant analysis (LDA), support vector machine (SVM) and one-class partial least squares (OCPLS). The results showed that the established LDA and SVM models performed well and had satisfactory classification ability, with the former 94.12% and the latter 100%. The OCPLS model was developed with prediction sensitivity of 0.937 and specificity of 0.886. Further detection of commercial EBN samples confirmed these results.

CONCLUSION

FTIR is applicable in the scene of rapid authentication of EBNs, especially for quality supervision departments, entry-exit inspection and quarantine, and customs administration. © 2017 Society of Chemical Industry.

Rapid Food Product Analysis by Surface Acoustic Wave Nebulization Coupled Mass Spectrometry

Rapid food product analysis is of great interest for quality control and assurance during the production process. Conventional quality control protocols require time and labor intensive sample preparation for analysis by state-of-the-art analytical methods. To reduce overall cost and facilitate rapid qualitative assessments, food products need to be tested with minimal sample preparation. We present a novel and simple method for assessing food product compositions by mass spectrometry using a novel surface acoustic wave nebulization method. This method provides significant advantages over conventional methods requiring no pumps, capillaries, or additional chemicals to enhance ionization for mass spectrometric analysis. In addition, the surface acoustic wave nebulization - mass spectrometry method is ideal for rapid analysis and to investigate certain compounds by using the mass spectra as a type of species-specific fingerprint analysis. We present for the first time surface acoustic wave nebulization generated mass spectra of a variety of fermented food products from a small selection of vinegars, wines, and beers.

High Ethanol Contents of Spirit Drinks in Kibera Slums, Kenya: Implications for Public Health

Cheap licit and artisanal illicit spirit drinks have been associated with numerous outbreaks of alcohol poisoning especially with methanol. This study aimed to evaluate the quality of cheap spirit drinks in Kibera slums in Nairobi County, Kenya. The samples consisted of cheap licit spirits (n = 11) and the artisanal spirit drink, ‘chang’aa’, (n = 28). The parameters of alcoholic strength and volatile composition were used as indicators of quality and were determined using gas chromatography with flame ionization detection (GC-FID) and gas chromatography-mass spectrometry (GC-MS) respectively. The ranges for alcoholic strength were 42.8–85.8% vol and 28.3–56.7% vol for chang’aa and licit spirit drinks respectively, while the pH ranges were 3.3–4.2 and 4.4–4.8 for chang’aa and licit spirit drinks respectively. The majority of volatiles were found in artisanal spirits and they included higher alcohols, ethyl esters and carbonyl compounds. The alcoholic strength of all the artisanal spirits (100%) and 91% of the licit spirits was above the 40% vol of standard spirits such as vodka. The high ethanol content of the alcohol products was the only element of public health significance in this study.

Assessment of alcoholic standard drinks using the Munich composite international diagnostic interview (M-CIDI): An evaluation and subsequent revision

The quantity and frequency of alcohol consumption are crucial both in risk assessment as well as epidemiological and clinical research. Using the Munich Composite International Diagnostic Interview (M-CIDI), drinking amounts have been assessed in numerous large-scale studies. However, the accuracy of this assessment has rarely been evaluated. This study evaluates the relevance of drink categories and pouring sizes, and the factors used to convert actual drinks into standard drinks. We compare the M-CIDI to alternative drink assessment instruments and empirically validate drink categories using a general population sample (n = 3165 from Germany), primary care samples (n = 322 from Italy, n = 1189 from Germany), and a non-representative set of k = 22503 alcoholic beverages sold in Germany in 2010-2016. The M-CIDI supplement sheet displays more categories than other instruments (AUDIT, TLFB, WHO-CIDI). Beer, wine, and spirits represent the most prevalent categories in the samples. The suggested standard drink conversion factors were inconsistent for different pouring sizes of the same drink and, to a smaller extent, across drink categories. For the use in Germany and Italy, we propose the limiting of drink categories and pouring sizes, and a revision of the proposed standard drinks. We further suggest corresponding examinations and revisions in other cultures.

Non-destructive determination of ethanol levels in fermented alcoholic beverages using Fourier transform mid-infrared spectroscopy

BACKGROUND

Traditional fermented alcoholic beverages are indigenous to a particular area and are prepared by the local people using an age-old techniques and locally available raw materials. The main objective of this work was the direct determination of ethanol in traditional fermented alcoholic beverages using mid infrared spectroscopy with partial least squares regression, verifying the robustness of the calibration models and to assess the quality of beverages.

RESULTS

The level of ethanol determination in Ethiopian traditional fermented alcoholic beverages was done using mid infrared spectroscopy with partial least squares regression (MIR-PLS). The calibration and validation sets, and real samples spectra were collected with 32 scans from 850-1200 cm-1. A total of 25 synthetic standards (calibration and validation sets) with ethanol (2-10% w/w) and sugars (glucose, fructose, sucrose and maltose) (0-5% w/w) compositions were used to construct and validate the models. Twenty-five different calibration models were validated by cross-validation approach with 25 left out standards. A large number of pre-treatments were verified, but the best pre-treatment was subtracting minimum + 2nd derivative. The model was found to have the highest coefficients of determination for calibration and cross-validation (0.999, 0.999) and root mean square error of prediction [0.1% (w/w)]. For practical relevance, the MIR-PLS predicted values were compared against the values determined by gas chromatography. The predicted values of the model were found to be in excellent agreement with gas chromatographic measurements. In addition, recovery test was conducted with spiking 2.4-6.4% (w/w) ethanol. Based on the obtained recovery percentage, 85.4-107% (w/w), the matrix effects of the samples were not considerable.

CONCLUSION

The proposed technique, MIR-PLS at 1200-850 cm-1 spectral region was found appropriate to quantify ethanol in fermented alcoholic beverages. Among the studied beverages (Tella, Netch Tella, Filter Tella, Korefe, Keribo, Borde and Birz), the average ethanol contents ranged from 0.77-9.1% (v/v). Tej was found to have the highest ethanol content whereas Keribo had the least ethanol content. The developed method was simple, fast, precise and accurate. Moreover, no sample preparation was required at all. However, it should be noted that the present procedure is probably not usable for regulatory purposes (e.g. controlling labelling).

Multicomposition EPSR: Toward Transferable Potentials To Model Chalcogenide Glass Structures

The structure of xAs₄₀Se₆₀-(1 - x)As₄₀S₆₀ glasses, where x = 1.000, 0.667, 0.500, 0.333, 0.250, and 0.000, is investigated using a combination of neutron and X-ray diffraction coupled with computational modeling using multicomposition empirical potential structure refinement (MC-EPSR). Traditional EPSR (T-EPSR) produces a set of empirical potentials that drive a structural model of a particular composition to agreement with diffraction experiments. The work presented here establishes the shortcomings in generating such a model for a ternary chalcogenide glass composition. In an enhancement to T-EPSR, MC-EPSR produces a set of pair potentials that generate robust structural models across a range of glass compositions. The structures obtained vary with composition in a much more systematic way than those taken from T-EPSR. For example, the average arsenic-sulfur bonding distances vary between 2.28 and 2.46 Å in T-EPSR but are 2.29 ± 0.02 Å in MC-EPSR. Similarly, the arsenic-selenium bond lengths from T-EPSR vary between 2.28 and 2.43 Å but are consistently 2.40 ± 0.02 Å in the MC-EPSR results. Analysis of these models suggests that the average separation of the chalcogen (S or Se) atoms is the structural origin of the changes in nonlinear refractive index with glass composition.