Backgrounds and Aims:Bentonite is commonly added to white wines to remove the grape proteins responsible for haze formation. Despite being effective, this technique has drawbacks; thus, new solutions are desirable. The ability of carrageenan and pectin to remove heat-unstable grape proteins, and the impact that such addition has on the physicochemical and sensorial profile of a wine were assessed. Methods and Results: Carrageenan and pectin were added separately or in combination to a Chardonnay juice prior to fermentation. Both adsorbents removed proteins (up to 75%), thus increasing wine protein stability. Carrageenan was more effective than pectin at increasing wine protein stability. Conclusions: Pectin and carrageenan removed protein and partially stabilized the samples of the wine. Significance of the Study: Pre-fermentation addition of pectin or carrageenan may provide the wine industry with an alternative protein stabilization procedure. AbbreviationsCIELAB Commission Internationale de l'Eclairage Lab transmission values L* a* b*; NTU nephelometric turbidity units; Tukey-Kramer HSD test Tukey-Kramer honestly significance difference test.

Magnetically controlled capsule endoscope (MCCE) is an emerging tool for the diagnosis of gastric diseases with the advantages of comfort, safety, and no anesthesia. In this paper, we develop algorithms to detect and measure human gastric peristalsis (contraction wave) using video sequences acquired by MCCE. We develop a spatial-temporal deep learning algorithm to detect gastric contraction waves and measure human gastric peristalsis periods. The quality of MCCE video sequences is prone to camera motion. We design a camera motion detector (CMD) to process the MCCE video sequences, mitigating the camera movement during MCCE examination. To the best of our knowledge, we are the first to propose computer vision-based solutions to detect and measure human gastric peristalsis. Our methods have great potential in assisting the diagnosis of gastric diseases by evaluating gastric motility.

Gastric motility disorders are caused by abnormal muscle contractions which may impede the digestive process. Traditional approaches for evaluating human gastric motility have limitations, including discomfort, use of sedation, risk of radiation exposure, and confusion in interpretation. Magnetically controlled capsule endoscopy (MCCE) provides a new way to evaluate human gastric with the advantages of comfort, safety, and no anesthesia. In this paper, we develop deep learning algorithms to detect human gastric waves captured by MCCE. We demonstrate promising experimental results both qualitatively and quantitatively. Our methods have great potential to assist in the diagnosis of human gastric disease by evaluating gastric motility.