Sweetness is one of the main drivers of consumer preference, and thus is given high priority in apple breeding programmes. Due to the complexity of sweetness evaluation, soluble solid content (SSC) is commonly used as an estimation of this trait. Nevertheless, it has been demonstrated that SSC and sweet taste are poorly correlated. Though individual sugar content may vary greatly between and within apple cultivars, no previous study has tried to investigate the relationship between the amount of individual sugars, or ratios of these, and apple sweetness. In this work, we quantified the major sugars (sucrose, glucose, fructose, xylose) and sorbitol and explored their influence on perceived sweetness in apple; we also related this to malic acid content, SSC and volatile compounds. Our data confirmed that the correlation between sweetness and SSC is weak. We found that sorbitol content correlates (similarly to SSC) with perceived sweetness better than any other single sugar or total sugar content. The single sugars show no differentiable importance in determining apple sweetness. Our predictive model based on partial least squares regression shows that after sorbitol and SSC, the most important contribution to apple sweetness is provided by several volatile compounds, mainly esters and farnesene.

A combined approach for perceptible quality profiling of apples based on sensory and instrumental techniques was developed. This work studied the correlation between sensory and instrumental data, and defined proper models for predicting sensory properties through instrumental measurements. Descriptive sensory analysis performed by a trained panel was carried out during two consecutive years, on a total of 27 apple cultivars assessed after two months postharvest storage. The 11 attributes included in the sensory vocabulary discriminated among the different apple cultivars by describing their sensory properties. Simultaneous instrumental profiling including colorimeter, texture analyser (measuring mechanical and acoustic parameters) and basic chemical measurements, provided a description of the cultivars consistent with the sensory profiles. Regression analyses showed effective predictive models for all sensory attributes (Q 2 ≥ 0.8), except for green flesh colour and astringency, that were less effective (Q 2 = 0.5 for both). Interesting relationships were found between taste perception and flesh appearance, and the combination of chemical and colorimeter data led to the development of an effective prediction model for sweet taste. Thus, the innovative sensory-instrumental tool described here can be proposed for the reliable prediction of apple sensory properties.

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