• Journal of Animal Environmental Science
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• v.18 no.3
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• pp.257-266
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• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal and dired animal forages. Analysis of forage quality by NIRS usually involves dry grinding 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 on prediction ability of chemical composition and fermentation parameter for Italian ryegrass silages by NIRS. A population of 147 Italian ryegrass silages representing a wide range in chemical parameters were used in this investigation. Samples were scanned at 1nm intervals over the wavelength range 680-2500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in oven-dried grinding and fresh ungrinding condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with four spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV) and maximizing the correlation coefficient of cross validation (${R^2}_{CV}$). The results of this study show that NIRS predicted the chemical parameters with high degree of accuracy in oven-dried grinding treatment except for moisture contents. Prediction accuracy of the moisture contents was better for fresh ungrinding treatment (SECV 1.37%, $R^2$ 0.96) than for oven-dried grinding treatments (SECV 4.31%, $R^2$ 0.68). Although the statistical indexes for accuracy of the prediction were the lower in fresh ungrinding treatment, fresh treatment may be acceptable when processing is costly or when some changes in component due to the processing are expected. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation parameter of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.4107-4107
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• 2001

Previous works have shown the viability of NIRS technology for the prediction of fatty acids in Iberian pig fat, but although the resulting equations showed high precision, in the predictions of new samples important fluctuations were detected, greater with the time passed from calibration development to NIRS analysis. This fact makes the use of NIRS calibrations in routine analysis difficult. Moreover, this problem only appears in products like fat, that show spectrums with very defined absorption peaks at some wavelengths. This circumstance causes a high sensibility to small changes of the instrument, which are not perceived with the normal checks. To avoid these inconveniences, the software WinISI 1.04 has a mathematic algorithm that consist of create a “Repeatability File”. This file is used during calibration development to minimize the variation sources that can affect the NIRS predictions. The objective of the current work is the evaluation of the use of a repeatability file in quantitative NIRS analysis of Iberian pig fat. A total of 188 samples of Iberian pig fat, produced by COVAP, were used. NIR data were recorded using a FOSS NIRSystems 6500 I spectrophotometer equipped with a spinning module. Samples were analysed by folded transmission, using two sample cells of 0.1mm pathlength and gold surface. High accuracy calibration equations were obtained, without and with repeatability file, to determine the content of six fatty acids: miristic (SECV$\sub$without/=0.07% r$^2$$\sub$without/=0.76 and SECV$\sub$with/=0.08% r$^2$$\sub$with/=0.65), Palmitic (SECV$\sub$without/=0.28 r$^2$$\sub$without/=0.97 and SECV$\sub$with/=0.24% r$^2$$\sub$with/=0.98), palmitoleic (SECV$\sub$without/=0.08 r$^2$$\sub$without/=0.94 and SECV$\sub$with/=0.09% r$^2$$\sub$with/=0.92), Stearic (SECV$\sub$without/=0.27 r$^2$$\sub$without/=0.97 and SECV$\sub$with/=0.29% r$^2$$\sub$with/=0.96), oleic (SECV$\sub$without/=0.20 r$^2$$\sub$without/=0.99 and SECV$\sub$with/=0.20% r$^2$$\sub$with/=0.99) and linoleic (SECV$\sub$without/=0.16 r$^2$$\sub$without/=0.98 and SECV$\sub$with/=0.16% r$^2$$\sub$with/=0.98). The use of a repeatability file like a tool to reduce the variation sources that can disturbed the prediction accuracy was very effective. Although in calibration results the differences are negligible, the effect caused by the repeatability file is appreciated mainly when are predicted new samples that are not in the calibration set and whose spectrum were recorded a long time after the equation development. In this case, bias values corresponding to fatty acids predictions were lower when the repeatability file was used: miristic (bias$\sub$without/=-0.05 and bias$\sub$with/=-0.04), Palmitic (bias$\sub$without/=-0.42 and bias$\sub$with/=-0.11), Palmitoleic (bias$\sub$without/=-0.03 and bias$\sub$with/=0.03), Stearic (bias$\sub$without/=0.47 and bias$\sub$with/=0.28), oleic (bias$\sub$without/=0.14 and bias$\sub$with/=-0.04) and linoleic (bias$\sub$without/=0.25 and bias$\sub$with/=-0.20).

• 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.

