Quantification of Coffea arabica and Coffea canephora var. robusta in roasted and ground coffee blends

Current applications of benchtop FT-NMR in food science: From quality control to adulteration detection

The introduction of benchtop FT-NMR spectrometers in recent years represents a remarkable innovation in various fields, including the food sector. Modern benchtop FT-NMR spectrometers are low-field instruments, with a magnetic field ranging from 1 T to 2.35 T (1H resonance frequency from 43 MHz to 100 MHz), characterized by compact design, ease of use, and low maintenance costs. As in the case of high-field NMR instruments, benchtop NMR spectra (obtained by Fourier transformation) contain important information useful for compound identification and quantification. In this review, a description of the fundamental steps useful both to acquire benchtop NMR spectra and to treat the obtained data is reported together with a wide range of applications in the food field. In particular, peculiar aspects of commercial benchtop instruments as well as NMR data acquisition, processing and treatment are reviewed reporting also a practical pipeline and a list of good practices for benchtop NMR applications. Benchtop FT-NMR applications, mainly focused on food adulteration detection and quality control, are discussed here using targeted, metabolomic, and fingerprinting approaches. Finally, the industrial applicability of benchtop NMR methods in either static or continuous mode is reported.

rIDIMS: A novel tool for processing direct-infusion mass spectrometry data

Metabolomics using mass spectrometry-only (MS) analysis either by continuous or intermittent direct infusion (DIMS) and ambient ionization techniques (AMS) has grown in popularity due to their rapid, high-throughput nature and the advantage of performing fast analysis with minimal or no sample pretreatments. But currently, end-users without programming knowledge do not find applications with Graphical User Interface (GUI) specialized in processing DIMS or AMS data. Specifically, there is a lack of standardized workflow for processing data from limited sample sizes and scans from different total ion chronograms (TIC).To address this gap, we present rIDIMS, a browser-based application that offers a straightforward and fast workflow focusing on high-quality scan selection, grouping of isotopologues and adducts, data alignment, binning, and filtering. We also introduce a novel function for selecting TIC scans that is reproducible and statistically reliable, which is a feature particularly useful for studies with limited sample sizes. After processing in rIDIMS, the result is exported in an HTML report document that presents publication-quality figures, statistical data and tables, ready to be customized and exported. We demonstrate rIDIMS functionality in three cases: (i) Classification of coffee bean species through the chemical profile obtained with Mass Spec Pen; (ii) Public repository DIMS data from lipid profiling in monogenic insulin resistance syndromes, and (iii) Lipids for lung cancer classification. We show that our implementation facilitates the processing of AMS and DIMS data through an easy and intuitive interface, contributing to reproducible and reliable metabolomic investigations. Indeed, rIDIMS function asa user-friendly GUI based Shiny web application for intuitive use by end-users (available at https://github.com/BioinovarLab/rIDIMS).

PGI Chianina meat traceability by means of multivariate HRMAS-NMR data analysis

Food quality is a crucial issue for producers and consumers, either dealing with commodities according to basic standards or with top quality products. Among the parameters contributing to quality, the place of origin is considered to be one of the most relevant, especially for protected denomination of origin and protected geographical indication foods, PDO and PGI, respectively. These labels have been designed by the EU to protect and valorise high quality foodstuff produced in limited areas and to ensure higher incomes to farmers. Such economic interest has prompted the use of several analytical techniques for the traceability issue. Here we proposed the multivariate HRMAS-NMR (i.e. High-Resolution Magic Angle Spinning-Nuclear Magnetic Resonance) data analysis for the traceability of PGI Chianina meat, specifically for the semitendinosus muscle. The metabolic profile of Chianina meat assessed by HRMAS-NMR spectroscopy was analysed by means of PCA (Principal Component Analysis), PLS-DA (Partial Least Square-Discriminant Analysis) and OPLS-DA (Orthogonal Partial Least Square-Discriminant Analysis) in order to classify samples according to the geographical origin. The built models provided an excellent separation between PGI and non-PGI, and the use of the VIP (Valuable Influence on Projection) values allowed us to identify metabolites contributing significantly to classification. Specifically, we found molecules such as amino acids, carnosine, some nucleosides, and fatty acids to be responsible for the discrimination: the fatty acid profile of meat is affected by the different feeding systems, while the other metabolites are involved in the ageing process of meat (ATP degradation during post mortem and proteolysis).

