ABSTRACT
Research on Agarwood Essential Oils (AEO) has undergone dynamic evolution, propelled by its diverse applications in industries such as perfumery, pharmaceuticals, and alternative medicine. The aromatic richness and therapeutic potential of these essential oils have sparked a surge in research interest. Despite extensive exploration, there is a need for a comprehensive analysis of trends, patterns, and the impact of AEO research to provide insights for future studies and applications.This work presents a meticulously curated dataset encompassing the last five years of Agarwood Essential Oil (AEO) research trends. Sourced from two reputable scholarly databases, namely Web of Science and Scopus, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, analyzing the data using Biblioshiny, and spanning the period from 2019 to 2023, the dataset is designed to facilitate a comprehensive understanding of the evolving landscape of AEO studies. It covers a wide array of parameters, including authorship, subject areas, citations, source titles, wordcloud, and keywords. This dataset is made available to researchers, institutions, and decision-makers to provide insights into the academic debates on agarwood oil studies, allowing for a nuanced understanding of the progression of scholarly endeavors within the field. The dataset aims to serve as a valuable resource for researchers, policymakers, and industry stakeholders interested in the multifaceted applications of essential oils. The structured and comprehensive nature of the dataset makes it a valuable asset for exploring historical trends, identifying key contributors, and fostering collaborative initiatives within the AEO research domain.
ABSTRACT
Aquilaria oil, specifically agarwood oil, is esteemed for its unique fragrance and potential therapeutic qualities, primarily attributed to the presence of significant chemical compounds. These compounds play a vital role in shaping the quality and attributes of Aquilaria oil. The distinct aroma, characterized by intricate, woody, and multifaceted notes, originates directly from specific sesquiterpenes, with notable contributors like agarospirol defining this aromatic profile. The richness and complexity of the oil's scent are closely linked to the concentration and variety of noteworthy compounds within it. Oils containing a diverse range of sesquiterpenes are often considered superior, providing a more refined olfactory experience. This dataset presents a statistical analysis of the chemical compounds present in agarwood oil obtained through the hydrodistillation method from three distinct Aquilaria (A.) species: A. crassna, A. malaccensis, and A. subintegra. The analysis of these chemical compounds utilized Gas Chromatography-Mass Spectrometer (GC-MS) coupled with Gas Chromatography - Flame Ionization Detector (GC-FID). This study's data is crucial for highlighting compounds that contribute to the significance of agarwood oil as a valuable and versatile natural resource. This significance is emphasized by the oil's diverse applications and distinctive chemical composition.
ABSTRACT
Exported fresh intact pineapples must fulfill the minimum internal quality requirement of 12 degree brix. Even though near-infrared (NIR) spectroscopic approaches are promising to non-destructively and rapidly assess the internal quality of intact pineapples, these approaches involve expensive and complex NIR spectroscopic instrumentation. Thus, this research evaluates the performance of a proposed pre-dispersive NIR light sensing approach in non-destructively classifying the Brix of pineapples using K-fold cross-validation, holdout validation, and sensitive analysis. First, the proposed pre-dispersive NIR sensing device that consisted of a light sensing element and five NIR light emitting diodes with peak wavelengths of 780, 850, 870, 910, and 940 nm, respectively, was developed. After that, the diffuse reflectance NIR light of intact pineapples was non-destructively acquired using the developed NIR sensing device before their Brix values were conventionally measured using a digital refractometer. Next, an artificial neural network (ANN) was trained and optimized to classify the Brix values of pineapples using the acquired NIR light. The results of the sensitivity analysis showed that either one wavelength that was near to the water absorbance or chlorophyll band was redundant in the classification. The performance of the trained ANN was tested using new pineapples with the optimal classification accuracy of 80.56%. This indicates that the proposed pre-dispersive NIR light sensing approach coupled with the ANN is promising to be an alternative to non-destructively classifying the internal quality of fruits.
