Infrared Reflectance Spectroscopy Research Articles

Vis-NIR Reflectance Spectroscopy and PLSR to Predict PCB Content in Severely Contaminated Soils: A Perspective Approach

Soil reclamation from polychlorinated biphenyls (PCBs) requires careful analysis in terms of their concentrations and spatial distribution. Conventional laboratory analysis, even if providing the careful evaluation of PCBs, is costly and time-consuming. Therefore, rapid and cost-effective techniques to replace traditional analytical approaches are required. The utility of visible-near infrared (vis-NIR) reflectance spectroscopy in conjunction with partial least square regression (PLSR) analysis was evaluated in this study. Spectral reflectance was measured in the laboratory on 28 soil samples collected in a highly contaminated area of southern Italy and chemically analysed to determine eighteen PCB congeners, their sum (PCBs18), and extractable organic halogen content (EOX). Spectroscopic data were pre-processed prior to data analysis by combining different methods. Using PLSR analysis, significant relationships were observed between the predicted and the measured content of PCBs18, EOX, and the percentage of several isomeric classes of PCBs. Although rigorous models could not be calibrated, due to the limited number of samples, the preliminary results of this study demonstrated that vis-NIR reflectance spectroscopy, coupled with PLSR, can be considered a promising method for a rapid and cost-effective prediction of PCBs.

Probing carrier concentration of doped GaN single crystals from LO phonon-plasmon coupled modes

Raman and infrared reflectance spectroscopy were used to study GaN single crystals with different doping types and carrier concentrations. In different samples, the characteristic spectra related to LO phonon-plasmon coupled (LOPC) mode are observed as changing regularly with carrier concentration. Furthermore, to obtain the carrier concentration and mobility of GaN crystals, Raman and infrared reflectance spectra with LOPC modes were fitted. The carrier concentration obtained agrees well with that obtained from the Hall measurement, which confirms that these non-contact and non-destructive spectroscopy methods with high spatial resolution are reliable to probe the carrier concentration of doped GaN single crystals.

Application of Infrared Spectroscopy Techniques for Identification of Ancient Vegetation and Soil Change on Loess Areas

The discussion on the formation of Chernozems still has no consensus, and one of the outstanding questions is the type of the vegetation that supported the persistence of these soils in Central Europe over the Holocene period. The transformation of Chernozems and related soil types may be clarified by paleoenvironmental studies, which integrate different investigation techniques and proxy data. We propose a procedure based on infrared reflectance spectroscopy of soil organic matter, that presumably contains specific fingerprints from land use and plant cover. A database of spectra for 337 samples representing vegetation classes (grassland, woodland and arable) and loess soil types (Chernozem, Phaeozem, Luvisol) was created to build a mathematical model, which allows to identify the origin of buried soils with unknown history. The comparison confirmed the applicability of both near-infrared and mid-infrared spectroscopy, with higher statistical affinity of MIR. A clear disjunction of land use/vegetation classes was proven and allowed reliable association of the samples from buried soils with grassland/woodland and episodes of arable land use, followed by prevailing forest vegetation after burial. The findings are consistent with proposed models in Poland and Czechia, and confirm the potential of spectroscopy techniques in identification of soil types and their evolution.

Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice.

Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide a rapid assessment tool but their applicability is limited by the sample diversity, used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions, and reference data were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with R2 = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant.

Study of vegetable oils and their blends using infrared reflectance spectroscopy and refractometry

The present study aims to perform a comparative analysis of vegetable oils and their two-component blends using infrared spectroscopy and refractometry. The study was conducted in Almaty (Kazakhstan) in 2020. Three samples of 44 vegetable oils and their blends made from two components were examined. Fractometry and infrared spectroscopy were used to investigate the properties of blended vegetable oils. To this end, the fatty acid fraction (in percentage), iodine number, and index of refraction (IOR) were calculated. Afterward, the spectrograms obtained for the blends were analyzed. It was found that the difference between the intensities of weak bands and the band expansion of 722 cm−1 indicates greater expressiveness. When low-intensity bands (1653 cm−1) become more distinct due to vibrations of double carbon bonds (C-bonds), the level of unsaturated fatty acids in the blend increases as well.

