Feifei Wei

Roasting Process of Coffee Beans as Studied by Nuclear Magnetic Resonance: Time Course of Changes in Composition

In this paper, we report a (1)H and (13)C nuclear magnetic resonance (NMR)-based comprehensive analysis of coffee bean extracts of different degrees of roast. The roasting process of coffee bean extracts was chemically characterized using detailed signal assignment information coupled with multivariate data analysis. A total of 30 NMR-visible components of coffee bean extracts were monitored simultaneously as a function of the roasting duration. During roasting, components such as sucrose and chlorogenic acids were degraded and components such as quinic acids, N-methylpyridinium, and water-soluble polysaccharides were formed. Caffeine and myo-inositol were relatively thermally stable. Multivariate data analysis indicated that some components such as sucrose, chlorogenic acids, quinic acids, and polysaccharides could serve as chemical markers during coffee bean roasting. The present composition-based quality analysis provides an excellent holistic method and suggests useful chemical markers to control and characterize the coffee-roasting process.

13C NMR-Based Metabolomics for the Classification of Green Coffee Beans According to Variety and Origin

(13)C NMR-based metabolomics was demonstrated as a useful tool for distinguishing the species and origins of green coffee bean samples of arabica and robusta from six different geographic regions. By the application of information on (13)C signal assignment, significantly different levels of 14 metabolites of green coffee beans were identified in the classifications, including sucrose, caffeine, chlorogenic acids, choline, amino acids, organic acids, and trigonelline, as captured by multivariate analytical models. These studies demonstrate that the species and geographical origin can be quickly discriminated by evaluating the major metabolites of green coffee beans quantitatively using (13)C NMR-based metabolite profiling.

Complex mixture analysis of organic compounds in green coffee bean extract by two-dimensional NMR spectroscopy

A complex mixture analysis by one‐ and two‐dimensional nuclear magnetic resonance (NMR) spectroscopy was carried out for the first time for the identification and quantification of organic compounds in green coffee bean extract (GCBE). A combination of 1H1H DQF‐COSY, 1H13C HSQC, and 1H13C CT‐HMBC two‐dimensional sequences was used, and 16 compounds were identified. In particular, three isomers of caffeoylquinic acid were identified in the complex mixture without any separation. In addition, GCBE components were quantified by the integration of carbon signals by use of a relaxation reagent and an inverse‐gated decoupling method without a nuclear Overhauser effect. This NMR methodology provides detailed information about the kinds and amounts of GCBE components, and in our study, the chemical makeup of GCBE was clarified by the NMR results. Copyright

Metabolic Discrimination of Mango Juice from Various Cultivars by Band-Selective NMR Spectroscopy

NMR-based metabolic analysis of foods has been widely applied in food science. In this study, we performed discrimination of five different mango cultivars, Awin, Carabao, Keitt, Kent, and Nam Dok Mai, using metabolic analysis with band-selective excitation NMR spectra. A combination of unsupervised principal component analysis (PCA) with low-field region (1)H NMR spectra obtained by band-selective excitation provided a good discriminant model of the five mango cultivars. Using F(2)-selective 2D NMR spectra, we also identified various minor components in the mango juice. Signal assignment of the minor components facilitated the interpretation of the loading plot, and it was found that arginine, histidine, phenylalanine, glutamine, shikimic acid, and trigonelline were important for classification of the five mango cultivars.

Two-dimensional 1H-13C nuclear magnetic resonance (NMR)-based comprehensive analysis of roasted coffee bean extract.

Coffee was characterized by proton and carbon nuclear magnetic resonance (NMR) spectroscopy. To identify the coffee components, a detailed and approximately 90% signal assignment was carried out using various two-dimensional NMR spectra and a spiking method, in which authentic compounds were added to the roasted coffee bean extract (RCBE) sample. A total of 24 coffee components, including 5 polysaccharide units, 3 stereoisomers of chlorogenic acids, and 2 stereoisomers of quinic acids, were identified with the NMR spectra of RCBE. On the basis of the signal assignment, state analyses were further launched for the metal ion-citrate complexes and caffeine-chlorogenate complexes. On the basis of the signal integration, the coffee components were successfully quantified. This NMR methodology yielded detailed information on RCBE using only a single observation and provides a systemic approach for the analysis of other complex mixtures.

Comprehensive NMR Analysis of Compositional Changes of Black Garlic during Thermal Processing

Black garlic is a processed food product obtained by subjecting whole raw garlic to thermal processing that causes chemical reactions, such as the Maillard reaction, which change the composition of the garlic. In this paper, we report a nuclear magnetic resonance (NMR)-based comprehensive analysis of raw garlic and black garlic extracts to determine the compositional changes resulting from thermal processing. (1)H NMR spectra with a detailed signal assignment showed that 38 components were altered by thermal processing of raw garlic. For example, the contents of 11 l-amino acids increased during the first step of thermal processing over 5 days and then decreased. Multivariate data analysis revealed changes in the contents of fructose, glucose, acetic acid, formic acid, pyroglutamic acid, cycloalliin, and 5-(hydroxymethyl)furfural (5-HMF). Our results provide comprehensive information on changes in NMR-detectable components during thermal processing of whole garlic.

