Near Infrared Reflectance Spectroscopy Analysis Research Articles

Rapid and cost‐effective assessment of nutrients in pistachio (Pistacia vera L.) leaves through Fourier transform near‐infrared spectroscopy (FT‐NIRS)

AbstractBackgroundPistachio nuts provide many health benefits in human diet. Nutrient levels in plant leaves and fertilizer schedules are determined based on traditional soil and leaf chemical analyses. However, these methods require additional labor, time, and cost, which is why most farmers do not prefer them and cannot detect nutrient deficiencies in time. Fast, easy‐to‐use and low‐cost nutrient level assessment techniques are needed.AimThis study aims to explore the viability of near‐infrared reflectance spectroscopy (NIRS) as a fast, user friendly, and cost effective technique for evluating the major macro‐ and micronutrient contents of dried and ground pistachio leaf sample.MethodsThe feasibility of NIRS for estimating nutrient contents was investigated by analyzing samples from 200 pistachio trees. Dried and ground pistachio leaves were subjected to NIRS analysis. PLSR (Partial least square regression) analyses were performed to develop nutrient content prediction models using spectral information of samples.ResultsIt was found that the NIRS system had a very good potential to estimate the K, Ca, Cu, and Mg contents of the leaf samples (R2 > 0.80). It was also found that Fe and Mn concentrations could also be estimated with good accuracy (R2 = 0.70–0.80). However, the NIRS system did not give good results for the prediction of N, P, and Zn (R2 < 0.40).ConclusionIn conclusion, the NIRS system can be used to quickly, easily, and economically assess the K, Ca, Cu, Mg, Fe, and Mn contents of dried and ground pistachio leaves. This technique has the potential to improve nutrient management practices in pistachio farming within a sustainable and environmentally conscious framework. Fourier transform NIRS (FT‐NIRS) can provide valuable insights by complementing rather than replacing traditional chemical analysis. Laboratory analysis is still required for definitive nutrient content information, but FT‐NIRS can significantly reduce the reliance on such analysis, thereby mitigating the environmental impact caused by the excessive use of chemical fertilizers and minimizing the health risks to laboratory staff. In addition, the rapid information‐gathering capabilities of the FT‐NIRS can be emphasized.

Improper sample preparation negatively affects near infrared reflectance spectroscopy (NIRS) nutrient analysis of ground corn

Near infrared reflectance spectroscopy (NIRS) is widely used in the feed industry for the determination of nutrient composition of feed ingredients and finished feeds. This technology is a useful tool that can provide instant nutrient values for nutritionists to make sure that feeds are manufactured to specification. However, to achieve proper nutrient estimations from NIRS, samples must be ground to a specific particle size for each evaluation. Previous studies have demonstrated that particle size (PS) of the sample may affect NIRS nutrient estimates of corn and other feedstuffs. Therefore, an experiment was conducted to evaluate the effect of corn PS on the accuracy of NIRS proximate nutrient estimations. Corn was ground using a 2-pair roller mill set at 8 different roller pair settings. Roller pair spacing ranged from 50.8 µm at setting 2 to 152.4 μm 152.4 µm at setting 6. Roller mill settings 3–2, 3–3, 4–3, 4–4, 5–4, 5–5, 6–5, 6–6 generated samples with mean PS of 693, 882, 968, 1750, 1877, 2862, 2911, and 3343 µm, respectively. A rotor grinder with a 500-mesh screen was used to grind corn to 267 µm, which served as a control for the study as it is the recommended method for feed sample preparation per NIRS equipment recommendations. An NIR spectrometer and Adisseo Precise Nutrition Evaluation software with a calibration for ground corn were used to determine dry matter, ash, crude fat, crude protein, and crude fiber from 3 replicate samples from each PS treatment. Data were analyzed as a 1-way ANOVA using the GLIMMIX procedure of SAS ver. 9.4 and least square means were separated using the PDIFF option at P ≤ 0.05. Samples ground with only the roller mill with PS larger than the control treatment were lower in dry matter, ash, and crude fat. Crude protein content of ground corn was also lower in samples with PS larger than the control except for samples ground to 3343 µm (6−6). In contrast, crude fiber was greater in samples with PS greater than the control treatment. Overall, the results of this study underscore the importance of proper sample preparation to obtain accurate nutrient content estimations from NIRS analysis.

