Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Adel M Al-Saif; Mahmoud Abdel-Sattar; Abdulwahed M Aboukarima; Dalia H Eshra; Krzysztof Górnik

doi:10.3390/foods11182766

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Foods. 2022 Sep 8;11(18):2766. doi: 10.3390/foods11182766.

Authors

Adel M Al-Saif¹, Mahmoud Abdel-Sattar^{1

2}, Abdulwahed M Aboukarima^{3

4}, Dalia H Eshra⁵, Krzysztof Górnik⁶

Affiliations

¹ Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia.
² Pomology Department, Faculty of Agriculture, Alexandria University, El-Shatby, Alexandria 21545, Egypt.
³ Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia.
⁴ Agricultural Engineering Research Institute, Agricultural Research Center, Nadi El-Said St. Dokki, Giza 12619, Egypt.
⁵ Food Science and Technology Department, Faculty of Agriculture, Alexandria University, Alexandria 21545, Egypt.
⁶ The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland.

Abstract

The grape is a very well-liked fruit that is valued for its distinct flavor and several health benefits, including antioxidants, anthocyanins, soluble sugars, minerals, phenolics, flavonoids, organic acids, and vitamins, which significantly improve the product's overall quality. Today's supply chain as a whole needs quick and easy methods for evaluating fruit quality. Thus, the objective of this study was to estimate the quality attributes of Flame Seedless grape berries cultivated under various agronomical management and other practices using color space coordinates (berry L*, berry a*, and berry b*) as inputs in an artificial neural network (ANN) model with the best topology of (3-20-11). Satisfactory predictions based on the R² range, which was 0.9817 to 0.9983, were obtained for physical properties (i.e., berry weight, berry length, and berry diameter as well as berry adherence strength) and chemical properties (i.e., anthocyanin, total soluble solids (TSS), TSS/titratable acidity, total sugars, titratable acidity, reducing sugars, and non-reducing sugars). Meanwhile, we also performed a contribution analysis to analyze the relative importance of CIELab colorimeter parameters of berries L*, a*, and b* to determine the main fruit quality. In terms of relative contribution, berry b* contributed relatively largely to berry weight, berry adherence strength, TSS, TSS/titratable acidity, titratable acidity, total sugars, reducing sugars, and non-reducing sugars and a* contributed relatively largely to anthocyanin, berry length, and berry diameter. The developed ANN prediction model can aid growers in enhancing the quality of Flame Seedless grape berries by selecting suitable agronomical management and other practices to avoid potential quality issues that could affect consumers of them. This research demonstrated how color space coordinates and ANN model may well be utilized to evaluate the Flame seedless grape berries' quality.

Keywords: agricultural practices; chemical properties; grape; machine learning; modeling; physical properties; quality.

Grants and funding

RSP-2021/334/This research was funded by Researchers Supporting Project (number: RSP-2021/334), King Saud University, Riyadh, Saudi Arabia