Rapid screening of glycoalkaloids in Solanum scabrum and S. nigrum berries using ultra-high-performance liquid chromatography with pathway-specified in-source fragmentation tandem mass spectrometry
See original article published in Rapid Communications in Mass Spectrometry.
The safe consumption of Solanum scabrum and S. nigrum berries (SNBs) depends on a reliable and rapid chemical screen for the testing of the fruit and/or final food and industrial products for the presence and level of toxic glycoalkaloids. Such a rapid and sensitive screen could also be used by those involved in food safety and forensics, industry, research labs and those in agriculture production, breeding and food processing. Significant variation in the content and composition of glycoalkaloids across SNBs has been reported. To facilitate high-throughput targeted analysis, this work overcame the slow scan speed of a traditional triple quadruple mass spectrometry (QqQ) method by development of a pseudo-MS3 method.
In-source fragmentation functioned as a pseudo-MS or pseudo-hydrolysis to trim down the structurally diverse and complex glycosides into five types of aglycone ions, which were then analyzed using multiple reaction monitoring (MRM). Characteristic product ions were selected based on the aglycone skeleton and substitution pattern and associated fragmentation pathway.
A compact method with only 15 MRM transitions were developed for high-throughput screening of very diverse glycoalkaloids. Glycosides of the same aglycone type were readily identified in the same transition window without the need for mass spectra interpretation. Validated using solamargine, the sole available standard, the accuracy was 99.7–101.3%, the intra- and inter-day precision were, respectively, 2.5–5.0% and 8.0–9.2%, and the lower limit of detection and quantification were, respectively, 3.1 and 10.2 ng/mL (with 1 μL injection volume).
The peudo-MS3 method allowed for high-throughput targeted analysis with compact MRM transitions to address a large number of glycoalkaloids with diverse structures. This method could serve to meet the most heavy-duty demand for rapid inspection of glycoalkaloids in SNBs. This method can be adopted and used by those involved in food safety and forensics, in developing food and industrial products and in genetics and breeding.
This Git repository records the R script used for data analysis and visualization developed in the original publication. see code and output
The R code has been developed with reference to R for Data Science (2e), and the official documentation of tidyverse, and DataBrewer.co. See breakdown of modules below:
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Data visualization with ggplot2 (tutorial of the fundamentals; and data viz. gallery).
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Data wrangling with the following packages: tidyr: transform (e.g., pivoting) the dataset into tidy structure; dplyr: the basic tools to work with data frames; stringr: work with strings; regular expression: search and match a string pattern; purrr: functional programming (e.g., iterating functions across elements of columns); and tibble: work with data frames in the modern tibble structure.
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