Determination of Quality Parameters in Mangetout (Pisum sativum L. ssp. arvense) by Using Vis/Near-Infrared Reflectance Spectroscopy.

Pisum sativum L. ssp. arvense, is colloquially called tirabeque or mangetout because it is eaten whole; its pods are recognized as a delicatessen in cooking due to its crunch on the palate and high sweetness. Furthermore, this legume is an important source of protein and antioxidant compounds. Quality control in this species requires the analysis of a large number of samples using costly and laborious conventional methods. For this reason, a non-chemical and rapid technique as near-infrared reflectance spectroscopy (NIRS) was explored to determine its physicochemical quality (color, firmness, total soluble solids, pH, total polyphenols, ascorbic acid and protein content). Pod samples from different cultivars and grown under different fertigation treatments were added to the NIRS analysis to increase spectral and chemical variability in the calibration set. Modified partial least squares regression was used for obtaining the calibration models of these parameters. The coefficients of determination in the external validation ranged from 0.50 to 0.88. The RPD (standard deviation to standard error of prediction ratio) and RER (standard deviation to range) were variable for quality parameters and showed values that were characteristic of equations suitable for quantitative prediction and screening purposes, except for the total soluble solid calibration model.

The Planetary Terrestrial Analogues Library (PTAL) – An exclusive lithological selection of possible martian earth analogues

The Planetary Terrestrial Analogues Library (PTAL) is a dedicated lithological collection that currently consists of 102 terrestrial rock samples selected to be possible Mars analogues. The ultimate goal is improving future remote mineralogical and petrological analysis on Mars and other planetary bodies based on selected analysis such as Near- Infrared Reflectance Spectroscopy (NIR), Raman spectroscopy, Laser Induced Breakdown Spectroscopy (LIBS) and X-ray diffraction (XRD).Most international standards applied in the remote martian mineralogical and petrological analysis have so far been based on single, pure mineral analysis, with minimal interferences from other naturally occurring minerals. Here we present detailed lithological sample evaluations based on field appearance along with optical and XRD analysis of key terrestrial rock types. The detailed mineralogical and petrological descriptions give good basis for more complete lithological understanding. In combination with NIR, LIBS and Raman analysis of the very same samples PTAL aims at improving mineralogical and petrographical information from future rovers on Mars e.g. NASA's Mars2020-Perseverance and ESA and Roscosmos's ExoMars - Rosalind Franklin.The PTAL sample collection covers exclusively collected volcanic, magmatic and various sedimentary rocks and regoliths from well-known locations all over the world. These samples have a general composition comparable to what is currently known from Mars. The strength of this sample collection is its origin as common whole rock samples, in which minerals occur in their natural settings. It thereby allows studying possible detection interferences and a comparison of the sensitivity of the different techniques. The collection, in addition, forms the base for various alteration studies to better understand and explain alteration and weathering conditions on Mars.The complete results and sample preparations will be available to all scientists interested.

Near Infrared Reflectance Spectroscopy Analysis to Predict Diet Composition of a Mountain Ungulate Species.

