Shauqi Abdai Lubis, Dimas Firmanda Al Riza, Jyh Jian Chen, Yusuf Hendrawan, Joni Kusnadi, Aryo Pinandito
Accurate species authentication of pork (Sus scrofa domesticus) is essential for food labeling integrity and religious dietary compliance. However, conventional polymerase chain reaction-based methods are hindered by bulky instrumentation and lengthy thermal cycling, limiting their point-of-need applicability. This study describes the development of a compact, low-cost (<300 USD), real-time isothermal recombinase polymerase amplification platform optimized for on-site porcine DNA detection. The device features a novel reaction chamber fabricated from polymethyl methacrylate via a rapid 5 min milling process. To ensure efficient heat transfer, a high-thermal-conductivity grease (10 W mK−1) is utilized at the heating interface, providing superior thermal contact and stability compared to traditional PDMS-based microfluidics. For signal transduction, an integrated AS7262 spectral sensor provides high-sensitivity digital fluorescence monitoring at 500 nm, utilizing a 488 nm ice-blue laser and a 90° orthogonal optical geometry to minimize background scatter. Operating at a constant 37 °C maintained by a proportional-integral control algorithm, the system—coupled with a rapid 10 min thermal extraction protocol for pork meats—achieves a total turnaround time of 30 min. The platform demonstrates a limit of detection of 1 pg µl−1, with a linear correlation (R2 = 0.9014) between the time-to-threshold and the log-transformed DNA concentration. The total sample-to-result time for complex processed meat matrices is established at 45–50 min due to matrix-induced kinetic delays. Successful validation via gel electrophoresis confirmed a specific 152 bp amplification product. By bridging the gap between sophisticated laboratory analysis and field-based inspection, this integrated platform offers a robust, cost-effective solution for rapid meat speciation and global food supply chain monitoring. © 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. This article is available under the terms of the https://publishingsupport.iopscience.iop.org/iop-standard/v1.
Department of Biosystems Engineering, Faculty of Agricultural Technology, University of Brawijaya, Jl. Veteran, Malang, 65145, Indonesia; Department of Biomechatronics Engineering, National Pingtung University of Science and Technology, 1, Shuefu Road, Neipu, Pingtung, 91201, Taiwan; Department of Food Science and Biotechnology, University of Brawijaya, Jl. Veteran, Malang, 65145, Indonesia; Department of Information Systems, University of Brawijaya, Jl. Veteran, Malang, 65145, Indonesia
