Chemical characterisation of non-defective and defective green arabica and robusta coffees by electrospray ionization-mass spectrometry (ESI-MS)

Bioactive Potential and Chemical Composition of Coffee By-Products: From Pulp to Silverskin

Processing the coffee cherry into roasted beans generates a large amount of by-products, which can negatively impact the environment. The aim of this study was to analyze the bioactive potential and chemical composition of different coffee by-products (pulp, husk, parchment, silverskin, defective beans, and green coffee sieving residue) having in mind their bioactive potential for health and well-being. The coffee by-products showed a distinct nutritional composition. The content of ash, protein, fat, and total dietary fiber was significantly higher (p < 0.05) in coffee pulp (10.72% dw), silverskin (16.31% dw), defective beans (8.47% dw), and parchment (94.19% dw), respectively. Defective beans and the sieve residue exhibited a higher content of total phenolics (6.54 and 5.11 g chlorogenic acid eq./100 g dw, respectively) as well as higher DPPH^• scavenging activity (3.11 and 2.85 g Trolox eq./100 g, respectively) and ferric-reducing antioxidant power (17.68 and 17.56 g ferrous sulfate eq./100 g dw, respectively). All the coffee by-products considered in this study are sources of caffeine and chlorogenic acids, in particular 5-caffeoylquinic acid (5.36-3787.58 mg/100 g dw, for parchment and defective beans, respectively). Thus, they can be recycled as functional ingredients for food, cosmetic and/or pharmaceutical industries, contributing to the social, economic, and environmental sustainability of the coffee industry.

Infrared-Photoacoustic Spectroscopy and Multiproduct Multivariate Calibration to Estimate the Proportion of Coffee Defects in Roasted Samples

Infrared-photoacoustic spectroscopy (IR-PAS) and partial least squares (PLS) were tested as a rapid alternative to conventional methods to evaluate the proportion of coffee defects in roasted and ground coffees. Multiproduct multivariate calibration models were obtained from spectra of healthy beans of Coffea canephora and C. arabica (Arabica) and blends composed of defective and healthy beans of Arabica in different proportions. The blends, named selections, contained sour, black, broken, whole beans, skin, and coffee woods. Six models were built using roasted and ground coffee samples. The model was optimized through outlier evaluation, and the parameters of merit such as accuracy, sensitivity, limits of detection and quantification, the inverse of analytical sensitivity, linearity, and adjustment were computed. The models presented predictive capacity and high sensitivity in determining defects, all being predicted with suitable correlation coefficients (ranging from 0.7176 to 0.8080) and presenting adequate performance. The parameters of merit displayed promising results, and the prediction models developed for %defects can be safely used as an alternative to the reference method. Furthermore, the new method is fast, efficient, and suitable for in-line application in quality control industrial coffee processing.

Rapid authentication of coffee bean varieties of different forms by using a pocket-sized spectrometer and multivariate data modelling

Coffee is the most consumed beverage and the second most valuable traded commodity in the world. In this current study, a pocket-sized spectrometer and multivariate analysis were used for rapid authentication of coffee varieties (Arabica and Robusta) in three states to check mislabelling (food fraud). Two main coffee varieties were collected from different locations in Africa. The samples were scanned in the 740-1070 nm wavelength and the spectral data were pre-treated with several methods: mean centering (MC), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD) and standard normal variate (SNV) independently while partial least squares discriminate analysis (PLS-DA), K-nearest neighbour (KNN) and support vector machine (SVM) were used to comparatively build the prediction models for coffee beans (raw, roasted and powdered). The performances of the models were evaluated by using accuracy and efficiency. Among the classification methods developed, the best results were obtained for the following: raw coffee bean SD-SVM had an accuracy of 0.92 and efficiency of 0.82. For roasted coffee beans, SD-KNN had an accuracy of 0.92 and efficiency of 0.87, while for roasted powdered coffee, FD-KNN showed an accuracy of 0.97 and efficiency of 0.97. These finding reveals that for a more accurate differentiation of coffee beans, the roasted powder offers the best results. The obtained results showed that a pocket-sized spectrometer coupled with chemometrics could be employed to provide accurate and rapid authentication of different categories of coffee bean varieties.