Simple SummaryUngulates are characterized by their ability to modify or maintain habitats through their impact on plant species composition and the structure of vegetation. Assessing the diet composition of ungulates is therefore important to understand their role in the ecosystem integrity and to develop monitoring and population management plans. The diet composition of free-ranging ungulates has most often been assessed by time-consuming and cost-intensive approaches such as the direct observation of animals, chemical analysis, molecular approaches or microhistological analysis of fecal samples. Near infrared reflectance spectroscopy analysis would be a quick, economic and non-destructive alternative to assess diet composition using fecal samples. In this work, we evaluated the use of this spectroscopy method to assess the diet composition of the Pyrenean chamois (Rupicapra pyrenaica pyrenaica), a medium-size subalpine ungulate with a broad dietary niche. Our results support the reliability of the fecal spectroscopy analysis to monitor diet composition of free-ranging ungulates.The diet composition of ungulates is important to understand not only their impact on vegetation, but also to understand the consequences of natural and human-driven environmental changes on the foraging behavior of these mammals. In this work, we evaluated the use of near infrared reflectance spectroscopy analysis (NIRS), a quick, economic and non-destructive method, to assess the diet composition of the Pyrenean chamois Rupicapra pyrenaica pyrenaica. Fecal samples (n = 192) were collected from two chamois populations in the French and Spanish Pyrenees. Diet composition was initially assessed by fecal cuticle microhistological analysis (CMA) and categorized into four functional groups, namely: woody, herbaceous, graminoid and Fabaceae plants. Regressions of modified partial least squares and several combinations of scattering correction and derivative treatments were tested. The results showed that models based on the second derivative processing obtained the higher determination coefficient for woody, herbaceous and graminoid plants (R2CAL, coefficient of determination in calibration, ranged from 0.86 to 0.91). The Fabaceae group, however, was predicted with lower accuracy (R2CAL = 0.71). Even though an agreement between NIRS and CMA methods was confirmed by a Bland–Altman analysis, confidence limits of agreement differed by up to 25%. Our results support the viability of fecal NIRS analysis to study spatial and temporal variations of the Pyrenean chamois’ diets in summer and winter when differences in the consumption of woody and annual plants are the greatest. This new use for the NIRS technique would be useful to assess the consequences of global change on the feeding behavior of this mountain ungulate and also in other ungulate counterparts.

Development of novel technological approaches for a reliable crop characterization under changing environmental conditions

The expansion of world population requires the development of new strategies and tools for agriculture. Extensive breeding and agronomic efforts over the past 50 years have been responsible for tripling cereal yields, while advances in grain quality have been less evident. Continuing advances in the techniques available to breeders offer the potential to increase the rate of genetic improvement aiming to develop resilient crop and better (more resource use efficient) varieties. Plant breeders want to be able to phenotype large numbers of lines rapidly and accurately identify the best progeny. For this purpose, different methodological approaches have been proposed to evaluate these traits in the field: (1) proximal (remote) sensing and imaging, (2) laboratory analyses of samples, and (3) lab-based near-infrared reflectance spectroscopy analysis in the harvestable part of the crop. However, near-infrared reflectance spectroscopy-based field evaluation of yield and grain quality is currently a real option. Thus the development of new technological approaches, such as the use of hyperspectral imaging sensors or near-infrared reflectance spectroscopy under field conditions may be critical as a phenotypic approach for efficient breeding as well as in field management of crops. This article reports the description of the CropYQualT-CEC project funded by the H2020-MSCA-RISE program. This project pursues the main objective of generating a common solid knowledge basis within the context of precision agriculture and digital farming. Further, within the project context, the article also provides a case study in which prediction models for total grain protein content, based on the reflectance spectrum of wheat canopies, are presented. Measurements were performed at around anthesis, using a full range near-infrared reflectance spectroscopy field spectrometer. Several models explaining >60% of grain protein variance in field trials illustrate the predictive capacity and robustness of this methodology for inferring grain quality traits well in advance of harvest.

Both genetic and environmental conditions affect wheat grain texture: Consequences for grain fractionation and flour properties

This review summarizes the results of studies on near-isogenic common wheat lines differing in the Pinb-D1 allele encoding puroindoline B or durum wheat into which both wild-type puroindoline genes were introduced. The material was grown in different environments to evaluate the respective effect of puroindoline genes or of the environmental factors on grain characteristics and milling behavior.Environmental conditions were found to impact grain porosity (=1/vitreousness) and the presence of both wild-type puroindoline genes was found to reduce the vitreousness threshold under 60%. Hardness measurements with single kernel characterization system were found to differ from near-infrared reflectance spectroscopy analysis and were linearly related to vitreousness but differently depending on the puroindoline allele carried.Puroindoline genes were found to play a major role in the grain porosity, breaking energy, size of generated particles and in the concentration of phytic acid and damaged starch into flour whereas vitreousness introduced variations in the ability to break and in the level of damaged starch.Finally, the highest flour yield is obtained from either vitreous common wheat grains carrying the wild-type puroindoline alleles or carrying mutated alleles and displaying low vitreousness. This result was confirmed using common French wheat cultivars whose puroindoline genes were identified.

