Quantification of α-Acids, β-Acids, and Phenolic Compounds in Corsican Hops Using LC-MS/MS and Metabolomic Approach Through Molecular Networks.

IF 3 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS Phytochemical Analysis Pub Date : 2025-03-01 Epub Date: 2024-11-28 DOI:10.1002/pca.3481

A Dabbous-Wach, L Majidi, J V Lorenzetti, J Paolini, J Costa

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Abstract

Introduction: Determining the bittering profile of hops is a prerequisite for their use in beer making industry. To fully grasp the brewing potential of Corsican hops, it is therefore essential to perform a precise quantification of the molecules responsible for their bittering power.

Objective: The aim of this study is highlighting of the bittering profile of Corsican hops.

Methodology: A method for the characterization and quantification of α-acids, β-acids, and phenolic compounds in Corsican hops using high performance liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has been developed. In addition to the six α- and β-acids commonly quantified in hops, seven others hop acids were identified using a new methodology based on the analysis of their fragmentation pattern in full-scan detection mode. The compounds were then quantified as humulone or lupulone equivalents. Subsequently, a metabolomic analysis of hop cones was conducted using the method of molecular networking.

Results: A total of 28 compounds were quantified. The influence of both annual climate variations and transplantation on the chemical composition of hops extractives was highlighted. The molecular network elucidation led to the identification of 34 compounds. Among them, eight were previously undescribed in hops, including one previously unknown to the literature.

Conclusion: The methodologies developed in this study have shed light on the "bittering" potential of Corsican hops which represents a significant economic opportunity for the local brewing industry potentially establishing a new, sustainable, and profitable hops market. This work focuses extensively on the phenolic compounds and the bittering acids of Corsican hops, aiming to highlight their unique organoleptic characteristics and the influence of the Corsican terroir on their chemical composition and abundance.

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利用LC-MS/MS和基于分子网络的代谢组学方法定量科西嘉啤酒花中α-酸、β-酸和酚类化合物

简介：测定啤酒花的苦味特征是啤酒花在啤酒制造工业中使用的先决条件。因此，为了充分掌握科西嘉啤酒花的酿造潜力，必须对产生苦味的分子进行精确的量化。目的：研究科西嘉啤酒花的苦味特征。方法：建立了科西嘉啤酒花中α-酸、β-酸和酚类化合物的高效液相色谱-串联质谱（LC-MS/MS）表征和定量方法。除了在啤酒花中常用的6种α-和β-酸外，还使用基于全扫描检测模式分析其破碎模式的新方法鉴定了另外7种啤酒花酸。然后将这些化合物量化为葎草酮或狼疮酮当量。随后，利用分子网络方法对啤酒花锥进行了代谢组学分析。结果：共鉴定出28个化合物。重点讨论了气候变化和移栽对啤酒花提取物化学成分的影响。分子网络解析鉴定出34个化合物。其中，八种以前在啤酒花中被描述过，包括一种以前在文献中未知的。结论：本研究中开发的方法揭示了科西嘉啤酒花的“苦味”潜力，这对当地酿酒业来说是一个重要的经济机会，有可能建立一个新的、可持续的、有利可图的啤酒花市场。本研究主要关注科西嘉啤酒花的酚类化合物和苦味酸，旨在突出其独特的感官特征以及科西嘉风土对其化学成分和丰度的影响。

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来源期刊

Phytochemical Analysis 生物-分析化学

CiteScore

6.00

自引率

6.10%

发文量

审稿时长

1.7 months

期刊介绍： Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.