Comparison of the Fruit Volatile Profiles of Five Muscadine Grape Cultivars ( Vitis rotundifolia Michx.) Using HS-SPME-GC/MS Combined With Multivariate Statistical Analysis

Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary ‘dummy’ y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q 2 and Discriminant Q 2 (DQ 2) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q 2 and Discriminant Q 2 (DQ 2). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ 2 and Q 2 (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies. Electronic supplementary material The online version of this article (doi:10.1007/s11306-011-0330-3) contains supplementary material, which is available to authorized users.

Fruits produce a range of volatile compounds that make up their characteristic aromas and contribute to their flavor. Fruit volatile compounds are mainly comprised of esters, alcohols, aldehydes, ketones, lactones, terpenoids and apocarotenoids. Many factors affect volatile composition, including the genetic makeup, degree of maturity, environmental conditions, postharvest handling and storage. There are several pathways involved in volatile biosynthesis starting from lipids, amino acids, terpenoids and carotenoids. Once the basic skeletons are produced via these pathways, the diversity of volatiles is achieved via additional modification reactions such as acylation, methylation, oxidation/reduction and cyclic ring closure. In this paper, we review the composition of fruit aroma, the characteristic aroma compounds of several representative fruits, the factors affecting aroma volatile, and the biosynthetic pathways of volatile aroma compounds. We anticipate that this review would provide some critical information for profound research on fruit aroma components and their manipulation during development and storage.