Metabolite characterization of fifteen by-products of the coffee production chain: From farm to factory

Approximately 11.4 million tonnes of solid by-products and an increased amount of waste water will be generated during the 2020/21 coffee harvest. There are currently no truly value-adding uses for these potentially environmentally threatening species. This work presents the most wide-ranging chemical investigation of coffee by-products collected from farms to factories, including eight never previously investigated. Twenty compounds were found for the first time in coffee by-products including the bioactive neomangiferin, kaempferol-3-O-rutinoside, lup-20(29)-en-3-one and 3,4-dimethoxy cinnamic acid. Five by-products generated inside a factory showed caffeine (53.0-17.0 mg.g^-1) and/or chlorogenic acid (72.9-10.1 mg.g^-1) content comparable to coffee beans, while mature leaf from plant pruning presented not only high contents of both compounds (16.4 and 38.9 mg.g-1, respectively), but also of mangiferin (19.4 mg.g-1) besides a variety of flavonoids. Such by-products are a source of a range of bioactive compounds and could be explored with potential economic and certainly environmental benefits.

Prediction of black, immature and sour defective beans in coffee blends by using Laser-Induced Breakdown Spectroscopy

One of the most important factors that interfere negatively in coffee global quality has been blends with defective beans, especially those called Black, Immature and Sour (BIS). The methods based on visual-manual estimation of defective beans have shown their inefficiency in coffee value chain for large-scale analysis. The lack of fast, accurate and robust analytical methods for BIS determination is still a research gap. Laser-Induced Breakdown Spectroscopy (LIBS) is a fast, low-cost and residue-free technique capable of performing multielemental determination and investigating organic composition of samples. In the present work, LIBS together with spectral processing and variable selection were evaluated to fit linear regression models for predicting BIS in blends. Models showed high capacity of prediction with RMSEP smaller than 3.8% and R² higher than 80%. Most importantly, measurements are guided by chemical responses, which make LIBS-based methods less susceptible to the visual indistinguishability that occurs in manual inspections.

Characterization of Fatty Acid, Amino Acid and Volatile Compound Compositions and Bioactive Components of Seven Coffee (Coffea robusta) Cultivars Grown in Hainan Province, China

Compositions of fatty acid, amino acids, and volatile compound were investigated in green coffee beans of seven cultivars of Coffearobusta grown in Hainan Province, China. The chlorogenic acids, trigonelline, caffeine, total lipid, and total protein contents as well as color parameters were measured. Chemometric techniques, principal component analysis (PCA), hierarchical cluster analysis (HCA), and analysis of one-way variance (ANOVA) were performed on the complete data set to reveal chemical differences among all cultivars and identify markers characteristic of a particular botanical origin of the coffee. The major fatty acids of coffee were linoleic acid, palmitic acid, oleic acid, and arachic acid. Leucine (0.84 g/100 g DW), lysine (0.63 g/100 g DW), and arginine (0.61 g/100 g DW) were the predominant essential amino acids (EAAs) in the coffee samples. Seventy-nine volatile compounds were identified and semi-quantified by HS-SPME/GC-MS. PCA of the complete data matrix demonstrated that there were significant differences among all cultivars, HCA supported the results of PCA and achieved a satisfactory classification performance.

Application of gas chromatography/flame ionization detector-based metabolite fingerprinting for authentication of Asian palm civet coffee (Kopi Luwak)

Development of authenticity screening for Asian palm civet coffee, the world-renowned priciest coffee, was previously reported using metabolite profiling through gas chromatography/mass spectrometry (GC/MS). However, a major drawback of this approach is the high cost of the instrument and maintenance. Therefore, an alternative method is needed for quality and authenticity evaluation of civet coffee. A rapid, reliable and cost-effective analysis employing a universal detector, GC coupled with flame ionization detector (FID), and metabolite fingerprinting has been established for discrimination analysis of 37 commercial and non-commercial coffee beans extracts. gas chromatography/flame ionization detector (GC/FID) provided higher sensitivity over a similar range of detected compounds than GC/MS. In combination with multivariate analysis, GC/FID could successfully reproduce quality prediction from GC/MS for differentiation of commercial civet coffee, regular coffee and coffee blend with 50 wt % civet coffee content without prior metabolite details. Our study demonstrated that GC/FID-based metabolite fingerprinting can be effectively actualized as an alternative method for coffee authenticity screening in industries.