Potential of GPR data fusion with hyperspectral data for precision agriculture of the future

Precision Agriculture (PA) requires accurate spatial and temporal information of soil properties at a very fine scale. Traditional soil characterization methods are time consuming, laborious and invasive and do not allow long-term repeatability of measurements. Ground Penetrating Radar (GPR) appears to be a particularly suitable methodology for characterizing soil and subsurface from a physical property point of view. Visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance spectroscopy has now become a widespread technique in soil analysis. Information on soil variability can be improved by the integration of data from multiple sensors. The overall objective of this paper was to examine the potential of fusing GPR data with hyperspectral data using multivariate geostatistics for delineating the management zones in the soil of an olive grove of centuries-old trees in Italy.A linear model of coregionalization (LMC) was individually fitted for the raw hyperspectral data and for GPR data including for each case a nugget effect and two spherical models at short scale and at longer scale. After that, one data set was obtained from the fusion of the two sensor data sets and a LMC was fitted for the combined data to be then used in factor cokriging. The application of this technique produced a delineation of the field into homogeneous zones, highlighting a wide southern-central zone, characterized by different granulometric and chemical properties. The proposed approach was then effective to discriminate areas with different properties by using multi-sensor data. It then has the potential to be used in PA.

Enlightening the structural, elastic, and luminescence properties of transparent Zn2SiO4 glass-ceramic by precipitation of Gd2O3 as dopant

This work focus on fabrication of new Gd3+-doped willemite (Zn2SiO4) glass-ceramics derived from the Gd2O3–ZnO–Al2O3–B2O3–SiO2 (Gd-ZABS) glass system by a controlled sintering process. The physical characteristic with formation of Gd3+-doped Zn2SiO4 glass-ceramic crystal phase, morphology and structural changes were examined by Archimedes method, X-ray diffraction (XRD), Field Emission Scanning Electron Microscope (FESEM) and Fourier-transform infrared (FTIR) reflection spectroscopy. The appearance of SiO2, ZnO4 and Zn–O–Si bands indicate that the formation of Zn2SiO4 crystal phase. As for elastic analysis, the ultrasonic velocity and elastic coefficients showed an increasing tendency with the increase in sintering temperature. The study on energy transfer has found that the optical band gap of Gd3+-doped Zn2SiO4 glass-ceramic has decreased as the sintering temperature increased. The luminescence spectra of Gd3+-doped Zn2SiO4 exhibit several strong emission peak located at 425, 447, 462, 485 and 530 nm after the samples were subjected to 310 nm excitation source. Prominent green emission of Gd3+-doped Zn2SiO4 glass-ceramic were strongly observed at 530 nm. These spectra show that when the sintering temperature increases, the luminescence performance of the glass-ceramics has improved.

Faecal near-infrared reflectance spectroscopy profiling for the prediction of dietary nutritional characteristics for equines

