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

A high performance thin layer chromatography (HPTLC) method for the quality assessment of agarwood (Aquilaria malaccensis) oil from Northeast India

The high-value agarwood oil, largely used in perfumery is generally graded by the traditional method of sensorial assessment. The compositional complexity and variation made its quality control challenging. Besides, non-volatile contaminants and adulterants are the bottlenecks in gas-chromatographic detection. Herein, a HPTLC based technique was developed for the quality assessment of agarwood oil from Northeast India. A 'marker band' (anisylacetone and oxygenated sesquiterpene rich) on HPTLC profile, containing major peaks of the oil and characteristic agarwood aroma was quantified to assess the quality. The developed method was validated in terms of specificity, linearity, sensitivity, recovery and precision. The application of the method in test samples of three different grades indicated a positive correlation between 'marker band' quantity and oil quality. Its abundance in the superior grade oil was >50% and <20% in poor grade. It can be an efficient analytical tool for the quality assessment and grading of agarwood oil.

Solid-Phase Microextraction/Gas Chromatography-Time-of-Flight Mass Spectrometry Approach Combined with Network Pharmacology Analysis to Evaluate the Quality of Agarwood from Different Regions against Anxiety Disorder

Agarwood (Aquilaria malaccensis Lam.) is a resinous material from different geographical locations. The current evaluation of agarwood quality is usually based on its physical properties and chemical compounds, yet only a few studies have linked agarwood quality with its anxiolytic effect, as indicated by characteristic compounds. In this study, using solid-phase microextraction/gas chromatography-time-of-flight mass spectrometry (SPME/GC-TOFMS) and multivariate analysis, we found 116 significantly different compounds in agarwood samples from four locations in Southeast Asia with regard to their quality. Brunei and Nha Trang agarwood had abundant sesquiterpenoids, exhibiting notable pharmacological efficacy in relieving anxiety. Malaysian and Irian agarwood had abundant alcohols and aldehydes, qualifying them as high-quality spices. Compound-target-disease network and pathway enrichment analysis were further employed to predict 79 gene targets and 20 pathways associated with the anxiolytic effects based on the 62 sesquiterpenoids. The correlated relationships among the sesquiterpenoids and targets suggest that agarwood treats anxiety via multiple compounds acting on multiple targets. Varying levels of sesquiterpenes across agarwood groups might lead to differences in the anxiolytic effects via signaling pathways, such as neurotransmitter- and hormone-regulated pathways. Our study originally evaluates agarwood quality and its anxiolytic effect by linking the characteristic compounds to potential gene targets and pathways.

Chemometric-Guided Approaches for Profiling and Authenticating Botanical Materials

Botanical supplements with broad traditional and medicinal uses represent an area of growing importance for American health management; 25% of U.S. adults use dietary supplements daily and collectively spent over $9. 5 billion in 2019 in herbal and botanical supplements alone. To understand how natural products benefit human health and determine potential safety concerns, careful in vitro, in vivo, and clinical studies are required. However, botanicals are innately complex systems, with complicated compositions that defy many standard analytical approaches and fluctuate based upon a plethora of factors, including genetics, growth conditions, and harvesting/processing procedures. Robust studies rely upon accurate identification of the plant material, and botanicals' increasing economic and health importance demand reproducible sourcing, as well as assessment of contamination or adulteration. These quality control needs for botanical products remain a significant problem plaguing researchers in academia as well as the supplement industry, thus posing a risk to consumers and possibly rendering clinical data irreproducible and/or irrelevant. Chemometric approaches that analyze the small molecule composition of materials provide a reliable and high-throughput avenue for botanical authentication. This review emphasizes the need for consistent material and provides insight into the roles of various modern chemometric analyses in evaluating and authenticating botanicals, focusing on advanced methodologies, including targeted and untargeted metabolite analysis, as well as the role of multivariate statistical modeling and machine learning in phytochemical characterization. Furthermore, we will discuss how chemometric approaches can be integrated with orthogonal techniques to provide a more robust approach to authentication, and provide directions for future research.