Diets selected and growth of steers grazing buffel grass (Cenchrus ciliaris cv. Gayndah)–Centro (Centrosema brasilianum cv. Oolloo) pastures in a seasonally dry tropical environment

Context Liveweight (LW) gain of grazing cattle in the seasonally dry tropics is usually moderate during the wet season (WS) and declines to slow growth or LW loss during the dry season (DS). Cattle growth can often be improved by inclusion of herbaceous legumes into pastures to improve their nutritional quality. Aims A study examined the quality of the diet selected and the growth of young cattle grazing a buffel grass (Cenchrus ciliaris)–Centro (Centrosema brasilianum) pasture in a high-rainfall, seasonally dry, tropical environment in northern Australia to relate the diet selected to cattle growth. Methods During three annual cycles, young steers grazed a grass–Centro legume pasture at moderate stocking rate. LW was measured monthly, and diet attributes (legume content, DM digestibility (DMD) and crude protein concentration) were measured fortnightly by near-infrared reflectance spectroscopy analyses of faeces. Pasture available and species were measured twice annually. Key results The annual LW gain and diet attributes followed a consistent profile through the annual cycles. Following the seasonal break, the diet DMD and crude protein concentration increased abruptly to maxima (means 732 and 184 g/kg respectively), and then declined approximately linearly during the remainder of the WS and the wet–dry transition season (TS); DMD decreased by 0.49, 0.74 and 0.88 g/kg units per day. DMD and crude protein averaged 561 and 61 g/kg respectively during the DS. Centro comprised 86–291 g/kg of the pasture on offer, and averaged 283 and 205 g/kg of the diet during the TS and DS, respectively, but only 58 g/kg during the WS. Cattle selected for Centro during the TS and the DS, but not during the WS. Cattle LW gain reflected diet quality averaging 0.86, 0.59 and 0.12 kg/day during the WS, TS and DS respectively. Conclusions The Centro legume contributed substantially to the diet of growing cattle during the TS and DS, but not during the WS. The LW gains of cattle were moderate during the WS and TS, and low during the DS. Implications Centro in a buffel grass pasture contributed substantially to the diet, but nevertheless annual LW gain was only modest (mean 179, range 159–209 kg/annum).

Effects of ensiling time on corn silage neutral detergent fiber degradability and relationship between laboratory fiber analyses and in vivo digestibility

Accurate analysis of degradability of silage neutral detergent fiber (NDF) is important for diet formulation and to predict lactational performance of dairy cows. In this study, 5 corn silage hybrids ensiled for 0 (unfermented), 30, 60, 120, and 150 d were used to determine the effects of ensiling time on silage neutral detergent fiber degradability (NDFD) and to assess the relationships between near-infrared reflectance spectroscopy (NIR) NDF-related analyses and in situ NDFD variables. In addition, the relationships between dietary concentration of indigestible NDF, 288-h incubation (iNDF288), or undegraded NDF, 240-h incubation (uNDF240), and in vivo total-tract apparent organic matter and NDF digestibility were studied in total mixed ration samples from 16 experiments with lactating dairy cows. Ensiling time had no effect on silage NDF concentration; however, the ratio of acid detergent fiber ÷ NDF increased, and estimated hemicellulose concentration decreased quadratically with ensiling time. Also, concentration of NDF-bound protein decreased, and that of lignin increased linearly with ensiling time. These changes in silage fiber composition resulted in a linear decrease in in situ effective degradability of silage NDF with increasing ensiling time. The indigestible fraction of NDF and concentration of structural carbohydrates were not affected by ensiling time. Correlations of in situ NDFD variables with laboratory NIR NDFD analyses were weak to moderate. The relationship of corn silage uNDF240 with lignin concentration or 30-h NDFD (all NIR analyses) was remarkably good (R2 = 0.73 and 0.88, respectively). The relationship between in situ iNDF288 concentration (but not uNDF240) and in vivo total-tract apparent digestibility of dietary organic matter and NDF was good (R2 = 0.72 and 0.80, respectively). In conclusion, in situ degradability of silage NDF linearly decreased from 0 to 150 d ensiling time, primarily caused by a decrease in concentrations of hemicellulose and NDF-bound protein. In situ NDF degradability measurements and common laboratory NIR NDF-related analyses were generally poorly correlated. We found a good relationship between in vivo NDF digestibility and dietary concentration of iNDF288 determined in situ, but the relationship with uNDF240 was poor.

