[en] In the present work, the performance of the multiple-cumulative trapping headspace solid-phase
microextraction technique used in the headspace linearity range and saturated headspace was investigated and compared, with the ultimate goal of maximizing the fingerprinting information extractable using a cross-sample comparison algorithm for olive oil quality assessment. It was highlighted as the use of 0.1 g of olive oil provides comparable or even better profiling than 1.5 g at a little expense of sensitivity. However, the use of multiple-cumulative-solid-phase microextraction, along with the correct sample volume, improved not only the overall sensitivity but significantly burst the level of information for cross-sample studies.
Disciplines :
Food science Chemistry
Author, co-author :
Mascrez, Steven ; Université de Liège - ULiège > Département GxABT > Chimie des agro-biosystèmes
Purcaro, Giorgia ; Université de Liège - ULiège > Département GxABT > Chimie des agro-biosystèmes
Language :
English
Title :
Enhancement of volatile profiling using multiple-cumulative trapping solid-phase microextraction. consideration on sample volume
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Pawliszyn, J., Theory of Solid-Phase Microextraction 38 (2000), 270–278.
Górecki, T., Yu, X., Pawliszyn, J., Theory of analyte extraction by selected porous polymer SPME fibres. Analyst 124 (1999), 643–649, 10.1039/a808487d.
Gionfriddo, E., Souza-Silva, É.A., Pawliszyn, J., Headspace versus direct immersion solid phase microextraction in complex matrixes: investigation of analyte behavior in multicomponent mixtures. Anal. Chem. 87 (2015), 8448–8456, 10.1021/acs.analchem.5b01850.
Pawliszyn, J., Handbook of Solid Phase Microextraction. 2012, Elsevier Inc., London First.
Kolb, B., Ettre, L.E., Static Headspace-Gas Chromatography: Theory and Practice. 2006, Wiley-VHC, New York, 10.5860/choice.44-1529.
Stilo, F., Cordero, C., Sgorbini, B., Bicchi, C., Liberto, E., Highly informative fingerprinting of extra-virgin olive oil volatiles: the role of high concentration-capacity sampling in combination with comprehensive two-dimensional gas chromatography. Separations, 6, 2019, 34, 10.3390/separations6030034.
Sghaier, L., Vial, J., Sassiat, P., Thiebaut, D., Watiez, M., Breton, S., Rutledge, D.N., Cordella, C.B.Y., An overview of recent developments in volatile compounds analysis from edible oils: technique-oriented perspectives. Eur. J. Lipid Sci. Technol. 118 (2016), 1853–1879, 10.1002/ejlt.201500508.
Angerosa, F., Servili, M., Selvaggini, R., Taticchi, A., Esposto, S., Montedoro, G., Volatile compounds in virgin olive oil: occurrence and their relationship with the quality. J. Chromatogr., A 1054 (2004), 17–31, 10.1016/j.chroma.2004.07.093.
Purcaro, G., Cordero, C., Liberto, E., Bicchi, C., Conte, L.S., Toward a definition of blueprint of virgin olive oil by comprehensive two-dimensional gas chromatography. J. Chromatogr., A 1334 (2014), 101–111, 10.1016/j.chroma.2014.01.067.
Stilo, F., Liberto, E., Reichenbach, S.E., Tao, Q., Bicchi, C., Cordero, C., Untargeted and targeted fingerprinting of extra virgin olive oil volatiles by comprehensive two-dimensional gas chromatography with mass spectrometry: challenges in long-term studies. J. Agric. Food Chem. 67 (2019), 5289–5302, 10.1021/acs.jafc.9b01661.
Romero, I., García-González, D.L., Aparicio-Ruiz, R., Morales, M.T., Validation of SPME-GCMS method for the analysis of virgin olive oil volatiles responsible for sensory defects. Talanta 134 (2015), 394–401, 10.1016/j.talanta.2014.11.032.
Oliver-Pozo, C., Aparicio-Ruiz, R., Romero, I., García-González, D.L., Analysis of volatile markers for virgin olive oil aroma defects by SPME-GC/FID: possible sources of incorrect data. J. Agric. Food Chem. 63 (2015), 10477–10483, 10.1021/acs.jafc.5b03986.
Aparicio, R., Morales, M.T., Aparicio-Ruiz, R., Tena, N., García-González, D.L., Authenticity of olive oil: mapping and comparing official methods and promising alternatives. Food Res. Int. 54 (2013), 2025–2038, 10.1016/j.foodres.2013.07.039.
