To preserve the quality of fresh pear fruit after harvest and deliver quality fruit year-round a controlled supply chain and long-term storage are applied. During storage, however, internal disorders can develop due to suboptimal storage conditions that may not cause externally visible symptoms. This makes them impossible to be detected by current commercial quality grading systems in a reliable and non-destructive way. A combination of a Support Vector Machine coupled with a feature extraction algorithm and X-ray Computed Tomography is proposed to successfully detect internal disorders in 'Conference' and 'Cepuna' pear fruit nondestructively. Classifiers were able to distinguish defective from sound fruit with classification accuracies ranging between 90.2 and 95.1 % depending on the cultivar and number of used features. Moreover, low false positive and negative rates were obtained, respectively ranging between 0.0 and 6.7 %, and 5.7 and 13.3 %. Classifiers trained on 'Conference' data were transferred effectively to the 'Cepuna' cultivar, suggesting generalizability to other cultivars as well. With continuing developments in both hardware and software to increase inspection speed and reduce equipment costs, the method can be implemented in industrial applications, e.g., inline translational X-ray CT.
In this study, insights into the effect of process parameters (temperature: 55, 60 and 65°C; thickness: 3, 5, and 8 mm; time: up to 5 hr) on drying (mechanical, solar) kinetics and sorption behavior of guava fruits (variety: local and hybrid) with a focus on the degradation of ascorbic acid (AA) were established for the first time to the best of existing knowledge. The experimental results showed the highest moisture diffusion coefficient and lower activation energy (Ea) of 0.000326 cm2/s and 9.62 kcal/g.mole, respectively for hybrid mature guava variety; whereas, the highest AA degradation rate (0.107 mg AA/hr) with lower Ea (4.3 kcal/g.mole) was obtained for local mature guava variety. Besides, a mixed‐mode solar dryer assisted with a fan seemed to be efficient for drying guava fruit in comparison to drying under direct sunlight. In terms of sorption behavior, both local and hybrid guava followed sigmoidal (type II) shape isotherm where the experimental data were well fitted with Brunauer–Emmett–Teller (BET) model. Practical application The drying conditions to have dried guava (Psidium guajava L.) fruit with high ascorbic acid (AA) were investigated. The mathematical model was successfully applied to describe the drying kinetics and degradation of AA of guava fruit. Process parameters and methods influencing drying kinetics and AA degradation rate were detailed. BET model fits well to describe the sorption isotherms of dried guava fruit, in understanding its storage behavior. The information of this study would help in the drying process optimization of guava fruit, in order to make them available in the off‐seasons.
Phenolic compounds (PCs) have been finding increasing applications in functional foods, cosmetic, pharmaceutical and nutraceuticals due to their nutritional and antioxidant properties. Considering the drawbacks of conventional extraction methods, there is an ongoing challenge for researchers to develop green extractions (GE) of PCs in a sustainable and eco-friendly way. A bibliometric study is a valuable tool to provide quantitative information for evaluating the scientific research activity based on the available published scientific literature. This bibliometric study aims for the first time to unravel the scientific exploration towards the GE of phenolics from plant by-products, that is, leaves by focussing on scientific publications collected through the Web of Science database. Consequently, insights into the evaluation of publication trends, authors, countries, organisations, keywords, journals, publishers and citations in support of GE were established. The critical evaluation of the results showed an increase in research, during the last decade, on GE of PCs from leaves. The study also revealed Europe as the leader in terms of publications, citations, countries, organisations, as well as authors contributing to this research topic. Following the analysis, directions for future research have also been suggested.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.