extfieldsofstudy: Medicine Chemistry

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A Metabolomic Study of the Variability of the Chemical Composition of Commonly Consumed Coffee Brews

Coffee drinking has been associated with a lower risk of certain chronic diseases and overall mortality. Its effects on disease risk may vary according to the type of coffee brew consumed and its chemical composition. We characterized variations in the chemical profiles of 76 coffee brew samples representing different brew methods, roast levels, bean species, and caffeine types, either prepared or purchased from outlets in Rockville, Maryland, United States of America. Samples were profiled using liquid chromatography coupled with high-resolution mass spectrometry, and the main sources of chemical variability identified by the principal component partial R-square multivariable regression were found to be brew methods (Rpartial2 = 36%). A principal component analysis (PCA) was run on 18 identified coffee compounds after normalization for total signal intensity. The three first principal components were driven by roasting intensity (41% variance), type of coffee beans (29%), and caffeine (8%). These variations were mainly explained by hydroxycinnamoyl esters and diketopiperazines (roasting), N-caffeoyltryptophan, N-p-coumaroyltryptophan, feruloylquinic acids, and theophylline (coffee bean variety) and theobromine (decaffeination). Instant coffees differed from all coffee brews by high contents of diketopiperazines, suggesting a higher roast of the extracted beans. These variations will be important to consider for understanding the effects of different coffee brews on disease risk.

Introduction
Coffee drinking has been consistently linked to lower risk of overall mortality [1][2][3][4], diabetes [5], cardiovascular diseases [6], and cancers of the liver [7,8] and endometrium [9,10] in epidemiological studies. Possible harmful effects have also been examined, but no consistent evidence of harmful associations with diseases could be found, except for outcomes related to pregnancy and the risk of fractures in women [11]. The mechanisms underlying these associations are likely mediated by any of the thousands of specific coffee phytochemicals present in the beans or produced during roasting. Coffee is prepared in many diverse ways, such as adding water to instant coffee powder, percolation, or filtering of ground coffee, and preparation of espresso in commercial machines. In addition, raw coffee beans include both Arabica and Robusta species that are then roasted to different degrees. All these parameters affect the levels of bioactive compounds in the coffee [12][13][14] and may influence the effects of coffee consumption on disease risk.
Epidemiological studies on coffee and disease risk require accurate measurements of coffee intake. However, food frequency questionnaires (FFQ) commonly used to assess coffee intake almost always lack information on coffee type and preparation. As such, these factors are rarely considered in epidemiological studies, and it remains unclear whether they affect associations with health. Investigating the composition of popular coffee brews and determining which parameters have the greatest influence on the content of important coffee bioactives should assist in the creation of a focused coffee FFQ module and food composition table. It will also inform the interpretation of coffee biomarkers measured in epidemiological studies [15][16][17].
Some previous studies on coffee composition have analyzed a wide range of brewed coffees, but they have typically focused on specific components of interest, such as flavor volatiles, caffeine, or lipids rather than overall profiles [18][19][20][21]. The aim of this study was to analyze through an agnostic untargeted metabolomic approach a wide range of coffee brews in order to identify coffee characteristics that have the greatest influence on their chemical profile.

Untargeted Constituent Profiles and Variation Due to Different Coffee Parameters
A total of 3670 spectral features were measured across the 76 coffee brews. PC-PR2 analysis revealed that brew method explained the largest proportion of variability in metabolomic data (R partial 2 = 36%), followed by roast (R partial 2 = 16%), bean type (R partial 2 = 9%) and caffeine (R partial 2 = 7%). In relation to technical variables, injection plate and order of injection explained R partial 2 = 10% and 3% of the variability, respectively. The total variability explained by all selected variables (R model 2 ) was 61% (Supplementary Figure S1).
The summed signal intensities from all spectral features measured varied with the brew method (Supplementary Figure S2). On average, espresso and boiled brews produced significantly greater total signal intensity than instant coffee brews, which were taken as a reference for brew strength (mean fold change = 1.62; Student's t-test p = 0.0007).

Annotation of Coffee Compounds in Coffee Brew Metabolomic Data
An in-house database on known coffee compounds was built and a targeted list of 64 tentative annotations was established based on the matching of their accurate masses with those of the known coffee compounds (Supplementary Table S1). A tentative annotation of 24 compounds was invalidated on the basis of their mass fragmentation spectra and/or retention time ('unknowns'). The identity of an additional 22 peaks could not be confirmed because of the lack of chemical standards or good quality mass fragmentation spectra (confidence level 4). In contrast, 14 compounds could be identified with a high level of confidence (levels 1 and 2) by comparison of their retention time and MS/MS fragmentation spectra with those of the commercially available reference compounds, or by comparison of their MS/MS spectra with those in public database when standards were not available (Table 1). Four additional compounds were tentatively identified based on their accurate masses, known occurrence in coffee brews, and the presence of fragments characteristic of specific structural residues in the molecule (e.g., coumaroyl group or amino acid group) (confidence level 3).

Association of Coffee Compound Profiles with Different Types of Coffee Brews
Intensity profiles of the 18 coffee compounds in the 76 unique coffee samples were analyzed using principal component analysis (PCA). Data were first normalized over total signal intensities to correct for differences in coffee brew dilution (Supplementary Figure S2). The first two principal components (PC) accounted for respectively 41% and 29% of the variance in the data. Different brew methods could be separated on the first two PCs. Instant coffee showed lower scores in both PC1 and PC2, and espresso and K-cup coffee brews showed lower scores in PC1 when compared to all other coffee brews ( Figure 1A). Loading plots showed that PC1 was largely explained by the high levels of the six phenolic acid esters and cafamarine, and low levels of the 5 diketopiperazines ( Figure 1C). The same score plot also showed a clear distinction between coffee brews according to the type of coffee beans used, either Arabica or blends of Arabica and Robusta ( Figure 1B). The second PC was mainly explained by high loadings of the two feruloylquinic acid isomers, the two phenolic acid amides, and the five diketopiperazines ( Figure 1C). Caffeinated and decaffeinated coffee brews could be differentiated along PC3 (accounting for 7.7% of the variability), a result consistent with the high loadings of paraxanthine and theobromine on this PC (Supplementary Figure S3).

Coffee Compounds Associated with Coffee Brew Characteristics
T-tests were run to identify coffee compounds most strongly associated with roasting, the type of coffee beans, instant coffee, and bean decaffeination (Supplementary Figure S4), showing significant differences of the relative levels of several coffee compounds after Bonferroni correction. The three caffeoyl esters showed lower concentrations and three diketopiperazines showed higher concentrations in dark roasted brews when compared with medium roasted beans ( Figure 2). Light roasted brews could not be compared because of a low number of samples. Coffee brews prepared with Arabica beans showed relatively high concentrations of N-caffeoyltryptophan, N-p-coumaroyltryptophan, the two feruloylquinic acid isomers, and theophylline when compared with coffee brews prepared with blends of Arabica and Robusta beans ( Figure 3). Instant coffees were also compared to all other coffee brew samples. The most discriminating compounds were the 5 diketopiperazines ( Figure 4). Decaffeinated coffees showed lower intensities of theobromine and cafamarine when compared with all caffeinated coffee brews ( Figure 5).
Metabolites 2019, 9 FOR PEER REVIEW 5 of 13 concentrations in dark roasted brews when compared with medium roasted beans ( Figure 2). Light roasted brews could not be compared because of a low number of samples. Coffee brews prepared with Arabica beans showed relatively high concentrations of N-caffeoyltryptophan, N-p-coumaroyltryptophan, the two feruloylquinic acid isomers, and theophylline when compared with coffee brews prepared with blends of Arabica and Robusta beans ( Figure 3). Instant coffees were also compared to all other coffee brew samples. The most discriminating compounds were the 5 diketopiperazines ( Figure 4). Decaffeinated coffees showed lower intensities of theobromine and cafamarine when compared with all caffeinated coffee brews ( Figure 5).   concentrations in dark roasted brews when compared with medium roasted beans ( Figure 2). Light roasted brews could not be compared because of a low number of samples. Coffee brews prepared with Arabica beans showed relatively high concentrations of N-caffeoyltryptophan, N-p-coumaroyltryptophan, the two feruloylquinic acid isomers, and theophylline when compared with coffee brews prepared with blends of Arabica and Robusta beans ( Figure 3). Instant coffees were also compared to all other coffee brew samples. The most discriminating compounds were the 5 diketopiperazines ( Figure 4). Decaffeinated coffees showed lower intensities of theobromine and cafamarine when compared with all caffeinated coffee brews ( Figure 5).

