A metabolomic platform to identify and quantify polyphenols in coffee and related species using liquid chromatography mass spectrometry

INTRODUCTION

Products of plant secondary metabolism, such as phenolic compounds, flavonoids, alkaloids, and hormones, play an important role in plant growth, development, stress resistance. The plant family Rubiaceae is extremely diverse and abundant in Central America and contains several economically important genera, e.g. Coffea and other medicinal plants. These are known for the production of bioactive polyphenols (e.g. caffeine and quinine), which have had major impacts on human society. The overall goal of this study was to develop a high-throughput workflow to identify and quantify plant polyphenols.

METHODS

First, a method was optimized to extract over 40 families of phytochemicals. Then, a high-throughput metabolomic platform has been developed to identify and quantify 184 polyphenols in 15 min.

RESULTS

The current metabolomics study of secondary metabolites was conducted on leaves from one commercial coffee variety and two wild species that also belong to the Rubiaceae family. Global profiling was performed using liquid chromatography high-resolution time-of-flight mass spectrometry. Features whose abundance was significantly different between coffee species were discriminated using statistical analysis and annotated using spectral databases. The identified features were validated by commercially available standards using our newly developed liquid chromatography tandem mass spectrometry method.

DISCUSSION

Caffeine, trigonelline and theobromine were highly abundant in coffee leaves, as expected. Interestingly, wild Rubiaceae leaves had a higher diversity of phytochemicals in comparison to commercial coffee: defense-related molecules, such as phenylpropanoids (e.g., cinnamic acid), the terpenoid gibberellic acid, and the monolignol sinapaldehyde were found more abundantly in wild Rubiaceae leaves.

Sub-hourly resolution quality control of rain-gauge data significantly improves regional sub-daily return level estimates

This research demonstrates how the use of high-resolution rain-gauge data for quality control (QC) significantly changes extreme rainfall estimates, with implications in scientific, meteorological and engineering applications. Current open QC algorithms only consider data at hourly or daily accumulations. Here we present the first open QC algorithm utilising sub-hourly rain-gauge data from official networks at a national, multi-decade scale. We use data from 1,301 rain-gauges in Great Britain (GB) to develop a threshold-based methodology for sub-hourly QC that can be used to complement existing, freely available hourly QC methods by developing an algorithm for sub-hourly QC that uses monthly thresholds for 1 hr, 15 min and 1 min rainfall totals. We then evaluated the effect of combining these QC procedures on rainfall distributions using graphical and statistical methods, with an emphasis on extreme value analysis. We demonstrate that the additional information in sub-hourly rainfall allows our new QC to remove spuriously large values undetected by existing methods which generate errors in extreme rainfall estimates. This results in statistically significant differences between extreme rainfall estimates for 15 min and 1 hr accumulations, with smaller differences found for 6 and 24 hr totals. We also find that extremes in the distributions of 15 min and 1 hr rainfall accumulations tend to grow more rapidly with return period than for longer accumulation periods. We observe similarities between the shape parameter populations for 15 min and 1 hr rainfall accumulations, suggesting that hourly records may be used to improve shape parameter estimates for extreme sub-hourly rainfall in GB. Sub-hourly QC moderates unrealistically large return level estimates for short-duration rainfall, with beneficial impacts on data required for the design of urban drainage infrastructure and the validation of high-resolution climate models.