Visible and Near-Infrared Reflectance Spectroscopy Analysis of a Coastal Soil Chronosequence

The soil chronosequence is a useful method for investigating pedological theories. Soil chemical, physical and mineralogical properties in chronosequences change over time and exhibit systematic and time-dependent trends, which can be used to analyze the rates and directions of pedogenic changes. The potential of soil spectroscopy as an emerging, rapid and cost-effective technique for predicting soil properties has been widely accepted and has motivated the application of spectroscopic techniques to the analysis of soil chronosequence. We present a soil chronosequence derived from 1000-year-old calcareous marine sediments and examine changes in six soil properties over this period. We evaluated the utility of a soil spectroscopic method to detect soil property changes and to predict the pedogenic properties and soil ages of the chronosequence. The results show that some soil pedogenic processes, such as soil organic matter accumulation, CaCO3 leaching and clay migration, can be identified in the millennium chronosequence. Power chronofunctions are formulated for soil organic matter (SOM) and Logarithmic chronofunctions are fitted for clay, CaCO3 and pH. These pedogenic processes are identified in the reflectance intensity and absorption features of soil spectroscopy, and pedogenic properties can be calibrated via soil reflectance spectroscopy. Profile ages can also be predicted via pseudo multi-depth spectra of soil profiles, and soil spectral curves for 0–30 cm generated the best prediction results (RPD = 1.85). We conclude that soil properties, changing due to weathering and soil formation, act as a bridge linking spectroscopy and weathering levels/pedogenic processes. The results imply that applying spectroscopy techniques to chronosequence study and mapping the degree of soil development in certain areas should be possible.

Prediction of fatty acid composition of sunflower seeds by near-infrared reflectance spectroscopy.

This study was performed in order to evaluate efficiency of near-infrared reflectance spectroscopy (NIRS) for the determination of fatty acid composition ratio of sunflower seeds and to compare performance of calibration methods. Calibration equations were developed using modified partial least squares (MPLS) and partial least squares (PLS) regression methods. Ninety-three sunflower seed varieties were from test field of East Mediterranean Agricultural Research Institute. In order to determine the reference fatty acid values needed to construct calibration in NIRS analysis, sunflower seed samples were analyzed by gas chromatography method. Coefficients of determination (R2) in calibration were developed using MPLS and PLS as follows: for palmitic acid 0.706-0.664, for stearic acid 0.615-0.654, for oleic acid 0.996-0.994, for linoleic acid 0.995-0.994, for arachidic acid 0.768-0.643, for linolenic acid 0.818-0.763, for behenic acid 0.891-0.776, for eicosapentaenoic 0.933-0.892, for unsaturated fatty acid 0.837-0.890 and for saturated fatty acid 0.837-0.890 respectively. The results showed that NIRS was a reliable technique that can be used as a tool for rapid pre-screening of fatty acid composition of sunflower seeds.