Faecal near infrared reflectance spectroscopy (NIRS) profiling involves the use of NIRS for the prediction of nutrient components of animal diets from their faecal samples. This study evaluated the potential use of faecal NIRS profiling for the prediction of minerals, fibre fractions, and organic matter (OM) in horses’ diet. Two feeding trials were conducted consisting of 29 horses housed in individual stables and fed unique diets (D) to obtain diet reference data and faecal (F) NIR spectra (D:F) pairs. The D:F pairs were used for the development of faecal NIRS predictive equations. The trials ran for a total of five weeks (7 days adaptation and 4 weeks of measurement). Diets were adjusted weekly by changing the forage and concentrate supplied to include variation in the diet. Faecal and diet samples were pooled per week per horse resulting in 116 D:F pairs (29 horses x 4 weeks). Samples from weeks 1–3 (n = 87) were used to develop calibration and week 4 samples were used as an independent validation set (n = 29). Modified partial least square regression was used for calibration development. Results obtained showed that faecal NIRS prediction of dietary OM was excellent [coefficient of determination of validation (R2val) = 0.92 and ratio performance deviation (RPD) = 3.54], while that of neutral detergent fibre (aNDF) was very good (R2val and RPD: 0.83 and 2.12). The validation statistics (R2val: 0.77 and RPD: 1.56) showed the prediction model for acid detergent fibre (ADF) was acceptable but not as precise as those of OM and aNDF. Prediction of acid detergent lignin (ADL) was the poorest (R2val = 0.41and RPD = 1.05). For the mineral components, prediction of copper (Cu) was very good (R2val = 0.81, RPD= 2.11) and better than those of Phosphorus (P), Sulphur (S) and Manganese (Mn) which all had R2val of 0.60–0.67 and RPD of 1.60–1.64. Overall, this study showed that faecal NIRS profiling can be used to successfully predict OM, aNDF and Cu concentration in horses’ diet. Although, the predictions of other fibre fractions were less satisfactory, faecal NIRS can still be used for monitoring purposes and improved for future application to independent samples.

Changes in the Properties of Native Low-Metamorphozed Coal in Contact with Air

Data on the analysis of the chemical composition and physicochemical properties of native low-metamorphosed coal at the initial moment of its contact with the air are presented. Diffuse reflectance IR spectroscopy, EPR spectroscopy, NMR spectroscopy, gas chromatography, chemical analysis of oxygen-containing groups, and the determination of the specific surface area and wettability of the contacting surface were used to identify changes in the organic matter of coal. The dynamics of changes in the numbers of paramagnetic centers and functional groups showed that the most intense transformations in the surface layer occurred in the first day of coal exposure to air. Next, oxidation at room temperature proceeded in a periodic mode of the accumulation and consumption of radicals and functional O groups. After four days, the process of low-temperature oxidation passed from the accessible outer surface into the diffusion region of the porous space of coal and gradually slowed down.

Shedding light on human tissue (in vivo) to predict satiation, satiety, and food intake using near infrared reflectance spectroscopy: A preliminary study

The aim of this study was to evaluate the ability of a portable near infrared (NIR) instrument to collect the spectra in vivo of different tissues in healthy individuals and to relate their spectral information with food and energy intake, satiation, and satiety data. In this study, a hand-held NIR instrument was used to collect the spectra of different human tissues (e.g. arm, ear, face, jaw and wrist) with partial least squares (PLS) regression used to relate the NIR data with food and energy intake, satiation, and satiety measured in healthy individuals. The coefficient of determination in cross-validation (R2CV) and the standard error in cross validation (SECV) for the prediction of satiety ranged between 0.58 and 0.62 and 223.3–235.0 total area under the curve (AUC), respectively, depending on the tissue analysed. The PLS cross-validation models based on the NIR spectra collected in both the arm and face tissues gave the best prediction of food intake (R2CV 0.47–0.51, SECV 110.8-115 g). No workable calibrations were developed for the prediction of satiation, which might be associated with the inherent complexity of this parameter as well as the experimental conditions used to collect the data (e.g. type of tissue analysed). These results demonstrated the potential ability of in vivo NIR spectroscopy to identify tissue differences associated with satiety and food intake in individuals. However, a wider variety of food types, diets, and human subjects (samples) are needed to develop robust relationships between the NIR spectra of a tissue with both satiety and food intake.

Probing the Drug Dynamics of Chemotherapeutics Using Metasurface-Enhanced Infrared Reflection Spectroscopy of Live Cells.

