• Tuberculosis and Respiratory Diseases
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• v.42 no.1
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• pp.84-92
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• 1995

Background: Nasal applied continuous positive airway pressure(CPAP) is a highly effective method of treatment for obstructive sleep apnea syndrome. More than a decade of accumulated experience with this treatment modality confirmed that it is unquestionably the medical treatment of choice for patients with obstructive sleep apnea syndrome. However it takes long time to reach optimal CPAP pressure. To save the time to reach optimal pressure, it is necessary to clarify the time to reach optimal pressure for treatment of obstructive sleep apnea syndrome. Method: CPAP pressure is titrated during an overnight study according to a standardized protocol. Just before the presleep bio-calibration procedures, the technician applies the nasal mask and switches on the clinical CPAP unit. Initial positive for pressure is typically 3.0 centimeters of water pressure. After sleep onset, the technician gradually increases the pressure until sleep-disordered breathing events disappear or become minimal. The pressure must maintain maximal airway patency during both NREM and REM sleep to be considered effective. Before recommending a final pressure setting, sleep recording and oximetry data are reviewed by an American Board of Sleep Medicine certified Sleep Specialist and a Registrered Polysomnographic Technologist. Results: We examined the time required to reach optimal pressure during routine CPAP titration in 127 consecutively evaluated individuals diagnosed with sleep-disordered breathing. Results indicate that 33% of patients required more than four hours to attain satisfactory titration. This indicates that a four-hour session is marginally enough time, at best, to determine a proper CPAP pressure setting. Moreover, 60 of 127 patients required further adjustment after optimal pressure was reached. These additional pressure trials were needed to confirm that higher pressures were not superior for eliminating sleep-disordered breathing events. Conclusions: The data presented underscore the logistical difficulty of titrating CPAP during split-night studies without modifying the titration procedure. Futhermore, the time needed to reach optimal pressure makes it improbable that proper CPAP titration can be performed during a 2-3 hour nap study.

• 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.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.4
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• pp.258-263
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• 2019

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. The objective of this study was to evaluate the potential of NIRS, applied to imported forage, to estimate the moisture and chemical parameters for imported hays. A population of 392 imported hay representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1 nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which 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 based on the highest coefficients of determination in cross validation(R²) 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 R² and SECV for imported hay calibration were 0.92(SECV 0.61%) for moisture, 0.98(SECV 0.65%) for acid detergent fiber, 0.97(SECV 0.40%) for neutral detergent fiber, 0.99(SECV 0.06%) for crude protein and 0.97(SECV 3.04%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of imported hay in Korea for routine analysis method to evaluate the feed value.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.1
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• pp.50-57
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• 2016

This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were 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 several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation ($R^2{_{cv}}$) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, $R^2{_{cv}}$, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy ($R^2{_{cv}}$ 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.