TY - JOUR
T1 - Free amino acids in African indigenous vegetables
T2 - Analysis with improved hydrophilic interaction ultra-high performance liquid chromatography tandem mass spectrometry and interactive machine learning
AU - Yuan, Bo
AU - Lyu, Weiting
AU - Dinssa, Fekadu F.
AU - Simon, James E.
AU - Wu, Qingli
N1 - Funding Information: This research was supported by the Horticulture Innovation Lab with funding from the U.S. Agency for International Development (USAID EPA-A-00-09-00004), as part of the U.S. Government's global hunger and food security initiative, Feed the Future, for project titled ?Improving nutrition with African indigenous vegetables? in eastern Africa. This study was made possible by the generous support of the American people through the United States Agency for International Development (USAID). The contents are the responsibility of the Horticulture Innovation Lab and do not necessarily reflect the views of USAID or the United States Government. This work is part of the senior author's dissertation research at Department of Food Science. We also thank Agilent Instruments, New Jersey Agricultural Experiment Station and the USDA (HATCH project 12131) for also providing funds and the World Vegetable Center. We also thank Drs. John Bowman, USAID-Washington, D.C. and Beth Mitcham, UC-Davis for their support and leadership in the Horticulture Innovation Lab, and Dr. Marcos Wopereis, Director WorldVeg Center. We thank Dr. David Byrnes, Lara Brindisi and Bernie Somers for their assistance with the samples and archiving of product inventory and Zhiya Yin for assisting with the experiment. Funding Information: This research was supported by the Horticulture Innovation Lab with funding from the U.S. Agency for International Development (USAID EPA-A-00-09-00004), as part of the U.S. Government's global hunger and food security initiative, Feed the Future, for project titled “Improving nutrition with African indigenous vegetables” in eastern Africa. This study was made possible by the generous support of the American people through the United States Agency for International Development (USAID). The contents are the responsibility of the Horticulture Innovation Lab and do not necessarily reflect the views of USAID or the United States Government. This work is part of the senior author's dissertation research at Department of Food Science. We also thank Agilent Instruments, New Jersey Agricultural Experiment Station and the USDA (HATCH project 12131) for also providing funds and the World Vegetable Center. We also thank Drs. John Bowman, USAID-Washington, D.C., and Beth Mitcham, UC-Davis for their support and leadership in the Horticulture Innovation Lab, and Dr. Marcos Wopereis, Director WorldVeg Center. We thank Dr. David Byrnes, Lara Brindisi and Bernie Somers for their assistance with the samples and archiving of product inventory and Zhiya Yin for assisting with the experiment. Publisher Copyright: © 2020
PY - 2021/1/25
Y1 - 2021/1/25
N2 - A hydrophilic interaction (HILIC) ultra-high performance liquid chromatography (UHPLC) with triple quadrupole tandem mass spectrometry (MS/MS) method was developed and validated for the quantification of 21 free amino acids (AAs). Compared to published reports, our method renders collectively improved sensitivity with lower limit of quantification (LLOQ) at 0.5~42.19 ng/mL with 0.3 μL injection volume (or equivalently 0.15~12.6 pg injected on column), robust linear range from LLOQ up to 3521~5720 ng/mL (or 1056 ~ 1716 pg on column) and a high throughput with total time of 6 min per sample, as well as easier experimental setup, less maintenance and higher adaptation flexibility. Ammonium formate in the mobile phase, though commonly used in HILIC, was found unnecessary in our experimental setup, and its removal from mobile phase was key for significant improvement in sensitivity (4~74 times higher than with 5 mM ammonium formate). Addition of 10 (or up to100 mM) hydrochloric acid (HCl) in the sample diluent was crucial to keep response linearity for basic amino acids of histidine, lysine and arginine. Different HCl concentration (10~100 mM) in sample diluent also excreted an effect on detection sensitivity, and it is of importance to keep the final prepared sample and calibrators in the same HCl level. Leucine and isoleucine were distinguished using different transitions. Validated at seven concentration levels, accuracy was bound within 75~125%, matrix effect generally within 90~110%, and precision error mostly below 2.5%. Using this newly developed method, the free amino acids were then quantified in a total of 544 African indigenous vegetables (AIVs) samples from African nightshades (AN), Ethiopian mustards (EM), amaranths (AM) and spider plants (SP), comprising a total of 8 identified species and 43 accessions, cultivated and harvested in USA, Kenya and Tanzania over several years, 2013~2018. The AN, EM, AM and SP were distinguished based on free AAs profile using machine learning methods (ML) including principle component analysis, discriminant analysis, naïve Bayes, elastic net-regularized logistic regression, random forest and support vector machine, with prediction accuracy achieved at ca. 83~97% on the test set (train/test ratio at 7/3). An interactive ML platform was constructed using R Shiny at https://boyuan.shinyapps.io/AIV_Classifier/ for modeling train-test simulation and category prediction of unknown AIV sample(s). This new method presents a robust and rapid approach to quantifying free amino acids in plants for use in evaluating plants, biofortification, botanical authentication, safety, adulteration and with applications to nutrition, health and food product development.