Infrared spectroscopy has drawn considerable interest in biological applications, but the measurement of live cells is impeded by the attenuation of infrared light in water. Metasurface-enhanced infrared reflection spectroscopy (MEIRS) had been shown to mitigate the problem, enhance the cellular infrared signal through surface-enhanced infrared absorption, and encode the cellular vibrational signatures in the reflectance spectrum at the same time. In this study, we used MEIRS to study the dynamic response of live cancer cells to a newly developed chemotherapeutic metal complex with distinct modes of action (MoAs): tricarbonyl rhenium isonitrile polypyridyl (TRIP). MEIRS measurements demonstrated that administering TRIP resulted in long-term (several hours) reduction in protein, lipid, and overall refractive index signals, and in short-term (tens of minutes) increase in these signals, consistent with the induction of endoplasmic reticulum stress. The unique tricarbonyl IR signature of TRIP in the bioorthogonal spectral window was monitored in real time, and was used as an infrared tag to detect the precise drug delivery time that was shown to be closely correlated with the onset of the phenotypic response. These results demonstrate that MEIRS is an effective label-free real-time cellular assay capable of detecting and interpreting the early phenotypic responses of cells to IR-tagged chemotherapeutics.

Estimation of forage quality by near infrared reflectance spectroscopy in dallisgrass, Paspalum dilatatum, poir

Dallisgrass ( Paspalum dilatatum Poir.) is an economically important and widely cultivated forage crop for livestock feeding in the tropical, subtropical, and warm temperate regions because of good adaptation to unsuitable pasture conditions. In this study, 216 dallisgrass samples were used to develop near infrared reflectance calibrations to estimate five forage quality parameters: dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and ash. Second derivative pretreatment was applied for calibration of DM, CP and NDF while a first derivative pretreatment was used for ADF and ash. The coefficients of determination in the internal validation set (r 2) were 0.78 for DM, 0.80 for CP, 0.95 for NDF 0.75 for ADF, and 0.71 for ash. The relative predictive determinant ratios for calibration indicate that the NDF equations were acceptable for quantitative prediction of dallisgrass quality, whereas the DM, CP, ADF, and ash equations were useful for screening purposes. The near infrared prediction models developed in this study can be used for screening in the forage breeding researches to be carried out for five quality parameters in the future.

A fast approach to measuring the thickness uniformity of a homoepilayer grown on to any type of silicon wafer

Optical reflection spectroscopy techniques offer a non-destructive and fast method of measuring the thickness of silicon (Si) epilayers, enabling very fast thickness uniformity mapping across the full surface of epiwafers up to 450 mm in diameter. However, their use for undoped or low doped epilayers has traditionally been constrained by a dependence on high levels of substitutional doping in the Si wafer, at values of approximately 5 × 1019 cm−3. Whilst the high dopant concentration of this wafer creates the necessary reflectance boundary for optical reflection, their commercial availability is mainly limited to the (001) surface orientation only. Optical reflectance techniques are therefore also limited in use to this orientation. In this article, an approach to measure the thickness of a Si epilayer on any Si wafer, independent of its crystallographic orientation, doping type and value, using Fourier transform infrared reflection spectroscopy is proposed and demonstrated. Because the use of non-destructive optical reflection spectroscopy is already common and well-understood within both industry and academia, this technique could easily be implemented within existing industrial and research fabrication facilities. Furthermore, this approach could be adapted, with further work, to suit other semiconductor materials and other optical reflection techniques.

