Met-myoglobin is a major component related to meat discoloration, and it gradually accumulates over time after the meat is slaughtered. Recently, studies have been conducted to observe the changes in the composition of met-myoglobin in the meat along with its storage time using Diffuse Reflectance Spectroscopy(DRS). DRS is an optical technique that is simple and can estimate the composition of chromophores without damaging the sample. However, since DRS requires high resolution and complicated fitting process, it is difficult to apply DRS to the mobile environment. Therefore, the purpose of our study is to classify the freshness of meat by extracting features from low spectral resolution diffuse reflectance spectrum by using the deep learning model. To improve the generality of the model, a data augmentation was used. To consider the applicability at low-resolution spectrometer, the diffuse reflectance spectrum was down-sampled 5, 10, 30 and 50 times.
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
Functional near-infrared spectroscopy (fNIRS) has been gaining much attention in biophotonics fields because it provides brain activity based on monitoring of hemodynamic changes. Even though fNIRS has shown significant results in brain research, the question has been raised about the origin of hemodynamic changes, due to the uncertainty of the light path in the brain structure. The goal of this study is to separate the scalp and brain layer hemodynamic by developing diffuse reflectance spectroscopy based on two-layered photon diffusion reflectance equation. In order to validate our approach, the simulation experiments were carried out. During the experiment, various tissue reflectance spectra corresponding to various hemodynamic conditions of the superficial and brain layers were generated by simulation. The results show the potential of our approach that separating brain hemodynamics from tissue reflectance spectrums.
We developed combined diffuse reflectance spectroscopy – near-infrared diffuse correlation spectroscopy system. Cuff occlusion test, blood phantom test and test with anesthetic agents in a rat model were conducted for system verification. When the result of system verification follows the expected change in hemoglobin concentration change and the result of test with anesthetic agents shows effect of agents, then we can say that this combined system can be used for monitoring depth of anesthesia. This verified system can be used as the system for establishment of brain disease biomarker under anesthesia state.
There are a number of commercially available low level light therapy (LLLT) devices in a market, and face whitening or wrinkle reduction is one of targets in LLLT. The facial improvement could be known simply by visual observation of face, but it cannot provide either quantitative data or recognize a subtle change. Clinical diagnostic instruments such as mexameter can provide a quantitative data, but it costs too high for home users. Therefore, we designed a low cost multi-spectral imaging device by adding additional LEDs (470nm, 640nm, white LED, 905nm) to a commercial USB microscope which has two LEDs (395nm, 940nm) as light sources. Among various LLLT skin treatments, we focused on getting melanin and wrinkle information. For melanin index measurements, multi-spectral images of nevus were acquired and melanin index values from color image (conventional method) and from multi-spectral images were compared. The results showed that multi-spectral analysis of melanin index can visualize nevus with a different depth and concentration. A cross section of wrinkle on skin resembles a wedge which can be a source of high frequency components when the skin image is Fourier transformed into a spatial frequency domain map. In that case, the entropy value of the spatial frequency map can represent the frequency distribution which is related with the amount and thickness of wrinkle. Entropy values from multi-spectral images can potentially separate the percentage of thin and shallow wrinkle from thick and deep wrinkle. From the results, we found that this low cost multi-spectral imaging system could be beneficial for home users of LLLT by providing the treatment efficacy in a quantitative way.
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