Does Coffee Have Terroir and How Should It Be Assessed?

Predicting best planting location and coffee cup quality from chemical parameters: An evaluation of raw Arabica coffee beans from São Paulo over two harvests

Arabica coffee is one of the most consumed beverages in the world. The chemical components present in raw Arabica coffee beans (RACB) are directly related to the cup quality of the beverage. Environmental and genetic factors influence the content and profile of these components. Then, this work aimed to evaluate different chemical parameters of RACB from 3 varieties planted in 3 different experimental farms located within the "Alta Mogiana" terroir harvested in 2021 and 2022 to identify a better variety for each farm to produce a high cup quality Arabica coffee. The harvest period had a strong influence on most of the studied parameters because atypical weather conditions occurred in the 2021 harvest. The RACB harvested in 2022 yielded better results and supposedly will produce a beverage of high cup quality. Samples harvested in this period presented mainly average moisture levels closer to optimum (11.02 against 8.56 % in 2021); low total titratable acidity (98.00 against 169.75 mL 0.1 M NaOH/100 g in 2021); high amounts of free amino acids (0.96 against 0.93 g GAE/100 g in 2021), low amounts of CGA (4.27 against 4.85 g/100 g in 2021) and caffeine (1.08 against 1.76 g/100 g in 2021) and high amounts of trigonelline (1.12 against 0.96 g/100 g in 2021). The Rome Sudan variety had the best combination of chemical results, mainly when cultivated in Farm 2 in 2022, presenting high amounts of protein content (15.24 %) and free amino acids (0.96 g GAE/100 g), low total titratable acidity (98.3 mL 0.1 M NaOH/100 g), low amounts of CGA (4.55 g/100 g) and caffeine (1.29 g/100 g) and high amounts of trigonelline (1.11 g/100 g). The analysis of chemical compounds could predict the best farm to cultivate each variety studied and was a guide to foresee a higher cup quality of RACB beverages.

Identifying the origin of Yemeni green coffee beans using near infrared spectroscopy: a promising tool for traceability and sustainability

Yemeni smallholder coffee farmers face several challenges, including the ongoing civil conflict, limited rainfall levels for irrigation, and a lack of post-harvest processing infrastructure. Decades of political instability have affected the quality, accessibility, and reputation of Yemeni coffee beans. Despite these challenges, Yemeni coffee is highly valued for its unique flavor profile and is considered one of the most valuable coffees in the world. Due to its exclusive nature and perceived value, it is also a prime target for food fraud and adulteration. This is the first study to identify the potential of Near Infrared Spectroscopy and chemometrics-more specifically, the discriminant analysis (PCA-LDA)-as a promising, fast, and cost-effective tool for the traceability of Yemeni coffee and sustainability of the Yemeni coffee sector. The NIR spectral signatures of whole green coffee beans from Yemeni regions (n = 124; Al Mahwit, Dhamar, Ibb, Sa'dah, and Sana'a) and other origins (n = 97) were discriminated with accuracy, sensitivity, and specificity ≥ 98% using PCA-LDA models. These results show that the chemical composition of green coffee and other factors captured on the spectral signatures can influence the discrimination of the geographical origin, a crucial component of coffee valuation in the international markets.

Metabolomics as a tool for geographic origin assessment of roasted and green coffee beans

Coffee is widely consumed across the globe. The most sought out varieties are Arabica and Robusta which differ significantly in their aroma and taste. Furthermore, varieties cultivated in different regions are perceived to have distinct characteristics encouraging some producers to adopt the denomination of origin label. These differences arise from variations on metabolite content related to edaphoclimatic conditions and post-harvest management among other factors. Although sensory analysis is still standard for coffee brews, instrumental analysis of the roasted and green beans to assess the quality of the final product has been encouraged. Metabolomic profiling has risen as a promising approach not only for quality purposes but also for geographic origin assignment. Many techniques can be applied for sample analysis: chromatography, mass spectrometry, and NMR have been explored. The data collected is further sorted by multivariate analysis to identify similar characteristics among the samples, reduce dimensionality and/or even propose a model for predictive purposes. This review focuses on the evolution of metabolomic profiling for the geographic origin assessment of roasted and green coffee beans in the last 21 years, the techniques that are usually applied for sample analysis and also the most common approaches for the multivariate analysis of the collected data. The prospect of applying a wide range of analytical techniques is becoming an unbiased approach to determine the origin of different roasted and green coffee beans samples with great correlation. Predictive models worked accurately for the geographic assignment of unknown samples once the variety was known.

Microbiomes associated with Coffea arabica and Coffea canephora in four different floristic domains of Brazil

Brazilian coffee production relies on the cultivation of Coffea arabica and Coffea canephora. Climate change has been responsible for the decreasing yield of the crops in the country yet the associated microbial community can mitigate these effects by improving plant growth and defense. Although some studies have tried to describe the microorganisms associated with these Coffea species, a study that compares the microbiome on a wider spatial scale is needed for a better understanding of the terroir of each coffee planting region. Therefore, our aim was to evaluate the microbial communities harbored in soils and fruits of these Coffea species in four Brazilian floristic domains (Amazon, Atlantic Forest Caatinga, and Cerrado). One hundred and eight samples (90 of soil and 90 of fruits) were used in the extraction and sequencing of the fungal and bacterial DNA. We detected more than 1000 and 500 bacterial and fungal genera, respectively. Some soil microbial taxa were more closely related to one coffee species than the other species. Bacillus bataviensis tends to occur more in arid soils from the Caatinga, while the fungus Saitozyma sp. was more related to soils cultivated with C. arabica. Thus, the species and the planting region (floristic domain) of coffee affect the microbial composition associated with this crop. This study is the first to report microbial communities associated with coffee produced in four floristic domains that include sites in eight Brazilian states. Data generated by DNA sequencing provides new insights into microbial roles and their potential for the developing more sustainable coffee management, such as the production of biofertilizers and starter culture for fermentation of coffee cherries.

Coffee Yield Stability as a Factor of Food Security

Yield fluctuation is a major risk in all agricultural sectors, and it influences Goal 2 (food security) of the UN SDGs. Yield fluctuations are expected due to climate change, risking stable coffee supplies, and compromising coffee-exporting countries' ability to earn revenue to pay for food imports. Technology minimizing yield fluctuations is crucial for food security and for coffee farmers to earn a stable income. Fluctuations are small if yields remain close to the mean yield trends. In this study, the coffee yields of major producers are analyzed, together with zonal temperature data, to see where coffee is grown with stable technology under rising temperatures; thus, we demonstrate the advantages of the Yield Stability Index (YSI) over traditional stability measurements in guiding policy formulation and managerial decisions. The Yield Stability Index (YSI) is applied for 1961-1994 and 1995-2020, for the world's 12 major coffee-producing countries. The YSI indicates that of the 12 countries, only Indonesia, Honduras, and Mexico maintain stable yield levels, while Brazil and Vietnam considerably improve their yield stability, which traditional stability measures cannot grasp. Country-wise differences exist in environmental vulnerability and adaptability, with implications for food security. The novelty is the application of the YSI, and the connection between yield stability, climate change, and food security.