Mascrez, S., Psillakis, E., Purcaro, G., A multifaceted investigation on the effect of vacuum on the headspace solid-phase microextraction of extra-virgin olive oil. Anal. Chim. Acta 1103 (2020), 106–114, 10.1016/j.aca.2019.12.053.
Magagna, F., Valverde-Som, L., Ruíz-Samblás, C., Cuadros-Rodríguez, L., Reichenbach, S.E., Bicchi, C., Cordero, C., Combined untargeted and targeted fingerprinting with comprehensive two-dimensional chromatography for volatiles and ripening indicators in olive oil. Anal. Chim. Acta 936 (2016), 245–258, 10.1016/j.aca.2016.07.005.
Mascrez, S., Purcaro, G., Exploring multiple-cumulative solid-phase microextraction for olive oil aroma profiling. J. Separ. Sci., 2020, 10.1002/jssc.202000098 in press.
IOC. Sensory analysis of olive oil - method for the organoleptic assessment of virgin olive oil. Int. Olive Counc., 2018 http://www.internationaloliveoil.org/estaticos/view/224-testing-methods.
Dieterle, F., Ross, A., Schlotterbeck, G., Senn, H., Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in1H NMR metabonomics. Anal. Chem. 78 (2006), 4281–4290, 10.1021/ac051632c.
Massart, D., Chemometrics : a Textbook. 1988, Elsevier Science Ltd., New York.
Smolinska, A., Hauschild, A.-C., Fijten, R.R.R., Dallinga, J.W., Baumbach, J., van Schooten, F.J., Current breathomics—a review on data pre-processing techniques and machine learning in metabolomics breath analysis. J. Breath Res., 8, 2014, 027105, 10.1088/1752-7155/8/2/027105.
Mascrez, S., Psillakis, E., Purcaro, G., A multifaceted investigation on the effect of vacuum on the headspace solid-phase microextraction of extra-virgin olive oil. Anal. Chim. Acta, 2019, 10.1016/j.aca.2019.12.053.
Oliver-Pozo, C., Trypidis, D., Aparicio, R., Garciá-González, D.L., Aparicio-Ruiz, R., Implementing dynamic headspace with SPME sampling of virgin olive oil volatiles: optimization, quality analytical study, and performance testing. J. Agric. Food Chem. 67 (2019), 2086–2097, 10.1021/acs.jafc.9b00477.
Abraham, M.H., Grellier, P.L., McGill, R.A., Determination of olive oil–gas and hexadecane–gas partition coefficients, and calculation of the corresponding olive oil–water and hexadecane–water partition coefficients. J. Chem. Soc., Perkin Trans. 2 (1987), 797–803, 10.1039/P29870000797.
Abraham, M.H., Ibrahim, A., Gas to olive oil partition coefficients: a linear free energy analysis. J. Chem. Inf. Model. 46 (2006), 1735–1741, 10.1021/ci060047p.
Trujillo-Rodríguez, M.J., Pino, V., Psillakis, E., Anderson, J.L., Ayala, J.H., Yiantzi, E., Afonso, A.M., Vacuum-assisted headspace-solid phase microextraction for determining volatile free fatty acids and phenols. Investigations on the effect of pressure on competitive adsorption phenomena in a multicomponent system. Anal. Chim. Acta 962 (2017), 41–51, 10.1016/j.aca.2017.01.056.
Cerretani, L., Salvador, M.D., Bendini, A., Fregapane, G., Relationship between sensory evaluation performed by Italian and Spanish official panels and volatile and phenolic profiles of virgin olive oils. Chemosens. Percept 1 (2008), 258–267, 10.1007/s12078-008-9031-3.
Cavalli, J.F., Fernandez, X., Lizzani-Cuvelier, L., Loiseau, A.M., Comparison of static headspace, headspace solid phase microextraction, headspace sorptive extraction, and direct thermal desorption techniques on chemical composition of French olive oils. J. Agric. Food Chem. 51 (2003), 7709–7716, 10.1021/jf034834n.
Purcaro, G., Stefanuto, P.H., Franchina, F.A., Beccaria, M., Wieland-Alter, W.F., Wright, P.F., Hill, J.E., SPME-GC×GC-TOF MS fingerprint of virally-infected cell culture: sample preparation optimization and data processing evaluation. Anal. Chim. Acta 1027 (2018), 158–167, 10.1016/j.aca.2018.03.037.
Krooshof, P.W.T., Üstün, B., Postma, G.J., Buydens, L.M.C., Visualization and recovery of the (Bio)chemical interesting variables in data analysis with support vector machine classification. Anal. Chem. 82 (2010), 7000–7007, 10.1021/ac101338y.
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