A green-footprint approach for parallel multiclass analysis of contaminants in roasted coffee via LC-HRMS.

The development and validation of a simple, comprehensive, and environment-friendly procedure to determine pesticide residues, naturally occurring and processing contaminants in roasted coffee is presented. A solid-liquid extraction of pesticides and mycotoxins with ethyl acetate and the concurrent partition of acrylamide to an aqueous phase follows a parallel analytical strategy that requires a single analytical portion to determine contaminants that are typically analyzed by dedicated single residue methods. The partition rules the lipids out of the aqueous extract before an "in-tube" dispersive solid phase microextraction (dSPME) for acrylamide retention. This is followed by the elution with buffer prior to injection. This extract is independently introduced into the system front end followed by the injection of the compounds from the organic phase, yet all spotted in the same run. A novel liquid chromatography high-resolution mass spectrometry (LC-HRMS) method setup enables the quantification of 186 compounds at 10 µg/kg, 226 at 5 µg/kg, and the acrylamide at 200 µg/kg for a total of 414 molecules, with acceptable recoveries (70-120%) and precision (RSD < 20%) making this strategy significantly faster and cost-effective than the dedicated single residue methods. Even though the presence of chlorpyrifos, acrylamide, and ochratoxin A was confirmed on samples of different origins, the findings were below the limit of quantification. During the storage of raw coffee, no proof of masking of OTA was found; however, condensation with glucose was evidenced during thermal processing experiments with sucrose by using stable isotope labeling (SIL). No detected conjugates were found in roasted nor in commercial sugar-added torrefacto samples, an industrial processing usually carried out above the decomposition temperature of the disaccharide.

Green synthesis of carbon nanotubes functionalized with iron nanoparticles and coffee husk biomass for efficient removal of losartan and diclofenac: Adsorption kinetics and ANN modeling studies.

The presence of emerging contaminants in wastewater poses a global environmental challenge, requiring the development of innovative materials or methods for their treatment. This study focused on the production of green functionalized carbon nanotubes (CNTs) and using them in the adsorption of the pharmaceuticals Losartan (LOS) and Diclofenac (DIC). The efficiency of the methodology was verified by characterization techniques. Elemental composition analysis indicated a significant increase in the iron content after the green functionalization, proving the effectiveness of the method. Thermogravimetric analysis showed similar thermal degradation profiles for pristine CNTs and functionalized CNTs, indicating better post-functionalization thermal stability. BET analysis revealed mesoporous characteristics of CNTs, with increased surface area and pore volumes after functionalization. X-Ray diffraction confirmed the preservation of the lattice structure of the CNTs post-functionalization and post-adsorption, with changes in peak broadening suggesting surface modifications. LOS and DIC adsorption were evaluated via kinetic studies at four different concentrations (0.1-0.4 mmol/L) that were best represented by the pseudo-second order model, suggesting chemisorption mechanisms, with faster and higher uptakes for DIC (0.084-0.261 mmol/g; teq = 5 min) when compared to LOS (0.058-0.235 mmol/g; teq = 20 min). The curves were also studied via artificial neural networks (ANN) and revealed that the best ANN architecture for representing the experimental data is a network with [3 5 5 2] neurons trained using the Bayesian-Regularization algorithm and the Log-sigmoid (hidden layers) and Linear (output layer) transfer functions. The desorption study showed that CaCl2 had better performance in CNT regeneration, reaching its removal capacity above 50% up to 3 cycles, for both pharmaceuticals. These findings reveal the potential of the developed material as a promising adsorbent for targeted removal of pollutants, contributing to advances in the remediation of emerging contaminants and the application of artificial intelligence in adsorption research.

Ochratoxin A detoxification potentials of basil, chan, and chia seeds.

