• Journal of the Korean Society of Tobacco Science
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• v.17 no.2
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• pp.177-183
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• 1995

This study was conducted to develop the most precise NIR(near infrared spectrometric) calibration for rapid determination of chemical composition in ground samples of toasted burley tobacco using stepwise, stepup, principal component regression(PCR), partial least square(PLS) and modified partial least square(MPLS) calibration method. The number of wavelength(W) selected by stepup multiple linear regression using: second derivative spectra was as follows: total sugar(TS)-4 W, nicotine-9 W, total nitrogen(TN)-2 W, ash-8 W, total volatile base(TVB)-5 W, chlorine4 W, L of color-6 W, a of color-6 W and b of color-7 W. Comparing the calibration equations followed by each chemical components, the most precise calibration equation was MPLS for 75, a and b of color, PLS for nicotine, ash, TVB, chlorine and L of color and stepup for TN. The standard error of calibration(SEC) and standard error of performance(SEP) between result of near infrared analysis and standard laboratory analysis were 0.18, 0.40% for 75, 0.06, 0.08% for nicotine, 0.18, 0.16% for TN, 0.33, 0.46% for ash, 0.04, 0.03% for TVB, 0.08, 0.06% for chlorine, 0.54, 0.58 for L of color, 0.22, 0.22 for a of color and 0.27, 0.27 for b of color, respectively. The SEC and SEP of ash and TVB were within allowable error of standard laboratory analysis, nicotine, TN and chlorine were 1.2-2.0 times and 75 were 2.1-4.0 times larger than allowable error of standard laboratory analysis. The ratio of SEC and SEP to mean were 1.5, 1.6% for L of color, 3.7, 3.8% for a of color and 1.8, 1.8% for b of color, respectively. Key words : burley tobacco chemistry, near infrared spectroscopy.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.

• Food Science and Preservation
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• v.12 no.6
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• pp.608-612
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• 2005

Fourier transform-near infrared (FT-NIR) spectroscopy is a simple, rapid, non-destructive technique which can be used to make quantitative analysis of chemical composition in grain. An interest in total dietary fiber (TDF) of grain such as rice has been increased due to its beneficial effects for health. Since measuring methods for TDF content were highly depending on experimental technique and time consumptions, the application of FT-NIR spectroscopy to determine TDF content in milled rice. Results of enzymatic-gravimetric method were $1.17-1.92\%$ Partial least square (PLS) regression on raw NIR spectra to predict TDF content was developed Accuracy of prediction model for TDF content was certified for regression coefficient (r), standard error of estimation (SEE) and standard error of prediction (SEP). The r, SEE and SEP were 0.9705, 0.0464, and 0.0604, respectively. The results indicated that FT-NIR techniques could be very useful in the food industry and rice processing complex for determination of TDF in milled rice on real time analysis.

• Journal of The Korean Society of Grassland and Forage Science
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• v.26 no.1
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• pp.53-62
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• 2006

Near infrared reflectance spectroscopy (NIRS) has been increasingly used as a rapid, accurate method of evaluating some chemical compositions in forages. Analysis of forage quality by NIRS usually involves dry ground samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations and spectral math treatments on prediction ability of chemical composition for corn silage by NIRS. A population of 112 corn silage representing a wide range in chemical parameters were used in this investigation. Samples of com silage were scanned at 2nm intervals over the wavelength range 400-2500nm and the optical data recorded as log l/Reflectance(log l/R) and scanned in overt-dried grinding(ODG), liquid nitrogen grinding(LNG) or intact fresh(IF) condition. Samples were analysed for neutral detergent fiber(NDF), acid detergent fiber(ADF), acid detergent lignin(ADL), crude protein(CP) and crude ash content were expressed on a dry-matter(DM) basis. The spectral data were regressed against a range of chemical parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with four spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation(SECV). The results of this study show that NIRS predicted the chemical parameters with very high degree of accuracy(the correlation coefficient of cross validation$(R^2cv)$ range from $0.70{\sim}0.95$) in ODG. The optimum equations were selected on the basis of minimizing the standard error of prediction(SEP). The Optimum sample preparation methods and spectral math treatment were for ADF, the ODG method using 2,10,5 math treatment(SEP = 0.99, $R^2v=0.93$), and for CP, the ODG method using 1,4,4 math treatment(SEP = 0.29. $R^2v=0.91$).

• Journal of Food Hygiene and Safety
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• v.21 no.4
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• pp.204-212
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• 2006

This study was conducted to investigate the possibility of rapid and non-des tructive evalution of AV (Acid Value), IV (Iodine Value) and fatty acids in sesame oils. The samples were scanned over the range $400\sim2500nm$ using transmittance spectrum of NIRS(Near-infrared spectroscopy). A calibration equation calculated by MPLS regression technique was developed and correlation coefficient of determination for AV, IV, palmitic acid, stearic acid, linoleic acid and linolenic acid content were 0.9907, 0.9677, 0.9527, 0.9210, 0.9829, 0.9736 and 0.9709 respectively. The validation model for measuring the AV content had R of 0.989, SEP of 0.058 and IV content had R of 0.944, SEP of 0.562 and palmitic acid content had R of 0.924, SEP of 0.194 and stearic acid content had R of 0.717, SEP of 0.168 and oleic acid content had R of 0.989, SEP of 0.221 and linoleic acid content had R of 0.967, SEP of 0.297 and linolenic acid content had R of 0.853, SEP of 0.480 by MPLS. The obtained results indicate that the NIRS procedure can potentially be used as a non-destructive analysis method for the purpose of rapid and simple measurement of AV, IV and fatty acids in sesame oils.

