RESULTS: In this research, chili pest and disease features extracted using the traditional approach were compared with features extracted using a deep-learning-based approach. A total of 974 chili leaf images were collected, which consisted of five types of diseases, two types of pest infestations, and a healthy type. Six traditional feature-based approaches and six deep-learning feature-based approaches were used to extract significant pests and disease features from the chili leaf images. The extracted features were fed into three machine learning classifiers, namely a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN) for the identification task. The results showed that deep learning feature-based approaches performed better than the traditional feature-based approaches. The best accuracy of 92.10% was obtained with the SVM classifier.
CONCLUSION: A deep-learning feature-based approach could capture the details and characteristics between different types of chili pests and diseases even though they possessed similar visual patterns and symptoms. © 2020 Society of Chemical Industry.
RESULTS: A total of 31 constituents comprising primary and secondary metabolites belonging to the chemical classes of fatty acids, amino acids, sugars, terpenoids and phenolic compounds were identified. Shade-dried leaves were identified to possess the highest concentrations of bioactive secondary metabolites such as chlorogenic acid, caffeic acid, luteolin, orthosiphol and apigenin, followed by microwave-dried samples. Freeze-dried leaves had higher concentrations of choline, amino acids leucine, alanine and glutamine and sugars such as fructose and α-glucose, but contained the lowest levels of secondary metabolites.
CONCLUSION: Metabolite profiling coupled with multivariate analysis identified shade drying as the best method to prepare OS leaves as Java tea or to include in traditional medicine preparation. © 2017 Society of Chemical Industry.
METHODS: The dry powder leaves of Tetrastigma were extracted with different organic solvent such as hexane, ethyl acetate, chloroform, butanol and aqueous methanol. The total phenolic and total flavonoids contents of the essential oil and various organic extracts such as hexane, ethyl acetate, chloroform, butanol and aqueous ethanol were determined by Folin - Ciocalteu method and the assayed antioxidant activity was determined in vitro models such as antioxidant capacity by radical scavenging activity using α, α-diphenyl- β-picrylhydrazyl (DPPH) method.
RESULTS: The total phenolic contents of the essential oil and different extracts as gallic acid equivalents were found to be highest in methanol extract (386.22 mg/g) followed by ethyl acetate (190.89 mg/g), chloroform (175.89 mg/g), hexane (173.44 mg/g), and butanol extract (131.72 mg/g) and the phenolic contents not detected in essential oil. The antioxidant capacity of the essential oil and different extracts as ascorbic acid standard was in the order of methanol extract > ethyl acetate extract >chloroform> butanol > hexane extract also the antioxidant activity was not detected in essential oil.
CONCLUSIONS: The findings show that the extent of antioxidant activity of the essential oil and all extracts are in accordance with the amount of phenolics present in that extract. Leaves of Tetrastigma being rich in phenolics may provide a good source of antioxidant.