The most toxic of the ochratoxins is ochratoxin A (OTA), which is primarily produced by species of Aspergillus and Penicillium that can be found in maize, wheat, coffee, red wine, and various grains. OTA induces immunotoxicity, nephrotoxicity, hepatotoxicity, teratogenicity, and carcinogenicity in both animals and humans. Thus, there is a need to identify mycotoxin detoxification agents that can effectively decontaminate OTA. Seeds of basil (Ocimum basilicum L.), chan (Hyptis suaveolens L.), and chia (Salvia hispanica L.) are functional foods capable of eliminating harmful substances. Despite this potential, the impact of these seeds on OTA detoxification remains unclear. This study reveals that milled basil, chan, and chia seeds adsorb significant levels of OTA, with chia demonstrating the highest adsorption capacity, followed by chan and basil seeds showing the least efficiency. Furthermore, milled basil, chan, and chia seeds effectively reduced OTA residues in artificial gastric and intestinal fluids, where they achieved up to 93% OTA adsorption in the former. In addition, these milled seeds were able to remove OTAs from canned, drip, and instant coffee. This study is the first to report the OTA elimination potential of basil, chan, and chia seeds.

Physicochemical Characterization, Antioxidant and Tyrosinase Inhibitory Activities of Coffea Robusta Monofloral Honey from Dak Lak Province, Vietnam.

Robusta coffee blossom honey stands as a key regional product in Dak Lak province, Vietnam. Despite its significance, there exists a dearth of scientific data for assessing its quality. This study aims to fill this gap by characterizing the physicochemical properties and biological activities of coffee blossom honeys from three distinct sub-regions within Dak Lak province, Vietnam. These activities include ferric reducing power (FRP), DPPH and ABTS radical scavenging, as well as tyrosinase inhibitory activities. Moreover, the study compares these honey samples with other popular varieties in Vietnam, such as Lychee and Longan honeys. The physicochemical parameters of the honey samples meet the standards set by Codex Alimentarius 2001. Through UPLC analysis, eleven compounds were identified, with caffeine serving as a marker for coffee honey. Furthermore, by employing multiple factor analysis (MFA), it was observed that certain physicochemical properties correlate positively with tyrosinase inhibitory, DPPH, ABTS free radicals scavenging activities, and FRP. Notably, tyrosinase inhibitory activity exhibited a positive correlation with antioxidant activity. These findings underscore the high quality of Coffea robusta honey, showcasing its potent antioxidant and tyrosinase inhibitory activities.

Whey protein concentrate and skimmed milk powder as encapsulation agents for coffee silverskin extracts processed by spray drying.

We tested the hypothesis that milk proteins, through microencapsulation, guarantee protection against bioactive substances in coffee silverskin extracts. Therefore, the aim of this study was to carry out technological, nutritional and physicochemical characterisation of a coffee silverskin extract microencapsulated using instant skim milk powder and whey protein concentrate as wall materials. The aqueous extract of coffee silverskin was spray-dried using 10% (w/v) skim milk powder and whey protein concentrate. The samples were characterised by determining the water content, water activity, particle size distribution, colour analysis and total phenolic compound content as well as antioxidant activity using 2,2-diphenyl-radical 1-picrylhydrazyl scavenging methods, nitric oxide radical inhibition and morphological analysis. The product showed water activity within a range that ensured greater stability, and the reduced degradation of the dried coffee silverskin extract with whey protein concentrate resulted in better rehydration ability. The luminosity parameter was higher and the browning index was lower for the encapsulated samples than for the pure coffee silverskin extract. The phenolic compound content (29.23 ± 8.39 and 34.00 ± 8.38 mg gallic acid equivalents/g for the coffee silverskin extract using skimmed milk powder and whey protein concentrate, respectively) and the antioxidant activity of the new product confirmed its potential as a natural source of antioxidant phenolic compounds. We conclude that the dairy matrices associated with spray drying preserved the bioactive and antioxidant activities of coffee silverskin extracts.

The Effect of Caffeine on Movement-Related Cortical Potential Morphology and Detection.

