Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.

[Primary study on computer-aided qualitative and quantitative radial thin-layer chromatography methods for analysis of Yiqing tablets]

Radial thin-layer chromatography (RTLC) is a form of chromatography in which the sample and developing agent/solvent are applied at the edge of a circle on the silica-layered TLC plate. The sample components are radially transported from the center of the circle toward the periphery by the solvent flow. RTLC is an efficient separation and semi-preparation method, with the advantages of fast separation, low diffusion, and absence of tailing at a certain distance. Although RTLC isolation has been well documented in the literature, there are only a few qualitative and quantitative reports on this technique. In this study, a self-programming module combined with image processing software was adopted for digital processing of RTLC images, and a digital atlas was created. Qualitative and quantitative verification of RTLC was then conducted. With the aid of computer technology, qualitative and quantitative methods of radially expanded TLC for Yiqing tablets were investigated. The results showed that the qualitative RTLC method has good reliability, but the quantitative RTLC method has inherent drawbacks, which may be associated with the experimental environment. We strongly believe that better separation and data acquisition equipment, as well as the combination of computer image processing and programming methods, will open up infinite possibilities of analysis/separation using RTLC-based techniques. We also believe that TLC will take a new step in the future with the assistance of computer technology.

Asian Rhinoplasty: Preoperative Simulation and Planning Using Adobe Photoshop

A rhinoplasty in Asians differs from a rhinoplasty performed in patients of other ethnicities. Surgeons should understand the concept of Asian beauty, the nasal anatomy of Asians, and common problems encountered while operating on the Asian nose. With this understanding, surgeons can set appropriate goals, choose proper operative procedures, and provide an outcome that satisfies patients. In this article the authors define the concept of an Asian rhinoplasty-a paradigm shift from the traditional on-top augmentation rhinoplasty to a structurally integrated augmentation rhinoplasty-and provide a step-by-step procedure for the use of Adobe Photoshop as a preoperative program to simulate the expected surgical outcome for patients and to develop a preoperative plan for surgeons.