Through metabolomics techniques like mass spectrometry, NMR spectroscopy, and gas chromatography, researchers can analyze the chemical diversity of plant metabolites. This allows identification of hundreds of compounds simultaneously and reveals patterns of metabolite responses. Metabolomics has applications in food science to analyze nutrients, assess GM crops, and detect compounds related to taste and aroma. It can also be used to characterize wines, discriminate varieties and vintages, monitor fermentation, and identify markers for authenticity and quality.

1. PRESENTATION
BY
TATHAGAT SAH
BTech Biotechnology
URN – 1901181
linkedin.com/in/tathagat-sah-b29349192

2. Emerging Applications of Metabolomics in the Field of
Agricultural Biotechnology
• The chemical machinery of plants is capable of synthesizing a wide range of molecular
structures, some of which are sources of food, fuel, polymers, fibres, adhesives, waxes,
dyes, fragrances and biologically active natural compounds.
• It is therefore essential to understand these capabilities as well as the capacities of the
production of those useful plant products, and to develop tools to make production as
efficient as possible.
• Through biotechnology, researchers can improve a crop’s resistance to diseases and
environmental stresses, or allow plants to be grown in areas where soil and climate were
previously unsuitable for agriculture. Recent developments in biotechnology are expected
to contribute to the development and production of more nutritious, tastier and healthier
food products.

3. • The most commonly utilized techniques for analysing the chemical diversity of plant
metabolome are:
• Mass spectrometry (MS) in combination with liquid chromatography (LC-MS)
• Gas chromatography (GC-MS)
• Nuclear magnetic resonance spectroscopy (NMR), together with sophisticated data
and statistical analyses.
This allows for the identification of hundreds of compounds simultaneously, and the
identification of patterns of metabolite responses upon different stimuli.
• The most important advantage of using NMR in metabolomics is that it is non-destructive
and therefore allows for the sample to be used for other analytical analyses. In addition,
NMR provides highly accurate quantification of compounds and the unambiguous
identification of targeted compounds of interest.
• A GC-MS analysis of a typical plant extract (nonvolatile derivatized) can separate up to 400
compounds including sugars and sugar alcohols, amino and organic acids, amines, sterols
and fatty acids.

5. Metabolomics of Cereals
• Metabolomics has several applications that can add significant value to crop and food
science, and therefore help deliver future food demands:
• Foods can be analysed at the molecular level using metabolomics technologies and
distinct chemical compounds can be detected and identified, which presents a
unique opportunity to detect and analyse chemical compounds that give certain
foods their taste, texture, aroma or colour, and can be used to assess their
nutritional quality.
• Other potential applications include the assessment of genetically modified (GM)
crops with the aim of developing a better understanding of potentially beneficial (or
harmful) metabolites

6. • Detailed comparisons of the bread wheat identified lines in which high levels of
phytochemicals (tocols, sterols, alkylresorcinols, phenolics and folates) and dietary fibre
components were combined with good yield, high seed weight and processing quality.
• In a study, Positive correlations were observed between total alkylresorcinols (phenolic
lipids), total tocols (important lipid-soluble anti-oxidants) and bran yield, whereas
significant negative correlations were found between total sterol content and 1000
grain weight.
• Other phenolic compounds including phenolic acids and lignans were a distinct target for
the analysis of variation due to their known health effects.
• Folate and sterols were also included in the study because grains are important sources
of these components in the diet. Metabolites such as folate were found to be
differentially accumulated, separated into winter and spring types, with total folate
content correlating positively with bran yield in the winter lines but negatively in the
spring lines.

7. Metabolomics for Safety Evaluation of Genetically
Modified (GM) Crops
• GM crops are required to undergo extensive safety and nutritional assessments prior to
marketing as human and animal feed in many countries. There has been significant
discussion about the use of metabolomics as a possible novel evaluation technique for
environmental risk assessment.
• Metabolic profiling of crops using a diverse set of analytical tools with a broad range of
detection capabilities, in combination with bioinformatics and statistical tools that can
support high-throughput data analysis, have the potential, when fully validated, to
enhance the understanding of crop composition.
• Metabolomics has been shown to be able to discriminate between adulterated and
unadulterated beverages, or accurately monitor previously undetectable batch-to-batch
quality and production site differences.