Movement-related cortical potential (MRCP) is observed in EEG recordings prior to a voluntary movement. It has been used for e.g., quantifying motor learning and for brain-computer interfacing (BCIs). The MRCP amplitude is affected by various factors, but the effect of caffeine is underexplored. The aim of this study was to investigate if a cup of coffee with 85 mg caffeine modulated the MRCP amplitude and the classification of MRCPs versus idle activity, which estimates BCI performance. Twenty-six healthy participants performed 2 × 100 ankle dorsiflexion separated by a 10-min break before a cup of coffee was consumed, followed by another 100 movements. EEG was recorded during the movements and divided into epochs, which were averaged to extract three average MRCPs that were compared. Also, idle activity epochs were extracted. Features were extracted from the epochs and classified using random forest analysis. The MRCP amplitude did not change after consuming caffeine. There was a slight increase of two percentage points in the classification accuracy after consuming caffeine. In conclusion, a cup of coffee with 85 mg caffeine does not affect the MRCP amplitude, and improves MRCP-based BCI performance slightly. The findings suggest that drinking coffee is only a minor confounder in MRCP-related studies.

On the Cholesterol Raising Effect of Coffee Diterpenes Cafestol and 16-O-Methylcafestol: Interaction with Farnesoid X Receptor.

The diterpene cafestol represents the most potent cholesterol-elevating compound known in the human diet, being responsible for more than 80% of the effect of coffee on serum lipids, with a mechanism still not fully clarified. In the present study, the interaction of cafestol and 16-O-methylcafestol with the stabilized ligand-binding domain (LBD) of the Farnesoid X Receptor was evaluated by fluorescence and circular dichroism. Fluorescence quenching was observed with both cafestol and 16-O-methylcafestol due to an interaction occurring in the close environment of the tryptophan W454 residue of the protein, as confirmed by docking and molecular dynamics. A conformational change of the protein was also observed by circular dichroism, particularly for cafestol. These results provide evidence at the molecular level of the interactions of FXR with the coffee diterpenes, confirming that cafestol can act as an agonist of FXR, causing an enhancement of the cholesterol level in blood serum.

Polyphenols vs. Caffeine in Coffee from Franchise Coffee Shops: Which Serving of Coffee Provides the Optimal Amount of This Compounds to the Body.

The scientific literature indicates that there is a limited number of data on the content of bioactive components in coffees consumed "on the go". Therefore, this study examined the polyphenol and caffeine content of different types of coffee from franchise coffee shops, and the caffeine/total polyphenol ratio. The five most popular types of coffee purchased in six franchise coffee shops in Warsaw were analysed. A total of 120 coffee samples were tested. A significant positive (r = 0.7407, p < 0.001) correlation was found between the total polyphenol and caffeine content in all coffee types tested. Per unit volume, espresso coffee had the highest significant (p < 0.005) average total polyphenol and caffeine contents (232.9 ± 63.9 mg/100 mL and 198.6 ± 68.3 mg/100 mL, respectively). After taking into account the coffee's serving size, a serving of Americano provided significantly (p < 0.05) the most total polyphenol (average 223.5 ± 81.5 mg), while the highest caffeine content was provided by a serving of ice latte/latte frappe (average 136 ± 57.0 mg). The most favourable ratio of caffeine to total polyphenols (0.56) was found in a serving of Americano coffee; therefore, it seems that this coffee can be considered optimal in terms of the content of both compounds. These findings demonstrate that the polyphenol and caffeine contents of coffees offered in franchise coffee shops are closely related to the serving size.

An Optimized Method for Evaluating the Preparation of High-Quality Fuel from Various Types of Biomass through Torrefaction.