Micro-Fourier-transform infrared reflectance spectroscopy as tool for probing IgG glycosylation in COVID-19 patients

It has been reported that patients diagnosed with COVID-19 become critically ill primarily around the time of activation of the adaptive immune response. However the role of antibodies in the worsening of disease is not obvious. Higher titers of anti-spike immunoglobulin IgG1 associated with low fucosylation of the antibody Fc tail have been associated to excessive inflammatory response. In contrast it has been also reported that NP-, S-, RBD- specific IgA, IgG, and IgM are not associated with SARS-CoV-2 viral load, indicating that there is no obvious correlation between antibody response and viral antigen detection. In the present work the micro-Fourier-transform infrared reflectance spectroscopy (micro-FTIR) was employed to investigate blood serum samples of healthy and COVID-19-ill (mild or oligosymptomatic) individuals (82 healthcare workers volunteers in “Instituto de Infectologia Emilio Ribas”, São Paulo, Brazil). The molecular-level-sensitive, multiplexing quantitative and qualitative FTIR data probed on 1 µL of dried biofluid was compared to signal-to-cutoff index of chemiluminescent immunoassays CLIA and ELISA (IgG antibodies against SARS-CoV-2). Our main result indicated that 1702–1785 hbox {cm}^{-1} spectral window (carbonyl C=O vibration) is a spectral marker of the degree of IgG glycosylation, allowing to probe distinctive sub-populations of COVID-19 patients, depending on their degree of severity. The specificity was 87.5 % while the detection rate of true positive was 100%. The computed area under the receiver operating curve was equivalent to CLIA, ELISA and other ATR-FTIR methods (>0.85). In summary, overall discrimination of healthy and COVID-19 individuals and severity prediction as well could be potentially implemented using micro-FTIR reflectance spectroscopy on blood serum samples. Considering the minimal and reagent-free sample preparation procedures combined to fast (few minutes) outcome of FTIR we can state that this technology is suitable for fast screening of immune response of individuals with COVID-19. It would be an important tool in prospective studies, helping investigate the physiology of the asymptomatic, oligosymptomatic, or severe individuals and measure the extension of infection dissemination in patients.

Impact damage of the surface layer of α-quartz amorphized by the Ar+ ions implantation

The goal of the performed experiments was to assess the effect of ion bombardment on the mechanical properties of α-quartz. The 40 keV Ar+-ions were implanted with fluences of 1014 and 1016 ion/cm2 were used. The accumulation of broken interatomic bonds in the sample surface layer was traced with the reflectance IR spectroscopy. The sample surface was subjected to the localized impact damaging by a pointed striker prior to and after the Ar+ implantation. The fractoluminescence (FL) excited in damaged samples was recorded with a 20 ns resolution during 1 ms. The statistical analysis of the energy distributions in impact-induced FL time series detected in unirradiated samples showed a random accumulation of newly formed point defects. The FL energy distributions in the ion-implanted samples followed a power law typical for the fracture of heterogeneous solids such as rocks, ceramics, etc.

Prediction of soil carbon and nitrogen contents using visible and near infrared diffuse reflectance spectroscopy in varying salt-affected soils in Sine Saloum (Senegal)

Soil organic carbon (C) and nitrogen (N) contents have an essential role in soil fertility, but they may be affected by salinity, which is especially responsible for land degradation in arid and semiarid regions. The objective of this work was to study the ability of visible and near infrared diffuse reflectance spectroscopy (VNIRS) to predict soil C and N contents and electrical conductivity (EC, a proxy for soil salinity) in variably salt-affected topsoils of the Sine Saloum region (Senegal). Different calibration procedures and spectral pretreatments were compared, and variable log-transformation usefulness was evaluated for prediction optimization.Predictions involved three calibration procedures: global partial least squares regression (PLSR), which used all calibration samples similarly; locally weighted (local) PLSR, with target samples predicted individually by giving higher weight to closest calibration spectra; and global PLSR per salinity class, after spectral discrimination of these classes. Predictions were performed with possible spectrum pretreatments (e.g., derivatization) and variable decimal log-transformation.The study was performed on 311topsoil samples (0–25 cm depth), either unsalted to slightly salty (Salt-, EC ≤ 2mScm−1; 262samples) or medium to highly salty (Salt+, EC > 2mScm−1; 49samples). Soil salinity was accurately discriminated using spectra: in validation, 100% and 95% of Salt- and Salt+ samples were correctly assigned on average, respectively. Best C and N content predictions were achieved after log-transformation using calibration by class (R2VAL = 0.87) and local calibration (R2VAL = 0.77), respectively; best EC prediction was achieved without log-transformation using global calibration (R2VAL = 0.90). This suggested C and N content predictions were affected by salinity; logC and logN distributions were almost symmetrical, hence log-transformation usefulness, while logEC distribution was very asymmetrical. No pretreatment yielded systematically good predictions; nevertheless, first-order derivative using 31-point gap often yielded good predictions, and second-order derivatives poor results.

