Classification and differentiation of agarwoods by using non-targeted HS-SPME-GC/MS and multivariate analysis

Hung, Cheng-Han; Lee, Chieh-Yen; Yang, Chan; Lee, Maw-Rong

doi:10.1039/c4ay01151a

Cited by 18 publications

(17 citation statements)

References 32 publications

(39 reference statements)

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“…However, analysis of chemical constituents in agarwood solvent extracts is not suitable for differentiation of agarwood because agarwood is usually used to produce incense smoke. A few research studies analyzed chemical ingredients in the incense smoke produced by heated agarwood [ 11 , 18 , 19 ]. For example, Ishihara et al [ 11 ] analyzed chemical constituents in the incense smoke that was trapped in the Tenax TA adsorbent resin, and extracted in diethyl ether for GC and GC-mass spectrometry (MS) analysis.…”

Section: Introductionmentioning

confidence: 99%

Chemical Profiles of Incense Smoke Ingredients from Agarwood by Headspace Gas Chromatography-Tandem Mass Spectrometry

Kao

Hsiang

et al. 2018

Molecules

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Agarwood, the resinous wood in the heartwood of Aquilaria trees, has been used as incense in traditional Chinese medicine for its sedative, aphrodisiac, carminative, and anti-emetic effects. Grading of agarwood is usually based on its physical properties. Therefore, it is important to develop analytic methods for judgment and grading of agarwood. Here, we created a headspace (HS) preheating system that is combined with gas chromatography-mass spectrometry (HS GC-MS) to analyze the chemical constituents in the incense smoke produced by agarwood. Incense smoke generated in the HS preheating system was injected directly to GC-MS for analysis. A total of 40 compounds were identified in the incense smoke produced by Kynam agarwood, the best agarwood in the world. About half of the compounds are aromatics and sesquiterpenes. By analyzing chemical constituents in the incense smoke produced by Vietnamese, Lao, and Cambodian varieties of agarwood, we found that butyl hexadecanoate, butyl octadecanoate, bis(2-ethylhexyl) 1,2-benzenedicarboxylate, and squalene were common in the aforementioned four varieties of agarwoods. 2-(2-Phenylethyl) chromone derivatives were identified only in the incense smoke produced by Kynam agarwood, and were the major ingredient (27.23%) in the same. In conclusion, this is the first study that analyzes chemical profiles of incense smoke produced by agarwood using HS GC-MS. Our data showed that 2-(2-phenylethyl) chromone derivatives could be used to assess quality of agarwoods. Moreover, HS GC/MS may be a useful tool for grading quality of agarwood.

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Section: Introductionmentioning

confidence: 99%

Chemical Profiles of Incense Smoke Ingredients from Agarwood by Headspace Gas Chromatography-Tandem Mass Spectrometry

Kao

Hsiang

et al. 2018

Molecules

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“…Other studies also showed that high grade gaharu contained high levels of 10-epi-γ-eudesmol, aromadendrane, β-agarofuran, α-agarofuran, γ-eudesmol, epoxybulnesene and α-guaiene [ 24 , 25 , 26 , 27 ]. Meanwhile, Hung et al [ 28 ] found that α-copaene, trans -caryophyllene, and δ-guaiene were present in the most expensive agarwood powders/extracts. Although these studies have provided useful insights into the chemical profile of gaharu, more information is needed in order to make meaningful correlations between chemical constituents and the quality of gaharu.…”

Section: Introductionmentioning

confidence: 99%

“…It combines the use of analytical measurements (e.g., FTIR, 1 H-NMR, GC-MS, LC-MS) and multivariate data analysis to classify and identify metabolites in biological samples [ 29 , 30 , 31 , 32 ]. Previously, metabolomics has been successfully used to classify different grades of agarwood powder [ 28 , 33 , 34 ] and oils [ 34 , 35 , 36 ]. The analytical tools used in these studies were mainly GC-MS [ 33 , 34 , 35 ], GC-MS coupled with solid-phase microextraction (SPME) [ 22 , 24 , 25 , 27 , 28 ] and two-dimensional GC coupled to accurate mass time-of-flight mass spectrometry (TOFMS) [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

“…Previously, metabolomics has been successfully used to classify different grades of agarwood powder [ 28 , 33 , 34 ] and oils [ 34 , 35 , 36 ]. The analytical tools used in these studies were mainly GC-MS [ 33 , 34 , 35 ], GC-MS coupled with solid-phase microextraction (SPME) [ 22 , 24 , 25 , 27 , 28 ] and two-dimensional GC coupled to accurate mass time-of-flight mass spectrometry (TOFMS) [ 36 ]. Although GC-MS can identify more metabolites in comparison to 1 H-NMR spectroscopy, the identification of sesquiterpenoids (major component in agarwood essential oil) in gaharu samples using GC-MS alone may lead to errors [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Discriminative Analysis of Different Grades of Gaharu (Aquilaria malaccensis Lamk.) via 1H-NMR-Based Metabolomics Using PLS-DA and Random Forests Classification Models