Design of encapsulation method for chlorogenic acid and caffeine in coffee waste by-product

INTRODUCTION

Coffee silver skin (CSS) is a thin covering over green coffee seeds inside coffee cherry. It is a good source of bioactive compounds like chlorogenic acid and caffeine. It is produced as a by-product of the roasting process.

OBJECTIVE

The goal of this study is to apply spray drying method to encapsulate 5-O-caffeoylquinic acid (chlorogenic acid) and caffeine extracted from CSS.

METHODS

The main-plots for optimisation were feed solid concentration (2.5, 5, 10°Bx), and the sub-plots of the whole-plot were carrier material type (maltodextrin, modified starch, arabic gum) and inlet air temperature (130, 160, 190°C). Responses included were drying yield, chlorogenic acid concentration, caffeine content, Carr index, and solubility values.

RESULTS

Suitable conditions were spray drying inlet temperature of 190°C, extract concentration of 10°Bx, and wall material composition [modified starch/arabic gum (MS:AG)] 10.5:9.5. As the feeding CSS extract concentration increased, the amount of chlorogenic acid and caffeine in the final powder increased, while the powder's flow characteristics improved.

CONCLUSIONS

The concentration stage might be used to produce free-flowing powdered particles with good bioactive retention for use in the food processing industry.

Novel authentication of African geographical coffee types (bean, roasted, powdered) by handheld NIR spectroscopic method

African coffee is among the best traded coffee types worldwide, and rapid identification of its geographical origin is very important when trading the commodity. The study was important because it used NIR techniques to geographically differentiate between various types of coffee and provide a supply chain traceability method to avoid fraud. In this study, geographic differentiation of African coffee types (bean, roasted, and powder) was achieved using handheld near-infrared spectroscopy and multivariant data processing. Five African countries were used as the origins for the collection of Robusta coffee. The samples were individually scanned at a wavelength of 740-1070 nm, and their spectra profiles were preprocessed with mean centering (MC), multiplicative scatter correction (MSC), and standard normal variate (SNV). Support vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN), random forests (RF), and partial least square discriminate analysis (PLS-DA) were then used to develop a prediction model for African coffee types. The performance of the model was assessed using accuracy and F1-score. Proximate chemical composition was also conducted on the raw and roasted coffee types. The best classification algorithms were developed for the following coffee types: raw bean coffee, SD-PLSDA, and MC + SD-PLSDA. These models had an accuracy of 0.87 and an F1-score of 0.88. SNV + SD-SVM and MSC + SD-NN both had accuracy and F1 scores of 0.97 for roasted coffee beans and 0.96 for roasted coffee powder, respectively. The results revealed that efficient quality assurance may be achieved by using handheld NIR spectroscopy combined with chemometrics to differentiate between different African coffee types according to their geographical origins.

Interactive deciphering electron-shuttling characteristics of Coffea arabica leaves and potential bioenergy-steered anti-SARS-CoV-2 RdRp inhibitor via microbial fuel cells

Due to the pandemics of COVID-19, herbal medicine has recently been explored for possible antiviral treatment and prevention via novel platform of microbial fuel cells. It was revealed that Coffea arabica leaves was very appropriate for anti-COVID-19 drug development. Antioxidant and anti-inflammatory tests exhibited the most promising activities for C. arabica ethanol extracts and drying approaches were implemented on the leaf samples prior to ethanol extraction. Ethanol extracts of C. arabica leaves were applied to bioenergy evaluation via DC-MFCs, clearly revealing that air-dried leaves (CA-A-EtOH) exhibited the highest bioenergy-stimulating capabilities (ca. 2.72 fold of power amplification to the blank). Furthermore, molecular docking analysis was implemented to decipher the potential of C. arabica leaves metabolites. Chlorogenic acid (-6.5 kcal/mol) owned the highest binding affinity with RdRp of SARS-CoV-2, showing a much lower average RMSF value than an apoprotein. This study suggested C. arabica leaves as an encouraging medicinal herb against SARS-CoV-2.

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.