Pretreatment and Integrated Analysis of Spectral Data Reveal Seaweed Similarities Based on Chemical Diversity

Extracting useful information from high dimensionality and large data sets is a major challenge for data-driven approaches. The present study was aimed at developing novel integrated analytical strategies for comprehensively characterizing seaweed similarities based on chemical diversity. The chemical compositions of 107 seaweed and 2 seagrass samples were analyzed using multiple techniques, including Fourier transform infrared (FT-IR) and solid- and solution-state nuclear magnetic resonance (NMR) spectroscopy, thermogravimetry-differential thermal analysis (TG-DTA), inductively coupled plasma-optical emission spectrometry (ICP-OES), CHNS/O total elemental analysis, and isotope ratio mass spectrometry (IR-MS). The spectral data were preprocessed using non-negative matrix factorization (NMF) and NMF combined with multivariate curve resolution-alternating least-squares (MCR-ALS) methods in order to separate individual component information from the overlapping and/or broad spectral peaks. Integrated analysis of the preprocessed chemical data demonstrated distinct discrimination of differential seaweed species. Further network analysis revealed a close correlation between the heavy metal elements and characteristic components of brown algae, such as cellulose, alginic acid, and sulfated mucopolysaccharides, providing a componential basis for its metal-sorbing potential. These results suggest that this integrated analytical strategy is useful for extracting and identifying the chemical characteristics of diverse seaweeds based on large chemical data sets, particularly complicated overlapping spectral data.

siRNA-mediated knockdown of aryl hydrocarbon receptor nuclear translocator 2 affects hypoxia-inducible factor-1 regulatory signaling and metabolism in human breast cancer cells

Recent human studies found that the mRNA expression level of aryl‐hydrocarbon receptor nuclear translocator 2 (ARNT2) was positively associated with the prognosis of breast cancer. In this study, we used small interfering RNA techniques to knockdown ARNT2 expression in MCF7 human breast cancer cells, and found that an almost 40% downregulation of ARNT2 mRNA expression increased the expression of sensitive to apoptosis gene (3.36‐fold), and decreased the expression of von Hippel‐Lindau (0.27‐fold) and matrix metalloproteinase‐1 (0.35‐fold). The metabolite analysis revealed the contents of glucose, glycine, betaine, phosphocholine, pyruvate and lactate involved in the hypoxia‐inducible factor (HIF)‐1‐dependent glycolytic pathway were significantly lower in cells treated with siARNT2. Our results suggested that ARNT2 might play an important role in the modulation of HIF‐1‐regulated signaling and metabolism.

The Effect of Acyclic Retinoid on the Metabolomic Profiles of Hepatocytes and Hepatocellular Carcinoma Cells

Background/Purpose Acyclic retinoid (ACR) is a promising chemopreventive agent for hepatocellular carcinoma (HCC) that selectively inhibits the growth of HCC cells (JHH7) but not normal hepatic cells (Hc). To better understand the molecular basis of the selective anti-cancer effect of ACR, we performed nuclear magnetic resonance (NMR)-based and capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS)-based metabolome analyses in JHH7 and Hc cells after treatment with ACR. Methodology/Principal Findings NMR-based metabolomics revealed a distinct metabolomic profile of JHH7 cells at 18 h after ACR treatment but not at 4 h after ACR treatment. CE-TOFMS analysis identified 88 principal metabolites in JHH7 and Hc cells after 24 h of treatment with ethanol (EtOH) or ACR. The abundance of 71 of these metabolites was significantly different between EtOH-treated control JHH7 and Hc cells, and 49 of these metabolites were significantly down-regulated in the ACR-treated JHH7 cells compared to the EtOH-treated JHH7 cells. Of particular interest, the increase in adenosine-5′-triphosphate (ATP), the main cellular energy source, that was observed in the EtOH-treated control JHH7 cells was almost completely suppressed in the ACR-treated JHH7 cells; treatment with ACR restored ATP to the basal levels observed in both EtOH-control and ACR-treated Hc cells (0.72-fold compared to the EtOH control-treated JHH7 cells). Moreover, real-time PCR analyses revealed that ACR significantly increased the expression of pyruvate dehydrogenase kinases 4 (PDK4), a key regulator of ATP production, in JHH7 cells but not in Hc cells (3.06-fold and 1.20-fold compared to the EtOH control, respectively). Conclusions/Significance The results of the present study suggest that ACR may suppress the enhanced energy metabolism of JHH7 cells but not Hc cells; this occurs at least in part via the cancer-selective enhancement of PDK4 expression. The cancer-selective metabolic pathways identified in this study will be important targets of the anti-cancer activity of ACR.

Application of Two-Dimensional Nuclear Magnetic Resonance for Signal Enhancement by Spectral Integration Using a Large Data Set of Metabolic Mixtures.

Nuclear magnetic resonance (NMR) spectroscopy has tremendous advantages of minimal sample preparation and interconvertibility of data among different institutions; thus, large data sets are frequently acquired in metabolomics studies. Previously, we used a novel analytical strategy, named signal enhancement by spectral integration (SENSI), to overcome the low signal-to-noise ratio (S/N ratio) problem in (13)C NMR by integration of hundreds of spectra without additional measurements. In this letter, the development of a SENSI 2D method and application to >1000 2D JRES NMR spectra are described. Remarkably, the obtained SENSI 2D spectrum had an approximate 14-fold increase in the S/N ratio and 80-250 additional peaks without any additional measurements. These results suggest that SENSI 2D is a useful method for assigning weak signals and that the use of coefficient of variation values can support the assignment information and extraction of features from the population characteristics among large data sets.