Gas Chromatography-Mass Spectrometry (GC-MS) Metabolite Profiling of Citrus limon (L.) Osbeck Juice Extract Evaluated for its Antimicrobial Activity Against Streptococcus mutans

Abed, Samer Salim; P, Kiranmayi; Imran, Khalid; Lateef, Syed Salman

doi:10.7759/cureus.33585

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…Gas Chromatography-Mass Spectrometry (GC-MS) is well recognized as a fundamental analytical technique in the field of coffee metabolomics research, offering researchers a comprehensive range of analytical instruments. The utilization of GC-MS in coffee extract analysis not only facilitates the isolation and characterization of distinct components related to the aroma properties, but it also holds a prominent position as a widely accepted baseline in the field of forensic research, namely in the domain of authentication [10][11][12]. Alongside the support of numerous commercial and publicly available databases, this adaptability also includes providing both untargeted and targeted modes for investigating phytochemical substances.…”

Section: Introductionmentioning

confidence: 99%

Headspace gas chromatography with various sample preparation and chemometric approaches to improve discrimination of wild and feeding civet coffee

Prajna,

Barea-Sepúlveda,

Calle

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Civet coffee, or kopi luwak, has attracted significant attention within the coffee industry in certain regions due to its distinct flavor characteristics that arise from the digestive processes of the civet. The ability to discriminate between wild and feeding civet coffee is of major importance in upholding the industry’s established standards of quality and transparency. This study introduces an innovative method to differentiate between these two coffee types using Headspace Gas Chromatography-Mass Spectrometry (HS-GCMS) with advanced data analysis using machine-learning techniques. This study encompasses seven samples collected from various regions, all of which were subjected to analysis in both roasted and unroasted forms. The data analysis consisted of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), which revealed clear trends that were mostly influenced by processing, indicating how roasting affects the chemical profiles of various coffee types. Further classification was conducted using Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. SVM exhibited notable accuracy at 90%, effectively discriminating between wild and feeding civet coffee, whereas RF outperformed it with a remarkable 100% accuracy. This study contributes to the field of coffee characterization by presenting a robust approach to discriminate between roasted and unroasted wild and feeding civet coffee. This tool serves as a starting step for a valuable resource for both farmers and customers, as it promotes sustainable and ethical practices while retaining the distinct flavor characteristics of this exceptional specialty coffee.

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Section: Introductionmentioning

confidence: 99%