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

Author

Listed:
  • Mahmoud Abdel-Sattar

    (Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia
    Pomology Department, Faculty of Agriculture, El-Shatby, Alexandria University, Alexandria 21545, Egypt)

  • Adel M. Al-Saif

    (Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia)

  • Abdulwahed M. Aboukarima

    (Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia
    Agricultural Engineering Research Institute, Agricultural Research Center, Giza 12619, Egypt)

  • Dalia H. Eshra

    (Food Science and Technology Department, El-Shatby, Alexandria University, Alexandria 21545, Egypt)

  • Lidia Sas-Paszt

    (The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland)

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 practices, in particular fertilization treatments, was carried out using nutritional status concentration (leaf mineral elements, total chlorophyll content, total carotenoids content) and multiple linear regression (MLR). The method was based on the development of two indices: the first is called index 1 (%) and was formulated by combing the mineral elements of N, P, K, Ca, and Mg concentrations; and the second is called index 2 (ppm) and was formulated by combing the elements of Fe, Cu, Mn, Zn, and B concentrations in leaf petioles. The results indicated that the established MLR models can obtain variation accuracy, based on values of coefficients of determination ( R 2 ) using the test set. The R 2 values were in the range of 0.9286 to 0.9972 for cluster weight, cluster length, shoulder length, berries’ color attributes (L*, a*, b*, chroma, hue, and color index for red grapes (CIRG)). This study highlighted that during a grown season, leaf mineral elements, total chlorophyll content, and total carotenoids coupled with a MLR model can be used successfully to evaluate the physical characteristics of the cluster and berries’ color attributes of Flame seedless grape. This method is easy, fast and reliable as it retains the physical appearance of the fruits by adjusting the concentration of mineral elements, total chlorophyll content, and total carotenoids in leaves. Moreover, total chlorophyll had the greatest weight of all the predicted quality attributes.

Suggested Citation

  • Mahmoud Abdel-Sattar & Adel M. Al-Saif & Abdulwahed M. Aboukarima & Dalia H. Eshra & Lidia Sas-Paszt, 2022. "Quality Attributes Prediction of Flame Seedless Grape Clusters Based on Nutritional Status Employing Multiple Linear Regression Technique," Agriculture, MDPI, vol. 12(9), pages 1-19, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1303-:d:897398
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    References listed on IDEAS

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    1. Muhammed Yasin Taskesenlioglu & Sezai Ercisli & Muhammed Kupe & Nazan Ercisli, 2022. "History of Grape in Anatolia and Historical Sustainable Grape Production in Erzincan Agroecological Conditions in Turkey," Sustainability, MDPI, vol. 14(3), pages 1-16, January.
    2. Muhammed Kupe & Sezai Ercisli & Mojmir Baron & Jiri Sochor, 2021. "Sustainable Viticulture on Traditional ‘Baran’ Training System in Eastern Turkey," Sustainability, MDPI, vol. 13(18), pages 1-12, September.
    3. Alwosheel, Ahmad & van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 28(C), pages 167-182.
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