  In this study, a novel active solubility sensing device using computer vision
is proposed to improve separation purification performance and prevent
malfunctions of separation equipment such as preparative liquid
chromatographers and evaporators. The proposed device actively measures the
solubility by transmitting a solution using a background image. The proposed
system is a combination of a device that uses a background image and a method
for estimating the dissolution and particle presence by changing the background
image. The proposed device consists of four parts: camera, display, adjustment,
and server units. The camera unit is made up of a rear image sensor on a mobile
phone. The display unit is comprised of a tablet screen. The adjustment unit is
composed of rotating and height-adjustment jigs. Finally, the server unit
consists of a socket server for communication between the units and a PC,
including an automated solubility analysis system implemented in Python. The
dissolution status of the solution was divided into four categories and a case
study was conducted. The algorithms were trained based on these results. Six
organic materials and four organic solvents were combined with 202 tests to
train the developed algorithm. As a result, the evaluation rate for the
dissolution state exhibited an accuracy of 95 %. In addition, the device and
method must develop a feedback function that can add a solvent or solute after
dissolution detection using solubility results for use in autonomous systems,
such as a synthetic automation system. Finally, the diversification of the
sensing method is expected to extend not only to the solution but also to the
solubility and homogeneity analysis of the film.