Impacts of rice-husk biochar on soil microbial biomass and agronomic performances of tomato (Solanum lycopersicum L.)

Tomato is beneficial to human health because it contains valuable vitamins such as vitamins A, C and several minerals. However, to meet up with the demands of the ever increasing population, there is need to improve tomato production. This research, thus, investigated the impact of rice-husk biochar on the agronomic performances of tomato plant and microbial biomass of carbon, nitrogen and phosphorus in different tomato growth stages. The rice husk biochar pyrolyzed at 350 °C was amended with soil at four different application rates: 0, 2.5, 5.0 and 7.5 t/ha. Physicochemical property of soil was conducted using Mid Infrared Reflectance Spectroscopy method. Impact of biochar on Microbial Biomass Carbon, Microbial Biomass Nitrogen and Microbial Biomass Phosphorous was conducted using fumigation extraction method and monitored at three functional stages. Biochar application appreciably increase the soil physicochemical properties such as pH, Ca, Na, H+, S, P, B, C, Zn and cation exchangeable capacity in comparison with the control. Biochar amended soil significantly enhanced tomato height, stem girth, leaf area, flowers, fruit yields and weight. Although, B3 recorded the lowest leaf area, it possessed the highest number of fruits and fruit weight of 3 and 40%, respectively. The ratio of Microbial biomass C:N:P for biochar amended soil at 7.5 t/ha (B3) was 302.30:18.81:11.75 µg/g, compared to control, which was 242.12:18.30:11.49 µg/g. This study revealed that biochar amendments significantly (p < 0.05) increased the yields and microbial biomass of tomato plants. Conclusively, the application of rice-husk biochar (7.5 t/ha) to soil is considered as a suitable approach to improve tomato growth and yield.

Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen Content Under Drought Stress Using Near Infrared Spectroscopy.

Drought is a climatic event that considerably impacts plant growth, reproduction and productivity. Toona sinensis is a tree species with high economic, edible and medicinal value, and has drought resistance. Thus, the objective of this study was to dynamically monitor the physiological indicators of T. sinensis in real time to ensure the selection of drought-resistant varieties of T. sinensis. In this study, we used near-infrared spectroscopy as a high-throughput method along with five preprocessing methods combined with four variable selection approaches to establish a cross-validated partial least squares regression model to establish the relationship between the near infrared reflectance spectroscopy (NIRS) spectrum and physiological characteristics (i.e., chlorophyll content and nitrogen content) of T. sinensis leaves. We also tested optimal model prediction for the dynamic changes in T. sinensis chlorophyll and nitrogen content under five separate watering regimes to mimic non-destructive and dynamic detection of plant leaf physiological changes. Among them, the accuracy of the chlorophyll content prediction model was as high as 72%, with root mean square error (RMSE) of 0.25, and the RPD index above 2.26. Ideal nitrogen content prediction model should have R2 of 0.63, with RMSE of 0.87, and the RPD index of 1.12. The results showed that the PLSR model has a good prediction effect. Overall, under diverse drought stress treatments, the chlorophyll content of T. sinensis leaves showed a decreasing trend over time. Furthermore, the chlorophyll content was the most stable under the 75% field capacity treatment. However, the nitrogen content of the plant leaves was found to have a different and variable trend, with the greatest drop in content under the 10% field capacity treatment. This study showed that NIRS has great potential for analyzing chlorophyll nitrogen and other elements in plant leaf tissues in non-destructive dynamic monitoring.

