دورية أكاديمية
Predicting the Chemical Attributes of Fresh Citrus Fruits Using Artificial Neural Network and Linear Regression Models
| العنوان: | Predicting the Chemical Attributes of Fresh Citrus Fruits Using Artificial Neural Network and Linear Regression Models |
|---|---|
| المؤلفون: | Adel M. Al-Saif, Mahmoud Abdel-Sattar, Dalia H. Eshra, Lidia Sas-Paszt, Mohamed A. Mattar |
| المصدر: | Horticulturae, Vol 8, Iss 11, p 1016 (2022) |
| بيانات النشر: | MDPI AG, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Plant culture |
| مصطلحات موضوعية: | artificial neural network, multiple linear regression, citrus tree, fruit chemical characteristics, Plant culture, SB1-1110 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2311-7524 41259734 |
| Relation: | https://www.mdpi.com/2311-7524/8/11/1016; https://doaj.org/toc/2311-7524 |
| DOI: | 10.3390/horticulturae8111016 |
| URL الوصول: | https://doaj.org/article/102ac41259734dddab4f02c677ff7767 |
| رقم الأكسشن: | edsdoj.102ac41259734dddab4f02c677ff7767 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23117524 41259734 |
|---|---|
| DOI: | 10.3390/horticulturae8111016 |