Discussion
Coffee beans contain a wide range of phytochemicals, many of which are potentially bioactive and may explain observed associations with health. These consist of chemical classes as diverse as alkaloids, amino acids, terpenes, lipids, phenolic acids, and many small volatiles that contribute to aroma and bitterness [24]. The aim of this study was to explore the chemical profiles of a wide range of commonly-consumed coffee brews and to determine which coffee characteristics most influence these profiles. Chemical profiles were acquired using high-resolution mass spectrometry and were found to vary substantially according to the type of coffee brew, the extent of roasting of beans, the variety of bean, and decaffeination. The data was mined for known coffee components. Eighteen compounds could be annotated, and PCA analysis was run after normalization over the total solute concentration to correct for variations in dilution. Much of the variations in the composition of these 18 compounds were driven by the type of coffee brew, with instant coffee being clearly different from all other types of coffee under study ( Figure 1A).
The analysis of the loadings on the first two PCs provides clues for how to interpret these differences. The first PC is mainly driven by high levels of chlorogenic acids (caffeoyl-, feruloyl-, and p-coumaroylquinic acids) and low levels of diketopiperazines ( Figure 1C, Figure 2). Diketopiperazines are cyclodipeptides formed by the degradation of proteins contained in green coffee beans during heat treatment [25,26]. They confer a bitter taste to highly roasted coffee. In contrast, chlorogenic acids are largely degraded during roasting [27]. This suggests that coffee brews with low scores on the first PC are more highly roasted than coffee brews with high scores. Instant

Discussion
Coffee beans contain a wide range of phytochemicals, many of which are potentially bioactive and may explain observed associations with health. These consist of chemical classes as diverse as alkaloids, amino acids, terpenes, lipids, phenolic acids, and many small volatiles that contribute to aroma and bitterness [24]. The aim of this study was to explore the chemical profiles of a wide range of commonly-consumed coffee brews and to determine which coffee characteristics most influence these profiles. Chemical profiles were acquired using high-resolution mass spectrometry and were found to vary substantially according to the type of coffee brew, the extent of roasting of beans, the variety of bean, and decaffeination. The data was mined for known coffee components. Eighteen compounds could be annotated, and PCA analysis was run after normalization over the total solute concentration to correct for variations in dilution. Much of the variations in the composition of these 18 compounds were driven by the type of coffee brew, with instant coffee being clearly different from all other types of coffee under study ( Figure 1A).
The analysis of the loadings on the first two PCs provides clues for how to interpret these differences. The first PC is mainly driven by high levels of chlorogenic acids (caffeoyl-, feruloyl-, and p-coumaroylquinic acids) and low levels of diketopiperazines ( Figure 1C, Figure 2). Diketopiperazines are cyclodipeptides formed by the degradation of proteins contained in green coffee beans during heat treatment [25,26]. They confer a bitter taste to highly roasted coffee. In contrast, chlorogenic acids are largely degraded during roasting [27]. This suggests that coffee brews with low scores on the first PC are more highly roasted than coffee brews with high scores. Instant coffee samples, which show particularly high levels of diketopiperazines (Figure 4), would then differ from other coffee brews by a particularly high degree of roast.
The second PC of the same PCA plot is mainly driven by the variety of coffee beans used to prepare the coffee brews. Coffee brews prepared from Arabica beans show high scores, whereas those prepared from blends of Arabica and Robusta beans show low scores ( Figure 1B). The compounds with the highest loadings on the second PC are the two phenolic acid amides (N-caffeoyltryptophan and N-p-coumaroyltryptophan) and the two feruloylquinic acid isomers ( Figure 1C, Figure 3). This is fully consistent with the known composition of both varieties of beans. The two phenolic acid amides are characteristic of Robusta beans and are either not found or found at very low concentrations in Arabica beans [28,29]. Feruloylquinic acids are known to be particularly abundant in Robusta beans when compared with Arabica beans [29]. The low scores of the instant coffee samples on PC2 are also consistent with the wide use of Robusta beans in the production of instant coffees (https://www.britannica.com/topic/coffee).
The third PC was notably explained by the high negative loadings of theobromine and paraxanthine, two known biosynthetic precursors of caffeine in the coffee plant [30]. T-tests showed in particular relatively low levels of theobromine in decaffeinated coffee samples, as would have been expected ( Figure 5).
The present metabolomic study shows the distinct chemical compositions of the various coffee brew samples. Several classes of coffee compounds (chlorogenic acids, phenolic acid amides, methylxanthines, and diketopiperazines) showed large variations in their concentrations, which were explained by the extent of bean roast, the variety of the coffee bean, and also by decaffeination. These coffee compounds showing wide variations in their concentrations are not only abundant in coffee but also have biological properties which contribute to explain the health effects of coffee. In particular, chlorogenic acids were shown to exhibit a wide range of biological activities, including indirect antioxidant effects through the induction of phase 2 enzyme activities, inhibition of DNA methyltransferase, inhibition of platelet activities, and interference with glucose absorption [24,31,32]. Caffeine, besides its effects as a stimulant of the central nervous system, increases metabolic rate and energy expenditure and has been used as an aid in weight loss, and may contribute to a reduced risk of metabolic syndrome [33].
In most epidemiological studies, assessment of coffee intake is generally limited to the number of cups of coffee consumed and sometimes whether the coffee consumed was usually caffeinated or decaffeinated. It will be important in future epidemiological studies on coffee to accurately record the type of coffee consumed in order to understand the impact of various types of coffee brews (brew method, roasting, coffee beans) on disease risk. A previous cross-over study with a dark roast and a light roast coffee brew suggested that the degree of roast may influence coffee health effects [34]. The intake of dark roast coffee was shown to elicit stronger antioxidant effects upon human erythrocytes and to significantly reduce body weight in pre-obese subjects when compared with light roast coffee, which had no effect.
The use of biomarkers may also help to clarify the role of the different types of coffee brews in disease risk in future epidemiological studies. Several biomarkers of habitual coffee intake have been identified in blood [15,[35][36][37]. Ratios of diketopiperazines or catechol sulfate over trigonelline measured in serum were recently proposed as indicators of roast of coffee brews consumed in four European countries differing in their coffee drinking habits [17].
The present study also has some limitations. Firstly, the present work is derived from the analysis of data on 18 compounds that could be successfully annotated and analyzed with the analytical method used in this study. Some important compounds could not be measured in this work such as caffeine (saturated signal) or trigonelline (insufficiently retained on the chromatographic column). A combination of several chromatographic methods would be needed for a more comprehensive analysis of coffee compounds. Secondly, the number of annotated compounds was limited by the limited availability of chemical standards or the lack of high-quality mass spectra. Further analytical work would be needed for more comprehensive coverage of coffee compounds. In future studies, it will be of particular importance to develop targeted methods of analysis to measure in human biospecimens the coffee compounds specific to the different types of coffee brews as described here, but also to measure all major bioactive compounds known in coffee that may explain the effects of coffee intake on health.

Standards and Reagents
UPLC-grade acetonitrile and formic acid were supplied by Sigma-Aldrich (France) and LCMS grade water was generated from a Milli-Q integral water purification system. Standards to aid compound identification were obtained as follows: N-methyl-2-pyrrolecarboxaldehyde, 5-caffeoylquinic acid, were purchased from Sigma, France; cyclo(leucyl-prolyl) and cyclo(prolyl-valyl) were purchased from Bachem, Switzerland; 3-caffeoylquinic acid and 3,5-dicaffeoylquinic acid were purchased from Extrasynthese, France; and cyclo(phenylalanyl-prolyl) was purchased from Chem-Impex, Ilinois, US. Standards of theophylline, theobromine, paraxanthine, caffeine, and guanosine were obtained as part of the Mass Spectrometry Metabolite Library (MSMLS) kit of standards by IROA Technologies (Bolton, MA, USA).