The pretreatment for torrefaction impacts the performance of biomass fuels and operational costs. Given their diversity, it is crucial to determine the optimal torrefaction conditions for different types of biomass. In this study, three typical solid biofuels, corn stover (CS), agaric fungus bran (AFB), and spent coffee grounds (SCGs), were prepared using fluidized bed torrefaction. The thermal stability of different fuels was extensively discussed and a novel comprehensive fuel index, "displacement level", was analyzed. The functional groups, pore structures, and microstructural differences between the three raw materials and the optimally torrefied biochar were thoroughly characterized. Finally, the biomass fuel consumption for household heating and water supply was calculated. The results showed that the optimal torrefaction temperatures for CS, AFB, and SCGs were 240, 280, and 280 °C, respectively, with comprehensive quality rankings of the optimal torrefied biochar of AFB (260) > SCG (252) > CS (248). Additionally, the economic costs of the optimally torrefied biochar were reduced by 7.03-19.32%. The results indicated that the displacement level is an index universally applicable to the preparation of solid fuels through biomass torrefaction. AFB is the most suitable solid fuel to be upgraded through torrefaction and has the potential to replace coal.

Climate and disease: tackling coffee brown-eye spot with advanced forecasting models.

BACKGROUND: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables. RESULTS: These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R2 = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82). CONCLUSION: The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry.

Models for predicting coffee yield from chemical characteristics of soil and leaves using machine learning.

BACKGROUND: Coffee farming constitutes a substantial economic resource, representing a source of income for several countries due to the high consumption of coffee worldwide. Precise management of coffee crops involves collecting crop attributes (characteristics of the soil and the plant), mapping, and applying inputs according to the plants' needs. This differentiated management is precision coffee growing and it stands out for its increased yield and sustainability. RESULTS: This research aimed to predict yield in coffee plantations by applying machine learning methodologies to soil and plant attributes. The data were obtained in a field of 54.6 ha during two consecutive seasons, applying varied fertilization rates in accordance with the recommendations of soil attribute maps. Leaf analysis maps also were monitored with the aim of establishing a correlation between input parameters and yield prediction. The machine-learning models obtained from these data predicted coffee yield efficiently. The best model demonstrated predictive fit results with a Pearson correlation of 0.86. Soil chemical attributes did not interfere with the prediction models, indicating that this analysis can be dispensed with when applying these models. CONCLUSION: These findings have important implications for optimizing coffee management and cultivation, providing valuable insights for producers and researchers interested in maximizing yield using precision agriculture. © 2024 Society of Chemical Industry.

Feasibility of using colorimetric devices for whole and ground coffee roasting degrees prediction.

BACKGROUND: Coffee roasting is one of the crucial steps in obtaining a high-quality product as it forms the product's color and flavor characteristics. Roast control is made by visual inspection or traditional instruments such as the Agtron spectrophotometer, which can have high implementation costs. Therefore, the present study evaluated colorimetric approaches (a bench colorimeter, smartphone digital images, and a colorimetric sensor) to predict the Agtron roasting degrees of whole and ground coffee. Two calibration approaches were assessed, that is, multiple linear regression and least-squares support vector machine. For that, 70 samples of whole and ground roasted coffees comprising the Agtron roasting range were prepared. RESULTS: The results showed that all three colorimetric acquisition types were efficient for the model building, but the bench colorimeter and the smartphone digital images generally performed with good determination coefficients and low errors as measured by external validation. For the whole bean coffee, the best model presented a determination coefficient (R2) of 0.99 and a root-mean-squared error (RMSE) of 1.91%, while R2 of 0.99 and RMSE of 0.87% was obtained for ground coffee, both using the colorimeter. CONCLUSION: The obtained models presented good prediction capability, as assessed by external validation and randomization tests. The obtained findings point to an alternative for coffee roasting monitoring that can lead to higher digitalization and local control of the process, even for smaller producers, due to its lower costs. © 2024 Society of Chemical Industry.

Profiling flavor characteristics of cold brew coffee with GC-MS, electronic nose and tongue: effect of roasting degrees and freeze-drying.

