• Clean Technology
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• v.18 no.1
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• pp.31-37
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• 2012

Biomass has become a major feedstock for bioenergy and other bio-based products because of its renewability and environmental benefits. Various researches have been done in the prediction of crucial characteristics of biomass, including the active utilization of spectroscopy data. Near infrared (NIR) spectroscopy has been widely used because of its attractive features: it's non-destructive and cost-effective producing fast and reliable analysis results. This work developed the multivariate statistical scheme for predicting weight loss profiles based on the utilization of NIR spectra data measured for six lignocellulosic biomass types. Wavelet analysis was used as a compression tool to suppress irrelevant noise and to select features or wavelengths that better explain NIR data. The developed scheme was demonstrated using real NIR data sets, in which different prediction models were evaluated in terms of prediction performance. In addition, the benefits of using right pretreatment of NIR spectra were also given. In our case, it turned out that compression of high-dimensional NIR spectra by wavelet and then PLS modeling yielded more reliable prediction results without handling full set of noisy data. This work showed that the developed scheme can be easily applied for rapid analysis of biomass.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2006.06a
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• pp.303-304
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• 2006

• Korean Journal of Food Science and Technology
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• v.24 no.5
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• pp.404-407
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• 1992

This study was conducted to determine the effect of the physical and chemical factors on the absorbance when the chemical component of food was rapidly and effectively analyzed with near infrared reflection spectrophotometer. The absorbance was measured within the wavelength range of 1100 and 2500m and at the various sample temperatures. The absorbance was greatly increased with the rising sample temperature. When the grinding time of samples prolonged, the absorbance was decreased. The absorption peaks occurring in the near infrared spectra of meat, meat protein, pork fat and water were attributed to the overtone and combination vibration of C-H, N-H, O-H or C=O bond within the macromolecules of each sample. The absorbance was lower for meat protein than other meat components. Pork fat was characterized by the ${\cdot}CH_2{\cdot}$ absorption peak at the wavelength of 1700 and 2300 nm and water by the relatively high absorption peak at the wavelength of 1450 and 1930 nm. As a result, the absorbance of near infrared reflection spectrum of meat was affected mainly by fat and water in meat.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.991-1000
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• 2021

Among non-invasive blood glucose detection technologies, the optical technique is a method that uses light reflection, absorption, and scattering characteristics when passing through a biological medium. It reduces pain or discomfort in measurement and has no risk of infection. So it is becoming a major flow of blood glucose detection research. Among them, near-infrared spectroscopy has a disadvantage in that the complexity increases when analyzing signals detected due to interferences between proteins and acids that share a similar absorption function with blood glucose molecules. In this study, a non-invasive sensor system with multiple near-infrared bands was designed and manufactured to alleviate the deterioration of blood glucose detection function that may occur due to skin absorption of near-infrared rays. A blood survey was conducted to verify the system, and the degree of blood glucose response in the blood was collected as spectral data, and the results of this study were quantitatively verified in terms of correlation between the data and blood glucose.

• Korean Chemical Engineering Research
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• v.45 no.6
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• pp.596-603
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• 2007

Rapid or on-line coal analysis is of great interest in coal industry as it would allow efficient plant operation. Multivariate analysis has been applied to near-infrared(NIR) spectra coal for investigating the relationship between coal properties(%) (moisture, ash, volatile matter, fixed carbon, carbon, hydrogen, nitrogen, oxygen, sulfur), heating value(kcal/kg) and corresponding near-infrared spectral data. The quantitative analysis was carried out by applying PLS(partial least squares regression) to determine a methodology able to establish a relationship between coal properties and NIR spectral data being applied mathematical pre-treatments for minimizing the physical features of the samples. As a results of the analysis, this technique is able to classify the species of coals and to predict the all coal properties except ash, nitrogen and sulfur. The efficient operation of coal fired power plant is expected owing to real time on-line coal analysis of moisture and heating value.

• Korean Deer Journal
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• v.5 no.6 s.29
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• pp.89-89
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• 2000

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.1
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• pp.31-37
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• 2014

The aim of this study was the non-destructive evaluation of bloody eggs using VIS/NIR spectroscopy. The bloody egg samples used to develop the sorting mode were produced by injecting chicken blood into the edges of egg yolks. Blood amounts of 0.1, 0.7, 0.04, and 0.01 mL were used for the bloody egg samples. The wavelength range for the VIS/NIR spectroscopy was 471 to 1154 nm, and the spectral resolution was 1.5nm. For the measurement system, the position of the light source was set to $30^{\circ}$, and the distance between the light source and samples was set to 100 mm. The minimum exposure time of the light source was set to 30 ms to ensure the fast sorting of bloody eggs and prevent heating damage of the egg samples. Partial least squares-discriminant analysis (PLS-DA) was used for the spectral data obtained from VIS/NIR spectroscopy. The classification accuracies of the sorting models developed with blood samples of 0.1, 0.07, 0.04, and 0.01 mL were 97.9%, 98.9%, 94.8%, and 86.45%, respectively. In this study, a novel nondestructive sorting technique was developed to detect bloody brown eggs using spectral data obtained from VIS/NIR spectroscopy.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.spc1
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• pp.42-46
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• 2006

The purpose of this study was to assess whether mechanical sensory score by Toyo Midometer can be substituted by near-infrared spectroscopy (NIRS) method in whole-grain milled rice samples. Toyo values of collected commercial brand rice (n=127) had comparatively wide ranges from 62.9 to 84.2 (Mean=70.5; S.D.=4.0). Calibration equation was developed using 73 samples. Standard error of calibration (SEC) for sensory score equation and $R^2$ were 0.95, and 0.94, respectively, however, percentage of variation in the reference method values (1-VR) which give a true indication of equation performance was slightly lower (1-VR=0.81) than calibration equation. It was demonstrated that, even though NIRS has potential as a rapid tools to predict rice sensory score, the prediction of sensory score in rice by NIRS needs to be further investigation on a large number of sample with different varieties and growing locations.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1353-1362
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• 2023

Various methods are being attempted to resolve the inconvenience of blood glucose meters used to check blood sugar levels. In this paper, we attempted to estimate blood sugar levels non-invasively using machine learning technology from spectral data acquired using a near-infrared sensor. The non-invasive blood glucose meter used in the study has a total of six near-infrared ray emitters, including visible rays, and a light receiver that receives them. It is a device created to collect spectral data on specific parts of the human body, such as the fingers. To verify whether there was a significant difference depending on blood sugar level, we attempted to estimate blood sugar level through machine learning algorithms. As a result of applying five machine learning algorithm techniques to the collected data and adjusting various hyper parameters, it was confirmed that the support vector regression algorithm showed the best performance.

• Proceedings of the Korean Society of Postharvest Science and Technology of Agricultural Products Conference
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• 2002.04a
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• pp.123.2-123
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• 2002