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Potato (Solanum tuberosum L.) is a staple food widely consumed in Tunisia and represents a source of various secondary metabolites with human health benefits. However, cold storage of potato tuber affects their bioactive properties and functional quality. Despite the importance of this fact, few studies have examined the effects of extended cold storage on potato tubers quality. Therefore, the objective of the present work was to evaluate the effect of cold storage on the main quality traits of different potato genotypes grown in Tunisia. This investigation focused on four cultivated potato varieties including three commercial (Selena, Elbeidha, and Naima) and a selected clone CL27 grown under late cropping season conditions with planting at the beginning of autumn and a winter tuberization. The tubers from this trial were stored for 90 days at 8°C. The results obtained showed that quality traits of potato tubers were strongly influenced by storage at 8°C throughout 90 days. Additionally, disregarding the genotype, total carotenoids, phenolics, and flavonoids contents increased significantly following 90 days storage at 8°C. However, genotypic differences underlying the antioxidant activity in both hydrophilic and lipophilic fractions were detected suggesting their sensibility to cold storage. This study might be useful for breeders working on potato genotypes suitable for extended storage duration and for growers storing their material for extended period under low temperature. Core Ideas: The effect of storage varies according to the considered genotype.Stored tubers at 8°C for 90 days exhibited higher nutritional quality with respect to fresh samples.Appropriate storage can contribute to maintain high‐quality potato tubers. [ABSTRACT FROM AUTHOR]

Potato (Solanum tuberosum L.) is a staple food widely consumed in Tunisia and a source of nutrients beneficial for human health. The tubers are, indeed, important source of vitamins, minerals, and bioactive compounds, including carotenoids, polyphenols, and flavonoids. However, data regarding the functional quality of potato varieties grown and consumed in Tunisia are very scarce. For this reason, two field trials were conducted to focus on the effect of the growing season on the functional quality of four potato genotypes (Selena, El Beidha, Naima, and CL07F172.5): a first autumnal trial in Teboulba, Monastir (September 2015–February 2016), which is the late cropping season (LCS) and a second summer cropping season (SCS) in El ksour, El Kef region at an altitude > 1000 m (July 2015–October 2015). Total carotenoids, total phenolics, and flavonoids contents, as well as the hydrophilic radical scavenging activity and lipophilic radical scavenging activity (LRSA) were determined. The obtained results confirmed the importance of potato tubers as a source of bioactive compounds. All quality traits varied significantly among varieties and cropping seasons. Tubers grown under colder weather conditions (LCS: minimum and maximum averages temperatures and relative humidity (RH) ranging between 14.25 and 22.55°C and 25.46% and 83.07%, respectively) accumulated the highest total vitamin C content and exhibited the highest LRSA values. However, temperature and warmer growing conditions (SCS: minimum and maximum averages temperatures and RH ranging between 19.74 and 34.22°C and 35.46% and 87.51%, respectively) probably enhanced the synthesis and accumulation of total carotenoids, total phenolics, and total flavonoids, associated with higher LRSA values. Core Ideas: The development of a new summer cropping season in Tunisia is examined.The production and quality traits are similar to other potato cropping season.Highland potato production is promising and contributes to fill the existing gap. [ABSTRACT FROM AUTHOR]

The tomato as a raw material for processing is globally important and is pivotal in dietary and agronomic research due to its nutritional, economic, and health significance. This study explored the potential of machine learning (ML) for predicting tomato quality, utilizing data from 48 cultivars and 28 locations in Hungary over 5 seasons. It focused on °Brix, lycopene content, and colour (a/b ratio) using extreme gradient boosting (XGBoost) and artificial neural network (ANN) models. The results revealed that XGBoost consistently outperformed ANN, achieving high accuracy in predicting °Brix (R² = 0.98, RMSE = 0.07) and lycopene content (R² = 0.87, RMSE = 0.61), and excelling in colour prediction (a/b ratio) with a R² of 0.93 and RMSE of 0.03. ANN lagged behind particularly in colour prediction, showing a negative R² value of −0.35. Shapley additive explanation's (SHAP) summary plot analysis indicated that both models are effective in predicting °Brix and lycopene content in tomatoes, highlighting different aspects of the data. SHAP analysis highlighted the models' efficiency (especially in °Brix and lycopene predictions) and underscored the significant influence of cultivar choice and environmental factors like climate and soil. These findings emphasize the importance of selecting and fine-tuning the appropriate ML model for enhancing precision agriculture, underlining XGBoost's superiority in handling complex agronomic data for quality assessment. [ABSTRACT FROM AUTHOR]