Purchase and Brew of Coffee Samples
A total of 76 unique coffee samples representing a diverse range of coffee brews were brewed or purchased in the Rockville, Maryland area, USA (Supplementary Table S2). We targeted brands that accounted for a majority of the market share to provide the best coverage of coffee exposure. For the ground coffees, we brewed all selections using a drip machine since this is the most common form of preparation in the US. Then, for comparison, we brewed ground beans from the same source (i.e., the bag) using the French Press, percolator, and cold brew methods, balancing these between 100% Arabica or blend of Arabica and Robusta, light, medium, or dark roast, and caffeinated and decaffeinated varieties. With limited resources for analysis and countless potential combinations of coffee types and brew methods, we focused on the most commonly consumed coffee types and brew methods. We tried to balance the number of samples in the compared groups to prevent factors other than the one being considered from impacting the results. Coffee brews prepared with Robusta bean exclusively are not commonly consumed in the US and were therefore not included in this study. Robusta beans are thus brewed as blends of Arabica and Robusta beans in proportions that are usually not disclosed by the food manufactures.
Ground coffee samples were brewed with a BUNN®drip coffee maker with a paper filter using 50 g of ground coffee per 1893 mL of tap water (i.e., 5 g ground coffee per 177 mL cup of tap water). The drip coffee machine heated the water to approximately 200 • F (93 • C). French Press coffees were brewed using 23 g of ground coffee per 798 mL of tap water. Water was on average 190 • F (88 • C) upon pouring, and French Press brews were left to steep at room temperature for 3 minutes. Percolated coffee was brewed using a Presto®Coffee Maker with a paper filter as described by the manufacturer using 40 g of ground coffee per 1183 mL of tap water. The Presto®coffee maker heated the water to approximately 200 • F (93 • C). Cold brew coffee concentrates were brewed using 24 g of ground coffee per 89 mL of tap water and were left in the refrigerator overnight. After 19 h, cold brew coffee concentrates were filtered using a paper filter and were then diluted using a 1:3 ratio of concentrate to tap water (i.e., 17 g ground coffee per 177 mL of tap water).
K-cups were brewed using pods made for Keurig®coffee machines, each containing 10 g of coffee grounds and brewed with 177 mL tap water heated to approximately 192 • F (89 • C). Three types of espresso were brewed using a Nespresso®machine with pods containing 6 g of ground coffee and approximately 74 mL of water. Twelve unique types of instant coffee were prepared from single-serving sachets containing 2 g of powdered coffee and 177 mL tap water heated to approximately 183 • F (84 • C). Espresso brews were purchased from national chains serving coffee. Turkish and Greek boiled coffees were brewed per package instructions with 6 g of coffee grounds per 59 mL of tap water, which was heated to approximately 190 • F (88 • C) prior to pouring.
All coffees were brewed or purchased in triplicate. The triplicate preparations were combined to make a single pooled sample for each coffee type to limit the total number of analyses. Coffee samples were cooled and stored in a refrigerator, and two aliquots of each pooled sample were transferred to a −80 • F freezer within 6 hours of brewing. Two coffee samples were aliquoted 13 times each and used as blinded QCs for the monitoring of analytical repeatability. QC1 consisted of replicate samples of a medium roast, decaffeinated, 100% Arabica coffee, and QC2 of replicate samples of a dark roast, caffeinated, Arabica/Robusta blend coffee.

Untargeted Chemical Profiling of Coffee Samples Using Liquid Chromatography-Mass Spectrometry
Coffee samples were thawed and agitated, and an aliquot of each was placed in Eppendorf tubes. Samples were centrifuged at 120× g for 10 min to separate each into a particulate pellet, with the clear aqueous portion and any fat from the coffee resting on top. Any fatty supernatant (in three instant coffee samples containing powdered milk) was first removed using a micro-spatula and a portion of the aqueous fraction was then taken, diluted 50-fold in water, and transferred to glass vials for instrumental analysis in a randomized order. An equal mixture of all coffee samples was also made and used as a pooled quality control (QC) sample. This QC was placed in the injection sequence after every 10 study samples, giving a total of 18 replicates. In addition, six aqueous methanol blanks were placed in the injection sequence after every 20 study samples, giving a total of 6 replicates, and blinded QC samples (2 × 13 samples) were randomly distributed across the sample batch. With column conditioning QCs, a total of 200 samples were thus injected into the instrument, requiring three 96-well injection plates.
Coffee brews were analyzed directly using liquid chromatography coupled to a time-of-flight mass spectrometer (LC-MS). The instrument used was an Agilent 1290 Binary LC system coupled to an Agilent 6550 quadrupole time-of-flight (QTOF) mass spectrometer with jet steam electrospray ionization source. Samples (2 µL) were injected onto a reversed-phase C18 column (ACQUITY UPLC HSS T3 2.1 × 100 mm, 1.8 µm, Waters) maintained at 45 • C. The mobile phase used was a mixture of ultrapure water and LC-MS grade methanol. A linear gradient made of ultrapure water (eluant A) and LC-MS grade methanol (eluant B) was used for elution: 0-100% B from 0-6 min, 100% B from 6-10.5 min, and 5% B for the remainder of the 13-min run. The mass spectrometer was operated in positive ionization mode and ions were detected across a mass range of 50-1000 daltons. Agilent Mass Hunter software and a recursive two-stage extraction pass were used to align and extract mass spectral data. First, all possible molecular features were extracted from every sample, and masses and retention times aligned according to tolerance windows of 15 ppm and 0.1 min, respectively. The resulting list was then used as a target for a second recursive extraction, which enabled features closer to the noise floor to be picked up. The result of this molecular feature extraction was a table of 3670 spectral features aligned across samples by mass to charge ratio (m/z) and retention time with accompanying relative intensities (peak areas).

Mining of Untargeted Metabolomic Data for Known Coffee Compounds
Untargeted profile data acquired by LC-MS were mined for known coffee components. Firstly, chemical formulae and exact masses of as many known coffee components as possible were collected from online resources such as the Dictionary of Food Ingredients, Duke's Phytochemical and Ethnobotanical database (www.phytochem.nal.usda.gov) and FooDB (www.foodb.ca). Data was collected for around 500 organic compounds (348 formulae). All formulae were then searched for directly in the raw data files of pooled QC samples using the Agilent Quantitative Analysis Find by Formula algorithm. This algorithm sums signals from different ion species to generate a peak for each formula. Peaks were found corresponding to 115 formulae matching those of the coffee compounds. In some cases, multiple peaks at different retention times were retained for the same formula.
Subsequently, 88 groups of peaks potentially corresponding to known coffee compounds remained and were checked for acceptable peak shapes and consistent retention times. An Agilent Personal Compound Database and Library (PCDL), populated using these masses and retention times, was then used as the target for further extraction using the Find by Formula algorithm on the 76 coffee samples. The resulting groups of peaks were carefully screened for inclusion. A total of 24 of these putative coffee compounds were then excluded due to detector saturation (caffeine only), large peaks in the injected blanks, missing peaks in >25% samples, or high coefficient of variation (>30%) in pooled or blinded QCs. For the remaining 64 peaks (Supplementary Table S1; Supplementary Spreadsheet), mass spectral data were mined to confirm the initial hypotheses or, if disproved, generate alternative hypotheses. Annotations were thus made taking into account inter-compound correlations (Supplementary Figure S5), and each attributed a level of confidence for annotation [23]. Level 1 corresponding to confirmed structure, based on comparison of retention time and MS/MS match with those of an authentic chemical standard. For level 2, no standard was available or analyzed, and probable identification was based on physicochemical properties, isotope pattern, and MS/MS spectra. For level 3, the tentative structure was based on comparison with a structurally related compound in the same chemical class and characteristic fragments in MS/MS spectra. For level 4, the tentative annotation was based on the unequivocal molecular formula. Level 5 correspond to unknown compounds. When no chemical standard was available, experimental MS/MS spectra were compared to library spectra in databases such as HMDB [38] and Metlin [39]. If MS/MS data were not available (for example, where the intensity of the [M + H] + ion was low), retention time only was used. If identities could not be confirmed by retention time or MS/MS spectra matching, the assigned identity was regarded as hypothetical (level 4). Technical reproducibility of the semi-quantitative measurements of peak areas is shown for annotated compounds in Supplementary Table S3 for the pooled QC samples and blinded duplicate QC samples.

