Coupling Proton Transfer Reaction−Mass Spectrometry with Linear Discriminant Analysis:  a Case Study

Abstract

Proton transfer reaction-mass spectrometry (PTR-MS) measurements on single intact strawberry fruits were combined with an appropriate data analysis based on compression of spectrometric data followed by class modeling. In a first experiment 8 of 9 different strawberry varieties measured on the third to fourth day after harvest could be successfully distinguished by linear discriminant analysis (LDA) on PTR-MS spectra compressed by discriminant partial least squares (dPLS). In a second experiment two varieties were investigated as to whether different growing conditions (open field, tunnel), location, and/or harvesting time can affect the proposed classification method. Internal cross-validation gives 27 successes of 28 tests for the 9 varieties experiment and 100% for the 2 clones experiment (30 samples). For one clone, present in both experiments, the models developed for one experiment were successfully tested with the homogeneous independent data of the other with success rates of 100% (3 of 3) and 93% (14 of 15), respectively. This is an indication that the proposed combination of PTR-MS with discriminant analysis and class modeling provides a new and valuable tool for product classification in agroindustrial applications.

Butter and butter oil classification by PTR-MS

“…Proton transfer reaction mass spectrometry (PTR-MS) is a promising technique for analysis of volatile compounds and has been used to investigate different issues in food science [4][5][6], including correlation with sensory data [7][8][9]. Proton transfer reactions are used to induce chemical ionization of the vapours to be analysed.…”

In vivo monitoring of strawberry flavour release from model custards: effect of texture and oral processing

“…Even though the most successful applications of PTR-MS are probably in the monitoring of atmospheric gases and related issues, 19 there are examples of the application of PTR-MS in many other fields from medicine 20 to plant physiology 21 or monitoring of industrial processes. 22 In recent years several laboratories have used PTR-MS to investigate different issues in food science [23][24][25][26] and, in particular, nose-space analysis 16,27 and the correlation with sensory data.…”

Reflectance of botanical, production and geographical origin on the unique compositional traits of purple grape juices

“…As compared to clustering methods and PCA, supervised multivariate methods, such as partial least squares discriminant analysis (PLSDA), k-nearest neighbours (KNN), and soft independent modelling of class analogy (SIMCA) have been shown to be more efficient because they do not consider the whole variability of the data set but only the variability related to the given task (Biasioli et al, 2003). In theory, SIMCA builds a confidence limit for each class, also known as interclass distance, using PCA and then project the unclassified samples into each principal components space aiming at assigning them to the class in which they fit best or even to no class (Wold, 1976).…”

“…Overall, Brazilian juices, regardless of the production management system used to produce the grapes, presented higher total phenolic content, flavonoids, anthocyanins, proanthocyanidins, flavonols (except for myricetin), flavanols, cyanidin-3-highlighted as a suitable analytical technique for monitoring the quality of food/feed and also non-food products by measuring the volatile organic compounds (VOCs) present in the sample (Biasioli et al, 2003;Biasioli et al, 2011).…”

“…Some of the various foods and beverages that have already been investigated, with promising and positive results are wines (Spitaler et al, 2007), butter (Macatelli et al, 2009), vegetable oils (Ruiz-Samblás et al, 2012, meat products , coffee (Özdestan et al, 2013), fruits (Biasioli et al, 2003;Granitto et al, 2007) and even fruit juices (Aprea et al, 2009;van Ruth, Frasnelli, & Carbonell, 2011). The advantage of PTR-MS over GC-MS and GC-FID is that it is a non-destructive and rapid technique that does not require sample preparation, thus saving time and avoiding adulteration prior to analysis.…”