Identification of 3-methylbutanoyl glycosides in green Coffea arabica beans as causative determinants for the quality of coffee flavors

The quality of coffee green beans is generally evaluated by the sensory cupping test, rather than by chemical compound-based criteria. In this study, we examined the relationship between metabolites and cupping scores for 36 varieties of beans, using a nontargeted LC-MS-based metabolic profiling technique. The cupping score was precisely predicted with the metabolic information measured using LC-MS. Two markers that strongly correlated with high cupping scores were determined to be isomers of 3-methylbutanoyl disaccharides (3MDs; 0.01-0.035 g/kg of beans) by spectroscopic analyses after purification, and one of them was a novel structure. Further, both the 3MDs were determined to be precursors of 3-methylbutanoic acid that enhance the quality of coffee. The applicability of 3MDs as universal quality indicators was validated with another sample set. It was concluded that 3MDs are the causative metabolites determining beverage quality and can be utilized for green bean selection and as key compounds for improving the beverage quality.

Rapid approach to identify the presence of Arabica and Robusta species in coffee using 1H NMR spectroscopy

NMR spectroscopy was used to verify the presence of Arabica and Robusta species in coffee. Lipophilic extracts of authentic roasted and green coffees showed the presence of established markers for Robusta (16-O-methylcafestol (16-OMC)) and for Arabica (kahweol). The integration of the 16-OMC signal (δ 3.165 ppm) was used to estimate the amount of Robusta in coffee blends with an approximate limit of detection of 1-3%. The method was successfully applied for the analysis of 77 commercial coffee samples (coffee pods, coffee capsules, and coffee beans). Furthermore, principal component analysis (PCA) was applied to the spectra of lipophilic and aqueous extracts of 20 monovarietal authentic samples. Clusters of the two species were observed. NMR spectroscopy can be used as a rapid prescreening tool to discriminate Arabica and Robusta coffee species before the confirmation applying the official method.

Electrospray ionization mass spectrometry and partial least squares discriminant analysis applied to the quality control of olive oil

Direct infusion electrospray ionization mass spectrometry in the positive ion mode [ESI(+)-MS] is used to obtain fingerprints of aqueous-methanolic extracts of two types of olive oils, extra virgin (EV) and ordinary (OR), as well as of samples of EV olive oil adulterated by the addition of OR olive oil and other edible oils: corn (CO), sunflower (SF), soybean (SO) and canola (CA). The MS data is treated by the partial least squares discriminant analysis (PLS-DA) protocol aiming at discriminating the above-mentioned classes formed by the genuine olive oils, EV (1) and OR (2), as well as the EV adulterated samples, i.e. EV/SO (3), EV/CO (4), EV/SF (5), EV/CA (6) and EV/OR (7). The PLS-DA model employed is built with 190 and 70 samples for the training and test sets, respectively. For all classes (1-7), EV and OR olive oils as well as the adulterated samples (in a proportion varying from 0.5 to 20.0% w/w) are properly classified. The developed methodology required no ions identification and demonstrated to be fast, as each measurement lasted about 3 min including the extraction step and MS analysis, and reliable, because high sensitivities (rate of true positives) and specificities (rate of true negatives) were achieved. Finally, it can be envisaged that this approach has potential to be applied in quality control of EV olive oils.

Ambient desorption/ionization mass spectrometry using a liquid sampling-atmospheric glow discharge (LS-APGD) ionization source

A novel approach to ambient desorption/ionization mass spectrometry (ADI-MS) is described, based on a recently developed liquid sampling-atmospheric pressure glow discharge (LS-APGD) ionization source. The device is essentially unmodified relative to its implementation in elemental mass spectrometry, where the operational space is characterized by low operation power (<10 W) and low solution delivery rates (<50 μL min(-1)). In this implementation, the plasma is produced between a Ni anode and an electrolytic liquid (1 M HNO3) cathode flowing through a glass capillary that is angled towards the sample surface, at a distance of ~2 mm away. Analyte species can be desorbed/ionized from neat solution residues and complex solid samples. The ADI-LS-APGD source is mounted onto the source interface of a Thermo Finnigan LCQ Advantage Max quadrupole ion trap mass spectrometer without modifications to the instrument faceplate or ion optics. Described here is the initial evaluation of the roles of source geometry and working parameters, including electrolytic solution composition and plasma current, on the response of caffeine residues, with preliminary limits of detection based on the relative standard deviation of the spectral background suggested to be on the 10-pg level. Demonstrative spectra are presented for green tea extracts and raw leaves, coffee beans, a dried (raw) tobacco leaf, an analgesic tablet, and paper currency. Versatility is further revealed through the determination of components in common cigarette smoke. In each case, the spectra are characterized by (M + H)(+) species of the expected constituents. The capacity for a single source to perform both in solution and particulate elemental analysis (as shown previously) and ADI of molecular species is unique in the realm of mass spectrometry.

