We introduce cell dynamic activity analysis method-based combination of dynamic full-field optical coherence tomography (DFFOCT) and machine learning (ML) models. DFFOCT can monitor intracellular migration label-free by capturing scatters movement inside of cells. Since ML builds classification criteria through learning a lot of data, based on the intracellular scatter migration observed through DFFOCT, it is possible to judge abnormal signs of cells regardless of changes in the external experimental environment. We compared the suggested analysis method and staining analysis method for the change of state of HeLa cells (including cell data) and verified the validity.
Previous studies in this area of research have reported that merchantability of meat drops when the meat color. The changes in meat color are caused by met-myoglobin concentration changes. Despite, a few methods are presented to measure met-myoglobin concentration, those methods have a number of problem in use. In general, met-myoglobin concentrations increase inside the meat and spread to the surface. The main purpose of this study is the measurement of met-myoglobin proportion inside the meat by using diffuse optical spectroscopy (DOS) to predict meat color changes. To conduct the experiments, the DOS system consists of a spectrometer and the broadband light source. And 30 beef samples were taken on the day that the cattle were slaughtered. In order to measure met-myoglobin changes over time, Data were collected every day. The results show us increase and decrease of met-myoglobin during the storage. This study will help us to predict meat color changes and to qualify merchantability
We propose a simple, rapid, and nondestructive method to investigate formation, accumulation, and degradation of met-myoglobin (met-Mb) and myoglobin oxygenation from the interior of porcine meat. For the experiment, color photos and attenuance spectra of porcine meat (well-bled muscle, fat, and mixed) were collected daily to perform colorimetric analysis and to obtain the differences of attenuance between 578 and 567 nm (A578-A567) and between 615 and 630 nm (A630-A615), respectively. Oxy-, deoxy-, and met-myoglobin concentration changes over storage time were also calculated using Beer–Lamberts’ law with reflectance intensities at 557, 582, and 630 nm. The change of A578-A567 was well matched with the change of myoglobin oxygenation, and the change of A630-A615 corresponded well with the formation and degradation of met-Mb. In addition, attenuation differences, A578-A567 and A630-A615, were able to show the formation of met-Mb earlier than colorimetric analysis. Therefore, the attenuance differences between wavelengths can be indicators for estimating myoglobin oxygenation and met-Mb formation, accumulation, and degradation, which enable us to design a simple device to monitor myoglobin activities in porcine meat.
Driver’s condition plays a critical role in driving safety. The fact that about 20 percent of automobile accidents occurred due to driver fatigue leads to a demand for developing a method to monitor driver’s status. In this study, we acquired brain signals such as oxy- and deoxyhemoglobin and neuronal electrical activity by a hybrid fNIRS/EEG system. Experiments were conducted with 11 subjects under two conditions: Normal condition, when subjects had enough sleep, and sleep deprivation condition, when subject did not sleep previous night. During experiment, subject performed a driving task with a car simulation system for 30 minutes. After experiment, oxy-hemoglobin and deoxy-hemoglobin changes were derived from fNIRS data, while beta and alpha band relative power were calculated from EEG data. Decrement of oxy-hemoglobin, beta band power, and increment of alpha band power were found in sleep deprivation condition compare to normal condition. These features were then applied to classify two conditions by Fisher’s linear discriminant analysis (FLDA). The ratio of alpha-beta relative power showed classification accuracy with a range between 62% and 99% depending on a subject. However, utilization of both EEG and fNIRS features increased accuracy in the range between 68% and 100%. The highest increase of accuracy is from 63% using EEG to 99% using both EEG and fNIRS features. In conclusion, the enhancement of classification accuracy is shown by adding a feature from fNIRS to the feature from EEG using FLDA which provides the need of developing a hybrid fNIRS/EEG system.
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