AB - A hydrophilic interaction (HILIC) ultra-high performance liquid chromatography (UHPLC) with triple quadrupole tandem mass spectrometry (MS/MS) method was developed and validated for the quantification of 21 free amino acids (AAs). Compared to published reports, our method renders collectively improved sensitivity with lower limit of quantification (LLOQ) at 0.5~42.19 ng/mL with 0.3 μL injection volume (or equivalently 0.15~12.6 pg injected on column), robust linear range from LLOQ up to 3521~5720 ng/mL (or 1056 ~ 1716 pg on column) and a high throughput with total time of 6 min per sample, as well as easier experimental setup, less maintenance and higher adaptation flexibility. Ammonium formate in the mobile phase, though commonly used in HILIC, was found unnecessary in our experimental setup, and its removal from mobile phase was key for significant improvement in sensitivity (4~74 times higher than with 5 mM ammonium formate). Addition of 10 (or up to100 mM) hydrochloric acid (HCl) in the sample diluent was crucial to keep response linearity for basic amino acids of histidine, lysine and arginine. Different HCl concentration (10~100 mM) in sample diluent also excreted an effect on detection sensitivity, and it is of importance to keep the final prepared sample and calibrators in the same HCl level. Leucine and isoleucine were distinguished using different transitions. Validated at seven concentration levels, accuracy was bound within 75~125%, matrix effect generally within 90~110%, and precision error mostly below 2.5%. Using this newly developed method, the free amino acids were then quantified in a total of 544 African indigenous vegetables (AIVs) samples from African nightshades (AN), Ethiopian mustards (EM), amaranths (AM) and spider plants (SP), comprising a total of 8 identified species and 43 accessions, cultivated and harvested in USA, Kenya and Tanzania over several years, 2013~2018. The AN, EM, AM and SP were distinguished based on free AAs profile using machine learning methods (ML) including principle component analysis, discriminant analysis, naïve Bayes, elastic net-regularized logistic regression, random forest and support vector machine, with prediction accuracy achieved at ca. 83~97% on the test set (train/test ratio at 7/3). An interactive ML platform was constructed using R Shiny at https://boyuan.shinyapps.io/AIV_Classifier/ for modeling train-test simulation and category prediction of unknown AIV sample(s). This new method presents a robust and rapid approach to quantifying free amino acids in plants for use in evaluating plants, biofortification, botanical authentication, safety, adulteration and with applications to nutrition, health and food product development.
KW - Classification prediction
KW - Cluster analysis
KW - HILIC
KW - R Shiny App
KW - UHPLC-MS/MS
KW - Underivatized amino acid
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U2 - https://doi.org/10.1016/j.chroma.2020.461733
DO - https://doi.org/10.1016/j.chroma.2020.461733
M3 - Article
C2 - 33385745
VL - 1637
JO - Journal of Chromatography A
JF - Journal of Chromatography A
SN - 0021-9673
M1 - 461733
ER -