Statistical Analysis
The untargeted feature table was first filtered to remove any features present in one or two samples only. All other features were retained. Multivariate data were log 2 transformed and Pareto scaled, and constituent profiles were explored by performing a principal component analysis (PCA) on multivariate data for all unique brews (n = 76). The variability in the untargeted profile explained by each factor was then determined using principal component partial R-square (PC-PR2) multivariable regression [40]. The covariates included in the models were the brew method, bean type, roast, caffeine type, injection order, and injection plate.
The effect of the most influential brew parameters on the profiles of 18 annotated compounds was then investigated using PCA for all 76 brews. Data were first normalized to total intensity (sum of all detected compounds; Supplementary Figure S2) to remove the effect of differing brew strengths from the analysis [41] and were log-transformed. Loadings were calculated to determine which compounds or groups of compounds contributed most to the largest principal components (PC) of variation. An unpaired Student's t-test was used to assess differences between different coffee subgroups (dark vs. medium roast, Arabica beans vs. blend, instant coffee vs other brew methods, regular coffee vs. decaffeinated coffee) for the 18 identified metabolites. Bonferroni-corrected p-values below 0.05 were considered significant. The PCA and PC-PR2 models were fit using R open-source statistical software. All analyses were performed using R software version 3.4.4 [42].

Conclusions
There is a high demand for better understanding of the health benefits or risks associated with the consumption of coffee, one of the most widely consumed beverages in the world. The coffee composition is highly variable, depending on coffee brew method, bean variety, level of roast and decaffeination. However, it is still unclear how these parameters affect coffee bioactivity and associations with disease. The current study is the first to compare the chemical profiles of the main types of coffee consumed in the USA and to identify factors that contribute to this variation. It is hoped that the results presented here will contribute to better estimations of coffee intake in epidemiological studies and to clarify the relationship with disease.
Supplementary Materials: The following are available online at http://www.mdpi.com/2218-1989/9/1/17/s1: Table S1: Brewed or purchased coffee samples and their characteristics, Table S2: Summary of mass spectral data supporting peak annotations in coffee brews, Table S3: Coefficients of variation for the coffee compounds identified or tentatively identified, Figure S1: Heatmap showing correlations between annotated coffee compounds, Figure  S2: Boxplots showing means of sums of total intensities of mass spectrometry signals (n = 3670) for each type of coffee brews, Figure S3: PC-PR2 analysis of metabolomic data from the coffee brew samples, Figure S4: Principal component analysis of constituent profiles of the 18 annotated coffee compounds measured in 76 coffee brew samples, Figure S5: T-tests comparing different groups of coffee brew samples. Dark line indicates the limit for significant difference after Bonferroni correction.

Conflicts of Interest:
All authors declare no conflicts of interest.


extfieldsofstudy:
Environmental Science
Medicine

provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2019.jsonl.gz.gz

text:
A global, empirical, harmonised dataset of soil organic carbon changes under perennial crops

A global, unified dataset on Soil Organic Carbon (SOC) changes under perennial crops has not existed till now. We present a global, harmonised database on SOC change resulting from perennial crop cultivation. It contains information about 1605 paired-comparison empirical values (some of which are aggregated data) from 180 different peer-reviewed studies, 709 sites, on 58 different perennial crop types, from 32 countries in temperate, tropical and boreal areas; including species used for food, bioenergy and bio-products. The database also contains information on climate, soil characteristics, management and topography. This is the first such global compilation and will act as a baseline for SOC changes in perennial crops. It will be key to supporting global modelling of land use and carbon cycle feedbacks, and supporting agricultural policy development.

Background & Summary
Perennial crops are those crops that have a productive life cycle longer than a year, such as apple trees or sugarcane. Perennial crops comprise 30% of global croplands 1 . Despite their large spatial coverage, the effects of perennial crop plantations on soil properties are not fully understood and results from the literature to date are inconclusive. For example, studies have reported both soil organic carbon (SOC) higher 2,3 , lower 4,5 or equal to 6 (2019) 6:57 | https://doi.org/10.1038/s41597-019-0062-1 www.nature.com/scientificdata www.nature.com/scientificdata/ after transition to perennial crops. This variation could be due to the diverse nature of published studies which have been carried out in different climates and soil types, with a wide range of crops and management practices. Furthermore, a variety of experimental designs, sampling strategies (frequency and soil depth) and analytical methods have also been used. As a result, studies are not directly comparable and conclusions on the effect of perennial crop cultivation on SOC stocks, and the underlying mechanisms, cannot be easily derived.
Accordingly, a major problem contributing to a lack of clear conclusions is that no harmonised data exist. The dataset we present fills that gap: we have compiled the largest dataset to date comprising studies which assess the effects on SOC and soil texture parameters of (a) temporal dynamics, e.g. presenting values across years (b) land use change to perennial crop, e.g. from annual crop or forest to perennial crop and (c) different management practices, e.g. soil amendments. We have included basic climate information: mean annual temperature and precipitation, and topography and bioclimatic region. We have also harmonised the data entry values. The process of harmonising data is to combine data from different and heterogeneous sources and formats into a single, consistent, integrated dataset. Information about SOC carbon stocks already exist and is available in repositories such as the harmonized world soil database 7,8 from the Global Soil Information System (GLOSIS) or SoilGrids 9 . The dataset we present not only complements those SOC stock data but provides further information on SOC changes.
We have included different final end-use types of perennials: (i) food and beverage crops such as citrus or coffee, (ii) bioenergy crops such as Miscanthus or switchgrass, (iii) short rotation coppice trees such as poplar and willow, (iv) animal-feed such us Brachiaria or Atriplex, (v) agroforestry and (vi) other bio-products such as cotton or ramie.
The data are at figshare 10 . In total, the dataset contains information from 1605 ID entries in 180 studies, 709 field studies, 58 crops, from 32 countries in all continents except for Antarctica, covering all the bio-climatic areas suitable for agriculture (Fig. 1). The list of crops and countries included is in Online-only Table 1.Some areas are still under-represented but we hope that publication of this dataset will encourage those gaps to be filled. Every ID-entry is a paired-comparison point. The ID-entry contains either single SOC measurements, when possible, or aggregated data at plot level when it was not possible to obtain individual values. This has been clearly indicated in the dataset, as is the number of subsamples in the aggregated data specified, where these data were available. Data aggregated at larger scale, i.e. local or regional, was not considered. Each ID-entry contains information on SOC dynamics, which can be either temporal data or paired comparison data including the crop age and time since conversion to perennial, which can be used as a surrogate of temporal information (see methods for more details). For every ID-entry, the dataset contains: information from the original study on crop type, crop age, climate, basic physio-chemical soil characteristics and SOC, number of samples, the original study source, plus additional information about climate, soil properties and biogeographical regions from global databases (details in Online-only Table 2). We only included studies that have information about the perennial crop plantation establishment date and crop age. This was an essential condition since otherwise temporal carbon dynamics cannot be quantified. The aforementioned essential requirements required us to discard many papers reporting some SOC changes in perennial crops, most of which lacked information about crop age. A second common cause for discarding papers was the reporting of SOC change alone rather than as empirical pair-wise values. Aggregation at a larger scales was also frequent.
This harmonized dataset will be of value to support global modelling of land use change and carbon cycle feedbacks, to underpin development of climate-smart agricultural strategies, policy development for sustainable agriculture, to calculate potential carbon savings from global deployment of perennial bioenergy crops, and to support the 4 per mille initiative 11,12 , among others. Indeed, as the first of many potential analyses using these data, an empirical model of global patterns of SOC changes under perennial crops is being developed using these datasets. www.nature.com/scientificdata www.nature.com/scientificdata/ Methods Empirical data gathering. We compiled the dataset from a systematic search of the literature and by consulting researchers globally to identify and share available data. We furthermore searched in ISI Web of Knowledge, GoogleScholar and figshare using the keywords "perennial crop", "soil organic carbon" and "soil organic carbon perennial". We additionally followed references from papers identified in these searches and contacted some authors via email.
For every ID-entry, we added all the available data from the original study in the original units given in the study. This included information about location, climate, crop, soil characteristics, and number of soil samples, temporal/paired site data and original study source. Full details and a list of all entries are presented in Online-only Table 2. Besides, we classified the crops in three main groups: woody plants, palms and grasses (Online-only Table 1).
For ID-entry, two SOC and soil characteristics values are given. The first pair of the set is named "Current SOC and soil characteristic" and indicates the values of SOC at the most recent time point (for temporal data), the perennial land use (for paired site data including different land use) or new management versus traditional (for paired site data including different crop management). The paired SOC and soil characteristic values are under "Previous SOC and soil characteristic". extra fields added to the dataset: climate, topography, soil characteristics. We added additional values of climate variables, topography, soil characteristics and FAO ecoregion classification values from different global free-source datasets. In every case, we used the geographical coordinates given in the original sources to extract additional information. We did not use this information to gap-filling but provide it alongside in a complementary dataset. The data points can be matched and merged using the ID-entry.
Climate information for the plot was extracted from the WorldClim v2 dataset 13 , which uses average monthly climate data for 1970-2000 (http://www.worldclim.org/). In addition, we added a climatic water deficit index 14 , and calculated an indicator of water or climate stress P/(T + 10), where P is the annual precipitation and T the annual mean temperature. Details and units are presented in Online-only Table 3.
Information about soil texture and chemical properties was obtained from the SoilGrid Database 15 , which provides information at 250 m resolution at seven soil depths (https://soilgrids.org/#!/?layer=TAXNWR-B_250m&vector=1). Details and units are given in Online-only Table 3.
To characterise the ecoregions, we used the FAO map of Eco-floristic regions (cdiac.ess-dive.lbl.gov/ftp/ global_carbon/ecofloristic_zones.zip). Details and regions are given in Online-only Table 3.
Unifying units. For soil properties, values of soil texture, clay, sand and silt were transformed to percentages if they were not in that unit in the original study. To do so, values of g/kg were divided by 10.
Values of soil bulk density (BD), texture and pH were not gap-filled but left as NA when not provided. This was to avoid masking which data were empirical and which data were not. Only 51% of the studies reported the values of BD, and 43% soil texture. But information on BD, texture and pH for each point can be found in the fields added to those obtained from the SoilGrids dataset, at the soil depth of the middle depth of the sample point. This information can be used to gap fill those fields if necessary.
Values of SOC in the original studies where either in concentration, g/kg, (SOC con ) or in carbon stocks, Mg/ha (SOC stock ). We initially considered transforming the values using the equation 18  However, that transformation would have added extra uncertainty and thus was not consistent with our principle of providing the SOC values as raw and close to empirical values as possible. Furthermore, when comparing the SOC values from transformed against untransformed data, we found a biased mismatch between the range of data using original units and the transformed data. We hypothesise that this may be due to the use of aggregated data or single use of BD for multiple samples 19,20 , or the use of different methodology 21 , or different sampling depth or soil core ring sizes, or lack of information about rock fragments. Considering this, we concluded that such a transformation was not appropriate, in line with previous reports 5 . Therefore, in this dataset, we provide SOC values using the units of the original study, which means about 30% are in g/kg (SOC con ) and the other 70% in Mg/ha (SOC stock ).
For climate variables, temperature and precipitation, the values reported in the original study were kept. Otherwise, we added the values obtained from the WorldClim dataset, which has a lag time of 30 years, averaging climate values from 1970 to 2000. The "temperature" and "precipitation" fields were the only ones that were gap-filled in the provided main database (in about 1% of the cases). Average values of temperature and precipitation were provided in most of the original studies, however, the period of time was specified hardly any case.