BACKGROUND: Roasting is an important process in the formation of coffee flavor characteristics, which determines the quality of coffee and consumer acceptance. However, the influence of roasting degree on the flavor characteristics of cold brew coffee has not been fully described. RESULTS: In the present study, the flavor characteristics of cold brew coffee with different roasting degrees were compared in detail by using chromatographic and electronic sensory approaches, and the flavor changes induced by freeze-drying were investigated. Pyrazine and heterocyclic compounds were the main aroma compounds in coffee, and gradually dominated with the increase of roasting. Pyridine was consistently present in cold brew coffees of different roasting degrees and showed significant gradient of quantity accumulation. Aroma compounds such as pyrazine, linalool and furfuryl acetate were the main contributors to coffee roasting, floral and fruity flavor. Freeze-drying preserved the fruity and floral aromas of medium-roasted cold brew coffee, whereas reducing the bitterness, astringency and acidity properties that are off-putting to consumers. CONCLUSION: The higher consumer acceptance and enjoyment in medium roast cold brew coffee may be related to its stronger floral and fruity aroma. The aroma profile qualities of freeze-drying processed medium roasted cold brewed coffee were more dominant and more suitable for freeze-drying processing than medium dark roasting. Application of freeze-drying for cold brew coffee will promote the convenience of drinking. The present study provides valuable technical guidance in improving the flavor and quality of cold brew coffee, and also promotes its commercialization process. © 2024 Society of Chemical Industry.

Bioactive functions of chlorogenic acid and its research progress in pig industry.

Chlorogenic acid (CGA), also known as 3-caffeioylquinic acid or coffee tannin, is a water-soluble polyphenol phenylacrylate compound produced through the shikimate pathway by plants during aerobic respiration. CGA widely exists in higher dicotyledons, ferns and many Chinese medicinal materials, and enjoys the reputation of 'plant gold'. Here, we summarized the source, chemical structure, biological activity functions of CGA and its research progress in pigs, aiming to provide a more comprehensive understanding and theoretical basis for the prospect of CGA replacing antibiotics as a pig feed additive.

Valorization of Pineapple (Ananas comosus) By-Products in Milk Coffee Beverage: Influence on Bioaccessibility of Phenolic Compounds.

The industrial processing of pineapples generates a substantial quantity of by-products, including shell, crown, and core. Bromelain, a proteolytic enzyme found naturally in pineapple, including its by-products, may positively influence the bioaccessibility of phenolics from milk coffee. Therefore, this study aimed to assess how the inclusion of extracts from pineapple by-products, namely shell, crown and core, could impact the bioaccessibility of coffee phenolics when combined with milk. After measuring the proteolytic activity of pineapple by-products, the standardized in vitro digestion model of INFOGEST was employed to evaluate changes in total phenolic content, total antioxidant capacity, and individual phenolic compounds in different coffee formulations. The results showed that incorporating extracts from the crown or core in both black and milk coffee increased the bioaccessibility of total phenolics (from 93 to 114% to 105-129%) and antioxidants (from 54 to 56% to 84-87%), while this effect was not observed for the shell. Moreover, adding core extracts also enhanced the bioaccessibility of caffeoylquinic acids and gallic acid in milk coffee (from 0.72 to 0.85% and 109-155%, respectively). Overall, the findings of this study highlight that bromelain from pineapple core may have a favorable effect on the recovery of phenolic compounds in milk coffee, possibly due to its ability to cleave proteins. These outcomes point out that industrial by-products can be transformed into economic value by being reintroduced into the production process through suitable treatment instead of disposal.

Molecular imprinting technology and poly (ionic liquid)s: Promising tools with industrial application for the removal of acrylamide and furanic compounds from coffee and other foods.

Coffee is one of the most consumed beverages in the world. Coffee provides to the consumer special sensorial characteristics, can help to prevent diseases, improves physical performance and increases focus. In contrast, coffee consumption supplies a significant source of substances with carcinogenic and genotoxic potential such as furan, hydroxymethylfurfural (HMF), furfural (F), and acrylamide (AA). The present review addresses the issues around the presence of such toxic substances formed in Maillard reaction (MR) during thermal treatments in food processing, from chemical and, toxicological perspectives, occurrences in coffee and other foods processed by heating. In addition, current strategies advantages and disadvantages are presented along with application of molecular imprinting technology (MIT) and poly (ionic liquid) s (PIL) as an alternative to reduce the furan, HMF, F and AA content in coffee and other foods.

