The aim of this study was to assess Annona muricata L. fruit extracts as an alternative to synthetic fungicide against Alternaria alternata (Fries) Keissler, the causative agent of black spots of tomato fruit. Antifungal activities of A. muricata pulp and seed extracts were tested both in vitro and in vivo. The seed extracts were more potent at inhibiting A. alternata than the pulp extracts. The in vitro assay showed maximum inhibition of radial mycelial growth of A. alternata (90%) by methanol seed extracts, at the highest concentration of 6%. Similarly, the in vivo assay showed marked reduction in lesion diameter (2.1 mm) and consequent disease inhibition (84%) on the tomato fruit treated with methanol seed extracts. Scanning electron microscopy showed that A. muricata extracts significantly damaged the morphology of hyphae and conidial structures. The FT-IR spectrum obtained from methanol extracts showed bands representing important bioactive compounds that possess antifungal activity. Based on our findings, Annona muricata fruit extracts can be further explored as a potential, excellent alternative approach to control the postharvest Alternaria spots of tomato fruit.

Recently, the use of mixed models for analyzing real data sets with infinite domains has gained favor. However, only a specific type of mixture model using mostly maximum likelihood estimation technique has been exercised in the literature, and fitting the mixture models for bounded data (between zero and one) has been neglected. In statistical mechanics, unit distributions are widely utilized to explain practical numeric values ranging between zero and one. We presented a classical examination for the trade share data set using a mixture of two log-Bilal distributions (MLBDs). We examine the features and statistical estimation of the MLBD in connection with three techniques. The sensitivity of the presented estimators with respect to model parameters, weighting proportions, sample size, and different evaluation methodologies has also been discussed. A simulation investigation is also used to endorse the estimation results. The findings on maximum likelihood estimation were more persuasive than those of existing mixture models. The flexibility and importance of the proposed distribution are illustrated by means of real datasets.