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 .

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.

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.

This study comprised of five different integrated fertilizers of calcium nitrate Ca(NO3)2 with ammonium sulphate (NH4)2SO4 ratios (0%:100%, 10%:90%, 20%:80%, 30%:70%, and 40%:60%) to enhance the physico-chemical properties, and the antioxidant and nutritional compounds of pomegranate fruits cv. ‘Wonderful’. The results discovered that the application of Ca(NO3)2: (NH4)2SO4 in different ratios significantly affected all measured parameters. Among integrated fertilizers, the 30%:70% combination showed an increment of 10.8% in fruit weight, 2.9% in fruit length, 11.8% in fruit volume, and 7.0% in fruit diameter. Similarly, total soluble solids, vitamin C, anthocyanin, total sugars, and reduced sugars, were also increased by 11.2%, 14.6%, 20.2%, 7.4%, and 5.2%, respectively. Likewise, values of both color variations from green to red (a*) and from blue to yellow (b*), and chroma, were also increased by 13.8%, 16.6%, and 14.4%, respectively. Moreover, the application of Ca(NO3)2:(NH4)2SO4 at a ratio of 40%:60% showed 25.1% decrease in titratable acidity, and 45.4% and 27.0% increase in maturity index and peel luminosity, respectively. Additionally, the 30%:70% combination showed an increment of 30.9% in total phenolic content, 70.5% in total tannin content, and 43.6% in total flavonoid content. Additionally, it showed 25.8% and 1.7% decrease in pH and moisture content, respectively. Moreover, P, K, Ca, Mg, Na, Fe, Mn, Zn, Cu, and Ni in fruit increased by different increments by application of the 30%:70% with an increasing range of 28% to 175%. A non-reducing sugar increase was observed at an application of Ca(NO3)2:(NH4)2SO4 at a ratio of 20%:80% by 47.0%. The findings of this study suggest that using calcium nitrate with ammonium sulphate at a ratio of 30%:70%, using the fertigation approach during the growth season, could be a safe, natural, and novel method for the pomegranate cv. ‘Wonderful’ to improve fruit quality, and its amount of antioxidants—specifically, phenolics, vitamin C, anthocyanin, and fruit minerals—with health benefits at harvest.