Post-sampling procedure affects the measured nutritive value of perennial ryegrass (Lolium perenne)

Abstract Estimates of pasture nutritive value are affected by how samples are stored and dried before laboratory analyses. To determine which post-sampling procedures best preserved nutritive value, a range of procedures were compared for one diploid and one tetraploid perennial ryegrass cultivar in June 2016 and April 2017. Treatments included different combinations of transportation from the field to the laboratory either in liquid nitrogen or in a chilly-bin with ice-packs, storage at room temperature (19°C), a chiller (3°C), standard freezer (-18°C) or a -80°C freezer, and drying either in a freeze-drier or an oven at 65°C for 48 hours. Samples were sent to a commercial laboratory for near-infrared reflectance spectroscopy analysis. Values were higher for one or more of metabolisable energy, soluble sugars, non-structural carbohydrate, and organic matter digestibility, and lower for neutral detergent fibre and ash, when comparing transportation in liquid nitrogen with a chilly-bin, freezer storage (-80°C and standard freezer) with chiller and room temperature storage, and freeze-drying with ovendrying (P

Application of fecal near-infrared reflectance spectroscopy profiling for the prediction of diet nutritional characteristics and voluntary intake in beef cattle.

The objective of this study was to evaluate the use of fecal near-infrared reflectance spectroscopy (NIRS) profiling to predict diet nutritional characteristics and voluntary DMI in beef cattle. Fecal samples were collected for growing cattle across 11 experiments in which individual animal performance and DMI was measured. Dried and ground fecal composite samples collected from each animal were subjected to fecal NIRS analysis by a Foss NIRS 6500 scanning monochromator (Foss, Eden Prairie, MN) at the Grazingland Animal Nutrition Laboratory (Temple, TX). Fecal spectra were then used to develop equations to predict diet composition (trials 1 to 11; = 408), digestibility (trials 1 to 5; = 155), and DMI (trials 1 to 11; = 408). Coefficients of determination for calibration () and cross-validation () for prediction of diet nutritional characteristics were lower for NDF ( = 0.85; = 0.82) than for CP ( = 0.90; = 0.88). For the prediction of DMI, and ranged from 0.69 and 0.67 for the prediction of trial-average DMI to 0.76 and 0.73 for the prediction of fecal-collection-period DMI. While the and obtained for the prediction of DMI were lower than those obtained for the prediction of diet composition or digestibility, fecal NIRS prediction equations for DMI were successful in predicting the mean DMI of groups, as no differences were found for the prediction of fecal-collection-period DMI (Diff. = 1.10; = 0.72) or trial DMI (Diff. = -0.47; = 0.86).

Titration and Spectroscopic Measurements of Poultry Litter pH Buffering Capacity.

The pH value of poultry litter is affected by nitrification, mineralization, and the addition of acidifying chemicals, all acting on the poultry litter pH buffering capacity (pHBC). Increased understanding of poultry litter pHBC will aid in modeling NH volatilization from surface-applied poultry litter as well as estimating rates of alum applications. Our objectives were to (i) determine the pHBC of a wide range of poultry litters; (ii) assess the accuracy of near-infrared reflectance spectroscopy (NIRS) for determining poultry litter pHBC; and (iii) demonstrate the use of poultry litter pHBC to increase the accuracy of alum additions. Litter pHBC was determined by titration and calculated from linear and sigmoidal curves. For the 37 litters measured, linear pHBC ranged from 187 to 537 mmol (pH unit) kg dry litter. The linear and sigmoidal curves provided accurate predictions of pHBC, with most > 0.90. Results from NIRS analysis showed that the linear pHBC expressed on an "as is" water content basis had a NIRS coefficient of calibration (developed using a modified partial least squares procedure) of 0.90 for the 37 poultry litters measured. Using the litter pHBC, an empirical model was derived to determine the amount of alum needed to create a target pH. The model performed well in the range of pH 6.5 to 7.5 (RMSE = 0.07) but underpredicted the amount of alum needed to reach pH <6. The lack of model performance at pH <6 was probably due to Al reacting with organic matter in the poultry litter, which prevented its hydrolysis.