Data aggregation quantification.
We added a new field to indicate whether the original dataset contains information about a single SOC empirical measurement or aggregated data (in the "Data" section, Online-only Table 2). In addition, the entries "N plots" and "N samples" specify the number of empirical measurements per entry, when provided in the original studies. We added a new field called "N measured" which is the number of plots multiplied by the number of samples. Therefore, this value also indicates the level of data aggregation. When www.nature.com/scientificdata www.nature.com/scientificdata/ only the number of plots or samples per plot were indicated in the original study, this was the value given in this field. When neither of these values were reported in the original study, the number of measurements was set to 1. In the dataset, 108 data-points contained a single measurement, 965 ID-entries were aggregated data, and for the remaining 532 samples no details of aggregation were provided. From the 965 entries with aggregated data, only 319 (33%) reported the standard deviation or variation in the original study.

Data Records
The dataset, in spreadsheet format, can be found in the figshare repository 10  The list of crops and countries is presented in Online-only Table 1. A summary Table summarising the different types of data included is in Table 1, and the percentage of data points in each category can be seen in Fig. 2.
Metadata information in ISA format (www.isa-tools.org) is provided in the associated Scientific Data Descriptor. The dataset we present is compliant with the FAIR principles for scientific data 22 : findable, accessible, intraoperative and reusable.

technical Validation
Variable distribution and unusual point values. We evaluated the distribution of all the quantitative variables. We examined the outliers and/or extreme points for potential errors. The distribution of all variables can be found in Supplementary Material 1 (Scatterplots, box-and-whiskers and histograms).

Quantifying errors and standard deviations in SOC.
We analysed the effect of the number of samples per plot in terms of uncertainty. To this end, we calculated the relative standard deviation (sd) of the SOC mean values for ID-entries with more than one measurement and reporting the sd of SOC values. Overall, the value of relative standard deviation of SOC mean values, sd/SOC, which is indeed the coefficient of variation (cv), decreased as the number of samples increased (Fig. 3). In other words, a greater sampling effort seems to decrease the cv. According to these data, the relative standard deviation is generally under 10-15% after gathering 20 sample points. Nonetheless, this depends on the soil characteristics and the sampling spatial distribution. All in all, having 20-30 replicates per plot seems to be an adequate sample size in SOC studies to constrain the coefficient of variation below 15% (Fig. 3).
On the SOC errors. Assessing SOC stocks is a complex process which involves several steps and measurements. It should thus be borne in mind that many types of error may have crept in the original data acquisition. Besides, while some errors may be random, others may be systematic thus introducing bias, the latter is of a greater concern and efforts should be made to minimise it. Not to mention that assessing SOC stocks is a complex matter itself. The dataset may contain different types of errors from different sources. For example measurement errors in the field, lab analytical and measurement errors and post-process data errors and transformations are all www.nature.com/scientificdata www.nature.com/scientificdata/ possible. A further description and comments on SOC errors is beyond the scope of this data paper and can be found elsewhere, e.g. 18,19,23-26 among others.
SOC measurement and stock estimation also usually involve measuring or estimating other soil parameters, namely BD, and SOC concentration and determination accuracy are often affected by other properties such as texture and iron oxide contents. Mathematical relationships to link those parameters may introduce further bias 18 . However, measurement and analytical errors can lead to both under-and over-estimation. If this is the case and the errors are randomly distributed, they somewhat compensate for each other in large datasets. Non-random, systematic errors, may also mask trends in the data. However, it is not possible to know to which extent these kind of errors are present in the SOC perennials dataset given the available source information. In any case, we recommend quantifying sources of uncertainty when possible, and at different temporal and spatial scales 23 and due to data aggregation 19 .
Different sampling depth is another potential source of error and uncertainty when comparing data from different studies 27 . In this dataset we have brought together SOC at different depths (the sample depth is clearly indicated in the dataset), but we suggest that values of SOC measured at different depths should not be mixed without using a suitable pedo-transfer function (PTF). Presenting new PTF for perennials is beyond the scope of this data paper yet is currently being explored for future publication.
A call for unification in reporting soil properties data. An increasing number of studies 18,19,21 are reporting problems in comparing SOC data, and some of the given reasons have been mentioned in the previous subsection. Values of bulk density and soil texture are measured in most studies but only at times reported in the resulting manuscript. This first source of uncertainty, and potential errors and bias, can be remedied by including this information in publications. Thus, we formally appeal to researchers to measure and report BD and soil texture in future SOC studies. BD is technically simpler to measure than texture, which is a time consuming analytics and could be constrained by budget and or time. However, easy methods to estimate soil texture directly in the field have been proposed and evaluated as accurate enough for most purposes 28 .
A second essential piece of information not always detailed in published papers is the number of samples and replicates. We encourage researchers to provide all of the relevant data and measurements done, and for reviewers   www.nature.com/scientificdata www.nature.com/scientificdata/ to request that authors provide this information, for the sake of transparency, reproducibility and quantification of uncertainty.
A particular drawback in current SOC studies is the lack of standardised carbon values to be reported. Until now, some studies use SOC concentration values (g/kg), while others use carbon stocks (Mg/ha) or volumetric values (g/m 3 ). This is not a matter of different units, since those carbon pools are different things. Nonetheless, they are interchangeable if and only if the required soil variables are accurately measured and provided (SOC content, BD, soil texture, rock fragment, maximum depth), as we suggested and discussed in the previous sections. This lack of consensus and unification is not only limiting values of some relevant studies 5 , but is hampering our understanding of soil carbon dynamics. It is timely that we gather and unify knowledge to better understand of the carbon cycle in general, and SOC dynamics in particular. This understanding is key to realistic estimation of the potential of SOC storage in perennial crops as a climate change mitigation option, and a potential tool to deliver the goals of the 4 per 1000 initiative. This knowledge is also important to predict potential changes on SOC dynamics under climate change conditions and to underpin development of climate-smart agricultural strategies.
The ISRIC WDC GlobalSoilMap from the World Soil Information Service (WoSIS) has recently provided some standards and procedures to acquire, harmonise and provide soil data 7,29 . In further SOC analysis and work, it may be adequate to align with those protocols.
The minimum information we recommend that should be provided in SOC studies is: (i) number of plots and number of replicates per plot (ii) depth of measurement and number of layers considered, (iii) BD and equivalent soil mass, (iv) soil texture, (v) rock fragment fractions (vi) SOC in concentration and stocks, (vii) method used to measure SOC and BD, (viii) climate, or at least mean annual temperature, precipitation and elevation, (ix) soil management (if any), and (x) land use (historic land use is also highly desirable). The WoSIS repository has a shorter list of requirements. Nevertheless, to align with its standards, cation exchange capacity, electrical conductivity and water holding capacity are also required 29 .


