An artificial class modelling approach to identify the most largely diffused cultivars of sweet cherry (Prunus avium L.) in Italy

Ceccarelli, D.; Antonucci, Francesca; Costa, Corrado; Talento, Carolina; Ciccoritti, Roberto

doi:10.1016/j.foodchem.2020.127515

Cited by 19 publications

(16 citation statements)

References 29 publications

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“…Moreover, Favette was discriminated in a sub-cluster probably due to the highest amount of TA (10 ± 1 mEq 100 g −1 FW) and TMA (26 ± 6 mg CGE 100 g −1 FW). The similar discrimination of cultivars in relation to the chemical characteristics was also reported by Ceccarelli et al [35] in a previous work on cherry. The second group was characterized by Cabrillo, Albion, Monterey, and San Andreas.…”

Section: Exploratory Analysis By Harvest Timesupporting

confidence: 87%

“…An aliquot of fresh fruits 10 g was used to titratable acidity (TA) determination using an automatic titration system (785 DPM Titrino, Metrohm Ldt, Herisau, Switzerland), and the results are expressed as g of malic acid (MA) 100 g −1 of fresh weight (FW). Instead, a fruit juice and digital refractometer (Refractometer 30PX, Mettler Toledo, Switzerland) were used for the TSS evaluation and data are expressed as g 100 g −1 of FW, as previously reported by Ceccarelli et al [35]. Approximately 5 g of defrosted samples were extracted with 25 mL of acidified (5 mM HCl) of methanol/water solution (70/30 v/v) for TPC and AA analyses, whereas 20 mL of methanol solution containing 0.2% of hydrochloric acid were used for the total anthocyanins content (TAC) evaluation, according to Ceccarelli et al [35].…”

Section: Methodsmentioning

confidence: 99%

“…Instead, a fruit juice and digital refractometer (Refractometer 30PX, Mettler Toledo, Switzerland) were used for the TSS evaluation and data are expressed as g 100 g −1 of FW, as previously reported by Ceccarelli et al [35]. Approximately 5 g of defrosted samples were extracted with 25 mL of acidified (5 mM HCl) of methanol/water solution (70/30 v/v) for TPC and AA analyses, whereas 20 mL of methanol solution containing 0.2% of hydrochloric acid were used for the total anthocyanins content (TAC) evaluation, according to Ceccarelli et al [35]. Briefly, the Folin-Ciocalteu method was applied for TPC determination and the results are expressed in mg of gallic acid equivalent (GAE) 100 g −1 of FW, whereas the antioxidant activity evaluation of the extracts was performed with 2,2-diphenyl-1-picrylhydrazyl (DPPH) and the data are expressed on a FW basis µg of Trolox equivalent (TE) mg −1 of FW [34].…”

Section: Methodsmentioning

confidence: 99%

“…Briefly, the Folin-Ciocalteu method was applied for TPC determination and the results are expressed in mg of gallic acid equivalent (GAE) 100 g −1 of FW, whereas the antioxidant activity evaluation of the extracts was performed with 2,2-diphenyl-1-picrylhydrazyl (DPPH) and the data are expressed on a FW basis µg of Trolox equivalent (TE) mg −1 of FW [34]. Finally, the pH-differential method [35] was used for TMA quantification and the results are expressed as mg cyanidin 3-O-glucoside equivalents (CGE) 100 g −1 of FW.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of Strawberries’ Quality Parameters Using Artificial Neural Networks

Amoriello¹,

Ciccoritti

Ferrante

2022

Agronomy

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Strawberry is a very popular fruit, appreciated for its unique flavor and many beneficial traits such as antioxidants and useful amino acids, which strongly contribute to the overall quality of the product. Indeed, the quality of fresh fruit is a fundamental aspect for consumers, and it is crucial for the success of breeding activities as well as for enhancing the competitiveness and profitability of the fruit industry. Nowadays, the entire supply chain requires simple and fast systems for quality evaluation. In this context, the pomological and chemical traits (i.e., soluble solids, firmness, titratable acidity, dry matter) as well as nutritional ones such as total phenols, total anthocyanins and antioxidant potential were evaluated and compared for seven strawberry cultivars and three harvest times. The prediction of the qualitative traits was carried out using color space coordinates (L*, a* and b*) and two statistical techniques, i.e., the multiple linear regression models (MLR) and artificial neural networks (ANNs). Unsatisfactory prediction performances were obtained for all parameters when MLR was applied. On the contrary, the good prediction of the internal quality attributes, using ANN, was observed, especially for both antioxidant activity and the total monomeric anthocyanin (R2 = 0.906, and R2 = 0.943, respectively). This study highlighted that color coordinates coupled with ANN can be successfully used to evaluate the quality of strawberry.

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Section: Exploratory Analysis By Harvest Timesupporting

confidence: 87%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Prediction of Strawberries’ Quality Parameters Using Artificial Neural Networks

Amoriello¹,

Ciccoritti

Ferrante

2022

Agronomy

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“…The global popularity of sweet cherry ( Prunus avium L.) among consumers can be attributed to its bright color, rich aroma, and high nutritional value ( Hayaloglu and Demir, 2016 ; Ceccarelli et al., 2020 ). High-density and dwarfing cultivation, in addition to mechanized orchard management, are emerging trends in sweet cherry cultivation ( Acero et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Strigolactones modulate stem length and diameter of cherry rootstocks through interaction with other hormone signaling pathways

Liu

Xu²,

Sun³

et al. 2023

Front. Plant Sci.

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Stem growth and development has considerable effects on plant architecture and yield performance. Strigolactones (SLs) modulate shoot branching and root architecture in plants. However, the molecular mechanisms underlying SLs regulate cherry rootstocks stem growth and development remain unclear. Our studies showed that the synthetic SL analog rac-GR24 and the biosynthetic inhibitor TIS108 affected stem length and diameter, aboveground weight, and chlorophyll content. The stem length of cherry rootstocks following TIS108 treatment reached a maximum value of 6.97 cm, which was much higher than that following rac-GR24 treatments at 30 days after treatment. Stem paraffin section showed that SLs affected cell size. A total of 1936, 743, and 1656 differentially expressed genes (DEGs) were observed in stems treated with 10 μM rac-GR24, 0.1 μM rac-GR24, and 10 μM TIS108, respectively. RNA-seq results highlighted several DEGs, including CKX, LOG, YUCCA, AUX, and EXP, which play vital roles in stem growth and development. UPLC-3Q-MS analysis revealed that SL analogs and inhibitors affected the levels of several hormones in the stems. The endogenous GA3 content of stems increased significantly with 0.1 μM rac-GR24 or 10 μM TIS108 treatment, which is consistent with changes in the stem length following the same treatments. This study demonstrated that SLs affected stem growth of cherry rootstocks by changing other endogenous hormone levels. These results provide a solid theoretical basis for using SLs to modulate plant height and achieve sweet cherry dwarfing and high-density cultivation.

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