Fennel seeds and their aromatic oil contain ingredients which are biologically active with high nutritional value, as well as their antimicrobial and fungal effects and alleviation of some disease symptoms. The aim of this research was to determine the chemical properties of fennel seeds, their aromatic oil, effect on the growth of microbes and alleviation of respiratory disease symptoms such as cough and sore throat. Drinks of fennel seeds and their aromatic oil were prepared with different ratios and used to treat a group of individuals suffering from cough and compare them with the control group. In addition, biscuits containing different ratios of fennel seeds and their aromatic oil were prepared. The results of this study showed that fennel seeds contained a high percentage of protein, Crude fiber, carbohydrates and minerals. The results also showed that fennel seeds and its aromatic oils contained substances, which had an effect in relieving cough and sore throat. Methanolic extract from fennel seeds had antioxidant and antimicrobial activities. This study also showed that biscuits containing fennel seeds and its aromatic oil could be stored for longer periods than biscuits without fennel seeds and its aromatic oil. No change in sensory properties or microbial growth during storage was observed. Therefore, we can use it as a natural preservative. In addition, biscuits prepared containing the different percentages of fennel seeds and their aromatic oil were accepted by the panelists when they were stored. Its sensory properties were well maintained. The results also showed that drink of fennel seeds and their oil had an effective effect in the treatment of cough.

Custard apple (Annona squamosal L.) seed kernel and the extracted oil were characterized for their physicochemical properties. Crude ether extract was found to be the main component where, the seed kernels had 31.22%. Moreover, protein content was 20.01%. On the other hand, the crude fiber and total ash were 15.43 and 1.89%, respectively. Total phenolic compounds, antioxidant activity and IC 50 of CASKF were 42.02 mg GAE/ 100g, 87.55% and 22.84 µg/ml, respectively. The results indicated that CASKF is rich in content of K, P, Ca, Mg and Na. Nevertheless, very low levels of Cd and Pb were detected. The amino acid composition of the defatted CASKF indicated that glutamic, aspartic, alanine, leucine and arginine were the predominant amino acids. The total amount of essential amino acids in the defatted CASKF was 37.77 g /100g protein (-) which is higher than that reported in FAO/ WHO pattern. The dominant fatty acids of custard apple seed kernel oil were oleic (49.75%), Linoleic (22.50%), palmitic (15.06%) and stearic and (4.63%). The oil could be classified as a semi-dry oil. Total lipid fractions consisted mainly of nine classes in which triacylglycerols were the major class.

In the fresh fruit industry, identification of fruit cultivars and fruit quality is of vital importance. In the current study, nine peach cultivars (Dixon, Early Grande, Flordaprince, Flordastar, Flordaglo, Florda 834, TropicSnow, Desertred, and Swelling) were evaluated for differences in skin color, firmness, and size. Additionally, a multilayer perceptron (MLP) artificial neural network was applied for identification of the cultivars according to these attributes. The MLP was trained with an input layer including six input nodes, a single hidden layer with six hidden nodes, and an output layer with nine output nodes. A hyperbolic tangent activation function was used in the hidden layer and the cross entropy error was given because the softmax activation function was functional to the output layer. Results showed that the cross entropy error was 0.165. The peach identification process was significantly affected by the following variables in order of contribution (normalized importance): polar diameter (100%), L∗ (89.0), b∗ (88.0%), a∗ (78.5%), firmness (71.3%), and cross diameter (37.5.3%). The MLP was found to be a viable method of peach cultivar identification and classification because few identifying attributes were required and an overall classification accuracy of 100% was achieved in the testing phase. Measurements and quantitative discrimination of peach properties are provided in this research; these data may help enhance the processing efficiency and quality of processed peaches.

This investigation aimed to develop a method to predict the total soluble solids (TSS), titratable acidity, TSS/titratable acidity, vitamin C, anthocyanin, and total carotenoids contents using surface color values (L*, Hue and chroma), single fruit weight, juice volume, and sphericity percent of fresh peach fruit. Multiple regression analysis (MLR) and an artificial neural network (ANN) were employed. An ANN model was developed with six inputs and 15 neurons in the first hidden layer for the prediction of six chemical composition parameters. The results confirmed that the ANN model R2 = 974–0.998 outperformed the MLR models R2 = 0.473–0.840 using testing dataset. Moreover, sensitivity analysis revealed that the juice volume was the most dominating parameter for the prediction of titratable acidity, TSS/titratable acidity and vitamin C with corresponding contribution values of 39.97%, 50.40%, and 33.08%, respectively. In addition, sphericity percent contributed by 23.70% to anthocyanin and by 24.08% to total carotenoids. Furthermore, hue on TSS prediction was the highest compared with the other parameters, with a contribution percentage of 20.86%. Chroma contributed by different values to all variables in the range of 5.29% to 19.39%. Furthermore, fruit weight contributed by different values to all variables in the range of 16.67% to 23.48%. The ANN prediction method denotes a promising methodology to estimate targeted chemical composition levels of fresh peach fruits. The information of peach quality reported in this investigation can be used as a baseline for understanding and further examining peach fruit quality.

This study aimed to develop a method for identifying different cultivars of Indian jujube fruits ( Ziziphus mauritiana Lamk.) based on a single Indian jujube fruit color and morphological attributes using an artificial neural network (ANN) classifier. Eleven Indian jujube fruit cultivars were collected during winter of season 2020 from a local orchard located at Riyadh region, Saudi Arabia to measure their lengths, major diameters, and minor diameters. Different morphological descriptors were calculated, including the arithmetic mean diameter, the sphericity percent, and the surface area. Moreover, the color values of L*, a*, and b* of the skin of fruits were recorded. The ANN classifier was used to identify the appropriate class of Indian jujube fruit by using a combination of morphological and color descriptors. The proposed method achieved an overall identification rate of 98.39% and 97.56% in training and testing phases, respectively. In addition to color and morphological features, ANN classifier is a useful tool for identifying Indian jujube fruit cultivars and circumventing the difficulties met during fruit grading .