Metabolite Characterization and Correlations with Antioxidant and Wound Healing Properties of Oil Palm (Elaeis guineensis Jacq.) Leaflets via 1H-NMR-Based Metabolomics Approach

Oil palm (Elaeis guineensis Jacq.) leaflets (OPLs) are one of the major agricultural by-products generated from the massive cultivation of Malaysian palm oil. This biomass is also reported to be of potential value based on its health-improving effects. By employing proton nuclear magnetic resonance (1H-NMR) spectroscopy combined with multivariate data analysis (MVDA), the metabolite profile of OPLs was characterized and correlated with their antioxidant and wound healing properties. Principal component analysis (PCA) classified four varieties of extracts, prepared using solvents ranging from polar to medium polarity, into three distinct clusters. Cumulatively, six flavonoids, eight organic acids, four carbohydrates, and an amine were identified from the solvent extracts. The more polar extracts, such as, the ethyl acetate-methanol, absolute methanol, and methanol-water, were richer in phytochemicals. Based on partial least square (PLS) analysis, the constituents in these extracts, such as (+)-catechin, (-)-epicatechin, orientin, isoorientin, vitexin, and isovitexin, were strongly correlated with the measured antioxidant activities, comprising ferric reducing antioxidant power (FRAP), 2,2-diphenyl-1-picrylhydrazyl (DPPH), and nitric oxide (NO) free radical scavenging activities, as well as with cell proliferation and migration activities. This study has provided crucial evidence on the importance of these natural antioxidant compounds on the wound healing properties of OPL.

Recent advances in the application of metabolomics for food safety control and food quality analyses

As one of the omics fields, metabolomics has unique advantages in facilitating the understanding of physiological and pathological activities in biology, physiology, pathology, and food science. In this review, based on developments in analytical chemistry tools, cheminformatics, and bioinformatics methods, we highlight the current applications of metabolomics in food safety, food authenticity and quality, and food traceability. Additionally, the combined use of metabolomics with other omics techniques for "foodomics" is comprehensively described. Finally, the latest developments and advances, practical challenges and limitations, and requirements related to the application of metabolomics are critically discussed, providing new insight into the application of metabolomics in food analysis.

Characterization of the incense ingredients of cultivated grafting Kynam by TG-FTIR and HS-GC-MS

Agarwood is a resinous wood of Aquilaria species and has been used for various applications. Burning agarwood incense is a common practice in temples and homes in Asia. Kynam is widely regarded as high-quality agarwood in the market. Recently, cultivated grafting Kynam (CGK) has emerged as a new agarwood product in the market, which greatly affects the price of high grading Kynam agarwood. In this study, the morphology, ethanol extract content, and incense chemical profile of CGK was investigated and compared with those of wild Kynam (WK) and cultivated common agarwood (CCA). The incense smoke of CGK was analyzed by thermogravimetric Fourier transform infrared spectroscopy (TG-FTIR) and headspace gas chromatography-mass spectrometry (HS-GC-MS). The results showed that the heating of most incenses occurred below 200 °C, and the mass-loss rate value of CGK was between those of WK and CCA. The HS-GC-MS analysis showed the chemical compounds of incense smoke of CGK at 40, 100, and 180 °C, corresponding to the head, middle, and tail of the heating process, respectively. The results suggested that the sesquiterpenes compounds were the major contributors to the mysterious and elegant odoriferous character of agarwood incense. However, a peak area percentage analysis revealed a significant difference in the predominant compounds between CGK and WK, especially at lower temperatures. Therefore, it is not straightforward to substitute WK with CGK. The results are helpful for the study and usage of the new cultivated grafting Kynam agarwood and the development of the agarwood incense industry.

Discovering Temporal Patterns in Longitudinal Nontargeted Metabolomics Data via Group and Nuclear Norm Regularized Multivariate Regression

Temporal associations in longitudinal nontargeted metabolomics data are generally ignored by common pattern recognition methods such as partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). To discover temporal patterns in longitudinal metabolomics, a multitask learning (MTL) method employing structural regularization was proposed. The group regularization term of the proposed MTL method enables the selection of a small number of tentative biomarkers while maintaining high prediction accuracy. Meanwhile, the nuclear norm imposed into the regression coefficient accounts for the interrelationship of the metabolomics data obtained on consecutive time points. The effectiveness of the proposed method was demonstrated by comparison study performed on a metabolomics dataset and a simulating dataset. The results showed that a compact set of tentative biomarkers charactering the whole antipyretic process of Qingkailing injection were selected with the proposed method. In addition, the nuclear norm introduced in the new method could help the group norm to improve the method’s recovery ability.

