DM is the ratio of methylated GalA to the total amount of GalA

To specifically understand the effects of different parameters on co-extraction, phenolic concentration could be a better indicator for kinetic study, as it excluded the evaporation of solution induced by high heat. Phenolic concentration was the highest in CA+85 °C group and the lowest in CA+25 °C group . Citric acid was a more effective extraction solvent compared with HCl, but the temperature was the dominating factor, like HCl, 85 °C achieved a higher equilibrium concentration than CA+55 °C . These values were lower than the results from Pan et al. , who applied ultrasound for polyphenol extraction in pomegranate peel using water and observed equilibrium concentration up to 2.8g/L. This indicated that acidic pH might interfere with the releasing of certain polyphenol components in source materials, such as phenolic acid. DSA varied from 3.09 g/g to 5.66 g/g. No significant trend was observed at different conditions. GalA content ranged from 47.78% to 83.20% and increased with higher temperature and longer extraction time. Pereira et al. applied conventional heating extraction of pomegranate peel pectin using citric acid and received similar GalA content . Citric acid-extracted pectin tended to have a higher level of GalA than the HCl-extracted one since citric acid could selectively extract pectin chains rich in homogalacturonans,growing blueberries which further released GalA . It is an important pectin property for applications in the industry.

The initial DM depended on the material intrinsic properties and DM reached an equilibrium of de-methylation at different processing conditions . As shown in the current study , the degree of methylation varied from 53.31% to 79.66%. For each condition, no significant change of DM over-extraction was observed. CA-extracted pectin had higher DM compared with the HCl-extracted ones , as HCl is a strong acid and could induce higher de-methylation. It was similar to research findings from Muñoz-Almagro et al. , who observed a lower DM in nitric acid-extracted pectin. Except for extraction with CA at 25 °C, other extractions showed the tendency of decreasing DM relative to higher extraction time and temperature, which was in agreement with Andersen et al. . Pereira et al. researched CA extraction of pectin in oven-dried pomegranate, at a temperature of 70 to 90 °C and pH 2 to 4 for 40 to 150 mins. They observed a set of DM ranging from 47.18 to 71.45% , while pectin yield and DM were negatively correlated in CA-extracted extracts from pomegranate peel. The current study demonstrated the same tendency. The percentage of GalA that is acetylated at positions O-2 and O-3 could be expressed as DA. DA is a major factor in pectin emulsifying properties. Hereby, DA ranged from 6.19% to 10.68% and slightly decreased with higher temperature and longer extraction time. The results were lower than research from Zhuang et al. , who found 12% to 15% of DA in 3 Chinese var. of pomegranate peel. Shakhmatov et al. also obtained a high-DM and highDA pectin from lyophilized pomegranate Punica granatum. However, the DA in the current study was still higher than that of some common pectin sources, including citrus and apple .

This indicated pomegranate peel had the potential to be applied as an effective emulsification agent, which was in agreement with the study from X. Yang et al., . Figure 5.10 Comparison of extraction rate reciprocal of polyphenol concentration and pectin yield from pomegranate peel over different extraction times using citric acid and HCl at different temperatures . It exhibited the second-order model for coextraction of polyphenol content and pectin yield in the linearized form. The kinetic parameters, including initial extraction rate , extraction rate constant , and equilibrium phenolic concentration , were determined by plotting t/PCt vs. extraction time . With a high coefficient of determination and great fitting of experimental data , these second-order models were sufficient to describe co-extraction kinetics. This suggested that there could be two phenomena involved in co-extraction, including the dissolution and degradation of polyphenol and pectin . Theoretically, the k parameter should increase with higher temperature as heat accelerate the mass transfer . However hereby when increasing the temperature, the extraction rate constant dropped while the equilibrium level elevated for both polyphenol concentration and pectin yield. This finding was consistent with research from Patil & Akamanchi , who extracted camptothecin from Nothapodytes nimmoniana using 76.4 to 191 Wcm−2 of ultrasound at 30 to 60 °C. Goula also extracted oil from pomegranate seeds using 130W ultrasound from 20 to 80 °C and observed a similar trend. In all these studies, the k parameter was negatively correlated to an equilibrium level. It was speculated that during ultrasound-assisted extraction, multiple processes could jointly affect the extraction rate and result in various responses. Hypothesis-driven research has led to many scientific advances, but hypotheses cannot be tested in isolation: rather, they require a framework of aggregated scientific knowledge to allow questions to be posed meaningfully. This framework is largely still lacking in microbiome studies, and the only way to create it is by discovery- and tool-driven research projects. Here we describe the value of several such projects from our own laboratories, including the American Gut Project, the Earth Microbiome Project , and the knowledge base-driven tools GNPS and Qiita. We argue that an investment of community resources in these infrastructure tasks, and in the controls and standards that underpin them, will greatly enhance the investment of hypothesis-driven research programs.

