• Korean Journal of Soil Science and Fertilizer
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• v.42 no.6
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• pp.522-528
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• 2009

This study focused on establishing prediction models using visible-near infrared spectrum to simultaneously detect multiple components of soils and enhancing the performance quality by suitably transformed input spectra and classification of soil spectral types for prediction model input. The continuum-removed spectra showed significant result for all cases in terms of soil properties and classified or bulk predictions. The prediction model using classified soil spectra at an absorption peak area around 500nm and 950nm efficiently indicating soil color showed slightly better performance. Especially, Ca and CEC were well estimated by the classified prediction model at $R^{2}$ > 0.8. For organic carbon, both classified and bulk prediction model had a good performance with $R^{2}$ > 0.8 and RPD> 2. This prediction model may be applied in global soil mapping, soil classification, and remote sensing data analysis.

• Analytical Science and Technology
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• v.16 no.4
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• pp.277-282
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• 2003

The amount of water for the cultivation of citrus is different based on the growing period. The effect of water stress induces to enhance of sugar accumulation in citrus. The water content in the leaves of citrus can be a index for watering during cultivation. The purpose of this study is to determine the water content of citrus leaves non-destructively by using near infrared spectroscopy (NIRS). Citrus leaves were prepared from 'Okitsu' Satusuma mandarin leaves (Citrus unshiu Marc.) ranging from 20.80 to 69.98% of water content by loss on drying method, and NIR reflectance spectra of citrus leaves were acquired by using a fiber optic probe. It was found that the variation of absorbance band 1450 nm from OH vibration of water depending on the water content change. Partial least squares regression (PLSR) was applied to develop a calibration model over the spectral range 1100-1700 nm. The calibration model predicted the water content for the validation set with a standard errors of prediction (SEP) of 0.97%. In order to validate the developed calibration model, routine analyses were performed using independently prepared citrus leaves. The NIR routine analyses showed good results with those of loss on drying method with a SEP of 0.81%. The rapid and non-destructive determination of the water content in citrus leaves was successfully performed by portable NIR system.

• Applied Chemistry for Engineering
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• v.28 no.5
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• pp.587-592
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• 2017

Uniform and optimum sized silver nanoplates were synthesized through the liquid phase reduction method by using silver nitrate solution as a starting chemical, dimethylformmide (DMF) as a reducing solvent, and polyvinylpyrrolidone (PVP) as reducing and surfactant agents. Synthesized and also film samples were characterized by using SEM, TEM, UV-Vis-NIR spectroscopy, particle size analyzer (PSA), and XRD. Triangle nanoplates with the size of 100~200 nm were found from the sample synthesized at $70^{\circ}C$ for 72 h using silver nitrate, DMF and 26 wt% PVP. The sample could reflect near-infrared light because it showed the maximum absorbing peak at about 1,000 nm. When the content or particle size of silver nanoplates increased in coating solutions, the transmittance decreased and the reflectance increased in film samples.

• Environmental Engineering Research
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• v.17 no.4
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• pp.179-184
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• 2012

The light absorbance of photocatalysts and reaction kinetics of environmental pollutants at the liquid-solid and gas-solid interfaces differ from each other. Nevertheless, many previous photocatalytic studies have applied the science to aqueopus applications without due consideration of the environment. As such, this work reports the surface and morphological characteristics and photocatalytic activities of carbon-embedded (C-$TiO_2$) photocatalysts for control of gas-phase methyl tertiary-butyl ether (MTBE) under a range of different operational conditions. The C-$TiO_2$ photocatalysts were prepared by oxidizing titanium carbide powders at $350^{\circ}C$. The characteristics of the C-$TiO_2$ photocatalysts, along with pure TiC and the reference pure $TiO_2$, were then determined by X-ray diffraction, scanning emission microscope, diffuse reflectance ultraviolet-visible-near infrared (UV-VIS-NIR), and Fourier transform infrared spectroscopy. The C-$TiO_2$ powders showed a clear shift in the absorbance spectrum towards the visible region, which indicated that the C-$TiO_2$ photocatalyst could be activated effectively by visible-light irradiation. The MTBE decomposition efficiency depended on operational parameters, including the air flow rate (AFR), input concentration (IC), and relative humidity (RH). As the AFRs decreased from 1.5 to 0.1 L/min, the average efficiencies for MTBE increased from 11% to 77%. The average decomposition efficiencies for the ICs of 0.1, 0.5, 1.0, and 2.0 ppm were 77%, 77%, 54%, and 38%, respectively. In addition, the decomposition efficiencies for RHs of 20%, 45%, 70%, and 95% were 92%, 76%, 50%, and 32%, respectively. These findings indicate that the prepared photocatalysts could be effectively applied to control airborne MTBE if their operational conditions were optimized.

