Paper Info
Title
A Fully Automated Robot for the Preparation of Fungal Samples for FTIR Spectroscopy Using Deep Learning
Abstract
Manual preparation of fungal samples for Fourier Transform Infrared (FTIR) spectroscopy involves sample washing, homogenization, concentration and spotting, which requires time-consuming and repetitive operations, making it unsuitable for screening studies. This paper presents the design and development of a fully automated robot for the preparation of fungal samples for FTIR spectroscopy. The whole system was constructed based on a previously-developed ultrasonication robot module, by adding a newly-designed centrifuge module and a newly-developed liquid handling module. The liquid handling module consists of a high accuracy electric pipette for spotting and a low accuracy syringe pump for sample washing and concentration. A dual robotic arm system with a gripper connects all of the hardware components. Furthermore, a camera on the liquid handling module uses deep learning to identify the labware settings, which includes the number and positions of well plates and pipette tips. Machine vision on the ultrasonication robot module can detect the sample wells and return the locations to the liquid handling module, which makes the system hand-free for users. Tight integration of all the modules enables the robot to process up to two 96-well microtiter (MTP) plates of samples simultaneously. Performance evaluation shows the deep learning based approach can detect four classes of labware with high average precision, from 0.93 to 1.0. In addition, tests of all procedures show that the robot is able to provide homogeneous sample spots for FTIR spectroscopy with high positional accuracy and spot coverage rate.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2941704
IEEE ACCESS
Keywords
DocType
Volume
Laboratory automation,robotics,deep learning,ultrasonication,spotting,FTIR spectroscopy
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Ya Xiong152.26
Volha Shapaval200.34
Achim Kohler321.15
Jichun Li429144.32
Pål Johan From5189.04