High-throughput metabolic profiling of diverse green Coffea arabica beans identified tryptophan as a universal discrimination factor for immature beans

The maturity of green coffee beans is the most influential determinant of the quality and flavor of the resultant coffee beverage. However, the chemical compounds that can be used to discriminate the maturity of the beans remain uncharacterized. We herein analyzed four distinct stages of maturity (immature, semi-mature, mature and overripe) of nine different varieties of green Coffea arabica beans hand-harvested from a single experimental field in Hawaii. After developing a high-throughput experimental system for sample preparation and liquid chromatography-mass spectrometry (LC-MS) measurement, we applied metabolic profiling, integrated with chemometric techniques, to explore the relationship between the metabolome and maturity of the sample in a non-biased way. For the multivariate statistical analyses, a partial least square (PLS) regression model was successfully created, which allowed us to accurately predict the maturity of the beans based on the metabolomic information. As a result, tryptophan was identified to be the best contributor to the regression model; the relative MS intensity of tryptophan was higher in immature beans than in those after the semi-mature stages in all arabica varieties investigated, demonstrating a universal discrimination factor for diverse arabica beans. Therefore, typtophan, either alone or together with other metabolites, may be utilized for traders as an assessment standard when purchasing qualified trading green arabica bean products. Furthermore, our results suggest that the tryptophan metabolism may be tightly linked to the development of coffee cherries and/or beans.

Discrimination between immature and mature green coffees by attenuated total reflectance and diffuse reflectance Fourier transform infrared spectroscopy

The objective of this work was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) in the characterization and discrimination between immature and mature or ripe coffee beans. Arabica coffee beans were submitted to FTIR analysis by reflectance readings employing attenuated total reflectance (ATR) and diffuse reflectance (DR) accessories. The obtained spectra were similar, but in general higher absorbance values were observed for nondefective beans in comparison to immature ones. Multivariate statistical analysis (principal component analysis, PCA, and agglomerative hierarchical clustering, AHC) was performed in order to verify the possibility of discrimination between immature and mature coffee samples. A clear separation between immature and mature coffees was observed based on AHC and PCA analyses of the normalized spectra obtained by employing both ATR and DR accessories. Linear discriminant analysis was employed for developing classification models, with recognition and prediction abilities of 100%. Such results showed that FTIR analysis presents potential for the development of a simple routine methodology for separation of immature and mature coffee beans. Practical Application: The ultimate goal of this research is to be able to propose improvements in the way immature coffee beans are separated from graded mature beans in coffee facilities (cooperatives and other coffee producer's associations). The results obtained herein point toward FTIR as a potential tool for the aimed improvements.

Application of supercritical fluid chromatography/mass spectrometry to lipid profiling of soybean

A metabolomics technology for lipid profiling based on supercritical fluid chromatography (SFC) coupled with mass spectrometry (MS) was applied to analyze lipids of soybean. Principal component analysis (PCA) was used to discriminate twelve soybean cultivars according to their suitability for different processed foods such as natto, tofu, edamame, and nimame. By PCA assay, triacylglycerol (TAG) was found as the main variable for discrimination of soybean cultivars. Therefore, a high-throughput and high-resolution TAG profiling method by SFC/MS was developed to more effective discrimination. By investigating several columns, three Chromolith Performance RP-18e columns connected in series were chosen as the most effective column for TAG profiling. Diverse TAGs were separated effectively for 8 min without purification. Additionally, each TAG was identified successfully by the programmed cone voltage fragmentation even without MS/MS analysis and any standard sample.