Chemical Composition and Calorific Value Prediction of Wheat Straw at Different Maturity Stages Using Near-Infrared Reflectance Spectroscopy

Rapid assessment of wheat straw at different maturity stages will help to reveal its growth mature and enable better process control that will optimize the sustainable value-added usage. This study explored the potential of near-infrared reflectance (NIR) spectroscopy to quantitatively and qualitatively analyze the multiple chemical composition and calorific value of wheat straw at different maturity stages. Partial least-squares (PLS) and genetic algorithm and partial least-squares (GA-PLS) models were used for NIR spectroscopy analysis. Results showed that PLS models and GA-PLS models could be both used for the estimation of chemical composition and calorific value of wheat straw at three maturity stages, and the GA-PLS method reduced the spectral variables for modeling and provided sensitive spectral variables correlated well with chemical composition and calorific value. NIR spectroscopy could successfully detect the contents of water-soluble carbohydrates (WSC), crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), ash, and nitrogen (N). It was also able to quantify dry matter (DM), cellulose (Cel), moisture (Moist), volatile matter (VM), higher heating value (HHV), and lower heating value (LHV). The NIR models for hemicellulose (Hem), lignin (Lig), carbon (C), sulfur (S), and hydrogen (H) had moderate accuracy, and could be used for qualitative analysis, whereas for fixed carbon (FC), it was suitable for screening purposes.

Near infrared reflectance spectroscopy (NIRS) for predicting glucocorticoid metabolites in lyophilised and oven-dried faeces of red deer

Interest in measuring faecal glucocorticoid metabolites (FGM) as indicators of physiological homeostasis and performance in wildlife is increasing. However, current reference techniques, specifically enzyme immunoassays (EIAs) and radioimmunoassays (RIAs), are expensive, time-consuming, reagent-based, and the samples are destroyed during their application. Conversely, near infrared reflectance spectroscopy (NIRS) is a rapid, reagent-free and non-destructive technique, which, once calibrated by standard laboratory methods, can be used at a low cost. The objectives of this study were to evaluate the feasibility of using NIRS to predict glucocorticoid metabolite concentrations in red deer (Cervus elaphus) faeces, as well as the effect of lyophilisation and oven drying on FGM quantification. Seventy-eight fresh faecal samples were collected directly from the rectum of hunter-harvested red deer and then divided into two equal portions; one portion of each individual sample was lyophilised and the other portion was oven-dried. After dehydration, all faecal samples were ground and then analysed by RIA (standard laboratory technique) and scanned with an NIR spectrophotometer. Modified partial least squares regression was used to generate NIRS calibration equations for both lyophilised and oven-dried samples and a cross-validation procedure was employed for their optimisation. Near infrared reflectance spectroscopy proved to be a feasible, acceptably accurate and reliable technique for predicting FGM concentrations in red deer faeces subjected either to lyophilisation or to oven drying. Calibration and cross-validation results indicated that predictive equations for lyophilised faeces were slightly more precise and robust than for the oven-dried ones (lyophilised: R2=0.90, r2cv=0.81, RPD=2.72; oven-dried: R2=0.88, r2cv=0.79, RPD=2.26; CV: cross-validation, RPD: ratio of performance to deviation). Nevertheless, oven-dried faeces may be used as an alternative to lyophilised ones to quantify FGM levels accurately, provided that an appropriate combination of dehydration time and temperature is used during the desiccation process. High degrees of association and statistically significant positive correlations (p<0.001) were found between the lyophilised and oven-dried samples regarding their FGM content, both for RIA assays and NIRS analyses. This study provides a new approach for assessing stress levels in free-ranging populations and has practical implications concerning wildlife monitoring as it makes it possible to improve the efficiency and reduce the cost and time constraints of current analytical techniques.

Prediction of the Chemical Composition of Fresh Whole Crop Barley Silages by Near Infrared Spectroscopy

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages and feedstuff. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of fresh whole crop barley silages. A representative population of 284 fresh whole crop barley silages was used as a database for studying the possibilities of NIRS to predict chemical composition. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data were recorded as log 1/Reflectance (log 1/R) and were scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh whole crop barley silages provided accurate predictions of moisture, acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP) and pH, as well as lactic acid content with correlation coefficients of cross-validation (<TEX>$R^2cv$</TEX>) of 0.96, 0.81, 0.79, 0.84, 0.72 and 0.78, respectively, and standard error of cross-validation (SECV) of 1.26, 2.83, 2.18, 1.19, 0.13 and 0.32% DM, respectively. Results of this experiment showed the possibility of the NIRS method to predict the chemical parameters of fresh whole crop barley silages as a routine analysis method in feeding value evaluation and for farmer advice.