• Journal of Animal Environmental Science
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• v.13 no.2
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• pp.113-120
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• 2007

This study was conducted to measure Nitrogen(N), Phosphate($P_2O_5$), Potassium ($K_2O$), Organic matter(OM) and Moisture content of liquid pig manure by Near Infrared Spectroscopy(NIRS) and to develop an alternative and analytical instrument which are used for measurement of N, $P_2O_5$, $K_2O$, OM, and Moisture contents for liquid pig manure. The liquid pig manure sample's transmittance spectra were measured with a NIRS in the wavelength range of 400 to 2,500 nm. Multiple linear regression and partial least square regression were used for calibrations. The correlation coefficient(RSQ) and standard error of calibration(SEC) obtained for nitrogen were 0.9190 and 2.1649, respectively. The RSQ for phosphate, potassium, organic matter and moisture contents were 0.9749, 0.5046, 0.9883 and 0.9777, and the SEC were 0.5019, 1.9252, 0.1180 and 0.0789, respectively. These results are indications of the rapid determination of components of liquid pig manure through the NIR analysis. The simple analytical instrument for liquid pig manure consisted of a tungsten halogen lamp for light source, a sample holder, a quartz cell, a SM 301 spectrometer for spectrum analyzer, a power supply, an electronics, a computer and a software. Results showed that the simple analytical instrument that was developed can approximately predict the phosphate, organic matter and moisture content of the liquid pig manure when compared to the analysis taken by NIRS. The low predictability value of potassium however, needs further investigation. Generally, the experiment proved that the simple analytical instrument was reliable, feasible and practical for analyzing liquid pig manure.

• Journal of Food Hygiene and Safety
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• v.22 no.4
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• pp.257-267
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• 2007

The possibility of rapid non-destructive qualitative and quantitative analysis of vegetable oils such as perilla, com, soybean and rapaseed oils in sesame oils was evaluated. A calibration equation calculated by MPLS(Modified Partial Least Squares) regression technique was developed and coefficients of determination for perilla oil, com oil, soybean oil and rapaseed oil contents were 0.9992, 0.9694, 0.9795 and 0.9790 respectively. According to the data obtained from validation study, $R^2$ of contents of perilla, com, soybean, rapaseed oils were 0.997, 0.848, 0.957 and 0.968, and SEP of content of them 0.747, 5.069, 3.063 and 3.000 by MPLS respectively. The results indicate that the NIRS procedure can potentially be used as a non-destructive analysis method for the rapid and simple measurement of sesame oil mixed with other vegetable oils. The detection limits of the NIRS for perilla oil, com oil, soybean oil and rapaseed oil were presumed as 2%, $15{\sim}20%,\;15{\sim}20%$ and 10%, respectively.

• Journal of environmental and Sanitary engineering
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• v.22 no.4
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• pp.65-76
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• 2007

The possibility of rapid quantitative analysis of AV(Acid Value), IV(Iodine Value) and fatty acids in vegetable oils with NIRS(Near-infrared spectroscopy) was evaluated. A calibration equation calculated by MPLS regression technique was developed and correlation coefficient of determination for AV, IV, $C_{16:0},\;C_{18:0},\;C_{18:1},\;C_{18:2},\;C_{18:3},\;and\;C_{20:0}$ content were 0.9727, 0.997, 0.9805, 0.942, 0.9987, 0.9994, 0.9966, and 0.975 respectively. According to the data obtained from validation study, $R^2$ of contents of perilla, corn, soybean, rapaseed oils were 0.897, 0.993, 0.935, 0.707, 0.994, 0.996, 0.984, 0.798, SEP of contents of 0.185, 1.367, 0.899, 0.640, 1.498, 1.360, 0.476, 0.076 by MPLS. The results indicate that the NIRS procedure can potentially be used as a non-destructive analysis method for the rapid and simple measurement of AV, IV and fatty acids in vegetable oils.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.57 no.3
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• pp.296-300
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• 2012

Discrimination of geographic origin of agricultural products is a important issue in Korea because the price difference between Korean domestic and imported cereals is a key among some reasons. NIRS (Near Infrared Reflectance Spectroscopy) has been applied to classify the geographical origin of soybeans. Total 135 samples (Korean domestic 92 and foreign 43) were used to obtain calibration equation through 400~2,500 nm wavelength. The math treatment with 1st derivative and 4 nm gap and the modified partial least squares(MPLS)regression was outstanding for calibration equation. The standard error of calibration and determination coefficient in calibration set(n=115) was 6.65 and 0.98, respectively. And it showed that the extra 20 samples for validation equation were identified their authentication correctly. This study describes that the application of NIRS would be possible for discrimination of geographical origin between Korean domestic and imported soybeans.

• Journal of The Korean Society of Grassland and Forage Science
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• v.32 no.3
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• pp.301-308
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• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of Italian ryegrass silages. A population of 267 Italian ryegrass silages representing a wide range in chemical parameters and fermentative characteristics was used in this investigation. Samples of silage were scanned at 2 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of the highest coefficients of determination in cross validation ($R^2$) and the lowest standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The $R^2$ and SECV were 0.98 (SECV 1.27%) for moisture, 0.88 (SECV 1.26%) for ADF, 0.84 (SECV 2.0%), 0.93 (SECV 0.96%) for CP and 0.78 (SECV 0.56), 0.81 (SECV 0.31%), 0.88 (SECV 1.26%) and 0.82 (SECV 4.46) for pH, lactic acid, TDN and RFV on a dry matter (%), respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation quality of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.