## Results and Discussion  
### Biometric and Physicochemical Characterization of Sweet Cherries and Cultivar Effect Evaluation  
Biometric and physicochemical parameters of the four sweet cherry cultivars (Durona, Lapins, Summit, and Van) were evaluated, as shown in Table 1. In general, cultivar significantly influenced the 13 biometric-physicochemical characteristics studied (\(p\)-value \(<\) 0.0001), which is in-line with the literature data. Summit cherries had more desirable biometric characteristics, including greater fruit mass, length, maximum diameter, and pulp/stone mass ratio. On the contrary, Durona showed the worst characteristics (in general, Summit \(>\) Lapins \(\geq\) Van \(>\) Durona). Regarding the penetration force and CIELAB color scale, Lapins and Summit cherries had the highest value, which were significantly greater (\(p\)-value \(<\) 0.0001) than those of Van and Durona cherries. On the other hand, Van and Summit cherries had the highest TSS values and were supposedly more sweet than Lapins and Durona cherries. Finally, no significant differences were observed for the TA between the four studied cherry cultivars, thus pointing out that all fruits had a similar acidity. The biometric-physicochemical value ranges are in accordance with the wide range of literature data for dozens of sweet cherry cultivars from different geographical origins (e.g., Croatia, Italy, Greece, Turkey, Portugal, Serbia, Spain, etc.) and are grown under different agro-climatic conditions [48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58]. Furthermore, it should be noticed that this agreement was also observed with the data reported in the literature for Lapins cherries [53; 53; Van cherries [51; 56; 57; 11], and Summit cherries [52; 49; 51]. Discrimination of Cherry Cultivar Using Biometric or Physicochemical Data or a Low Level Data Fusion  
The discrimination power of the biometric and physicochemical parameters was assessed using a supervised multivariate pattern recognition method (LDA) coupled with a meta-heuristic variable selection algorithm (SA). Different LDA approaches have been reported for sweet cherry cultivar discrimination, based on physicochemical, spectroscopic, and/or chromatographic data, the success of which depended on the type of data and the number of cultivars studied [6; 9; 11]. Thus, LDA-SA models were developed using, initially, only biometric or physicochemical parameters. The study revealed that using only one type of data did not allow a satisfactory discrimination of the cultivars. The LDA-SA model, based on five biometric parameters, enabled correct classifying of 84% and 76% of the original grouped data and LOO-CV. Similarly, a LDA-SA model, based on five physicochemical parameters, showed sensitivities of 84% and 68% for the original grouped data and LOO-CV. Therefore, a low-level data fusion abstraction level technique was implemented, allowing concatenating of the two data sets without a prior preprocessing of either of them [59]. The strategy allowed developing of a LDA-SA model with a slightly improved classification performance. The model comprised three discriminant functions based on nine selected biometric-physicochemical parameters (L*, a*, TA, penetration force, length, maximum and minimum diameter, stone weight, and pulp/stone mass ratio). Sensitivities of 100% (Figure 3), 72%, and 75 \(\pm\) 15% were achieved for the original grouped data, LOO-CV, and repeated K-fold-CV, respectively. These results showed that the biometric and physicochemical parameters evaluated had a discrimination power (mainly for discriminating Lapins cherries form the others). However, other parameters should be considered, namely chemical composition (e.g., mineral profile, volatile profile, and/or phenolic compounds) and antioxidant activity, to improve the predictive classification performance. However, this strategy would require a huge experimental and technical effort, which may turn out into an expensive and time-consuming task.