• Journal of The Korean Society of Grassland and Forage Science
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• v.35 no.3
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• pp.257-263
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• 2015

This study was conducted to assess the potential of using NIRS to accurately determine the chemical composition and fermentation parameters in fresh coarse sorghum and sudangrass silage. Near Infrared Spectroscopy (NIRS) has been increasingly used as a rapid and accurate method to analyze the quality of cereals and dried animal forage. However, silage analysis by NIRS has a limitation in analyzing dried and ground samples in farm-scale applications because the fermentative products are lost during the drying process. Fresh coarse silage samples were scanned at 1 nm intervals over the wavelength range of 680~2500 nm, and the optical data were obtained as log 1/Reflectance (log 1/R). The spectral data were regressed, using partial least squares (PLS) multivariate analysis in conjunction with first and second order derivatization, with a scatter correction procedure (standard normal variate and detrend (SNV&D)) 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 showed that NIRS predicted the chemical constituents with a high degree of accuracy (i.e. the correlation coefficient of cross validation ($R^2{_{cv}}$) ranged from 0.86~0.96), except for crude ash which had an $R^2{_{cv}}$ of 0.68. Comparison of the mathematical treatments for raw spectra showed that the second-order derivatization procedure produced the best result for all the treatments, except for neutral detergent fiber (NDF). The best mathematical treatment for moisture, acid detergent fiber (ADF), crude protein (CP) and pH was 2,16,16 respectively while the best mathematical treatment for crude ash, lactic acid and total acid was 2,8,8 respectively. The calibrations of fermentation products produced poorer calibrations (RPD < 2.5) with acetic and butyric acid. The pH, lactic acid and total acids were predicted with considerable accuracy at $R^2{_{cv}}$ 0.72~0.77. This study indicated that NIRS calibrations based on fresh coarse sorghum and sudangrass silage spectra have the capability of assessing the forage quality control

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1513-1513
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• 2001

Present Food Legislation compels dairy industry to carry out analyses in order to guarantee the food safety and quality of products. Furthermore, in many cases industry pays milk according to bacteriological or/and nutritional quality. In order to do these analyses, several expensive instruments are needed (Milkoscan, Fossomatic, Bactoscan). NIRS technology Provides a unique instrument to deal with all analytical requirements. It offers as main advantages its speed and, specially, its versatility, since not only allows determine all the parameters required in milk analysis, but also allows analyse other dairy products, like cheese or whey. The objective of this study is to develop NIRS calibration equations to predict several quality parameters in goat milk, cheese and whey. Three sets of 123 milk samples, 190 cheese samples and 109 whey samples, have been analysed in a FOSS NIR Systems 6500 I spectrophotometer equipped with a spinning module. Milk and whey were analysed by folded transmission, using circular cells with gold surface and pathlength of 0.1 m, while intact cheese was analysed by reflectance using standard circular cells. NIRS calibrations were obtained for the prediction of chemical composition in goat milk, for fat (r$^2$=0.92; SECV=0.20%), total solids (r$^2$=0.95: SECV=0.22%), protein (r$^2$=0.94; SECV=0.07%), casein (r$^2$=0.93; SECV=0.07%) and lactose (r$^2$=0.89; SECV=0.05%). Moreover, equations have been performed to determine somatic cells (r$^2$=0.81; SECV=276.89%) and total bacteria (r$^2$=0.58; SECV=499.32%) counts in goat milk. In the case of cheese, calibrations were obtained for the prediction of fat (r$^2$=0.92; SECV=0.57), total solids (r$^2$=0.80; SECV=0.92%) and protein (r$^2$=0.70; SECV=0.63%). In whey, fat (r$^2$=0.66; SECV=0.08%), total solids (r$^2$=0.67; SECV=0.19%) and protein (r$^2$=0.76; SECV=0.07%) NIRS equations were obtained. These results proved the viability of NIRS technology to predict chemical and microbiological parameters and somatic cells count in goat milk, as well as chemical composition of goat cheese and whey.