Targeted and Non-Targeted HPLC Analysis of Coffee-Based Products as Effective Tools for Evaluating the Coffee Authenticity

Coffee is a very popular beverage worldwide. However, its composition and characteristics are affected by a number of factors, such as geographical and botanical origin, harvesting and roasting conditions, and brewing method used. As coffee consumption rises, the demands on its high quality and authenticity naturally grows as well. Unfortunately, at the same time, various tricks of coffee adulteration occur more frequently, with the intention of quick economic profit. Many analytical methods have already been developed to verify the coffee authenticity, in which the high-performance liquid chromatography (HPLC) plays a crucial role, especially thanks to its high selectivity and sensitivity. Thus, this review summarizes the results of targeted and non-targeted HPLC analysis of coffee-based products over the last 10 years as an effective tool for determining coffee composition, which can help to reveal potential forgeries and non-compliance with good manufacturing practice, and subsequently protects consumers from buying overpriced low-quality product. The advantages and drawbacks of the targeted analysis are specified and contrasted with those of the non-targeted HPLC fingerprints, which simply consider the chemical profile of the sample, regardless of the determination of individual compounds present.

The use of multispectral imaging for the discrimination of Arabica and Robusta coffee beans

Arabica coffee beans are sold at twice the price, or more, compared to Robusta beans and consequently are susceptible to economically motivated adulteration by substitution. There is a need for rapid, non-destructive, and efficient analytical techniques for monitoring the authenticity of Arabica coffee beans in the supply chain. In this study, multispectral imaging (MSI) was applied to discriminate roasted Arabica and Robusta coffee beans and perform quantitative prediction of Arabica coffee bean adulteration with Robusta. The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, built using selected spectral and morphological features from individual coffee beans, achieved 100% correct classification of the two coffee species in the test dataset. The OPLS regression model was able to successfully predict the level of adulteration of Arabica with Robusta. MSI analysis has potential as a rapid screening tool for the detection of fraud issues related to the authenticity of Arabica coffee beans.

Effects of Coffee on Sirtuin-1, Homocysteine, and Cholesterol of Healthy Adults: Does the Coffee Powder Matter?

Background: Coffee is one of the most popular beverages globally and contains several bioactive compounds that are relevant to human health. Many nutritional strategies modulate sirtuin-1, thereby impacting aging and cardiometabolic health. This study investigated the influence of different blended coffees on serum sirtuin-1, blood lipids, and plasma homocysteine. Methods: An eight-week randomized clinical trial that included 53 healthy adults of both sexes analyzed the effects of daily intake of 450 to 600 mL of pure Arabica or blended (Arabica + Robusta) coffee intake of filtered coffee on blood sirtuin-1, lipids, and homocysteine. Results: Both Arabica and blended coffees similarly increased serum sirtuin-1 concentration, from 0.51 to 0.58 ng/mL (p = 0.004) and from 0.40 to 0.49 ng/mL (p = 0.003), respectively, without changing plasma homocysteine, folic acid, glucose, and CRP. However, the blended coffee intake increased total cholesterol from 4.70 to 5.17 mmol/L (p < 0.001) and LDL-cholesterol from 2.98 to 3.32 mmol/L (p < 0.001), as well as HDL-c from 1.26 to 1.36 mmol/L (p < 0.001). Conclusion: Both coffee powders increased sirtuin-1 expression, but our results suggest that blended coffee had hypercholesterolemic effects which could increase cardiovascular risk. Therefore, preference should be given to Arabica coffee for the best cardiometabolic benefits of coffee.

Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview

Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.

Metabolite Profiles of the Green Beans of Indonesian Arabica Coffee Varieties

The green beans of 3 Indonesian arabica coffee varieties, namely, ateng, buhun, and sigararutang, were analyzed with ¹H NMR-based metabolomics coupled with alpha-glucosidase inhibitory activity assay. These coffees were cultivated in the same geographical conditions. The PLSDA model successfully classified the green coffee beans based on their varieties. To reveal the characteristic metabolites for each coffee variety, S-plot of two-class OPLSDA models was generated and analyzed. Ateng coffee was characterized with trigonelline, sucrose, 5-CQA, and acetic acid. The characteristic metabolites of buhun coffee were citric acid and malic acid. Meanwhile, the most discriminant compound of sigararutang coffee was quinic acid. HCA analysis revealed the lineage relationship of the 3 coffee varieties. Ateng coffee had closer lineage relationship to sigararutang compared to the buhun coffee. Alpha-glucosidase inhibitory activity of the coffee samples did not differ widely. IC₅₀ values of alpha-glucosidase inhibitory activity of ateng, sigararutang, and buhun coffees were 3.01 ± 0.16, 3.14 ± 0.20, and 5.05 ± 0.28 mg/mL, respectively. Although grown in the same geographical conditions, our results revealed that each coffee variety possessed a unique metabolome clarifying the diversity of Indonesian arabica coffees. This study verified that ¹H NMR-based metabolomics is an excellence method for discovering the lineage relationship in the samples with different varieties or cultivars.

