Yuan Bo, Lyu Weiting, Dinssa Fekadu F, Simon James E, Wu Qingli
New Use Agriculture and Natural Plant Products Program, Department of Plant Biology and Center for Agricultural Food Ecosystems (RUCAFE), The New Jersey Institute of Food, Nutrition & Health, Rutgers, The State University of New Jersey, 59 Dudley Road, New Brunswick, NJ 08901, USA; Department of Food Science, Rutgers, The State University of New Jersey, 65 Dudley Road, New Brunswick, NJ 08901, USA.
New Use Agriculture and Natural Plant Products Program, Department of Plant Biology and Center for Agricultural Food Ecosystems (RUCAFE), The New Jersey Institute of Food, Nutrition & Health, Rutgers, The State University of New Jersey, 59 Dudley Road, New Brunswick, NJ 08901, USA; Department of Medicinal Chemistry, Rutgers, The State University of New Jersey, 160 Frelinghuysen Road, Piscataway, NJ 08854, USA.
J Chromatogr A. 2021 Jan 25;1637:461733. doi: 10.1016/j.chroma.2020.461733. Epub 2020 Dec 5.
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 0.542.19 ng/mL with 0.3 μL injection volume (or equivalently 0.1512.6 pg injected on column), robust linear range from LLOQ up to 35215720 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 (474 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 (10100 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 75125%, matrix effect generally within 90110%, 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, 20132018. 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.
建立了一种亲水作用（HILIC）超高效液相色谱（UHPLC）与三重四极杆串联质谱（MS/MS）联用的方法，并对其进行了验证，用于定量分析21种游离氨基酸（AAs）。与已发表的报告相比，我们的方法总体上提高了灵敏度，在进样体积为0.3 μL时，定量下限（LLOQ）为0.542.19 ng/mL（或等效于柱上进样0.1512.6 pg），线性范围稳健，从LLOQ到35215720 ng/mL（或柱上进样10561716 pg），每个样品总分析时间为6分钟，具有高通量，并且实验设置更简便，维护需求更少，适应性更强。流动相中常用的甲酸铵在我们的实验设置中被发现是不必要的，将其从流动相中去除是灵敏度显著提高的关键（比使用5 mM甲酸铵时高474倍）。在样品稀释剂中添加10（或高达100 mM）盐酸（HCl）对于保持组氨酸、赖氨酸和精氨酸等碱性氨基酸的响应线性至关重要。样品稀释剂中不同的HCl浓度（10100 mM）也对检测灵敏度有影响，使最终制备的样品和校准品保持相同的HCl水平很重要。使用不同的跃迁来区分亮氨酸和异亮氨酸。在七个浓度水平上进行验证，准确度在75%125%之间，基质效应一般在90%110%之间，精密度误差大多低于2.5%。使用这种新开发的方法，对2013年至2018年期间在美国、肯尼亚和坦桑尼亚种植和收获的544份非洲本土蔬菜（AIVs）样品中的游离氨基酸进行了定量分析，这些样品来自非洲茄属植物（AN）、埃塞俄比亚芥菜（EM）、苋菜（AM）和吊兰（SP），共包括8个已鉴定的物种和43个种质。使用包括主成分分析、判别分析、朴素贝叶斯、弹性网正则化逻辑回归、随机森林和支持向量机在内的机器学习方法（ML），根据游离氨基酸谱区分AN、EM、AM和SP，在测试集上的预测准确率约为83%~97%（训练/测试比例为7/3）。使用R Shiny在https://boyuan.shinyapps.io/AIV_Classifier/构建了一个交互式ML平台，用于对未知AIV样品进行建模训练-测试模拟和类别预测。这种新方法为植物中游离氨基酸的定量提供了一种稳健且快速的方法，可用于评估植物、生物强化、植物鉴定、安全性、掺假情况，并应用于营养、健康和食品产品开发。