Microbiome research is making dramatic progress, with thousands of papers now published each year linking specific microbes and/or host-microbe co-metabolites to specific diseases, physiological properties, or environmental parameters. Much of this research is performed in a traditional, hypothesis-driven way, or at least presented as a rational reconstruction that fits this model, much as Darwin re-wrote much of his discovery-driven work as hypothesis driven to increase its respectability under the influence of contemporary philosophers of science such as William Whewell . However, it should be noted that hypothesis-driven science was not always so respectable — Isaac Newton famously wrote “Hypotheses non fingo”, or “I feign no hypotheses”, in an essay appended to the second edition of the Principia— so the tradition of modifying how science is framed in order to meet respectability criteria dates back at least 300years. In any case, what can be framed as a singular hypothesis suffers important limitations based on what we can measure, and what we already know.One constant in microbiome research has been that most factors that we would intuitively suspect to drive differences in the microbiome are of minor importance. For example, although long-term dietary changes have a major effect on the microbiome, short-term changes don’t. Similarly, sex has a very limited impact on microbiomes across the human body and has a much weaker effect than many other variables such as age and the time of year the sample was collected . Perhaps more surprisingly,square plant pots factors such as temperature and pH have a much smaller impact on environmental microbiomes than salinity , and even the saline vs. non-saline difference is much smaller than the host-associated vs free-living difference . Samples from different sites of the same person’s body can be more different from one another in terms of their overall microbial communities than radically different free-living microbial communities, such as soils versus oceans . Differences of this magnitude can also occur within the gut of a single person, with sufficiently large perturbation . As a consequence, it is easy to incorrectly frame hypotheses, especially when supervised ordination and classification techniques are used in experiments with many confounding variables. For example, suppose that for mouse experiments we don’t know that cage effects are important in the microbiome , then we profile the microbiomes in each of two cages of each of two different genotypes of mice. Our results are likely to be driven by which pair of cages happens to resemble each other more closely. If the variable of cage is not measured, or not tested in an unsupervised model, we might never know that our results are driven by this important confounding variable! There may be many more important confounding variables that we are not yet aware of, so longitudinal studies with meticulous metadata annotation will be crucial for defining which environmental factors matter. This is especially important in the context of clinical samples, where single data points are often collected and obtaining contextual information in retrospect is exceedingly difficult . Similarly, a frequent practice is to discard unannotated microbes or unannotated molecules, focusing on the subset of microbes or molecules that can be matched to an existing database. Because databases of both microbes and molecules are heavily biased , the entities that actually best discriminate among classes of samples may be lost in the analysis: often, only 60% of sequences and 2% of molecular features from an untargeted metabolomics experiment can be annotated by existing references . However, a rational reconstruction of why the annotatable microbes or molecules are plausible can always be developed by creative scientists looking to respond to their reviewers’ criticism that their manuscript is “too descriptive”.

An important metaphor in science and information visualization is the idea of the map, whether of real spaces or of abstract spaces. Indeed, as data volumes increase, it is frequent that the field moves from tests of hypotheses among sites, to tests of these hypotheses with replicates at each site, to spatially or temporally explicit sampling, to detailed spatial maps. This progression has already occurred in 16S rRNA amplicon-based microbiome studies over the past decade , and has increasingly been taking place in mass spectrometry-based metabolome studies over the past four years . The value of spatial maps is so self-evident that the results are often cursed by obviousness. For example, the finding that metabolomes cluster by individual, as revealed by principal coordinates analysis , is interesting . However, the finding that a given molecule such as lauryl sulphate covers one individual, but is absent from the other individual is obvious , especially when you know that individual subject A uses a stereotypically gendered product such as Nivea for Men, which is the source of the molecule . How such personal lifestyle influences the microbiome is not known; it is also not known how even some basic parameters such as, skin temperature, skin pH, amount of sebum influences the microbial communities on the skin. Similarly, the finding that samples from four individuals differ to a statistically significant extent in their levels of specific purines and that within an individual, such molecules are also non-randomly distributed, might well be an intriguing finding prompting more investigation. However, a spatial map with dense sampling of the same individuals makes it obvious that the molecule is something that is touched and consumed, and sometimes spilled, allowing one to guess that it is probably caffeine and that one person likely spends time in the ocean based on the distribution of Synechococcus spp. .However, the fact remains that for most microbes and for most molecules, we have no idea where they are in and on the human body, in natural environments, or in human-impacted environments including built environments. Just as John Snow’s map of cholera instantly led to the hypothesis that this disease was water-borne and stemmed from the Broad Street pump, reinforced by the map’s revelation that the block that drank alcohol had no incidence of disease . The power of maps is shown by the history that this visual display of disease incidences by street became the foundation for the science and practice of epidemiology. In an analogous manner, systematically collected maps of microbes and of molecules across different spatial scales will dramatically improve our ability to make useful inferences from this data. Integration of these maps with other data layers ranging from air pollution to food deserts and neighborhood walk ability, together with zoomable user interfaces , will fundamentally transform the types of questions that can be asked of microbiome and metabolomics data. The value of abstract maps, whether ordinations such as principal coordinates analysis , non-metric multidimensional scaling , t-distributed stochastic neighbor embedding , network diagrams obtained from object similarity , or from co-occurrence across samples, is also considerable. In particular, when the right data frame and metrics are chosen, the key result is often immediately obvious.