Search

[Display omitted] • Sensor array for precise differentiation of coffee aromas. • Digitalizing coffee aroma by the PCA dimensionality-reduction method. • Combining DBSCAN with LDA to identify adulteration in specialty coffees. Coffee is a valuable commodity with diverse varieties and unique specifications, particularly in Indonesia, a coffee-producing country. Ensured classification and quality of coffee throughout the supply chain are crucial for industry stakeholders, particularly in terms of flavor and aroma. This study focuses on the authenticity of coffee, particularly specialized coffee beans from microclimates that may be impacted by climate change. Advanced technologies, such as electronic nose (E-Nose) systems paired with machine learning techniques, play a significant role in achieving accurate and efficient authentication. Furthermore, the research addresses adulteration issues, such as blending different types of coffee beans, which can discourage farmers from participating in coffee cultivation. In this study, we develop an authentication method leveraging the olfactory characteristics of coffee aroma, emphasizing the unique interplay between microclimate conditions and flavor profiles. The clustering method of density-based spatial clustering of applications with noise (DBSCAN) technique is used to authenticate tropical climate specialty coffee and recognize adulterated specialty coffee beans. In our case study, adulteration in Arabica and Luwak specialty coffees was successfully detected using the DBSCAN technique combined with dimensionality reduction methods. This work has demonstrated a novel method for identifying specialty coffee authenticity and adulteration using the E-Nose system paired with machine learning techniques. [ABSTRACT FROM AUTHOR]

Introduction: The consumption of high quality coffee such as specialty Arabica and fine Robusta coffee is increasing steadily in recent years. Development of single origin coffee is an important strategy to maintain coffee quality, grade and high cupping score. Indonesia is a top exporting country for Arabica coffee with high variety of specialty coffees from different origins. Despite its long standing reputation in global coffee market, very few is known about the variability among Indonesian specialty coffees. Objectives: This study aims to observe metabolite variability among Indonesian coffees from different species and geographical origins by means of non-targeted GC/MS metabolite profiling. Methods: Sixty-four compounds were tentatively identified from 16 green and roasted coffee beans from different species and cultivation areas in Indonesia and were subjected to principal component analysis (PCA). Ten Specialty Arabica coffee and five Fine Robusta representing all important high quality coffees of Indonesia were also analyzed independently to further classify Indonesian coffee according to their origin. Results: PCA results of 16 green and roasted coffee beans of different species and cultivation areas showed that samples were separated along PC1 based on different roasting condition (green and roasted) with 52.9% variance and were separated along PC2 based on different species with 19.3% variance. The result from this study showed the clustering of samples based on three major cultivation areas in Indonesia (western, central, eastern part). Metabolites showing higher concentration in Sulawesi, Papua, Flores and Sumatra samples were glycerol, glucuno-1,5-lactone, gluconic acid and sorbitol. A clear distinction in galactitol and galactinol concentration between all samples from eastern part of Indonesia and western and middle part of Indonesia was also observed. Conclusions: Our results showed that each region (western, central and eastern part of Indonesia) has signature compounds that may serve as discriminant markers for coffee authentication. This is the first report on the classification of Indonesian specialty coffee based on their metabolic profiles and can act as a basis for marker identification for routine procedures in industry. [ABSTRACT FROM AUTHOR]