Chemical Profiles of Cultivated Agarwood Induced by Different Techniques

Agarwood is the resinous wood produced in some Aquilaria species and is highly valued for wide usages in medicine, incense, and perfume. To protect the threatened Aquilaria species, the cultivation of Aquilaria sinensis and artificial agarwood induction techniques have been effectively established in China. To evaluate the quality of agarwood induced by different techniques, patterns of chemical constituents in artificial agarwood by four methods (wounding using an axe, burning-chisel-drilling, chemical inducer, and biological inoculation) were analyzed and compared by UPLC-ESI-MS/MS and GC-EI-MS in this study. Results of GC-MS gave a panorama of chemical constituents in agarwood, including aromatic compounds, steroids, fatty acids, sesquiterpenoids, and 2-(2-phenlyethyl)-chromones (PECs). Sesquiterpenoids were dominant in agarwood induced by wounding using an axe. PEC comprised over 60% of components in agarwood produced by biological inoculation and chemical inducers. PECs were identified by UPLC-ESI-MS/MS in all artificial agarwood and the relative contents varied in different groups. Tetrahydro-2-(2-phenylethyl)-chromones (THPECs) in wounding by axes induced agarwood were lower while 2-(2-phenylethyl)-chromones (FPECs) were higher than other groups. The results showed that methods used for inducing agarwood formation in Aquilaria sinensis affect the chemical constituents of agarwood.

Chyawanprash: A Traditional Indian Bioactive Health Supplement

Chyawanprash (CP) is an Ayurvedic health supplement which is made up of a super-concentrated blend of nutrient-rich herbs and minerals. It is meant to restore drained reserves of life force (ojas) and to preserve strength, stamina, and vitality, while stalling the course of aging. Chyawanprash is formulated by processing around 50 medicinal herbs and their extracts, including the prime ingredient, Amla (Indian gooseberry), which is the world's richest source of vitamin C. Chyawanprash preparation involves preparing a decoction of herbs, followed by dried extract preparation, subsequent mixture with honey, and addition of aromatic herb powders (namely clove, cardamom, and cinnamon) as standard. The finished product has a fruit jam-like consistency, and a sweet, sour, and spicy flavor. Scientific exploration of CP is warranted to understand its therapeutic efficacy. Scattered information exploring the therapeutic potential of CP is available, and there is a need to assemble it. Thus, an effort was made to compile the scattered information from ancient Ayurvedic texts and treatises, along with ethnobotanical, ethnopharmacological, and scientifically validated literature, that highlight the role of CP in therapeutics. Citations relevant to the topic were screened.

Predictive modelling of colossal ATR-FTIR spectral data using PLS-DA: empirical differences between PLS1-DA and PLS2-DA algorithms

In response to our review paper [L. C. Lee et al., Analyst, 2018, 143, 3526-3539], we present a study that compares empirical differences between PLS1-DA and PLS2-DA algorithms in modelling a colossal ATR-FTIR spectral dataset. Over the past two decades, partial least squares-discriminant analysis (PLS-DA) has gained wide acceptance and huge popularity in the field of applied research, partly due to its dimensionality reduction capability and ability to handle multicollinear and correlated variables. To solve a K-class problem (K > 2) using PLS-DA and high-dimensional data like infrared spectra, one can construct either K one-versus-all PLS1-DA models or only one PLS2-DA model. The aim of this work is to explore empirical differences between the two PLS-DA algorithms in modeling a colossal ATR-FTIR spectral dataset. The practical task is to build a prediction model using the imbalanced, high dimensional, colossal and multi-class ATR-FTIR spectra of blue gel pen inks. Four different sub-datasets were prepared from the principal dataset by considering the raw and asymmetric least squares (AsLS) preprocessed forms: (a) Raw-global region; (b) Raw-local region; (c) AsLS-global region; and (d) AsLS-local region. A series of 50 models which includes the first 50 PLS components incrementally was constructed repeatedly using the four sub-datasets. Each model was evaluated using six different variants of v-fold cross validation, autoprediction and external testing methods. As a result, each PLS-DA algorithm was represented by a number of figures of merit. The differences between PLS1-DA and PLS2-DA algorithms were assessed using hypothesis tests with respect to model accuracy, stability and fitting. On the other hand, confusion matrices of the two PLS-DA algorithms were inspected carefully for assessment of model parsimony. Overall, both the algorithms presented satisfactory model accuracy and stability. Nonetheless, PLS1-DA models showed significantly higher accuracy rates than PLS2-DA models, whereas PLS2-DA models seem to be much more stable compared to PLS1-DA models. Eventually, PLS2-DA also proved to be less prone to overfitting and is more parsimonious than PLS1-DA. In conclusion, the relatively high accuracy of the PLS1-DA algorithm is achieved at the cost of rather low parsimony and stability, and with an increased risk of overfitting.

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

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.