‘Lemonato’ is a Greek peach melting-flesh white-flesh cultivar with high nutritional value highly appreciated by the consumers. This study aimed to evaluate the effect of pre-harvest foliar calcium application on fruit quality, primary metabolite profile, antioxidant activity, total phenolic content, and phenolic profile of the ‘Lemonato’ peach, clone ‘Stamatis’. The experiment was conducted for two years, 2019 and 2020, in two commercial orchards at Kato Lehonia and Agios Vlasios regions, central Greece, where the ‘Lemonato’ clone ‘Stamatis’ is traditionally cultivated. The treatments were organic calcium (Ca), calcium–silicate in nanoparticles (Ca–Si), and calcium chloride (CaCl₂). Foliar application of the different Ca formulations, commonly used as a horticultural practice, were not effective at improving the fruit quality characteristics in this clone, which is characterized by fruit softening during ripening. The study revealed the sugars and organic acid composition and phenolic profile of the ‘Lemonato’ peach, clone ‘Stamatis’. Peach fruit quality, primary metabolites, and phenolic compounds of the two orchards showed a different response to organic Ca and Ca–Si, indicating that genetic or environmental factors may also be involved. A higher concentration of organic Ca and CaCl₂ increased the peach fruit phenolic compounds content and the total antioxidant activity, improving the fruit nutritional quality.

The current study investigates the use of a non-destructive hyperspectral imaging approach for the evaluation of kiwifruit cv. “Hayward” internal quality, focusing on physiological traits such as soluble solid concentration (SSC), dry matter (DM), firmness, and tannins, widely used as quality attributes. Regression models, including partial least squares regression (PLSR), bagged trees (BTs), and three-layered neural network (TLNN), were employed for the estimation of the above-mentioned quality attributes. Experimental procedures involving the Specim IQ hyperspectral camera utilization and software were followed for data acquisition and analysis. The effectiveness of PLSR, bagged trees, and TLNN in predicting the firmness, SSC, DM, and tannins of kiwifruit was assessed via statistical metrics, including R squared (R²) values and the root mean square error (RMSE). The obtained results indicate varying degrees of efficiency for each model in predicting kiwifruit quality parameters. The study concludes that machine learning algorithms, especially neural networks, offer substantial accuracy, surpassing traditional methods for evaluating kiwifruit quality traits. Overall, the current study highlights the potential of such non-destructive techniques in revolutionizing quality assessment during postharvest by yielding rapid and reliable predictions regarding the critical quality attributes of fruits.

Flesh firmness in sweet cherries is determined using the measurement of normalized deformation force, i.e., determining the required force for a distance equal to 5 or 10% of the diameter of the cherries per millimeter. However, a firmness method involving a defined distance is quite simple and suitable for easy applications. Hence, our study focuses on the impact of fruit physiology under various and fixed distances. To assess the firmness evaluation, two sweet cherry cultivars (Canada Giant and Regina) were selected and subjected to three different levels of compression distance equal to 1%, 5%, 10% of the fruit’s small thickness dimension along with a consistent compression distance of 0.16 mm. There was a strong correlation between panelists’ preferences and the fruit that had been subjected to both a 1% deformation force and a fixed distance of 0.16 mm within each cultivar. Physiological traits, membrane integrity, and the metabolome of the fruit in these categories were mostly unaffected by the control (0%), or 1%, deformation force, as shown by clustering and PCA analysis. The control and 1% deformation force groups showed similar patterns, contrary to those of the 5% and 10% deformation force groups. Given these considerations, a fixed distance of 0.16 mm and a minimal 1% deformation force possess the potential to be employed and implemented for monitoring the firmness of sweet cherries during postharvest preservation.

The impact of different maturity levels of peaches on fruit quality, metabolic pattern and transcriptomic alternation was studied. To achieve this, ‘Lemonato’ peaches were sampled at commercial harvest time and then were separated based on their peel ground color into immature (light green exocarp), semi-mature (partly yellow exocarp) and mature (entirely yellow exocarp). Data indicated that several key maturity parameters, such as color index, fruit firmness, chlorophyll content, dry matter, total phenol content and antioxidant activity were notably induced by the maturity transition. Consistent with this observation, increased levels of phenolic compounds, including catechin, epicatechin, procyanidin B1/B2/B4 and chlorogenic acid, were identified at the last maturity stage. Mature fruit was also characterized by an elevated abundance of various primary metabolites (e.g. sorbitol, arabinose, xylose) and decreased levels of main organic acids and putrescine. Genes predominantly associated with flavonoid biosynthesis, calcium signaling, cell wall integrity, auxin metabolism, and aquaporins and chloroplast function were remarkably affected by maturity. Several genes were also identified (e.g. EXORDIUM , PCC13-62 and SDI1 ), including transcription factors, such as IBH1, homeobox ATHB-13 and bHLH63 , undergoing major changes during fruit maturation. These results enhance understanding of on-tree metabolic and transcriptomic events that coordinate peach fruit maturation, helping to identify the optimal harvest time for best peach fruit quality. Clinical trial number Not applicable.