Current strategies to guarantee the authenticity of coffee

As they become more health conscious, consumers are paying increasing attention to food quality and safety. In coffee production, fraudulent strategies to reduce costs and maximize profits include mixing beans from two species of different economic value, the addition of other substances and/or foods, and mislabeling. Therefore, testing for coffee authenticity and detecting adulterants is required for value assessment and consumer protection. Here we provide an overview of the chromatography, spectroscopy, and single-nucleotide polymorphism-based methods used to distinguish between the major coffee species Arabica and Robusta. This review also describes the techniques applied to trace the geographical origin of coffee, based mainly on the chemical composition of the beans, an approach that can discriminate between coffee-growing regions on a continental or more local level. Finally, the analytical techniques used to detect coffee adulteration with other foods and/or coffee by-products are discussed, with a look at the practice of adding pharmacologically active compounds to coffee, and their harmful effects on health.

Aluminum in Coffee

This study investigated the aluminum content in one of the most consumed daily beverages: coffee. The total Al concentration in 10 different samples of coffee beans and their water-extractable fraction were determined. We then tested the influence of different brewing methods on the concentration of the extracted Al in the final beverage. Metal analyses were performed using graphite furnace atomic absorption spectroscopy (GF-AAS) after microwave-assisted acid digestion. The results showed highly variable Al contents in coffee beans (1.5-15.5 mg kg^-1), of which ∼2-10% were water-extractable. The brewing technique had a major influence on the Al content in the beverage: significantly higher Al concentrations (72.57 ± 23.96 μg L^-1) occurred in coffee brewed in an aluminum moka pot. Interestingly, using ground coffee with this method even reduced the Al content in the final beverage compared to the brewing water used. Coffee brewed from Al capsules did not contain significantly higher Al concentrations compared to other methods.

A data fusion model merging information from near infrared spectroscopy and X-ray fluorescence. Searching for atomic-molecular correlations to predict and characterize the composition of coffee blends

This article aims to develop and validate a multivariate model for quantifying Robusta-Arabica coffee blends by combining near infrared spectroscopy (NIRS) and total reflection X-ray fluorescence (TXRF). For this aim, 80 coffee blends (0.0-33.0%) were formulated. NIR spectra were obtained in the wavenumber range 11100-4950 cm-1 and 14 elements were determined by TXRF. Partial least squares models were built using data fusion at low and medium levels. In addition, selection of predictive variables based on their importance indices (SVPII) improved results. The best model reduced the number of variables from 1114 to 75 and root mean square error of prediction from 4.1% to 1.7%. SVPII selected NIR regions correlated with coffee components, and the following elements were chosen: Ti, Mn, Fe, Cu, Zn, Br, Rb, Sr. The model interpretation took advantage of the data fusion between atomic and molecular spectra in order to characterize the differences between these coffee varieties.

NMR-Based Plant Metabolomics in Nutraceutical Research: An Overview

Few topics are able to channel the interest of researchers, the public, and industries, like nutraceuticals. The ever-increasing demand of new compounds or new sources of known active compounds, along with the need of a better knowledge about their effectiveness, mode of action, safety, etc., led to a significant effort towards the development of analytical approaches able to answer the many questions related to this topic. Therefore, the application of cutting edges approaches to this area has been observed. Among these approaches, metabolomics is a key player. Herewith, the applications of NMR-based metabolomics to nutraceutical research are discussed: after a brief overview of the analytical workflow, the use of NMR-based metabolomics to the search for new compounds or new sources of known nutraceuticals are reviewed. Then, possible applications for quality control and nutraceutical optimization are suggested. Finally, the use of NMR-based metabolomics to study the impact of nutraceuticals on human metabolism is discussed.

