Determination of forage quality of available species is one of the fundamental factors for the management of rangelands. Near-Infrared REFLECTANCE SPECTROSCOPY ((NIRS)) was used to analysis the Nitrogen (N), Acid Detergent Fiber (ADF), Dry Matter Digestibility (DMD) and Metabolizable Energy (ME) content of three phenological stages (vegetative, flowering and seeding) of Bromus tomentellus samples in grazing pastures of Iran. The sample set consisted of 40 samples for calibration and 23 samples for validation was used to prediction N, ADF, DMD and ME, separately. The samples were measured by REFLECTANCE NIR in a 950-1650 nm range. Calibration models between chemical data and NIR spectra were produced using the method of Partial Least Squares (PLS). The coefficients of determination (R2) and standard error of cross validation (SECV) were 0.94 (SECV: 0.208%), 0.98 (SECV: 1.76%), 0.98 (SECV: 1.97%), and 0.97 (SECV: 0.34%) for N, ADF, DMD and ME, respectively. The results obtained from this study indicated that (NIRS) had a potentiality to be used in predict the N, ADF, and the estimated DMD and ME of forage samples content.

Chemical assessments of forage clearly determine the forage quality; however, traditional methods of analysis are somehow time consuming, costly, and technically demanding. Near Infrared REFLECTANCE SPECTROSCOPY ((NIRS)) has been reported as a method for evaluating chemical composition of agriculture products, food, and forage and has several advantages over chemical analyses such as conducting cost-effective and rapid analyses with non-destructive sampling and small number of samples. This study aims to estimate Nitrogen (N) and Acid Detergent Fiber (ADF) content of grass species using (NIRS). A total of 171 samples of grasses (Poaceae) at vegetative, flowering, and seeding stages were collected from different regions in Iran. The samples were scanned in a (NIRS) DA 7200 (Perten instruments, Sweden) in a range of 950-1650 nm. The sample set consisted of 110 samples for calibration and 61 samples for validation was used to predict N and ADF. Samples were previously analyzed chemically for Nitrogen (N) and Acid Detergent Fiber (ADF) and then were scanned by (NIRS). Calibration models between chemical data and (NIRS) were developed using partial least squares regression with the internal cross validation. The coefficients of determination (r2) of linear regression between chemical analyses and (NIRS) were 0.90 and 0.94 for N and ADF, respectively. The standard errors of prediction were 0.30% and 3.10% for N and ADF, respectively. The results achieved from this study indicated that (NIRS) has a potential to be used in the measurement of N and ADF contents regarding the forage samples.

Nowadays, most attention is focused on physical and non-destructive methods, such as (NIRS), to measure the chemical composition of rangeland species. Therefore, the purpose of this study is to provide calibration models for Infrared (NIRS) to estimate the forage quality of shrub species, so that in addition to saving time and cost, the quality of these plants could be estimated with proper accuracy. For this purpose, 654 samples of vegetative, flowering and seeding stages were irradiated by the DA7200 Perten Instrument to estimate the values of nitrogen (N), crude protein (CP), acid detergent fiber (ADF), dry matter digestiblility (DMD) and metabolizable energy (ME) via (NIRS). Then, the data were transferred to the Unscrambler software for multivariate analysis. Before fitting the model, S. Golay and SNV methods were used for normalization of data distribution. Calibration and validation of model were performed using PLS1 method and Cross Validation method, respectively. Then, the predictability of models was evaluated by considering the calibration statistics. A total of 18 calibration programs were developed. Considering the calibration statistics, it could be said that the coefficient of determination was above 80% in all the factors studied. Also, at all growth stages, the correlation coefficient between the reference data and the data estimated by NIR was above 90%. Our results clearly showed that NIR calibrations obtained in this study could be used in current and future programs to assess the forage quality of shrub species used by livestock.

Near-infrared SPECTROSCOPY ((NIRS)) is a spectroscopic method that uses the near-infrared region of the electromagnetic spectrum. Typical applications include medical and physiological diagnostics. (NIRS) can be used for non-invasive assessment of brain function through the intact skull in human subjects by detecting changes in blood hemoglobin concentrations associated with neural activity, e. g., in branches of cognitive psychology as a partial replacement for fMRI techniques. (NIRS) cannot fully replace fMRI because it can only be used to scan cortical tissue, where fMRI can be used to measure activation throughout the brain. In this approach we compared (NIRS) with different functional brain tests.