Quantitative Prediction of Biochar Soil Amendments by Near-Infrared Reflectance Spectroscopy

Any sale of C credits in a cap-and-trade system based on soil biochar applications will require an effective and inexpensive audit system. We investigated the ability of near-infrared reflectance spectroscopy (NIRS) to predict biochar amendment levels, total C, and C/N ratios within and between two independent soil sample sets collected from an agricultural field in Boone County, Iowa. One set had high intrinsic diversity of soil properties, while the other set had low diversity. The calibration cross-validation procedure showed the ability of NIRS to quantitatively model total C and biochar C along with their normalized values with R2- values >0.82 and ratios of prediction deviation (RPDs) >2.2 for the high-diversity set. Modeling total C of the no-biochar controls (R2 = 0.69, RPD = 1.56) and normalized total C (R2 = 0.51, RPD = 1.31) from the low-diversity sample set was less accurate. Validation using the independent partial least squares regression models of the high-diversity sample set to predict constituent concentrations for the low-diversity sample set showed predictions of total C, biochar C, and normalized biochar C with bias-corrected RPDs >2.1 and R2 values >0.87. Model validation R2 (0.92) between the measured biochar C and model-predicted normalized biochar C was significantly greater (α < 0.05) than the autocorrelation between the measured total C and biochar C (R2 = 0.89), thus demonstrating that NIRS responds to biochar C apart from total C. The results indicate that inexpensive NIRS analyses can be used to audit soil biochar applications.

Prediction of biogas yield and its kinetics in reed canary grass using near infrared reflectance spectroscopy and chemometrics

A rapid method is needed to assess biogas and methane yield potential of various kinds of substrate prior to anaerobic digestion. This study reports near infrared reflectance spectroscopy (NIRS) as a rapid alternative method to the conventional batch methods for prediction of specific biogas yield (SBY), specific methane yield (SMY) and kinetics of biogas yield (k-SBY) of reed canary grass (RCG) biomass. Dried and powdered RCG biomass with different level of maturity was used for biochemical composition analysis, batch assays and NIRS analysis. Calibration models were developed using partial least square (PLS) regression from NIRS spectra. The calibration models for SBY (R(2)=0.68, RPD=1.83) and k-SBY (R(2)=0.71, RPD=1.75) were better than the model for SMY (R(2)=0.53, RPD=1.49). Although the PLS model for SMY was less successful, the model performance was better compared to the models based on chemical composition.

Inability of Near Infrared Reflectance Spectroscopy (NIRS) to identify belowground earthworm casts in no-tillage soil

Several studies have emphasised the ability of Near Infrared Reflectance Spectroscopy (NIRS) to identify surface earthworm casts in the field. However, less is known about casts deposited within the soil, which usually represent the majority found in the field. This study tested the ability of NIRS to identify belowground casts in agricultural systems. Casts and surrounding soils were sampled at depths of 20–30cm in a loamy soil under no tillage for 12 years. To distinguish different types of cast, sizes and orientations relative to the horizontal plane were measured. NIRS analyses and analyses of carbon and nitrogen content were also performed to compare casts to surrounding soils. Casts were classified into 4 size classes, with no preferential orientation. Cast carbon and nitrogen content were not influenced by their size and did not differ from surrounding soils. PCAs performed on the NIRS data did not allow casts to be differentiated from surrounding soils, regardless of size class. However, soil aggregates were clearly differentiated probably due to their spatial distribution in the soil. Although this study did not identify specific NIRS signatures for casts, it shows the utility of this method to investigate the origin of the soil consumed by earthworms. In our case, NIRS analyses suggest that the high bulk density of the soil (1.42gcm−3) forced ingestion by endogeic earthworms, simply to move around, without preferential selection for organic matter. Consequently, their casts were deposited a few mm from where they had ingested soil with similar organic matter quality.