Review of Analytical Methods to Detect Adulteration in Coffee

As one of the most consumed beverages in the world, coffee plays many major socioeconomical roles in various regions. Because of the wide coffee varieties available in the marketplaces, and the substantial price gaps between them (e.g., Arabica versus Robusta; speciality versus commodity coffees), coffees are susceptible to intentional or accidental adulteration. Therefore, there is a sustaining interest from the producers and regulatory agents to develop protocols to detect fraudulent practices. In general, strategies to authenticate coffee are based on targeted chemical profile analyses to determine specific markers of adulterants, or nontargeted analyses based on the “fingerprinting” concept. This paper reviews the literature related to chemometric approaches to discriminate coffees based on nuclear magnetic resonance spectroscopy, chromatography, infrared/Raman spectroscopy, and array sensors/indicators. In terms of chemical profiling, the paper focuses on the detection of diterpenes, homostachydrine, phenolic acids, carbohydrates, fatty acids, triacylglycerols, and deoxyribonucleic acid. Finally, the prospects of coffee authentication are discussed.

Chemometric Authentication of Brazilian Coffees Based on Chemical Profiling

In this work, different chemometric tools were compared to classify n = 26 conventional (CONV) and n = 19 organic (ORG) coffees from the main Brazilian producing regions based on the chemical composition, physicochemical properties, and antioxidant activity. Principal component analysis separated ORG and CONV coffees but the distinction among the producing regions of Brazilian coffee was not possible. Partial least squares discriminant analysis classified all ORG and CONV coffees in the external validation. Similarly, linear discriminant analysis was able to discriminate 100% and 81% of ORG and CONV coffees in the external validation, respectively, in which total phenolic content (TPC), ferric reducing antioxidant activity, and caffeic acid were the main discriminant variables. Overall 100% of samples from Paraná, Minas Gerais, and blended samples were correctly classified, where TPC, flavonoids, inhibition of lipid peroxidation, caffeic acid, pH, and soluble solids were the main discriminant variables. Support vector machines classified 95% ORG and 88% CONV, 100% Coffea arabica, and 88% and 78% coffees produced in São Paulo and Minas Gerais. k-Nearest neighbors was effective in distinguishing 100% CONV, 89% ORG, 100% coffees from São Paulo, and 100% C. arabica coffees. Overall, HPLC data and simple physicochemical parameters allied to chemometrics were effective in authenticating the cultivation system and the botanical origin of Brazilian coffees. PRACTICAL APPLICATION: Coffee adulteration is a serious problem in the food chain as some fraudsters replace coffee powder by other cheaper products. In the case of organic coffee, this scenario is even worse as still there is not a universal method to differentiate conventionally grown coffee from its organic counterpart. In addition, Brazilian coffee is produced in different regions and the commercial value varies. Therefore, we analyzed some physicochemical, chemical, and antioxidant properties of Brazilian coffees from distinct origins and classified the samples using chemometrics. Our approach seems to be interesting for quality control purposes.

The potentiality of NMR-based metabolomics in food science and food authentication assessment

In the last years, there was an increasing interest on nuclear magnetic resonance (NMR) spectroscopy, whose applications experienced an exponential growth in several research fields, particularly in food science. NMR was initially developed as the elective technique for structure elucidation of single molecules and nowadays is playing a dominant role in complex mixtures investigations. In the era of the "omics" techniques, NMR was rapidly enrolled as one of the most powerful methods to approach metabolomics studies. Its use in analytical routines, characterized by rapid and reproducible measurements, would provide the identification of a wide range of chemical compounds simultaneously, disclosing sophisticated frauds or addressing the geographical origin, as well as revealing potential markers for other authentication purposes. The great economic value of high-quality or guaranteed foods demands highly detailed characterization to protect both consumers and producers from frauds. The present scenario suggests metabolomics as the privileged approach of modern analytical studies for the next decades. The large potentiality of high-resolution NMR techniques is here presented through specific applications and using different approaches focused on the authentication process of some foods, like tomato paste, saffron, honey, roasted coffee, and balsamic and traditional balsamic vinegar of Modena, with a particular focus on geographical origin characterization, ageing determination, and fraud detection.

Raman spectroscopy for the differentiation of Arabic coffee genotypes

The objective of this study was to evaluate the ability of Raman spectroscopy to identify the genotype of green coffee beans. Four genotypes of Arabic coffee: one Mundo Novo line (G1) and three Bourbon lines (G2, G3, and G4). The harvest was selected using a wet processing method. Raman spectra of the samples were obtained using a FT-Raman RFS/100 spectrometer in the spectral range of 3500-400 cm^-1. The data were treated using chemometric unsupervised classification tools and supervised analysis. Using the unsupervised analysis (PCA), the apparent tendency of agglomeration between samples G1 and G3 was verified. These differences were present in the spectral bands that are characteristic of fatty acids and kahweol. Based on this information, a classification model to discriminate (PLS-DA) the Mundo Novo and Bourbon samples was utilized. Raman spectroscopy allowed the building of an adequate model to differentiate between coffee genotypes.