• Clinical and Experimental Pediatrics
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• v.48 no.6
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• pp.649-654
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• 2005

Purpose : The present study examined how changes in cerebral hemodynamics in newborn piglets with bilirubin infusion can be evaluated by near infrared sepctroscopy(NIRS). Methods : Seventeen newborn piglets were randomly divided into the following three experimental groups : six in the control group(CG); seven in the bilirubin infusion group(BG), and four in the bilirubin infusion with 7-nitroindazole group(NG). To achieve the concentration of bilirubin above 20 mg/dL, we injected a bolus of 40 mg/kg of bilirubin intravenously, followed by 30 mg/kg/hr of bilirubin continuous intravenous infusion. All groups were monitored with cerebral hemodynamics using near infrared spectroscopy(NIRS) and their brain cortexes were harvested and the activities of $Na^+$, $K^+$-ATPase, level of conjugated dienes, ATP and phosphocreatine(PCr) were determined biochemically. Results : No changes took place in CG. In BG and NG, base excess, pH, and MABP decreased, and lactate level in blood increased. Cerebral $Na^+$, $K^+$-ATPase activity and ATP, PCr level in BG significantly decreased and conjugated dienes increased compared to CG. These abnormalities observed in the BG were significantly improved in the NG. In continuous NIRS monitoring, [$HbO_2$], [HbT], and [HbD] in BG were significantlly decreased compared to CG. However these abnormalities between NG and CG were not significantly different. There were no significant differences in $ScO_2$ between the study groups. Conclusion : Our study suggests cerebral hemodynamic changes could be monitored by non-invasive NIRS in newborn piglets with bilirubin infusion.

• Journal of the Korean Chemical Society
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• v.47 no.3
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• pp.250-256
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• 2003

In this work, the effect of cationic initiator content on the electron-beam (EB) curing process of diglycidylether of bisphenol-A (DGEBA) resin was studied using near-infrared spectroscopy (NIRS), thermogravimetric analysis (TGA), and critical stress intensity factor $(K_{IC})$. Benzylquinoxalinium hexafluoroantimonate (BQH) were used as an initiator and its content was varied from 0.5 to 3 phr. NIRS measurements showed that the hydroxyl group of EB-cured epoxy resin was increased with increasing the BQH content. Thermal stability and $K_{IC}$ value of EB-cured epoxy resin were increased with increasing the BQH content but were decreased above 2 phr content. These results could be attributed to the decrease of the conversion and degree of crosslinking. In another word, the conversion and degree of crosslinking were restricted by the incomplete network structure from high reactivity at the BQH content above 2 phr, resulting in decreasings of thermal stability and $K_{IC}$.

• Journal of the Korean Society of Tobacco Science
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• v.20 no.2
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• pp.183-190
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• 1998

This study was conducted to analyze chemical components in flue-cured tobacco using near infrared spectroscopy(NIRS). Samples were collected in '96 and '97 crop year and were scanned in the wavelengths of 400 ～ 2500 nm by near infrared analyzer(NIRSystem Co., Model 6500). Calibration equations were developed and then analyzed flue-cured samples by NIRS. The standard error of calibration(SEC) and performance (SEP) of '96 crop year samples between NIRS and standard laboratory analysis(SLA) were 0.18% and 0.24% for nicotine, 1.60% and 1.77% for total sugar, 0.13% and 0.15% for total nitrogen, 0.58% and 0.68% for crude ash, 0.23% and 0.28% for ether extracts, and 0.09% and 0.08% for chlorine, respectively. The coefficient of determination($R^2$) of calibration and prediction samples between NIRS and SLA of '96 crop year samples was 0.94～0.99 and 0.83～0.97 depending on chemical components, respectively. The SEC and SEP of '97 crop year samples were similar to those of '96 crop year samples. The SEP of '97 crop year samples which were analyzed using '96 calibration equation was 0.32 % for nicotine, 2.72% for total sugar, 0.14 % for total nitrogen, 1.00 % for crude ash, 0.48 for ether extracts and 0.17% for chlorine, respectively. The prediction result was more accurate when calibration and prediction samples were produced in the same crop year than those of the different crop year. The SEP of '96 and '97 crop year samples using calibration equation which was developed '96 plus '97 crop year samples was similar to that of '96 crop year samples using 96 calibration equation and that of '97 crop year samples using '97 calibration equation, respectively. The SEP of '97 crop year samples using calibration equation which was developed '96 plus '97 crop year samples was lower than that of '97 crop year samples analyzed by '96 calibration equation. To improve the analytical inaccuracy caused by the difference of crop year between calibration and prediction samples, we need to include the prediction sample spectra which are different from calibration sample spectra in recalibration sample spectra, and then develop recalibration equation. Although the analytical result using NIR is not as good as SLA, the chemical component analysis using NIR can apply to tobacco leaves, leaf process or tobacco manufacturing process which demand the rapid analytical result.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.6
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• pp.394-399
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• 2005

Near Infrared Spectroscopy (NIRS) has been used as a tool for the rapid, accurate and nondestructive assay of the fresh rice leaf in nitrogen content. NIRS used in this study was visible and near infrared spectroscopy type instrument, Foss model 6500. To obtain a useful calibration equation, standard regression between the data was analyzed by chemical analysis and by NIRS method. Accuracy of calibration equation for nitrogen content on fresh leaf of rice were 0.879, 0.858 and 0.819, respectively. Accuracy of calibration equation after outlier treatment increased as 0.017, 0.02 and 0.061 improved each with 0.896, 0.878 and 0.880, respectively. Calibration equation combined using merge function after accuracy of calibration equation more increased by 0.911. Difference analysis value between calibration equation and lab value by kjeldahl showed $0.001\%$. With this as same result is the possibility of closing the deterioration of the sample in order to omit a construction and pulverization process it is judged with the fact that the nitrogen content measurement of the fresh rice leaf which the possibility of reducing an hour and an expense is by a near infrared spectroscopy technique will be possible.

