Vacuum-assisted headspace solid-phase microextraction in food analysis: basics and applications
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In: Analytica Chimica Acta, Vol. 1386, 344939, 08.02.2026.
Research output: Journal contributions › Scientific review articles › Research
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TY - JOUR
T1 - Vacuum-assisted headspace solid-phase microextraction in food analysis
T2 - basics and applications
AU - Syrgabek, Yerkanat
AU - Alimzhanova, Mereke
AU - Haque, Shaikh Manirul
AU - Psillakis, Elefteria
AU - Fuente-Ballesteros, Adrián
N1 - Publisher Copyright: © 2025 Elsevier B.V.
PY - 2026/2/8
Y1 - 2026/2/8
N2 - 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.
AB - 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.
KW - Food analysis
KW - Green analytical chemistry
KW - Headspace solid-phase microextraction
KW - Sustainable sample preparation
KW - Vac-HSSPME
KW - Vacuum-assisted headspace solid-phase microextraction
KW - Chemistry
UR - https://www.scopus.com/pages/publications/105023484887
U2 - 10.1016/j.aca.2025.344939
DO - 10.1016/j.aca.2025.344939
M3 - Scientific review articles
C2 - 41545131
AN - SCOPUS:105023484887
VL - 1386
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
SN - 0003-2670
M1 - 344939
ER -