Raman spectroscopy: A new strategy for monitoring the quality of green coffee beans during storage

Raman spectroscopy was used to identify chemical changes associated sensory quality of coffee beans, for natural and pulped natural coffee stored in different packaging. The green beans of natural coffee and pulped natural coffee were stored in three types of packaging materials in a commercial warehouse. Sensory analyses were performed, and Raman spectra were collected after 0, 3, 6, 9, 12, and 18 storage months. Raman spectra were used to construct multivariate control charts. The charts, which were constructed using principal component analysis, can only be used to identify chemical changes in the green beans from pulped natural coffee stored in different packaging materials. Raman spectroscopy is more sensitive than sensory analysis for detecting chemical changes in stored pulped natural coffee. The measured changes ultimately affect the quality of the beverage because samples stored for six months in paper packaging were determined to no longer meet the quality control requirements.

Coffee variety, origin and extraction procedure: Implications for coffee beneficial effects on human health

We set up an efficient protocol for the rapid analysis of NMR spectra of green and roasted coffee extracts, enabling the automatic identification and quantification of metabolites in approximately two minutes per spectrum. This method allowed for the metabolic profiling and the subsequent evaluation of the content of bioactive compounds and antioxidant activity of coffee samples, depending on their species (Arabica and Robusta), geographical origin and extraction procedure (hydroalcoholic, espresso and moka). The hydroalcoholic extraction is the most efficient method in terms of yields of low molecular weight compounds (in particular chlorogenic acids), while moka extraction provides the highest amounts of melanoidins. Moreover, that the ratio between health-giving compounds (chlorogenic acids, trigonelline and choline) and caffeine is higher in Arabica coffees. The data collected provide useful insights for the selection of coffee raw material to be used in the preparation of coffee-based dietary supplements, nutraceuticals and functional beverages.

Coffea arabica and C. canephora discrimination in roasted and ground coffee from reference material candidates by real-time PCR

To produce specific desirable coffee blends, Coffea arabica and C. canephora are mixed each other, in some cases to suit consumer preference, but in others to reduce production costs. In this scenario, the aim of this work was to evaluate standard candidate reference materials (RMc) for analysis of different blends of roasted and ground coffee. For this purpose, we analyzed different percentages of C. arabica and C. canephora (100:0; 50:50; 25:75; and 0:100, respectively). These RMc samples were developed in a previous study with green coffee beans submitted to medium roasting. In this work, coffee species differentiation (C. arabica and C. canephora) was analyzed by real-time PCR, using specific primers previously developed, called ARA primers. The RMc material with 100% C. canephora did not present amplification, in contrast with the samples containing C. arabica, which all presented amplification. These results indicate the specificity of ARA primers for C. arabica and that the detection system assay can be used as a promising molecular tool to identify and quantify percentages of C. arabica in different coffee blends.

In-Depth Aroma and Sensory Profiling of Unfamiliar Table-Grape Cultivars

We present an in-depth analysis of aroma profiles and sensory attributes, employing solid-phase microextraction gas chromatography/mass spectrometry (SPME-GC-MS) to identify the key compounds driving consumer preference in 19 unfamiliar cultivars. In combination with popular cultivars, we identified a total of 100 compounds in all table grapes, of which 26 key volatiles were correlated with consumer liking. Based on this relationship, five aroma combinations (AC) were formulated, wherein 33 compounds contributed to aroma intensity, and thus, were viewed as active volatiles. The fruity, floral, and sweet aromas were further divided into secondary aromatic series, of which the apple, citrus, orange, rose, geranium, violet, and honey aromas constituted the predominant series in unfamiliar cultivars. Xiangyue and Heikuixiang emerged as the preferred table grapes according to our analysis. By comparison, the popular cultivars showed relatively fewer volatiles, but their contents were much greater than the large number of volatiles identified in the unfamiliar cultivars.