extfieldsofstudy: Biology Medicine

s2fieldsofstudy:
Agricultural And Food Sciences
Biology

text:
Biochemical and nutritional characteristics of buffalo meat and potential implications on human health for a personalized nutrition

The human consumption of food animal products is the main topic of an important debate among professionals in this sector: dietologists, dietitians and nutritional biologists. The red meat provides all the essential amino acids, bioavailable iron, zinc, calcium, lipids and B-group vitamins. A valid alternative to beef could be the buffalo meat. Italy is the largest European producer of buffalo meat and derivatives. The high nutritional characteristics of buffalo meat make it suitable to be included in the Mediterranean diet to customize it in relation to the needs and conditions of the population. Polyunsaturated/saturated fatty acids ratio can be influenced by diet, breed and type of breeding, but muscle tissue fat percentage is the main factor in determining a favorable fatty acid composition. This review focuses on the biochemical and nutritional characteristics of the buffalo meat (content of fats, cholesterol, amino acids, vitamins and minerals), explaining their variability depending on the different breeds, and the favorable implications on the human health. These results suggest that buffalo meat can be a healthier alternative to beef, not only for healthy people in particular physiological conditions (i.e. pregnancy), but also for persons at risk for cardiovascular and cerebrovascular diseases, thus achieving the goal of a personalized nutrition.

Introduction
Nowadays, the consumption of animal origin products in human nutrition is the main topic of an important debate among professionals: dietologists, dieticians, nutritional biologists and hygienists. Many scientific works, in which observational studies and physio-pathological findings have been carried out, show a correlation between the intake of meat and meat-based products and the onset of degenerative, cardiovascular, cerebrovascular and tumor diseases (Keys, 1980;Barnard et al., 1995;Brunner et al., 2008;Trichopoulou et al., 2009). However, these data must be related to the frequency of intake of the food, the nutritional value and the lifestyle of the population. In particular, red meat provides all the essential amino acids, highly bioavailable heme iron, zinc, calcium, lipids and B-group vitamins (specifically vitamin B12 involved, for example, in brain development and prevention of pernicious anemia). During the evolution of the species, men used hunting, fishing and wild fruit gathering as food source. Currently many populations follow almost the same diet. Cordain et al. (2002) reported that 229 hunter-gatherer populations, whose diet was made up of 26-35% of vegetables and 66-75% of animal origin products, were not affected by cardiovascular diseases.
In the world, the per capita consumption of beef (according to the statistical index of apparent consumption that includes inedible parts, drops in processing and various waste) is gradually being reduced, even in the large countries producing red meat such as South America, USA, Australia, Canada and New Zealand (Assocarni, 2018a).
In Europe, in the last 5 years, the trend of apparent per capita consumption of beef has undergone an inconstant trend, with a slight decline in the years 2013-2014, stabilizing at a consumption of 15.6 kg per capita per year in 2016 -2017 (Assocarni, 2018b) .
In Italy, in the last 10 years, a clear reduction in apparent consumption of beef has been reported: it decreased from 24.9 kg per capita per year in 2007 to 17.1 kg per capita per year in 2017 (Assocarni, 2018c).
A valid alternative to beef could be buffalo meat. Italy is the first European country for buffaloes breeding. The current size of the Italian buffalo heritage is 403,093 animals, distributed in a total of 2208 farms (Anagrafe Nazionale Zootecnica -Statistiche, 2018). The buffalo breed in Italy belongs to the Italian Mediterranean breed, typically used to produce milk, which is then transformed into mozzarella and to a lesser extent into provola, scamorza, caciotta and caciocavallo (typical Italian cheeses). The use of buffalo in meat production, on the other hand, only affects a small production niche. Only a few breeders, in fact, dedicate themselves to male calves breeding, because of a lower growth index than the bovine species, even though the feed conversion ratio is almost identical.
Recent scientific evidence presents Foodomics as a new vision, based on Metabonomics and Proteomics, to deepen inside the effects of digestion and to compare the different digestibility and bioavailability of protein-rich foods, developing a new concept of the nutritional value of food . Food of plant and animal origin, including meat, contains bioactive peptides, released during gastrointestinal digestion or during food processing, that exhibit many positive effects on human body (i.e. antioxidative, antimicrobial, antihypertensive, antithrombotic, cytomodulatory, immunomodulatory, anticancer, hypocholesterolemic and antiobesity) (Baltic et al., 2014;Ferranti et al., 2014;Chakrabarti et al., 2018). Bordoni et al. (2014) and Marcolini et al. (2015) suggest the first combined application of Bradford and SDS-PAGE assays, combined to NMR spectroscopy and relaxometry for the evaluation of protein bioavailability in bresaola, a typical meat-based food of northern Italy, after in vitro digestion. In this way it was possible to evaluate changes in nutritional values and in protein bioavailability occurring in meat-based food during digestion process. No studies on bio-accessibility and bio-availability of buffalo meat nutrients are available so far.
On the other hand, the nutritional and organoleptic characteristics of buffalo meat give it a high dietary value and make it suitable to be included in the Mediterranean diet, in case of personalized nutritional health needs. Ordovas et al. (2018) defined Personalized Nutrition as "an approach that uses information on individual characteristics to develop targeted nutritional advice, products, or services". Personalized nutrition refers to tailored nutritional recommendations aimed at the promotion, maintenance of health and diseases prevention. These recommendations consider differential responses to certain individualized food-derived nutrients that arise due to the interaction between nutrients and biological processes (internal factors such as genetics, microbiome, metabolome interactions, and external factors such as dietary habits and physical activity) (Verma et al., 2018).

Biochemical and nutritional characteristics of buffalo meat
Biochemical and nutritional characteristics of buffalo meat have been investigated by various researchers. They focused on the evaluation of its content in cholesterol, fatty acids, amino acids, vitamins, and minerals. Meat characteristics are also significantly influenced by breeding methods that include animal feeding.

