Global biosecurity threats such as the spread of emerging infectious diseases (i.e., avian influenza, SARS, Hendra, Nipah, etc.) and bioterrorism have generated significant interest in recent years. There is considerable effort directed towards understanding and negating the proliferation of infectious diseases. Biosensors are an attractive tool which have the potential to detect the outbreak of a virus and/or disease. Although there is a host of technologies available, either commercially or in the scientific literature, the development of biosensors for the detection of emerging infectious diseases (EIDs) is still in its infancy. There is no doubt that the glucose biosensor, the gene chip, the protein chip, etc. have all played and are still playing a significant role in monitoring various biomolecules. Can biosensors play an important role for the detection of emerging infectious diseases? What does the future hold and which biosensor technology platform is suitable for the real-time detection of infectious diseases? These and many other questions will be addressed in this review. The purpose of this review is to present an overview of biosensors particularly in relation to EIDs. It provides a synopsis of the various types of biosensor technologies that have been used to detect EIDs, and describes some of the technologies behind them in terms of transduction and bioreceptor principles.

The direct qualitative and quantitative determination of mineral components in shale rocks is a problem that has not been satisfactorily resolved to date. Infrared spectroscopy (IR) is a non-destructive method frequently used in mineral identification, yet challenging due to the similarity of spectral features resulting from quartz, clay, and feldspar minerals. This study reports on a significant improvement of this methodology by combining infrared attenuated total reflection spectroscopy (IR-ATR) with partial least squares (PLS) regression techniques for classifying and quantifying various mineral components present in a number of different shale rocks. The developed multivariate classification model was calibrated using pure component mixtures of the most common shale minerals (i.e., kaolinite, illite, montmorillonite, calcite, and quartz). Using this model, the IR spectra of 11 real-world shale samples were analyzed and evaluated. Finally, the performance of the developed IR-ATR method was compared with results obtained via X-ray diffraction (XRD) analysis.

Reflectance spectroscopy allows cost-effective and rapid mineral characterisation, addressing mineral exploration and mining challenges. Shortwave (SWIR), mid (MIR) and thermal (TIR) infrared reflectance spectra are collected in a wide range of environments and scales, with instrumentation ranging from spaceborne, airborne, field and drill core sensors to IR microscopy. However, interpretation of reflectance spectra is, due to the abundance of potential vibrational modes in mineral assemblages, non-trivial and requires a thorough understanding of the potential factors contributing to the reflectance spectra. In order to close the gap between understanding mineral-diagnostic absorption features and efficient interpretation of reflectance spectra, an up-to-date overview of major vibrational modes of rock-forming minerals in the SWIR, MIR and TIR is provided. A series of scripts are proposed that allow the extraction of the relative intensity or wavelength position of single absorption and other mineral-diagnostic features. Binary discrimination diagrams can assist in rapidly evaluating mineral assemblages, and relative abundance and chemical composition of key vector minerals, in hydrothermal ore deposits. The aim of this contribution is to make geologically relevant information more easily extractable from reflectance spectra, enabling the mineral resources and geoscience communities to realise the full potential of hyperspectral sensing technologies.