Quality of Commercial Coffees: Heavy Metal and Ash Contents

This study aimed to quantify the ash content and to determine the concentration of heavy metals in roasted ground coffee and their respective infusions. The ash content was determined by incineration of the samples, and the quantification of heavy metals was performed by flame atomic absorption spectrophotometry for the following metals: cadmium, lead, copper, chromium, nickel, and zinc. According to the ash analysis, 15% of the roasted ground coffee samples were within the standards established by the legislation of the state of São Paulo, which has set an ash content of below 5%. In the roasted ground coffee samples, the Cd, Cu, Ni, and Zn contents did not exceed the limit established by Brazilian legislation. In several samples, both Pb and Cr were found in high levels, exceeding the limits established by Brazilian legislation. In the infusions of roasted ground coffee, the Cd, Cu, Cr, and Ni contents were below the detection limit of the equipment. Zn was found in all infusions and Pb was only detected in seven coffee infusion samples. Overall, the concentrations of heavy metals found in the commercially roasted ground coffee and their respective infusions are lower than the limits recommended by the official inspection agencies and, thus, are suitable for consumption.

Comparison of Attenuated Total Reflectance Mid-Infrared, Near Infrared, and 1H-Nuclear Magnetic Resonance Spectroscopies for the Determination of Coffee's Geographical Origin

The sensorial properties of Colombian coffee are renowned worldwide, which is reflected in its market value. This raises the threat of fraud by adulteration using coffee grains from other countries, thus creating a demand for robust and cost-effective methods for the determination of geographical origin of coffee samples. Spectroscopic techniques such as Nuclear Magnetic Resonance (NMR), near infrared (NIR), and mid-infrared (mIR) have arisen as strong candidates for the task. Although a body of work exists that reports on their individual performances, a faithful comparison has not been established yet. We evaluated the performance of ¹H-NMR, Attenuated Total Reflectance mIR (ATR-mIR), and NIR applied to fraud detection in Colombian coffee. For each technique, we built classification models for discrimination by species (C. arabica versus C. canephora (or robusta)) and by origin (Colombia versus other C. arabica) using a common set of coffee samples. All techniques successfully discriminated samples by species, as expected. Regarding origin determination, ATR-mIR and ¹H-NMR showed comparable capacity to discriminate Colombian coffee samples, while NIR fell short by comparison. In conclusion, ATR-mIR, a less common technique in the field of coffee adulteration and fraud detection, emerges as a strong candidate, faster and with lower cost compared to ¹H-NMR and more discriminating compared to NIR.

How coffee affects metabolic syndrome and its components

Metabolic syndrome, with its increasing prevalence, is becoming a major public health problem throughout the world. Many risk factors including nutrition play a role in the emergence of metabolic syndrome. Of the most-consumed beverages in the world, coffee contains more than 1000 components such as caffeine, chlorogenic acid, diterpenes and trigonelline. It has been proven in many studies that coffee consumption has a positive effect on chronic diseases. In this review, starting from the beneficial effects of coffee on health, the relationship between coffee consumption and metabolic syndrome and its components has been investigated. There are few studies investigating the relationship between coffee and metabolic syndrome, and the existing ones put forward different findings. The factors leading to the differences are thought to stem from coffee variety, the physiological effects of coffee elements, and the nutritional ingredients (such as milk and sugar) added to coffee. It is reported that consumption of coffee in adults up to three cups a day reduces the risk of Type-2 diabetes and metabolic syndrome.

Low-field (1)H NMR spectroscopy for distinguishing between arabica and robusta ground roast coffees

This work reports a new screening protocol for addressing issues of coffee authenticity using low-field (60MHz) bench-top (1)H NMR spectroscopy. Using a simple chloroform-based extraction, useful spectra were obtained from the lipophilic fraction of ground roast coffees. It was found that 16-O-methylcafestol (16-OMC, a recognized marker compound for robusta beans) gives rise to an isolated peak in the 60MHz spectrum, which can be used as an indicator of the presence of robusta beans in the sample. A total of 81 extracts from authenticated coffees and mixtures were analysed, from which the detection limit of robusta in arabica was estimated to be between 10% and 20% w/w. Using the established protocol, a surveillance exercise was conducted of 27 retail samples of ground roast coffees which were labelled as "100% arabica". None were found to contain undeclared robusta content above the estimated detection limit.

