Quality Attributes Prediction of Flame Seedless Grape Clusters Based on Nutritional Status Employing Multiple Linear Regression Technique

Abdel-Sattar, Mahmoud; Al-Saif, Adel M.; Aboukarima, Abdulwahed M.; Eshra, Dalia H.; Sas-Paszt, Lidia

doi:10.3390/agriculture12091303

Agriculture

2022

DOI: 10.3390/agriculture12091303

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Quality Attributes Prediction of Flame Seedless Grape Clusters Based on Nutritional Status Employing Multiple Linear Regression Technique

Mahmoud Abdel-Sattar

Adel M. Al-Saif

Abdulwahed M. Aboukarima

et al.

Abstract: Flame Seedless grape is considered one of the most popular and favorite grapes for consumers, since it ripens early, and has good cluster quality. Flame seedless grape marketing value depends upon its desirable appearance, berry, cluster size, and shape. Therefore, it is imperative that the cluster yield and quality are enhanced to ensure profitability. In this study, the prediction of physical characteristics of clusters and berries’ color attributes of Flame Seedless grape grown under different culture pract… Show more

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Cited by 4 publications

References 82 publications

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Mineral composition modulates Erwinia amylovora resistance in pear based on path analysis

Mertoğlu,

Evrenosoğlu,

Akkurt

et al. 2024

Eur J Plant Pathol

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The effects of mineral composition (N, P, K, Mg, Fe, Cu, and Zn) against fire blight caused by Erwinia amylovora, the most devastating disease of pome fruits, was investigated in pear. Due to the transport physiology of minerals, the leaf contained higher concentrations of every mineral analyzed, especially macro-minerals (N, P, K, and Mg) compared to the fruit. Minerals obtained from the leaves were not statistically correlated with resistance to fire blight, however all the minerals examined in the fruit, except for K, were found to be significant. Increased P and Mg concentrations were associated with disease resistance, while N, Zn, Fe, and Cu were associated with susceptibility. Nitrogen-causing susceptibility exerted 61% of this impact through itself directly and was sharply distinguished from other mineral compounds. Furthermore, the indirect effect of nitrogen on disease susceptibility through Cu (39%), Zn (33%), and Fe (30%) was even higher than the direct effect of these minerals (21%, 24%, and 29%, respectively). The direct effects of P (13%) and Mg (10%), which are associated with an increase in resistance, were lower than the indirect effects (19% and 29%, respectively) due to their negative correlation with nitrogen, showing that the main effect of these minerals was in suppressing the negative effects of nitrogen on susceptibility by maintaining mineral balance.

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Mineral composition modulates Erwinia amylovora resistance in pear based on path analysis

Mertoğlu,

Evrenosoğlu,

Akkurt

et al. 2024

Eur J Plant Pathol

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Analysis of Vine Nutrition and Productivity Based on Statistical Indicators

Sala,

Camen,

Herbei

et al. 2024

Horticulturae

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Different fertilization systems, methods and techniques result in differences in plant nutrition, with variations in the mineral element contents in the leaves and values of physiological, yield, and production quality indices. This results in large datasets that are often relatively difficult to interpret. Starting from these aspects, the present study analyzed data on the state of plant nutrition (N, P, K, Ca, and Mg content in leaves, in % of dry matter; Fe, Mn, Cu, and Zn content in leaves, in mg/kg of dry matter), physiological indices (chlorophyll leaf content—Chl; leaf area—LA), yield (kg/vine, t/ha), and grape berry quality (sugar and acidity content in berries) of the ‘Burgund mare’ grape cultivar in relation to organic, mineral and foliar fertilization. According to a PCA multivariate analysis, four groups of components were obtained: PC1 including five factors, with positive action in the case of K (r = 0.963), P (r = 0.770) and Cu (r = 0.752), and negative action in the case of Mg (r = −0.940) and Mn (r = −0.759); PC2 including three factors, with positive action for Chl (r = 0.938), LA (r = 0.938) and Ca (r = 0.722); PC3 including one factor (N), with negative action (r = −0.916); and PC4 including two factors, with positive action for Fe (r = 0.869) and Zn (r = 0.761). Based on the one-sample t test (parametric) and the Wilcoxon test (non-parametric) and a threshold value of α = 0.05, the differences between the index values for fertilization variants T2–T13 compared with those for the control variant (T1) were verified. Significant differences were recorded in PC1 for P, Mg and Cu; in PC2 for Chl, LA and Ca; and in PC3 for N. Insignificant differences were recorded in PC1 for K and Mn and in PC4 for Fe and Zn. In the case of the productivity elements and quality indices, significant differences were recorded.

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Predicting the Chemical Attributes of Fresh Citrus Fruits Using Artificial Neural Network and Linear Regression Models

et al. 2022

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Different chemical attributes, measured via total soluble solids (TSS), acidity, vitamin C (VitC), total sugars (Tsugar), and reducing sugars (Rsugar), were determined for three groups of citrus fruits (i.e., orange, mandarin, and acid); each group contains two cultivars. Artificial neural network (ANN) and multiple linear regression (MLR) models were developed for TSS, acidity, VitC, Tsugar, and Rsugar from fresh citrus fruits by applying different independent variables, namely the dimensions of the fruits (length (FL) and diameter (FD)), fruit weight (FW), yield/tree, and soil electrical conductivity (EC). The results of ANN application showed that a feed-forward back-propagation network type with four input neurons (Yield/tree, FW, FL, and FD) and eight neurons in one hidden layer provided successful modeling efficiencies for TSS, acidity, VitC, Tsugar, and Rsugar. The effect of the EC variable was not significant. The hyperbolic tangent of both the hidden layer and the output layer of the developed ANN model was chosen as the activation function. Based on statistical criteria, the ANN developed in this study performed better than the MLR model in predicting the chemical attributes of fresh citrus fruits. The root mean square error of TSS, acidity, VitC, Tsugar, and Rsugar ranged from 0.064 to 0.453 and 0.068 to 0.634, respectively, for the ANN model, and 0.568 to 4.768 and 0.550 to 4.830, respectively, for the MLR model using training and testing datasets. In addition, the relative errors obtained through the ANN approach provided high model predictability and feasibility. In chemical attribute modeling, the FD and FL variables exhibited high contribution ratios, resulting in a reliable predictive model. The developed ANN model generally showed a good level of accuracy when estimating the chemical attributes of fresh citrus fruit.

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Quality Attributes Prediction of Flame Seedless Grape Clusters Based on Nutritional Status Employing Multiple Linear Regression Technique

Cited by 4 publications

References 82 publications

Mineral composition modulates Erwinia amylovora resistance in pear based on path analysis

Mineral composition modulates Erwinia amylovora resistance in pear based on path analysis

Analysis of Vine Nutrition and Productivity Based on Statistical Indicators

Predicting the Chemical Attributes of Fresh Citrus Fruits Using Artificial Neural Network and Linear Regression Models

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