Near infrared reflectance spectroscopy as a tool to predict non-starch polysaccharide composition and starch digestibility profiles in common monogastric cereal feed ingredients

Carbohydrates are the main components of cereal grain ingredients and are comprised predominantly of starch which varies in the rate and extent of digestibility, and non-starch polysaccharides (NSP) from plant cell walls. These components are traditionally measured by chemical methods but there is interest in developing more rapid, cost effective predictions by use of near infrared reflectance spectroscopy (NIRS) technology. In this study, around 400 samples of wheat, corn, barley, wheat bran, oats, rye, corn germ, and sorghum were collected over several years to develop global NIR calibrations. Chemical analysis was performed for NSP, including details on solubility and individual constituent sugars, as well as separate determinations of cellulose and lignin. Total starch content and starch digestibility were characterized by the method of Englyst as rapidly (RDS) and slowly (SDS) digestible starch, and resistant starch (RS) determined as total starch content minus RDS and SDS. The same samples were scanned (400 to 2500 nm, every 0.5 nm) by two NIRS machines: a Foss model DS2500 and a Bruker Tango FT-NIR (Fourier transform near-infrared spectroscopy). The NIRS calibrations were developed by using the combined set of different cereal grains. The modified Partial Least Squares Regression method was employed to establish correlations between the reference chemical analysis and the collected NIRS spectra. The correlation between the NIRS and reference data for total and insoluble NSP and the main component sugars (arabinose, xylose, and glucose) were robust (R2 = >0.97) with a 1-VR > 0.96 and RPD values > 5.3. Similarly, the NIRS predicted lignin (R2 = 0.94; 1-VR = 0.93) and to a lesser extent cellulose (R2 = 0.70; 1-VR = 0.98) but still highly correlated with the reference data. The predicted total starch content was excellent (R2 = 0.99; 1-VR = 0.99). The NIRS prediction for RDS and SDS were lower in linearity (R2 = 0.81 and R2 = 0.97, respectively) but more than sufficient to provide an estimate of the starch digestion profile for many of the raw materials evaluated. In addition, the robust NIRS predictions for total starch content, RDS and SDS could then provide information to calculate the RS fraction of the ingredients. In conclusion, compared with the reference chemical data for this large set of cereal grains the global NIR calibrations developed provide a good prediction of NSP, monosaccharide sugars, lignin, cellulose and total starch content in cereal grains, and for the first time an NIR relationship with starch digestibility fractions in the grains has been shown.

Prediction of quality of total mixed ration for dairy cows by near infrared reflectance spectroscopy and empirical equations

ABSTRACT This study aimed to evaluate the predictive ability of NIRS for the estimation of the chemical composition and organic matter digestibility (OMD) of total mixed rations (TMR) for dairy cows. Moreover, herein, empirical equations based on chemical parameters were developed for calculating the OMD. Samples were collected from Galician dairy farms and were scanned in duplicate a Foss NIRSystem 6500 monochromator (1100-2500 nm). The predictive ability of NIRS models was evaluated according to the coefficient of determination in external validation ( 2). High 2 (equal to or higher than 0.90) was shown in predicting chemical composition, however, the estimation of OMD was acceptable ( 2 = 0.83). The ability of the NIRS models for estimating the chemical composition was considered excellent, with the ratio performance deviation of external validation () higher than 3.0, allowing for quantitative predictions. The estimation of the OMD value by NIRS was considered acceptable ( = 2.5), and led to reduction in the standard error of external validation, in comparison of the best empirical model based on the chemical composition of samples (from ± 2.06% to ± 1.89%). Therefore, this study demonstrated that the NIRS is an effective technology for the rapid and precise nutritional evaluation of TMR in Galicia.