## 1 Introduction  
Variability among fruits in the analysis of volatile aroma compounds (VOCs) of non-climacteric melons is usually a problem due to the differential effect of fruit maturity, and the complex interaction between genotype and environment found for other quality attributes [1]. There is even variability among the years in which the fruits of each cultivar or breeding line are harvested because of difficulties in harvesting melons in the same stage of maturity, particularly in non-climacteric types. These visual indices, such as the skin color in contact with the soil, are affected by differences in the environment depending on the season.  
Studies of the aroma of the parental line 'Piel de sapo' have demonstrated the inter-season variability and the lower aromatic potential compared with other climacteric varieties and, to a lesser extent, compared with almost non-climacteric or hybrid isogenic lines of the same type [2; 3; 4; 5; 6; 7]. This pattern is typical of the _inodorus_ cultivars studied by other authors [8; 9]. Although certain data considered anomalous for this reference parental are usually discarded, no one methodology has been described for making the decision to remove outliers in a consistent way across years or seasons, although statistical tools, such as Grubbs'test for a single outlier based on the assumption of normality, are available [10]. However, this type of test is designed to detect the presence of only one outlier, which is why some authors have proposed tests for multiple outliers (e.g., [PERSON] and [PERSON]'s two sided and robust test), or outlier high dimensional data analysis [11; 12], the latter also applicable to fruit aromas which are composed of many individual chemical compounds.  
In previous studies we tested different multivariate and univariate statistical methods for the data analysis of VOCs and other quality traits [2; 3; 5; 6; 13], also considering \"zero\" type data, when the VOC variable should be considered consistent [5], or even the use of deconvolution to reduce this problem [6; 7]. The groups of different compound classes are of interest for the potential discrimination of physiological behavior or for quantitative trait loci (QTL) mapping of VOCs [5; 6]. In particular, principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were applied to data for unsupervised and supervised dimension reduction, respectively. The projection of data in a lower dimension space is a useful technique for identifying outlier points based on the distance between the rest of the points.  
The box-whisker plot, originally introduced by [PERSON] [14] as a tool in exploratory data analysis, has become one of the most widely used statistical graphs for the detection of univariate outliers. Outliers in a box-whisker plot are the data with distance to the box of at least 1.5 times the interquartile range (IQR) while, in the case of extreme outliers, the distance is at least three times the IQR.  
Data classified as outliers using different criteria have been considered as strong outliers. The objective of the present work was to develop a methodology for the systematic observation of strong outliers in VOC analysis of samples in an integrated manner that allows some potentially anomalous data to be excluded in the determination of fruit aromas. This is particularly critical for QTL mapping and in the determination of QTL \(\times\) environment effects because the variability could be higher than in other cases, particularly in plots in open fields for breeding purposes, and/or in fruit where the exact degree of maturity is difficult to assess due to biological variance, e.g., melon [15]. For this, the study was based on the same parental studied in two different seasons.