Cholesterol and fatty acids composition
Given the implications on human health and product quality, in the last decades increasingly sophisticated techniques have been used to study the composition of fatty acids and the levels of cholesterol present in the meat of animals meant for food consumption.
The buffalo carcass has a different distribution of fat than cattle carcass. In buffalo meat the fat cover is prevalent, and it is con-centrated in the subcutaneous area with few infiltrations in the muscular tissue; vice versa, in the beef, the fat infiltrates the muscular masses (typical marbling of the meat). This means that, at the time of consumption, the fat on the outer surface of the buffalo meat is easily detachable from the lean part.
The ratio of polyunsaturated/saturated fatty acids in the meat of ruminants is unfavorably low, as the unsaturated fatty acids that come from the animal's diet are hydrogenated by rumen microorganisms. This ratio, in relation to the nutritional value of food, is considered an important factor for the human health. The recommended range is 0.45-0.65 (Department of Health and Social Security, 1994); a lower ratio in an individual's diet can increase the incidence of cardiovascular and cerebrovascular diseases. Larick et al. (1992) examined buffalo and bovine meat samples, finding differences in the composition of unsaturated and polyunsaturated fatty acids. Monounsaturated fatty acids, in their cis configuration, lower the cholesterol levels and do not reduce the high-density lipoprotein (HDL), a protecting factor against coronary heart disease.
For this reason, the main objective of the new strategies for animal feeding, both for ruminants and non-ruminants, is to increase the concentration of polyunsaturated fatty acids and the levels of conjugated linoleic acid (CLA) in fodder (in particular those of the n-3 series) and to reduce the concentration of saturated fatty acids (mainly present in animal feeds) (Scollan et al., 2006).
The type and proportion of the different fatty acids depend on the type of the evaluated muscle. Calabrò et al., (2014) observed significant differences comparing three types of muscle (P<0.01). The Semitendinosus muscle showed better quality indices and a better fatty acid profile with lower SFA, higher PUFA, MUFA, ω-6, ω-3, and CLA. Infascelli et al. (2005), in their research, evaluated the cholesterol content and the acidic composition of intramuscular fat in the meat of buffalo and beef steers belonging to the Marchigiana breed (from Marche region of Italy). For this research the following animals were recruited: 8 buffalo steers on the fed a TMR with 14.1% CP and 0.84 UFV/Kg DM and 18 beef calves, belonging to the Marchigiana breed, fed diet with 15% CP and 0.91 UFV/Kg DM. The animals were slaughtered at an average live weight (PV) of 400 kg and 620 kg respectively and the analytical investigations were carried out on the Longissimus dorsi muscle. The protein content was overlapping; the buffaloes also showed lipid concentrations of 1.36% vs 2.40% of the Marchigiana cattle, cholesterol levels equal to 48.8 vs 53.7 mg/100 g, (SFA) levels of 38.4% vs 46.6%, MUFA levels of 37.3% vs 31.2%, PUFA levels of 24.3% vs 22.3%, n-6 fatty acids levels of 23.7% vs 21.5%. Therefore, the atherogenicity index (IA) and the thrombogenicity index (IT) were significantly lower in the buffalo species (IA of 0.41 vs 0.57 and IT of 1.16 vs 1.36). Lambertz et al. (2014), on the other hand, investigated the characteristics of the carcasses and the quality of the buffalo meat in the Swamp subspecies, widely distributed in Thailand, bred as a working animal, to supply manure used as fuel and fertilizer and for meat production.
The presence of n-3 and n-6 fatty acids, as well as conjugated linoleic acid, helps to determine the nutritional quality of buffalo meat. The n-3 fatty acids, in particular, have the ability to reduce the serum concentration of triglycerides in humans (Lambertz et al., 2014).
The polyunsaturated/saturated fatty acids ratio is influenced by diet, breed and type of farming. However, the percentage of fat mass in muscle tissue remains the main factor in determining the fatty acid composition and consequently the polyunsaturated/saturated fatty acids ratio (Cifuni et al., 2014). (Landi et al., 2016) analyzed meat samples (Longissimus dorsi) of male buffaloes collected from Campania region in Italy (Bubalus bubalis) and confirmed that water buffalo meat has great advantages for human consumption because of the better protein composition (21.13 g/100 g) than beef meat (19.23 g/100 g) (USDA, 2015). The total free amino acid content for 100 g meat ranged 155.79 to 181.78 mg.
Glutamic acid was the most abundant among the free amino acids (34.66-45.76%) as well as alanine. Among the free amino acids, glycine, arginine, and asparagine were highly present.
The total amounts of carnosine (βalanyl-L-histidine) ranged 124.73 to 139.2 mg/100 g, whereas the content of anserine (β-alanyl-N-methylhistidine) ranged 13.79 to 25.59 mg/100 g. Furthermore, the content of carnosine and anserine represents 50% of the total free amino acids. Ilavarasan et al. (2016) analyzed the amino acid content of 12 male Toda buffalos. The glutamic acid was found to be higher (1.80-2.22 g/100 g) followed by lysine (0.84-1.07 g/100 g) and aspartic acid (0.89-1.05 mg/100 g). Similar results were recorded by Syed Ziauddinet al. (1994). The results of this study revealed that Toda buffalo meat is a rich source of all essential and non-essential amino acids.
Finally, these analyses show that water buffalo meat is a source of carnosine, a natural anti-aging constituent of the human body (Gariballa, 2000).

Vitamins
In 1997 analyzed 12 buffaloes for content of selected vitamins and selenium. The buffaloes came from the United States and Canada and consumed concentrate diets plus hay free choice for at least 180 days.
The mean nutrient concentrations (per 100 grams of wet weight) were as follows: 0.045 mg thiamine (vitamin B1), 0.253 mg pyridoxine (vitamin B6), 2.131 mg cobalamin (vitamin B12), no detectable vitamin C, 0.848 mg vitamin A, 0.047 mg a-tocopherol (vitamin E), 0.013 mg β-tocopherol (vitamin E), and 25.464 mg selenium. All vitamin content of the buffalo cuts was lower than those of beef, pork, chicken, and turkey (except for vitamin A, and sometimes vitamin B12).
The consumption of 100 g of buffalo composite cuts (raw) would provide approximately (of the Dietary Reference Intake): 3% of vitamin B1, 12.7% of vitamin B6, 35.5% of vitamin B12, negligible for vitamin A and E, no vitamin C (Intakes, 2011).
The nutrient content data were for raw or uncooked buffalo cuts. Cooked buffalo cuts most likely would be rich sources of selenium and vitamin B12 and good sources of vitamin B6. The buffaloes were given relatively low levels of vitamins as part of their feeding regimen. Producers should consider adding a supplement containing adequate quantities of the vitamins to the diets of buffalo. Galbraith et al. (2006) analyzed buffalo meat samples from the USA. They found a vitamin A content in all buffalo cuts 1.74 to 2.46 µg/100 g, which was 2-3 times higher than the average value of 0.848 µg/100 g reported by Driskell et al. (1997). The vitamin A content was similar in all cuts.
In 2000, Driskell et al. focused on riboflavin (vitamin B2) and niacin (vitamin B3) concentrations of 24 buffaloes that were raised in various geographic areas of the United States and Canada. The analyzed cuts contained 5.5% of the daily requirement for riboflavin (1.7 mg/100 g) and 9.6% for niacin (20 mg/100 g) (Intakes, 2011). Buffalo meat was lower than the other meats in riboflavin and niacin contents.
The nutrient content of meat does vary considerably depending on many factors, including genetics, age, feed, and time of year slaughtered. A relevant variation also in riboflavin and niacin from individual buffaloes was found. Furthermore, appreciable quantities of vitamins are known to be lost during the cooking of foods (Driskell et al., 2000).