The development of (NIRS) calibration model as a rapid, precise, robust, and cost-effective method to estimate oil content in ground seeds of worldwide safflower germplasm collection grown under different agro-climatic conditions was the key objective of this research project. The oil content was measured by accelerated solvent extraction method in a total of 328 samples collected across 2004 (165 samples) and 2005 (163) growing seasons and used as reference values. Two thirds of the measured samples were used for building the calibrations and one third for the validations.Combined and annual calibration and validation models were carried out by NIRCal 4.21 using the partial least squares (PLS) regression. Different data pretreatments such as full multiplicative scatter correction (MSC), first derivative or smoothing way of Savitzky-Golay with a gap of 9 data points were used to improve the calibration models. The optimum PLS factors for developing the best calibration were 12, 10, and 14 for combined model, annual model of 2004 and of 2005, respectively. In combined and annual models, the statistical parameters in calibration model were consistent with the respective parameters in validation model. Coefficient of variation (15.5 to 25.1) demonstrated high variability in calibration and validation models. The standard error of estimation (SEE) and standard error of prediction (SEP) for combined model were 1.40 and 1.43, respectively. Although the quality value (Q-value) of calibration was slightly higher in annual models (0.66 for both), the combined calibration model (0.64) precisely predicted oil content as indicated by higher coefficient of determination (0.90) and RPD (3.2%) compared to annual calibration. The accuracy and precision of the combined calibration model were sufficient to use (NIRS) as a tool for screening of oil content in a diverse safflower germplasm in the range obtained.

Near infrared REFLECTANCE SPECTROSCOPY ((NIRS)) is a nondestructive and rapid technique applied increasingly for food quality evaluation in recent years. In this research optical method based on nearinfrared SPECTROSCOPY (900-1600 nm) has been used to determine sugar content in sugar beet. A total of 120 samples were used for the modeling, whereas 80 samples were used for the calibration set and 40 samples were used for prediction set. Four pre-processing methods, including average smoothing, multiplicative scatter correction (MSC), first and second derivatives, were applied to improve the predictive ability of the models. Then models were developed by partial least squares (PLS). The correlation coefficient (r) and root mean square error of prediction (RMSEP) were 0.84 and 1.8 for SC, whereas 0.95 and 1.7 for SSC, respectively. The results show that NIR can be used as a rapid method to determine soluble solid content and sucrose in sugar beet.

Introduction: Single fiber REFLECTANCE SPECTROSCOPY (SFRS) is a noninvasive procedure to quantitate tissue absorption and scattering properties. It can be used to diagnose different diseases such as malignancy and pre-cancerous conditions. The measurement is done with a fiber optic probe in contact with the tissue surface. Herein, the effect of probe pressure on the extracted parameters from human lip spectra was studied.Methods: Thirty-three normal subjects were examined with three exerted pressure levels on the right, middle and left parts of their lips.Results: The results showed variation of spectroscopic parameters with different pressure levels. However, the effect was seen between a very mild contact (pressure 1) and the other reasonably practical pressure levels normally used in the medical centers.Conclusion: SFRS can be used as a reliable diagnostic tool in clinics.

The potential of Near Infrared REFLECTANCE SPECTROSCOPY ((NIRS)) as a fast method to predict the Crude Protein (CP) and Moisture (M) content in fishmeal by scanning spectra between 1000 and 2500 nm using multivariate regression technique based on Partial Least Squares (PLS) was evaluated. The coefficient of determination in calibration (R2 C) and Standard Error of Calibration (SEC) were 0.95 and 14.03 g/kg Dry Matter (DM) and 0.80 and 3.52 g/kg, for CP and M content, respectively. This study proved that the application of (NIRS) using PLS is well fitted to evaluate the protein and moisture content of fishmeal.

In this research, the ability of the REFLECTANCE near-infrared (NIR) spectrometry was investigated for non-destructive assessment of the sugar content of sugar beet roots. To this end, spectrometry of 120 samples of sugar beet was performed in the interactance measurement mode within the spectral range of 350-2500 nm using a contact probe. Spectral data obtained from the spectrophotometer included unwanted information and noise in addition to the information about the samples. In order to arrive at accurate analytical models, pre-processing of the spectral data was required prior to regression model simulation. For this purpose, multivariate calibration models of partial least squares (PLS) were developed based on the reference measurements and the information of the preprocessed spectra. A combination of different methods for assessment and prediction of sugar content was employed: smoothing, normalizing as well as increasing the spectral resolution. Prediction of the sugar content of intact samples with the PLS model based on SG + D2, had the best discrimination ability. Thus, SG+D2 preprocessing (R_C^2=0. 973, RMSEC = 0. 306, R_P^2= 0. 977, RMSEP = 0. 265) is suitable for predicting beet root sugar content with high accuracy (SDR= 6. 660).