Dataset Information

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

ABSTRACT: 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-?-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.

SUBMITTER: Ismail SN

PROVIDER: S-EPMC6151416 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Publications

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

Ismail Siti Nazirah SN Maulidiani M M Akhtar Muhammad Tayyab MT Abas Faridah F Ismail Intan Safinar IS Khatib Alfi A Ali Nor Azah Mohamad NAM Shaari Khozirah K

Molecules (Basel, Switzerland) 20170925 10

Gaharu (agarwood, <i>Aquilaria malaccensis</i> 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 classific ...[more]

PMID: 28946701

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Project description:Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary 'dummy' y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q(2) and Discriminant Q(2) (DQ(2)) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q(2) and Discriminant Q(2) (DQ(2)). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ(2) and Q(2) (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0330-3) contains supplementary material, which is available to authorized users.

Dataset Information

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

Publications

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

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