Minerals
In 2017, Joele et al. analyzed minerals in meat from Longissimus thoracis muscle of 30 crossbred male Murrah X Mediterranean buffaloes (Bubalus bubalis). They found and analyzed a series of micronutrients (iron, copper, zinc and selenium) and contaminants (aluminum and antimony).
All samples had contents in micronutrients above the recommendation by the United States Department of Agriculture (USDA -nutrient database, 2010) of 1.61 g kg −1.48 , which confirms that 100 g of buffalo meat supplies the daily requirements of these micronutrients.
Giuffrida-Mendoza et al. (2007) analyzed mineral composition of Longissimus dorsi thoracis of 64 water buffalo and 68 Zebu-influenced cattle. Water buffalo had higher concentrations in K, Mn, Fe, Zn, Cu and P (Fe ranged 1.74 to 2.56 mg/100 g of fresh tissue). Meat contributes to over 25% of an adult's daily requirement of K, P, Fe, and Zn (Intakes, 2011). Zhang et al. (2016) analyzed the muscle tissue gene expression, including iron ion metabolism, in water buffalo and in domestic cattle. They found an increased expression of genes involved in iron homeostasis and transport, that indicates it's very important for maintaining muscle structure and function. The higher mRNA expression of genes involved in iron ion homeostasis was found in buffalo muscle than cattle such as TF (transferrin), and FTL (ferritin, light polypeptide) which is noteworthy.
Considering buffaloes as draft animals, they probably store more iron to ensure that they have sufficient myoglobin in muscle tissue for their heavier workloads, since it is needed for providing oxygen to the working muscles and its storage (Mancini & Hunt, 2005).
The darker color of the buffalo meat is usually attributed to a higher myoglobin content (Tateo et al., 2007). This charac-teristic is associated with the high levels of iron (Fe) homeostasis genes expressed in buffalo muscle tissues (Zhang et al., 2016).

Breeding methods
Breeding methods of buffalo are various in different countries, as well as the breeds are varied. The main breeds in the world come from the water buffalo (or Asian buffalo -Bubalus bubalis), with two subspecies, river buffalo and swamp buffalo, that differ for behavioral, morphological and genetic features (50 chromosomes in river vs 48 chromosomes in swamp type) and territorial distribution. The river buffalo is bred mainly in Europe, Egypt, Asia, China, Australia, South America and in some African countries. The swamp buffalo is bred mainly in China and India. In Italy there is only one breed: the Mediterranean, which derives from the river buffalo of Asian origin (Borghese, 2005(Borghese, , 2013.
Over the last decade, numerous researchers have studied the chemical-physical and nutritional properties of buffalo meat of the Italian Mediterranean breed, detecting different characteristics in relation to the type of feed.
The fatty acid composition of the meat is mainly influenced by the farming system (grazing or stable) and by the type, quantity and quality of the plants and feed that make up the diet (simple or composed, administered as a single meal or split during the day) (Lambertz et al., 2014). For this reason, the main objective of the new strategies for animal feeding, both for ruminants and non-ruminants, is to increase the concentration of polyunsaturated fatty acids and the levels of conjugated linoleic acid in fodder (in particular those of the n-3 series) and to reduce the concentration of saturated fatty acids (mainly present in animal feeds) (Scollan et al., 2006). Calabrò et al. (2014) evaluated the influence of a fava bean diet (Vicia faba var. minor) on the nutritional characteristics of buffalo meat. The different type of diet has influenced the qualitative characteristics of the meat analyzed. The meat taken from buffalo fed with fava beans (FB) showed lower values of fat, protein and cholesterol compared to the meat of buffalo fed with soybeans (P<0.05). Furthermore, the considered parameters were visibly different depending on the type of muscle observed (P<0.001); in particular, the Semitendinosus (ST) muscle has the lowest fat content (P<0.01), while the Iliopsoas muscle contains the highest amount of protein (P<0.01).

Effects on health
In 2010 Giordano et al. published a longitudinal observational study on the usual intake of buffalo meat in the diet of 300 adults, divided into 3 groups of 100 individuals (non-consumers, recent consumers and regular consumers of buffalo meat), resident in the Campania region, non-smokers, with no dyslipidemia or type-2 diabetes mellitus.
After 12 months, regular consumers showed a better lipidic profile in their blood, with lower levels of total cholesterol, high levels of HDL and low levels of triglycerides, compared to recent consumers and non-consumers. Furthermore, the same group of adults showed a better oxidative-reductive homeostasis and an improvement of the parameters associated with cardiovascular events, such as reduction of the sphygmic wave speed, of the carotid intima-media thickness and atheromatous plaques.
During the study, regular consumers of buffalo meat did not show an increase in cardiovascular diseases, unlike those who never consumed buffalo meat, in which there was a statistically significant increase in cardiovascular diseases. As a final point, recent consumers presented intermediate values, since they had begun to consume buffalo meat only during the research.
Evaluating the impact on the cardiovascular risk profile related to the intake of buffalo meat, Giordano et al. (2010) concluded that the transition from a beef-based diet to a buffalo meat-based diet seems to be associated, after a few weeks, to an improvement of the different markers of cardiovascular risk, thus detecting a more favorable serum lipidic profile, a lower atherosclerotic involvement at carotid level and a lower susceptibility to oxidative stress.
In 2013 McDaniel et al. conducted a randomized trial to examine the consequence of a single 12 oz serving and 7 weeks of chronic consumption of beef and buffalo on a total of 14 healthy male subjects. Blood lipids, inflammatory markers, oxidative stress and endothelial function were monitored.
After a single meal, buffalo, in contrast to beef, resulted in an attenuated increase in triglycerides, but no elevation in oxidative stress and vascular function were observed. Moreover, after 7 weeks of buffalo consumption, reduced inflammation, lower oxidative stress and a better vascular function were observed, compared to beef consumption. Authors concluded that consumption of buffalo meat, both acutely and chronically, was associated with a lower atherogenic risk than consumption of equal portions of beef (buffalo meat did not generate an increased inflammation and oxidative stress or a decreased vascular function).
The two studies reported, comparing the cardiovascular health risks associated with the acute and chronic consumption of buffalo, showed this meat a healthier alternative, in a society where red meat is widely consumed.
Personalized nutrition is equally applicable to patients and to healthy people who may or may not have enhanced genetic susceptibilities to specific diseases and can be applied in two broad areas: firstly, for the dietary management of people with specific diseases or who need special nutritional support (i.e. pregnancy, old age) and, secondly, for the development of more effective interventions aimed at improving the public health.
In this perspective buffalo meat consumption, which has a favorable content in Zn, Cholesterol and B-complex vitamins, can meet women's nutritional needs (Ordovas et al., 2018). High contents in Selenium and Iron, can extend the concept of personalized nutrition, that requires further investigations.

Conclusions
In relation to the healthiness aspects of buffalo meat, many studies show that the composition of the intramuscular fat has no role in the onset of cardiovascular and cerebrovascular diseases. In fact, lipids perform different functions in addition to energy supply (9 Kcal/g), such as internal organ protection, body temperature maintenance, appetite stimulation and hydrophilic substances transportation. Diets rich in C18:0 (stearic acid) do not cause an increase in serum cholesterol; long-chain fatty acids C12:0 (lauric acid), C14:0 (myristic acid), C16:0 (palmitic acid), as well as short-chain ones, are atherogenic (Ulbricht & Southgate, 1991).
In 2009, Infascelli et al. made a publication as a part of a project for the enhancement and the promotion of local agri-food excellences to transform them into a source of attraction and investment for new economic opportunities. In this work, a comparison between the Italian Mediterranean buffalo and beef meat shows that many dietary and nutritional characteristics are superior in buffalo for different organoleptic parameters (Tables 1-5): (1.28 mg/100 g vs 1.00 mg/100 g). v) higher concentration of iron (1.4 mg/100 g vs 1.2 mg/100 g) and zinc (4.0 mg/100 g vs 3.2 mg/100 g); Zhang et al. (2016) conducted a study on 6 buffalo females and 6 cattle females, native to the city of Nanning and Baoji, comparing their genome and in particular the genes related to homeostasis of protein, lipid, glucose and other nutritional values. Genetic analysis showed that buffaloes have a high expression of genes involved in the homeostasis of iron, cholesterol and other lipids, which confers their meat a high protein and a low lipid content and an intense dark color.

Review
Furthermore, an evaluation and a comparison of the organoleptic quality of the two investigated species meat were carried out by studying the Longissimus thoracis muscle. Buffalo meat had a more intense color than beef (P<0.05), but the latter had a lower protein, higher fat and ether extracts content. Despite this, the two types of meat had similar chemical and physical charac-teristics. The most intense color of buffalo meat was due to the greater amount of myoglobin contained in its muscles (Zhang et al., 2016).
According to these results, the buffalo meat is presented as a healthier alternative to the widespread beef. Further investigations, especially from an economic and organoleptic point of view, are needed to confirm these results and to stimulate the diffusion of this food in the Mediterranean diet.