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Updated: Jan 18, 2026

Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry
Published on: June 9, 2021
Vacuum-assisted headspace solid-phase microextraction in food analysis: basics and applications
Yerkanat Syrgabek1, Mereke Alimzhanova2, Shaikh Manirul Haque3
1Al-Farabi Kazakh National University, Center of Physical Chemical Methods of Research and Analysis, Almaty, 050012, Kazakhstan; Al-Farabi Kazakh National University, Faculty of Medicine and Healthcare, Almaty, 050040, Kazakhstan.
Background:
Vacuum-assisted headspace solid-phase microextraction (Vac-HSSPME) is increasingly recognized as a powerful and environmentally friendly technique for extracting volatile and semi-volatile compounds from complex food matrices. While conventional HSSPME has been widely applied, its limitations in extracting low-volatility and matrix-bound compounds have spurred interest in vacuum-assisted approaches. In this review, we provide a comprehensive summary of Vac-HSSPME basics, applications, and limitations in food analysis.
Results:
This review evaluates recent advances and applications of Vac-HSSPME across seven main food categories: (i) dairy products; (ii) edible oils and fats; (iii) honey; (iv) meat, fish, and high-protein samples; (v) fruits and vegetables; (vi) beverages and drinking water; and (vii) grains and plant-based products. Compared to conventional HSSPME, vacuum conditions consistently improve detection limits, extraction efficiency, and analyte coverage. Key operational parameters, including extraction time, temperature, sample volume, agitation, and vacuum level, are discussed in relation to their influence on performance and matrix effects. The review also highlights persistent challenges such as analyte losses during vacuum application, pronounced matrix effects in viscous samples, and the need for careful optimization. Recent developments in SPME fiber materials, automation, and coupling with high-resolution mass spectrometry are expanding the technique's applicability and analytical throughput. Moreover, the integration of machine learning offers promising avenues for optimizing conditions and predicting analyte behavior based on sample characteristics.
Significance:
Vac-HSSPME is expected to gain wider adoption in food, environmental, and pharmaceutical analysis, supporting high-throughput, sensitive, and green analytical workflows. This review provides an up-to-date perspective of Vac-HSSPME's advantages, limitations, and emerging perspectives in the context of food analysis.
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