Deuscher Zoé, Andriot Isabelle, Sémon Etienne, Repoux Marie, Preys Sébastien, Roger Jean-Michel, Boulanger Renaud, Labouré Hélène, Le Quéré Jean-Luc
Centre des Sciences du Goût et de l'Alimentation (CSGA), AgroSup Dijon, CNRS, INRA, Université Bourgogne Franche-Comté, F-21000, Dijon, France.
CIRAD, UMR 95 QUALISUD, F-34000, Montpellier, France.
J Mass Spectrom. 2019 Jan;54(1):92-119. doi: 10.1002/jms.4317.
Direct-injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e-noses) in classification tasks are briefly reviewed, with an emphasis on food-related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR-MS), and many results obtained using the powerful PTR-time of flight-MS (PTR-ToF-MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR-ToF-MS. A supervised multivariate data analysis based on partial least squares regression-discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR-MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.
直接进样质谱(DIMS)技术已发展成为无需色谱分离即可分析挥发性有机化合物(VOC)的强大方法。与化学计量学相结合,它们已被广泛应用于许多领域,以基于挥发物测定解决样品分类问题。本文简要回顾了在分类任务中大大优于传统电子鼻(e-nose)的不同DIMS方法,重点是与食品相关的应用。特别关注质子转移反应质谱(PTR-MS),并回顾了使用强大的PTR-飞行时间质谱(PTR-ToF-MS)仪器获得的许多结果。还总结并讨论了数据分析和特征选择问题。作为一个案例研究,提出了一个具有挑战性的黑巧克力分类问题,该问题先前已通过感官评价分为四个不同类别进行评估。通过PTR-ToF-MS分析了一组206个巧克力样品的VOC谱图,这些样品被分类为四个感官类别。基于偏最小二乘回归判别分析的监督多变量数据分析构建了一个具有出色预测能力的分类模型:在一个包含62个样品的测试集中,97%的样品被正确预测到感官类别中。离子的初步鉴定有助于巧克力类别的表征。使用专用方法进行变量选择确定了一些对巧克力区分很重要的挥发性化合物。其中,CovSel方法首次应用于PTR-MS数据,结果选择了10个特征,实现了良好的预测。最后,讨论了该领域的挑战和未来需求。