Comparison of Drying Yield, Meat Quality, Oxidation Stability and Sensory Properties of Bone-in Shell Loin Cut by Different Dry-aging Conditions.

This study aimed to investigate the drying yield, meat quality, oxidation stability and sensory properties of Hanwoo beef loin subjected to different dry-aging conditions. A total of 54 Hanwoo beef loins (bone-in, 6th-13th ribs) with fat cover at 2 d postmortem were assigned to four groups and hung in a dry-aging room at a controlled temperature (2℃-4℃), humidity (65%-85%) and air velocity (3 m/s). Four treatment conditions were tested; the respective temperature, humidity and treatment duration are as follows: T1, 2℃, 85%, 60 d; T2, 2℃, 65%, 20 d+2℃, 75%, 20 d+4℃, 85%, 20 d; T3, 2℃, 75%, 20 d+4℃, 85%, 40 d; T4, 4℃, 85%, 60 d. The drying yield, total aerobic counts and fat contents increased, whereas the moisture content, meat color (CIE L*, a*, and b*), and Warner-Bratzler shear force values decreased significantly during the aging period for all treatments (p<0.05). The cooking loss (%) did not change significantly until 40 d, and it was significantly higher in T1 and T4 than in T2 and T3 at 60 d (p<0.05). The water-holding capacity, pH and lipid oxidation [thiobarbituric acid reactive substance (TBARS) values] increased during aging (p<0.05). The sensory scores for tenderness, juiciness, flavor, and overall acceptability of dry-aged loin muscles increased as the aging period increased. T2 and T3 had significantly higher sensory scores and T1 had significantly lower scores for tenderness, juiciness, flavor, and overall acceptability at 20, 40, and 60 d for loin muscles (p<0.05).

Optimal sensor location for body sensor network to detect self-stimulatory behaviors of children with Autism Spectrum Disorder.

In this study, we investigate various locations of sensor positions to detect stereotypical self-stimulatory behavioral patterns of children with Autism Spectrum Disorder (ASD). The study is focused on finding optimal detection performance based on sensor location and number of sensors. To perform this study, we developed a wearable sensor system that uses a 3 axis accelerometer. A microphone was used to understand the surrounding environment and video provided ground truth for analysis. The recordings were done on 2 children diagnosed with ASD who showed repeated self-stimulatory behaviors that involve part of the body such as flapping arms, body rocking and vocalization of non-word sounds. We used time-frequency methods to extract features and sparse signal representation methods to design over-complete dictionary for data analysis, detection and classification of these ASD behavioral events. We show that using single sensor on the back achieves 95.5% classification rate for rocking and 80.5% for flapping. In contrast, flapping events can be recognized with 86.5% accuracy using wrist worn sensors.