Search

We explore the properties of an 'almost' dark cloud of neutral hydrogen (HI) using data from the Widefield ASKAP L-band Legacy All-sky Survey (WALLABY). Until recently, WALLABY J103508-283427 (also known as H1032-2819 or LEDA 2793457) was not known to have an optical counterpart, but we have identified an extremely faint optical counterpart in the DESI Legacy Imaging Survey Data Release 10. We measured the mean g-band surface brightness to be $27.0\pm0.3$ mag arcsec$^{-2}$. The WALLABY data revealed the cloud to be closely associated with the interacting group Klemola 13 (also known as HIPASS J1034-28 and the Tol 9 group), which itself is associated with the Hydra cluster. In addition to WALLABY J103508-283427/H1032-2819, Klemola 13 contains ten known significant galaxies and almost half of the total HI gas is beyond the optical limits of the galaxies. By combining the new WALLABY data with archival data from the Australia Telescope Compact Array (ATCA), we investigate the HI distribution and kinematics of the system. We discuss the relative role of tidal interactions and ram pressure stripping in the formation of the cloud and the evolution of the system. The ease of detection of this cloud and intragroup gas is due to the sensitivity, resolution and wide field of view of WALLABY, and showcases the potential of the full WALLABY survey to detect many more examples., Comment: 13 pages, 7 figures, accepted for publication in MNRAS

Over the past three decades, near infrared reflectance (NIR) spectroscopy has been proved to be one of the most efficient and advanced tools for the estimation of quality attributes in meat and meat products. This review focuses on the use of NIR spectroscopy to predict different meat properties, considering the literature published mainly in the last decade. Firstly, the potential of NIR to predict chemical composition (crude protein, intramuscular fat, moisture/dry matter, ash, gross energy, myoglobin and collagen), technological parameters (pH value; V, a*, b* colour values; water holding capacity; Warner-Bratzler and slice shear force) and sensory attributes (colour, shape, marbling, odour, flavour, juiciness, tenderness or firmness) are reviewed. Secondly, the usefulness of NIR for classification into meat quality grades is presented and thirdly its potential application in the industry is shown. The review indicates that NIR showed high potential to predict chemical meat properties and to categorize meat into quality classes. In contrast, NIR showed limited ability for estimating technological and sensory attributes, which may be mainly due to the heterogeneity of the meat samples and their preparation, the low precision of the reference methods and the subjectivity of assessors in taste panels. Hence, future work to standardize sample preparation and increase the accuracy of reference methods is recommended to improve NIR ability to predict those technological and sensory characteristics. In conclusion, the review shows that NIR has a considerable potential to predict simultaneously numerous meat quality criteria.