8. Metabolomics in the Production of Wine
• Wine contains a diverse range of metabolites including primary (e.g. sugars, organic acids
and amino acids) and secondary metabolites (e.g. flavonoids, hydroxycinnamates,
hydroxybenzoates and anthocyanins) present at varying concentrations.
• Chemical analysis has mainly involved the analysis of volatile compounds (sensory analysis),
flavour, wine quality, mouthfeel (descriptors such as body, warmth, astringency and length),
taste (sour, bitter and sweetness) and colour using various spectroscopic techniques such as
GC-MS, LC-MS, UV-absorption spectroscopy, colourimetric analyses, near infra-red (NIR),
Fourier Transform infra-red spectroscopy (FT-IR) and acid-based titrimetry.
• 1H NMR-based metabolomics provides useful information on understanding and
characterizing wine, including the effects of climate and fermentative behaviour or
mechanisms in wine, and grape and origin. For eg, 1H NMR in combination with multivariate
statistics is able to discriminate between wines fermented with different genera of lactic
acid bacteria Oenococcus oeni and Lactobacillus plantarum, track the alcoholic fermentation
of grape varietals and monitor the aging process in wine production.

9. • Exposure of wines to excessive amounts of oxygen causes irreparable damage, leading to
the production of off-flavours associated with aldehydes and bacterial spoilage.
• Wine micro-oxygenation is a globally used treatment and its effects were investigated by
LC-MS to analyse eight different procedural variations, marked by the addition of oxygen
and iron applied to a Sangiovese wine before and after malo-lactic fermentation.
• In a study, Various pigments and tannins were identified along with known candidate
biomarkers using multivariate analyses.
• Correlations between oxygen and metal contents and changes in primary metabolites
such as arginine, proline, tryptophan and raffinose and secondary metabolites were
identified, providing hypotheses regarding the formation and reactivity of wine pigment
production during micro-oxygenation.

10. Wine Fermentation
• NMR-based metabolomics coupled with multivariate statistical analysis has been applied
to monitor wine fermentation and evaluate the fermentative characteristics of yeast
strains and wines during aging.
• Various studies have analysed the yeast activity in the presence of varying amounts of
ethanol of a D. bruxellensis strain isolated from wine. Several ethyl esters and phenyl
ethanol, together with 4-ethyl guaiacol, were found to be produced in significantly higher
amounts in response to the increase in ethanol stress.
• Another study evaluated the impact of Saccharomyces cerevisiae metabolism in a model
wine during fermentation in a medium supplemented with minimal phenolic acids. It was
found that acetic acid, 2-phenylethanol and isoamyl acetate were the significant
discriminating compounds. S. cerevisiae had the capacity to produce chlorogenic acid in
the supplemented medium fermentation from simple precursors present in the minimal
medium.

11. Wine Characterization
• Detection of markers for wine authenticity is valuable when determining geographical
locality, vintage and grape varietal. The recent application of metabolomics to identify
potential markers to the wine industry and consumers has also been utilized in the
production of spirits.
• Polyphenols are widespread in nature and display a number of biological activities;
however, subtle structural variations and relative ratios are closely associated with
botanical origin and the plant’s environment.
• These compounds, derived from grape tannins and anthocyanin pigments, are related to
key sensory properties of red wines including colour, taste and mouth feel. The analysis of
the polyphenolic composition is therefore expected to highlight relevant biomarkers for
quality assessment and authentication in relation to grape variety, production area and
vintage.

12. • In a study, a 2D NMR-based metabolomic approach was used to profile varietals and
vintages. Riesling wines were characterized by higher levels of catechin, caftarate, valine,
proline, malate and citrate, whereas compounds such as quercetin, resveratrol, gallate,
leucine, threonine, succinate and lactate were discriminated for Mueller-Thurgau.
• The 2006 vintage was dominated by leucine, phenylalanine, citrate, malate and phenolics,
whereas valine, proline, alanine and succinate were predominantly present in the 2007
vintage.
• Not only are the polar metabolites potential markers, but the presence of volatile
aminothiols in wine can be associated with quality, price and taste. A fluorescence-specific
derivatization of thiols using UPLC-TOF/MS to screen for unknown thiol-containing
compounds is used. The screening assay consists of monitoring the UPLC-TOF/MS peaks for
unknown thiols, which decreased due to derivatization as compared to the non-derivatized
thiols.
• L-glutathione in rice wine was also reported on the basis of the available metabolomics
databases and standard matching.