Discrimination of green arabica and Robusta coffee beans by Raman spectroscopy

This paper presents an approach that may be applied as an accurate and rapid tool for classifying coffee beans on the basis of the specific kahweol content. Using Fourier-transform Raman spectroscopy with 1064 nm excitation it is possible to monitor the characteristic Raman bands of kahweol in green coffee beans without chemical and physical processing of the beans. The procedure was optimized on the basis of 83 and 125 measurements of whole and ground beans, respectively, using coffee samples of two different species, Coffea arabica L. and Coffea canephora L. (var. Robusta), and different origins (Asia, Africa, and South America). The relative contribution of the kahweol in individual beans can be determined quantitatively by means of a component analysis of the spectra, yielding a spectral kahweol index (σka) that is proportional to the relative content of kahweol in a coffee bean. The reproducibility of the spectroscopic measurement and analysis was found to be 3.5%. Individual beans of the same type and origin reveal a scattering of the σka values. Nevertheless, an unambiguous distinction between Arabica and Robusta samples is possible on the basis of single-bean measurements as the σka values are greater than and less than 10 for Arabica and Robusta coffees, respectively. Measurements of whole and ground beans afforded very similar results, despite the heterogeneous distribution of kahweol within a bean. Unlike conventional analytical techniques, the single-bean sensitivity of the present approach may also allow for a rapid detection of unwanted admixtures of low-value Robusta coffee to high-quality and more expensive Arabica coffee.

Hierarchical scheme for liquid chromatography/multi-stage spectrometric identification of 3,4,5-triacyl chlorogenic acids in green Robusta coffee beans

Liquid chromatography/multi-stage spectrometry (LC/MS(n)) (n = 2-4) has been used to detect and characterize in green Robusta coffee beans eight quantitatively minor triacyl chlorogenic acids with seven of them not previously reported in nature. These comprise 3,4,5-tricaffeoylquinic acid (Mr 678); 3,5-dicaffeoyl-4-feruloylquinic acid, 3-feruloyl-4,5-dicaffeoylquinic acid and 3,4-dicaffeoyl-5-feruloylquinic acid (Mr 692); 3-caffeoyl-4,5-diferuloylquinic acid and 3,4-diferuloyl-5-caffeoylquinic acid (Mr 706); and 3,4-dicaffeoyl-5-sinapoylquinic acid and 3-sinapoyl-4,5-dicaffeoylquinic acid (Mr 722). Structures have been assigned on the basis of LC/MS(n) patterns of fragmentation. A new hierarchical key for the identification of triacyl quinic acids is presented, based on previously established rules of fragmentation. Fifty-two chlorogenic acids have now been characterized in green Robusta coffee beans. In this study five samples of green Robusta coffee beans and fifteen samples of Arabica coffee beans were analyzed with triacyl chlorogenic acids only found in Robusta coffee bean extracts. These triacyl chlorogenic acids could be considered as useful phytochemical markers for the identification of Robusta coffee beans.

Profile and characterization of the chlorogenic acids in green Robusta coffee beans by LC-MS(n): identification of seven new classes of compounds

LC-MS(n) (n = 2-4) has been used to detect and characterize in green Robusta coffee beans 15 quantitatively minor sinapic acid and trimethoxycinnamoylquinic acid-containing chlorogenic acids, all reported for the first time from this source, with 13 of them not previously reported in nature. These comprise 3-sinapoylquinic acid, 4-sinapoylquinic acid, and 5-sinapoylquinic acid (M(r) 398); 3-sinapoyl-5-caffeoylquinic acid, 3-sinapoyl-4-caffeoylquinic acid, and 4-sinapoyl-3-caffeoylquinic acid (M(r) 560); 3-(3,5-dihydroxy-4-methoxy)cinnamoyl-4-feruloylquinic acid (M(r) 560); 3-sinapoyl-5-feruloylquinic acid, 3-feruloyl-4-sinapoylquinic acid, and 4-sinapoyl-5-feruloylquinic acid (M(r) 574); 4-trimethoxycinnamoyl-5-caffeoylquinic acid, 3-trimethoxycinnamoyl-5-caffeoylquinic acid (M(r) 574); and 5-feruloyl-3-trimethoxycinnamoylquinic acid, 3-trimethoxycinnamoyl-4-feruloylquinic acid, and 4-trimethoxycinnamoyl-5-feruloylquinic acid (M(r) 588). Furthermore, a series of structures including nine new triacyl quinic acids have been assigned on the basis of LC-MS(n) patterns of fragmentation, relative hydrophobicity, and analogy of fragmentation patterns if compared to feruloyl, caffeoyl, and dimethoxycinnamoyl quinic acids. Sixty-nine chlorogenic acids have now been characterized in green Robusta coffee beans.