High-throughput metabolic phenotyping is a challenge, but it provides an alternative and comprehensive access to the rapid and accurate characterization of plants. In addition to the technical issues of obtaining quantitative data of plenty of metabolic traits from numerous samples, a suitable data processing and statistical evaluation strategy must be developed. We present a simple, robust and highly scalable strategy for the comparison of multiple chemical profiles from coffee and tea leaf extracts, based on direct-injection electrospray mass spectrometry (DIESI-MS) and hierarchical cluster analysis (HCA). More than 3500 individual Coffea canephora and Coffea arabica trees from experimental fields in Mexico were sampled and processed using this method. Our strategy permits the classification of trees according to their metabolic fingerprints and the screening for families with desired characteristics, such as extraordinarily high or low caffeine content in their leaves.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
🌿 Green Tea Sales Analysis Dashboard I’m excited to share my latest Power BI project — a dynamic and interactive dashboard designed to analyze Green Tea sales data. This comprehensive solution offers actionable insights into key metrics such as revenue, product performance, customer behavior, and geographical distribution. With this dashboard, stakeholders can easily monitor sales trends, compare year-over-year performance, and make data-driven decisions.
🖥️ Key Dashboard Features Net Revenue & Total Bills Generated: Provides a clear view of overall financial performance.
Salesman Experience Analysis: Visualizes the average experience of sales representatives and its impact on sales.
Geographical Sales Distribution: An interactive map highlights sales performance across different regions.
Customer Type Breakdown: A detailed pie chart categorizes customers into Retail, Institutional, and Online segments.
Product Performance: A combination of treemap and bar chart visualizations showcase top-selling and underperforming products.
Revenue Trend & Discount Analysis: Year-over-year revenue and discount trends are analyzed to identify patterns and anomalies.
Date & Quarter Filters: Users can filter data using interactive controls for year, month, or quarter-based analysis.
📊 Dataset Overview The dataset used for this analysis contains essential information, including:
Sales Date
Total Sales Revenue
Product Category
Sales Volume (Tons)
Customer Type
Region & Country
Salesman Experience (Years)
🛠️ Tools Used Power BI – For data visualization and dashboard development
DAX (Data Analysis Expressions) – For complex calculations and dynamic data representation
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Figure S2. A comparison of current and previous genetic maps (map 1 is our SNP genetic map, map 2 quoted from previous study). (ZIP 210362 kb)
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High-throughput metabolic phenotyping is a challenge, but it provides an alternative and comprehensive access to the rapid and accurate characterization of plants. In addition to the technical issues of obtaining quantitative data of plenty of metabolic traits from numerous samples, a suitable data processing and statistical evaluation strategy must be developed. We present a simple, robust and highly scalable strategy for the comparison of multiple chemical profiles from coffee and tea leaf extracts, based on direct-injection electrospray mass spectrometry (DIESI-MS) and hierarchical cluster analysis (HCA). More than 3500 individual Coffea canephora and Coffea arabica trees from experimental fields in Mexico were sampled and processed using this method. Our strategy permits the classification of trees according to their metabolic fingerprints and the screening for families with desired characteristics, such as extraordinarily high or low caffeine content in their leaves.