Portable, low cost and sensitive cavity enhanced absorption (CEA) detection.

Absorption is a widely used technique for a range of different applications. It has lower sensitivity than many other techniques such as fluorescence which has 100 to 1000 times higher sensitivity than absorption. Optical cavity approaches have been developed where the light passes back and forth, within the sample, between two high reflectivity mirrors to increase the pathlength and sensitivity. These approaches have not yet, however, been widely used for analytical applications and for point-of-care diagnostics. Here we show a portable cavity enhanced absorption (CEA) spectrometer and a low cost point-of-care (POC) reader with CEA detection with mechanical elements fabricated using 3D printing. The CEA spectrometer can be used in both single pass and multi-pass cavity enhanced mode to provide measurements in the visible region that are very sensitive and over a wide dynamic range. The CEA mode was shown for Rhodamine B dye to increase the pathlength 57.8 fold over single pass measurements and an LOD of 7.1 × 10-11 M. The cost of the CEA POC reader was reduced by use of narrow band LEDs, photodiodes and removal of fibre optic coupling and with a 14 fold increase in the pathlength over conventional single pass microplate readers. The CEA POC reader was demonstrated for immunoassay of C-Reactive Protein (CRP), Procalcitonin (PCT) and Interleukin 6 (IL-6), towards a three biomarker panel to aid the diagnosis of sepsis. The CEA POC reader can be integrated with wireless connectivity for cloud based data sharing. We show here the potential for the wider use of optical cavity approaches where there is a need for sensitive absorption measurements and also for low cost point-of-care diagnostics.

Low-cost hyper-spectral imaging system using a linear variable bandpass filter for agritech applications.

Hyperspectral imaging for agricultural applications provides a solution for non-destructive, large-area crop monitoring. However, current products are bulky and expensive due to complicated optics and electronics. A linear variable filter was developed for implementation into a prototype hyperspectral imaging camera that demonstrates good spectral performance between 450 and 900 nm. Equipped with a feature extraction and classification algorithm, the proposed system can be used to determine potato plant health with ∼88% accuracy. This algorithm was also capable of species identification and is demonstrated as being capable of differentiating between rocket, lettuce, and spinach. Results are promising for an entry-level, low-cost hyperspectral imaging solution for agriculture applications.