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.

Acids in coffee: A review of sensory measurements and meta-analysis of chemical composition.

Coffee contains a variety of organic acids (OAs) and chlorogenic acids (CGAs) that contribute to overall sensory properties. Large variations in preparation and measurement methodology across the literature complicate interpretation of general trends. Here, we perform a systematic review and meta-analysis of the published literature to elucidate the concentrations of OAs and CGAs in both Coffea arabica (arabica) and Coffea canephora (robusta), for both green coffee and roasted coffee at multiple roast levels. A total of 129 publications were found to report acid concentration measurements, yielding 8,634 distinct data points. Analysis of the full data set reveals several trends. First, roasted robusta has considerably more acidic compounds than arabica with 2 to 5 times as much total OAs, and much larger amounts of formic and acetic acid. As for CGAs, in both arabica and robusta 5-CQA is the major component, and progressive roasting decreases the concentration of all CGAs. The total amount of CGA present was more dependent on roast level than the type of coffee (arabica vs. robusta). Overall, this meta-analysis suggests that the increases in certain OAs with roast level might play more of a role in the sensory profile of dark roast coffees than previously suspected.

Discrimination of Robusta Amazônico coffee farmed by indigenous and non-indigenous people in Amazon: comparing benchtop and portable NIR using ComDim and duplex.

Robusta Amazônico is the name given to the Amazonian coffee that has been becoming popular and has recently been registered as a geographical indication in Brazil. It is produced by indigenous and non-indigenous coffee producers in regions that are geographically very close to one another. There is a need to authenticate whether coffee is truly produced by indigenous people and near-infrared (NIR) spectroscopy is an excellent technique for this. To meet the substantial trend towards NIR spectroscopy miniaturization, this work compared benchtop and portable NIR instruments to discriminate Robusta Amazônico samples using partial least squares discriminant analysis (PLS-DA). To ensure the results to be fairly comparable and, at the same time, to guarantee representative selection of both training and test set for the discriminant analysis, a sample selection strategy based on coupling ComDim multi-block analysis and the duplex algorithm was applied. Different pre-processing techniques were tested to create multiple matrices to be used in ComDim, as well as to build the discriminant models. The best PLS-DA model for benchtop NIR provided an accuracy of 96% for the test samples, while for the portable NIR the correct classification rate was 92%. It was demonstrated that portable NIR provides similar results to benchtop NIR for coffee origin classification by performing an unbiased sample selection strategy.

New terbium complex as a luminescent probe for determination of chlorogenic acid in green coffee and roasted coffee infusions.

Green coffee is coming into vogue as a food that contains remarkable contents of antioxidants like chlorogenic acid (ChA) and induces mild stimulation to the consumer. While most methods for determination of ChA require chromatographic separation prior its quantitation, we present the first probe and a simple, sensitive and validated luminescence method for the determination of chlorogenic acid in green and roasted coffee infusion samples that does not require a chromatographic separation. ChA can remarkably quench the luminescence intensity of the Tb3+ complex with 1-(furan-2-ylmethyl)-4-hydroxy-N-(4-methylpyridin-2-yl)-2-oxo-1,2,5,6,7,8-hexahydroquinoline-3-carboxamide (R3) in aqueous solution containing urotropine buffer at a near neutral pH 7.5 at λexc = 315 nm and λem = 545 nm. Under optimal conditions, the quenching of the luminescence intensity is directly proportional to the concentration of ChA in the range of 0.5-30 µg/mL, and the detection limit is 180 ng/mL. From measurements of luminescence decay time, it was determined that both static and dynamic quenching is induced upon coordination of ChA to Tb-R3. The related quenching constants are KS = 5.97∙104 M-1 and KD = 1.05⋅104 M-1. Finally, the method was applied successfully to the determination of ChA in real green and roasted coffee infusion samples and validated by HPLC to yield very closely matching concentrations of both methods. Therefore, this method can serve for a simple quality control of total ChA contents in coffee prior and after roasting.