• Journal of The Korean Society of Grassland and Forage Science
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• v.37 no.2
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• pp.154-160
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• 2017

This study was conducted to find out an alternative way of rapid and accurate analysis of chemical composition of permanent pastures in hilly grazing area. Near reflectance infrared spectroscopy (NIRS) was used to evaluate the potential for predicting proximate analysis of permanent pastures in a vegetative stage. 386 pasture samples obtained from hilly grazing area in 2015 and 2016 were scanned for their visible-NIR spectra from 400~2,400nm. 163 samples with different spectral characteristics were selected and analysed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF) and neutral detergent fiber (NDF). Multiple linear regression was used with wet analysis data and spectra for developing the calibration and validation mode1. Wavelength of 400 to 2500nm and near infrared range with different critical T outlier value 2.5 and 1.5 were used for developing the most suitable equation. The important index in this experiment was SEC and SEP. The $R^2$ value for moisture, CP, CA, CF, Ash, ADF, NDF in calibration set was 0.86, 0.94, 0.91, 0.88, 0.48 and 0.93, respectively. The value in validation set was 0.66, 0.86, 0.83, 0.71, 0.35 and 0.88, respectively. The results of this experiment indicate that NIRS is a reliable analytical method to assess forage quality for CP, CF, NDF except ADF and moisture in permanent pastures when proper samples incorporated into the equation development.

• Journal of The Korean Society of Grassland and Forage Science
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• v.37 no.4
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• pp.350-357
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• 2017

Near infrared spectroscopy (NIRS) is a rapid and accurate method for analyzing the quality of cereals, and dried animal forage. However, one limitation of this method is its inability to measure fermentation parameters in dried and ground samples because they are volatile, and therefore, respectively lost during the drying process. In order to overcome this limitation, in this study, fresh coarse haylage was used to test the potential of NIRS to accurately determine chemical composition and fermentation parameters. Fresh coarse Italian ryegrass haylage samples were scanned at 1 nm intervals over a wavelength range of 680 to 2500 nm, and optical data were recorded as log 1/reflectance. Spectral data, together with first- and second-order derivatives, were analyzed using partial least squares (PLS) multivariate regressions; scatter correction procedures (standard normal variate and detrend) were used in order to reduce the effect of extraneous noise. Optimum calibrations were selected based on their low standard error of cross validation (SECV) values. Further, ratio of performance deviation, obtained by dividing the standard deviation of reference values by SECV values, was used to evaluate the reliability of predictive models. Our results showed that the NIRS method can predict chemical constituents accurately (correlation coefficient of cross validation, $R_{cv}^2$, ranged from 0.76 to 0.97); the exception to this result was crude ash ($R_{cv}^2=0.49$ and RPD = 2.09). Comparison of mathematical treatments for raw spectra showed that second-order derivatives yielded better predictions than first-order derivatives. The best mathematical treatment for DM, ADF, and NDF, respectively was 2, 16, 16, whereas the best mathematical treatment for CP and crude ash, respectively was 2, 8, 8. The calibration models for fermentation parameters had low predictive accuracy for acetic, propionic, and butyric acids (RPD < 2.5). However, pH, and lactic and total acids were predicted with considerable accuracy ($R_{cv}^2$ 0.73 to 0.78; RPD values exceeded 2.5), and the best mathematical treatment for them was 1, 8, 8. Our findings show that, when fresh haylage is used, NIRS-based calibrations are reliable for the prediction of haylage characteristics, and therefore useful for the assessment of the forage quality.

• Journal of The Korean Society of Grassland and Forage Science
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• v.31 no.4
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• pp.415-422
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• 2011

This study evaluated the feasibility of using a portable near infrared reflectance spectrometer working in the 900~1,600 nm range for the measurement of quality-related parameters (moisture, pH, Acid detergent fiber (ADF), Neutral detergent fiber (NDF), Crude protein (CP), lactic acid) in intact silage. The calibration result for the Phazir (handheld, Polychromix) was compared with the result for the Spectrastar (Lab. based, Unity). A total of 67 Italian ryegrass silages were used to build calibration models using different spectral signal pre-treatments and the partial least squares regression (PLS) method. The good calibration statistics in two instruments was obtained for moisture content of Italian ryegrass silages with over $R^2$ = 0.95. The Phazir spectrometer was less accurate in measuring of ADF, NDF and CP contents. The Spectrastar instrument yielded greater precision for pH and lactic acid content; statistic values were over $R^2$ = 0.82 and the standard error of calibration (SEC) = 0.21% and 0.24%. Thus, the NIR measurement of forage quality in the field by portable NIR analyzers was shown not to be feasible, but additional investigations are required to discern the key instrumental and operational parameters that may influence the portable NIR measurement.