et al. 2017

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Gaharu (agarwood, Aquilaria malaccensis Lamk.) is a valuable tropical rainforest product traded internationally for its distinctive fragrance. It is not only popular as incense and in perfumery, but also favored in traditional medicine due to its sedative, carminative, cardioprotective and analgesic effects. The current study addresses the chemical differences and similarities between gaharu samples of different grades, obtained commercially, using 1H-NMR-based metabolomics. Two classification models: partial least squares-discriminant analysis (PLS-DA) and Random Forests were developed to classify the gaharu samples on the basis of their chemical constituents. The gaharu samples could be reclassified into a ‘high grade’ group (samples A, B and D), characterized by high contents of kusunol, jinkohol, and 10-epi-γ-eudesmol; an ‘intermediate grade’ group (samples C, F and G), dominated by fatty acid and vanillic acid; and a ‘low grade’ group (sample E and H), which had higher contents of aquilarone derivatives and phenylethyl chromones. The results showed that 1H- NMR-based metabolomics can be a potential method to grade the quality of gaharu samples on the basis of their chemical constituents.

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“…This approach helps highlight certain regions of the chromatogram which is important when interpreted simultaneously with the entire peak profile. TIC has been applied successfully for predicting the composition of biodiesel blends (Pierce et al, 2011), classifying edible oil type (Bagur-González et al, 2015), discriminating counterfeit medicines (Custers et al, 2014) or classifying and differentiating agarwoods (Hung et al, 2014). These studies confirmed this method as a high-throughput fingerprint analysis technique (Pierce et al, 2011), as valuable tools to discriminate between genuine and counterfeit medicines (Bagur-González et al, 2015) or as a simple, convenient and robust method to extract volatile compounds successfully for evaluating agarwoods and sandalwoods (Hung et al, 2014).…”

Section: Volatiles Analysismentioning

confidence: 51%

Genomic studies of biochemical compounds determining arabica coffee (Coffea arabica L.) quality

Tran¹

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Coffee is an important crop globally. Improving coffee quality is a primary breeding target that would benefit from understanding of trait inheritance and ancestral genetic information. However, comparatively little information exists on the genetics of quality and the relationships between wild and cultivated coffee genotypes, especially arabica coffee (Coffea arabica L.) which accounts for almost 60% of coffee production globally. The complex polyploidy genome and limited genomic information for C. arabica have impeded the progress of genetic studies. Arabica coffee is among the major organisms that do not have a reference genome. This project aimed to develop and utilise genomic resources in genetic studies of arabica coffee quality. The relationship between coffee species and the position of arabica within the Coffeeae tribe were investigated using complete chloroplast genome sequences to determine the value of these genetic resources in breeding. A draft genome sequence of arabica coffee was developed. Marker-trait associations were studied for caffeine and trigonelline, the two principal compounds known to be related to coffee quality, paving the way for developing new improved varieties with preferred levels of these compounds in the coffee beans.Chloroplast genomes of 16 coffee species were sequenced and their phylogeny constructed. Results support distinct Psilanthus and Coffea clades. It is likely that C. canephora is a hybrid that has a Psilanthus maternal genome but received much of its nuclear genome from Coffea. The maternal genomes of C. arabica and C. canephora are divergent. This result is in agreement with the fact that the chloroplast genome of arabica should be that of the maternal parent i.e. C. eugenioides. There were two species (C. humblotiana and C. tetragona) close to C. arabica and one species (P. ebracteolatus) close to C. canephora containing almost no caffeine. They could serve as important materials in arabica quality breeding and research.For the association study, 232 diverse arabica coffee accessions originating from 27 countries were harvested from the germplasm collection at CATIE (Tropical Agricultural Research and Higher Education Centre), Costa Rica. Substantial variation between genotypes was observed for bean morphology attributes. Non-volatiles including caffeine and trigonelline showed larger variation in range than was previously reported. Results of targeted analysis of 18 volatiles from 35 accessions also showed significant variation. No strong correlation was found between bean morphology and the levels of non-volatile or volatile compounds, implying that it is difficult to select for low or high non-volatile and volatile compounds based on bean physical characteristics. However, it also indicates that breeding for desirable combinations of traits (i.e. large bean size, low caffeine, high trigonelline, and favourable volatiles) is possible.ii The genome of the most popular arabica variety (K7) in Australia was sequenced. Genome assembly was performed using b...

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Classification and differentiation of agarwoods by using non-targeted HS-SPME-GC/MS and multivariate analysis

Cited by 18 publications

References 32 publications

Chemical Profiles of Incense Smoke Ingredients from Agarwood by Headspace Gas Chromatography-Tandem Mass Spectrometry

Chemical Profiles of Incense Smoke Ingredients from Agarwood by Headspace Gas Chromatography-Tandem Mass Spectrometry

Discriminative Analysis of Different Grades of Gaharu (Aquilaria malaccensis Lamk.) via 1H-NMR-Based Metabolomics Using PLS-DA and Random Forests Classification Models

Genomic studies of biochemical compounds determining arabica coffee (Coffea arabica L.) quality

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