以銅基金屬有機骨架於分散式固相萃取結合高效液相層析串聯質譜儀分析胺基酸之研究

Abstract

胺基酸廣泛參與生物體內之蛋白質合成、能量代謝及訊息傳遞等生命活動，並在醫學診斷、營養評估、食品品質控管及環境監測中扮演關鍵角色。然而，複雜樣品基質中含有的干擾物質常導致胺基酸分析面臨高背景雜訊、離子抑制及偵測靈敏度不足等問題。為克服上述挑戰，本研究合成一種陰離子銅基金屬有機骨架材料 (Cu-PyC NH4+ MOF)，其負電荷骨架源自雙去質子化之吡唑-4-羧酸酯配體，並由 NH4+陽離子平衡，應用於分散式固相萃取 (dSPE) 以陽離子交換機制選擇性吸附胺基酸，其中胺基酸的質子化銨基團 (-NH3+) 取代 NH4+ 離子。此 MOF 具備高比表面積、規則微孔結構與含氮/含氧官能基團，可與胺基酸形成氫鍵與靜電作用，提供良好的萃取選擇性與效率。實驗中進行萃取與洗脫條件之系統性優化，達成多數分析物回收率超過 80% 並具可接受的重現性，並將所得萃取液以高效液相層析串聯質譜儀 (HPLC-MS/MS) 進行分析，搭配醯胺管柱進行非衍生化分離。應用於即溶咖啡樣品，該方法展現足夠的靈敏度以偵測複雜基質中的微量胺基酸。本研究所建立之陰離子 Cu-MOF/dSPE 結合 LC-MS/MS 方法，提供一選擇性高、快速且環保的胺基酸分析平台，具應用於食品、醫療與環境等領域的實用潛力。

Amino acids play essential roles in protein biosynthesis, energy metabolism, and cellular signaling, making their accurate quantification critical in clinical, nutritional, food, and environmental analyses. However, complex sample matrices often contain interfering substances that hinder detection by causing ion suppression and elevated background noise. To overcome these challenges, this study synthesized an anionic copper-based metal-organic framework (Cu-PyC NH₄⁺ MOF), characterized by a negatively charged framework derived from doubly deprotonated pyrazole-4-carboxylate ligands and balanced by NH4+ counterions, and applied it as a dispersive solid-phase extraction (dSPE) adsorbent for the selective enrichment of amino acids via a cation-exchange mechanism, where protonated ammonium groups of zwitterionic amino acids displace NH4+ ions.The synthesized MOF exhibits high surface area, microporosity, and functionalized surfaces capable of forming hydrogen bonding and electrostatic interactions with zwitterionic amino acids. Extraction and elution conditions were systematically optimized, resulting in recoveries exceeding 80% for most analytes and acceptable reproducibility. The extracted analytes were subsequently analyzed using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) with an amide column, enabling derivatization-free separation and quantification of 15 amino acids. Applied to instant coffee samples, the method demonstrated sufficient sensitivity for trace-level detection in complex matrices.This anionic MOF-based dSPE method coupled with LC-MS/MS provides a selective, rapid, and environmentally friendly platform for amino acid analysis, with practical applicability in food, clinical, and environmental fields.