• Journal of The Korean Society of Grassland and Forage Science
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• v.29 no.3
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• pp.253-262
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• 2009

The present study was conducted to develop a rapid and accurate method of evaluating chemical composition of total mixed ration (TMR) for dairy cows using near infrared reflectance spectroscopy (NIRS). A total of 253 TMR samples were collected from TMR manufacturers and dairy farms in Korea. Prior to NIR analysis, TMR samples were dried at $65^{\circ}C$ for 48 hour and then ground to 2 mm size. The samples were scanned at 2 nm interval over the wavelength range of 400-2500 nm on a FOSS-NIR Systems Model 6500. The values obtained by NIR analysis and conventional chemical methods were compared. Generally, the relationship between chemical analysis and NIR analysis was linear: $R^2$ and standard error of calibration (SEC) were 0.701 (SEC 0.407), 0.965 (SEC 0.315), 0.796 (SEC 0.406), 0.889 (SEC 0.987), 0.894 (SEC 0.311), 0.933 (SEC 0.885) and 0.889 (SEC 1.490) for moisture, crude protein, ether extract, crude fiber, crude ash, acid detergent fiber (ADF) and neutral detergent fiber (NDF), respectively. In addition, the standard error of prediction (SEP) value was 0.371, 0.290, 0.321, 0.380, 0.960, 0.859 and 1.446 for moisture, crude protein, ether extract, crude fiber, crude ash, ADF and NDF, respectively. The results of the present study showed that the NIR analysis for unknown TMR samples would be relatively accurate. Use of the developed NIR calibration curve can obtain fast and reliable data on chemical composition of TMR. Collection and analysis of more TMR samples will increase accuracy and precision of NIR analysis to TMR.

• 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. $R^2v=0.91$).

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.2
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• pp.217-223
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• 2008

Amylose and protein contents are important traits determining the edible quality of rice, especially in East Asian countries. Near-Infrared Reflectance Spectroscopy (NIRS) has become a powerful tool for rapid and nondestructive quantification of natural compounds in agricultural products. To test the practically of using NIRS for estimation of brown rice amylose and protein contents, the spectral reflectances ($400{\sim}2500\;nm$) of total 9,483 accessions of rice germplasm in Rural development Administration (RDA) Genebank ere obtained and compared to chemically determined amylose and protein content. The protein content of tested 119 accessions ranged from 6.5 to 8.0% and 25 accessions exhibited protein contents between 8.5 to 9.5%. In case of amylose content, all tested accessions ranged from 18.1 to 21.7% and the grade from 18.1 to 19.9% includes most number of accessions as 152 and 4 accessions exhibited amylose content between 20.5 to 21.7%. The optimal performance calibration model could be obtained from original spectra of brown rice using MPLS (Modified Partial Least Squares) with the correlation coefficients ($r_2$) for amylose and protein content were 0.865 and 0.786, respectively. The standard errors of calibration (SEC) exhibited good statistic values: 2.078 and 0.442 for amylose and protein contents, respectively. All these results suggest that NIR spectroscopy may serve as reputable and rapid method for quantification of brown rice protein and amylose contents in large numbers of rice germplasm.

• Journal of agriculture & life science
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• v.46 no.1
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• pp.189-194
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• 2012

Measurement of compositions and somatic cells in raw milk by chemical methods usually requires a lot of time, skilled labor and expensive analytical equipments. Recently, near-infrared reflectance spectroscopy (NIRS), which is a rapid, cost-effective and non-destructive technique, has been extensively used for safety and quality evaluation in the field of dairy products. However, less study has been performed to evaluate the effect of transmitted light on milk quality during NIRS analysis. Therefore, the objective of this study was to analyze the changes in milk quality using transmitted light. Raw milk samples collected from dairy farm from Siga prefecture in Japan were analyzed for fat, protein, lactose, solids not fat, total solids, milk urea and citric acid using the Milko scan 4000. Somatic cells in raw milk samples were counted by the Fossomatic 5000. Transmittance spectra of 50 ml raw milk samples were obtained by the Lax-Cute lighter in the 400 nm or less, 689 nm, 773 nm, 900 nm and 979 nm. As a result, milk fat as well as somatic cell count was increased by 2.6% and 9.0%, respectively. The other compositions were, however, changed within the relative error of the measurement. Further studies are needed to apply raw milk quality evaluation using the UV band by accumulating more samples and more data.