Characterization of New Ent-kaurane Diterpenoids of Yunnan Arabica Coffee Beans

Five new ent-kaurane diterpenoids, named mascaroside III-V (1-3), and 20-nor-cofaryloside I-II (4-5), together with seven known diterpenoids, were isolated from methanol extracts of the green coffee beans of Yunnan Arabica Coffee. Their chemical structures were elucidated by extensive spectroscopic analyses. Meanwhile, cytotoxicity assay against HL-60, A-549, SMMC-7721, MCF-7 and SW480 cell lines showed that they have not evident inhibition of cytotoxicity.

Classification of Coffee Beans by GC-C-IRMS, GC-MS, and (1)H-NMR

In a previous work using (1)H-NMR we reported encouraging steps towards the construction of a robust expert system for the discrimination of coffees from Colombia versus nearby countries (Brazil and Peru), to assist the recent protected geographical indication granted to Colombian coffee in 2007. This system relies on fingerprints acquired on a 400 MHz magnet and is thus well suited for small scale random screening of samples obtained at resellers or coffee shops. However, this approach cannot easily be implemented at harbour's installations, due to the elevated operational costs of cryogenic magnets. This limitation implies shipping the samples to the NMR laboratory, making the overall approach slower and thereby more expensive and less attractive for large scale screening at harbours. In this work, we report on our attempt to obtain comparable classification results using alternative techniques that have been reported promising as an alternative to NMR: GC-MS and GC-C-IRMS. Although statistically significant information could be obtained by all three methods, the results show that the quality of the classifiers depends mainly on the number of variables included in the analysis; hence NMR provides an advantage since more molecules are detected to obtain a model with better predictions.

Authentication of Coffea arabica according to Triacylglycerol Stereospecific Composition

Stereospecific analysis is an important tool for the characterization of lipid fraction of food products. In the present research, an approach to characterize arabica and robusta varieties by structural analysis of the triacylglycerol (TAG) fraction is reported. The lipids were Soxhlet extracted from ground roasted coffee beans with petroleum ether, and the fatty acids (FA) were determined as their corresponding methyl esters. The results of a chemical-enzymatic-chromatographic method were elaborated by a chemometric procedure, Linear Discriminant Analysis (LDA). According to the total and intrapositional FA composition of TAG fraction, the obtained results were able to characterize roasted pure coffee samples and coffee mixtures with 10% robusta coffee added to arabica coffee. Totally correct classified samples were obtained when the TAG stereospecific results of the considered coffee mixture (90 : 10 arabica/robusta) were elaborated by LDA procedure.

Metabolomics of dates (Phoenix dactylifera) reveals a highly dynamic ripening process accounting for major variation in fruit composition

BACKGROUND

Dates are tropical fruits with appreciable nutritional value. Previous attempts at global metabolic characterization of the date metabolome were constrained by small sample size and limited geographical sampling. In this study, two independent large cohorts of mature dates exhibiting substantial diversity in origin, varieties and fruit processing conditions were measured by metabolomics techniques in order to identify major determinants of the fruit metabolome.

RESULTS

Multivariate analysis revealed a first principal component (PC1) significantly associated with the dates' countries of production. The availability of a smaller dataset featuring immature dates from different development stages served to build a model of the ripening process in dates, which helped reveal a strong ripening signature in PC1. Analysis revealed enrichment in the dry type of dates amongst fruits with early ripening profiles at one end of PC1 as oppose to an overrepresentation of the soft type of dates with late ripening profiles at the other end of PC1. Dry dates are typical to the North African region whilst soft dates are more popular in the Gulf region, which partly explains the observed association between PC1 and geography. Analysis of the loading values, expressing metabolite correlation levels with PC1, revealed enrichment patterns of a comprehensive range of metabolite classes along PC1. Three distinct metabolic phases corresponding to known stages of date ripening were observed: An early phase enriched in regulatory hormones, amines and polyamines, energy production, tannins, sucrose and anti-oxidant activity, a second phase with on-going phenylpropanoid secondary metabolism, gene expression and phospholipid metabolism and a late phase with marked sugar dehydration activity and degradation reactions leading to increased volatile synthesis.

CONCLUSIONS

These data indicate the importance of date ripening as a main driver of variation in the date metabolome responsible for their diverse nutritional and economical values. The biochemistry of the ripening process in dates is consistent with other fruits but natural dryness may prevent degenerative senescence in dates following ripening. Based on the finding that mature dates present varying extents of ripening, our survey of the date metabolome essentially revealed snapshots of interchanging metabolic states during ripening empowering an in-depth characterization of underlying biology.