A Novel Classification Model of Date Fruit Dataset Using Deep Transfer Learning

Date fruits are the most common fruit in the Middle East and North Africa. There are a wide variety of dates with different types, colors, shapes, tastes, and nutritional values. Classifying, identifying, and recognizing dates would play a crucial role in the agriculture, commercial, food, and health sectors. Nevertheless, there is no or limited work to collect a reliable dataset for many classes. In this paper, we collected the dataset of date fruits by picturing dates from primary environments: farms and shops (e.g., online or local markets). The combined dataset is unique due to the multiplicity of items. To our knowledge, no dataset contains the same number of classes from natural environments. The collected dataset has 27 classes with 3228 images. The experimental results presented are based on five stages. The first stage applied traditional machine learning algorithms for measuring the accuracy of features based on pixel intensity and color distribution. The second stage applied a deep transfer learning (TL) model to select the best model accuracy of date classification. In the third stage, the feature extraction part of the model was fine-tuned by applying different retrained points to select the best retraining point. In the fourth stage, the fully connected layer of the model was fine-tuned to achieve the best classification configurations of the model. In the fifth stage, regularization was applied to the classification layer of the best-selected model from the fourth stage, where the validation accuracy reached 97.21% and the best test accuracy was 95.21%

Optimization of tannase production by Aspergillus glaucus in solid-state fermentation of black tea waste

Abstract Tannases are valuable industrial enzymes used in food, pharmaceutical, cosmetic, leather manufacture and in environmental biotechnology. In this study, 15 fungal isolates were obtained from Egyptian cultivated soil and marine samples. The isolated fungi were qualitatively and quantitatively screened for their abilities to produce tannase. The selected fungal isolate NRC8 giving highest tannase activity was identified by molecular technique (18S rRNA) as Aspergillus glaucus. Among different tannin-containing wastes tested, the black tea waste was the best substrate for tannase production by Aspergillus glaucus in solid-state fermentation (SSF). Optimization of the different process parameters required for maximum enzyme production was carried out to design a suitable SSF process. Maximal tannase production was achieved with moisture content of 75%, an inoculums size of 6 × 108 spore/ml and sodium nitrate 0.2% (pH of 5.0) at 30 °C after 5 days of incubation. Box–Behnken experiment was designed to get a quadratic model for further optimization studies. Four-factor response-surface method with 27 runs was prepared using independent parameters including (moisture content %, initial pH, substrate concentration (g) and sodium nitrate concentration (g) for tannase model. The F- and P-values of the model were 4.30 and 0.002, respectively, which implied that the model is significant. In addition, the lack-of-fit was 1040.37 which indicates the same significance relative to the pure error. A. glaucus tannase was evaluated by the efficiency of conversion of tannic acid to gallic acid. Moreover, production of gallic acid from SSF process of A. glaucus using black tea waste was found to be 38.27 mg/ml. The best bioconversion efficiency was achieved at 40 °C with tannic acid concentration up to 200 g/L. Graphical Abstrac

Purification, Characterization and anticancer activity of L-methionine γ-lyase from thermo-tolerant Aspergillus fumigatus

Abstract Purification of L-methionine γ-lyase (MGL) from A. fumigatus was sequentially conducted using heat treatment and gel filtration, resulting in 3.04 of purification fold and 73.9% of enzymatic recovery. The molecular mass of the purified MGL was approximately apparent at 46 KDa based on SDS-PAGE analysis. The enzymatic biochemical properties showed a maximum activity at pH 7 and exhibited plausible stability within pH range 5.0–7.5; meanwhile the highest catalytic activity of MGL was observed at 30–40 °C and the enzymatic stability was noted up to 40 °C. The enzyme molecule was significantly inhibited in the presence of Cu2+, Cd2+, Li2+, Mn2+, Hg2+, sodium azide, iodoacetate, and mercaptoethanol. Moreover, MGL displayed a maximum activity toward the following substrates, L-methionine < DL-methionine < Ethionine < Cysteine. Kinetic studies of MGL for L-methioninase showed catalytic activity at 20.608 mM and 12.34568 µM.min−1. Furthermore, MGL exhibited anticancer activity against cancerous cell lines, where IC50 were 243 ± 4.87 µg/ml (0.486 U/ml), and 726 ± 29.31 µg/ml (1.452 U/ml) against Hep-G2, and HCT116 respectively. In conclusion, A. fumigatus MGL had good catalytic properties along with significantly anticancer activity at low concentration which makes it a probably candidate to apply in the enzymotherapy field