Rapid Assessment of the Nutritional Value of Foods Eaten by Mountain Gorillas: Applying Near-Infrared Reflectance Spectroscopy to Primatology

The increasing recognition of the considerable intraspecific spatial and temporal variability in the nutritional contents of primate foods has necessitated development of fast and cost-effective analytical methods. Used widely for agricultural products, near-infrared reflectance spectroscopy (NIRS) is a quick, inexpensive means of assessing nutritional chemistry. The general principle of NIRS is that when the sample is irradiated with near-infrared light, the reflectance spectrum is characteristic of the mixture of chemical bonds present in the sample. These spectra, when calibrated against reference values—determined via traditional nutritional analysis—to develop regression equations, can be used to estimate nutritional values of similar samples without doing traditional nutritional analysis. We validated the use of NIRS for estimating the nutritional attributes of African herbs and trees, which were foods eaten by mountain gorillas (Gorilla beringei) collected as part of a larger study on gorilla nutritional ecology. We determined the near-infrared spectra (1100–2400 nm) of 241 dried samples of 13 species of tropical herbs and trees that formed the staple diet of the gorillas. We used modified partial least-squares regression to develop calibration equations that could predict nutritional attributes of gorilla foods, and we performed an independent validation of the calibrations. The equations had robust predictive power similar to those used in agricultural and ecology, and we found no differences between samples measured via NIRS and traditional nutritional analysis. Our analysis indicates that NIRS offers a rapid and cost-effective means of analysis of tropical leaves and herbs, and has the potential to transform primate feeding ecology studies by allowing us to evaluate the importance of intraspecific variation in nutritional value.     

Near- and mid-infrared spectroscopic determination of algal composition

The objective of this study was to evaluate whether near-infrared reflectance spectroscopy (NIRS) or mid-infrared reflectance spectroscopy (MIRS) could be used to determine the composition of algal turf scrubber samples. We assayed a set of algal turf scrubber (ATS) samples (n?=?117) by NIRS, MIRS, and conventional means for ash, total sugar, mono-sugar, total N, and P content. A subset of these samples (n?=?64) were assayed by conventional means, MIRS, and NIRS for total lipid and total fatty acid content. We developed calibrations using all the samples and a one-out cross-validation procedure under partial least-squares regression. This process was repeated using 75% of randomly selected samples to develop the calibration and the remaining samples as an independent test set. Results using the entire sample set demonstrated that NIRS and MIRS can accurately determine ash (r ²?=?0.994 and 0.995, respectively) and total N (r ²?=?0.787 and 0.820, respectively) content, but not phosphorus, total sugar, or mono-sugar content in ATS samples. Results using the 64 sample subset indicated that neither NIRS nor MIRS can accurately determine lipid or total fatty acid content in ATS samples.     

Prediction of tree species composition in fine root mixed samples using near-infrared reflectance spectroscopy

Fine roots ( ≤ 2 mm diameter) are of great value when investigating belowground interactions among different plant species and soil nutrient cycling in forest ecosystems. However, fine root separation and species identification are labor-intensive and time-consuming processes. This study aimed to evaluate the aptitude of near-infrared reflectance spectroscopy (NIRS) in predicting tree species composition in fine root mixed samples. The coniferous species Cunninghamia lanceolata and Pinus massoniana, the deciduous species Alniphyllum fortunei and Liquidambar formosana, and the evergreen broadleaved species Cyclobalanopsis glauca represent the five subtropical tree species selected for this investigation. To obtain near-infrared reflectance spectral data, 20 samples taken in the field and 70 artificially mixed samples of the five species were produced after root samples were oven-dried and ground. Calibration was performed with partial least squares regression and leave-one-out cross-validation. Root mass proportions of the mixed samples showed good predictive capacity for C. lanceolata, P. massoniana, and C. glauca with low root mean square error of prediction ( < 6.82%) and high determination coefficients (R²>0.944). Predictions for A. fortunei and L. formosana were acceptable with R²>0.819. NIRS shows potential in predicting tree species composition with suitable accuracy.     

Rapid estimation of single cell oil content of solid-state fermented mass using near-infrared spectroscopy

Calibration model using near-infrared reflectance spectroscopy (NIRS) for estimation of SCO content in solid-state fermented mass was established. The NIRS calibration model was derived by partial least-squares (PLS) regression and prediction of SCO contents of independent solid-state fermented mass samples fermented by different oleaginous fungi showed the model to be rapid and accurate, giving R(2)-value higher than 0.9552 and root mean standard error of prediction (RMSEP) value lower than 0.5772%. The established NIRS calibration model could be used to estimate the SCO contents of the solid-state fermented masses and will provide much convenience to the research of SCO production in solid-state fermentation.     

Assessing near-infrared reflectance spectroscopy for the rapid detection of lipid and biomass in microalgae cultures

With intensification of interest in microalgae as a source of biomass for biofuel production, rapid methods are needed for lipid screening of cultures. In this study, near-infrared reflectance spectroscopy (NIRS) was assessed as a method for analysing lipid (specifically, total fatty acid methyl esters (FAME) obtainable from processing) and biomass in late logarithmic and stationary phase cultures of the green alga Kirchneriella sp. and the eustigmatophyte Nannochloropsis sp. Culture samples were filtered, scanned by NIRS and chemically analysed; by combining these sets of information, models were developed to predict total biomass, FAME content and FAME as a percentage of dry weight in samples. Chemically derived (actual) and NIRS-predicted data were compared using the coefficient of determination (R ²) and the ratio of the standard deviation (SD) of actual data to the SD of NIRS prediction (RPD). For Kirchneriella sp. samples, models gave excellent prediction (R ²?≥?0.96; RPD?≥?4.8) for all parameters. For Nannochloropsis sp., the model metrics were less favourable (R ²?=?0.84–0.94; RPD?=?2.5–4.2), though sufficient to provide estimations that could be useful for screening purposes. This technique may require further validation and comparison with other species, but this study shows the potential of the NIRS as a rapid screening method (e.g. up to 200 sample analyses per day) for estimating FAME or other microalgal constituents and encourages further investigation.     

Comparison between UV spectroscopy and Nirs to assess humification process during sewage sludge and green wastes co-composting

The humification of organic matter during composting was studied by the quantification and monitoring of the evolution of humic substances (Humic Acid-HA and Fulvic Acid-FA) by UV spectra deconvolution (UVSD) and near-infrared reflectance spectroscopy (NIRS) methods. The final aim of this work was to compare UVSD to NIRS method, already applied on the same compost samples in previous studies. Finally, UVSD predictions were good for HA and HA/FA (r² of 0.828 and 0.531) but very bad for FA (r² of 0.092). In contrary, all NIRS correlations were accurate and significant with r² of 0.817, 0.806 and 0.864 for HA, FA and HA/FA ratio respectively. From these results, HA/FA ratio being a well-used index of compost maturity, UVSD and NIRS represent two invaluable tools for the monitoring of the composting process. However, we can note that NIRS predictions were more accurate than UVSD calibrations.     

Early pregnancy diagnosis in sheep using near-infrared spectroscopy on blood plasma

The objective of this study was to evaluate the ability of near-infrared reflectance spectroscopy (NIRS) to discriminate between pregnant and nonpregnant ewes in early stages of pregnancy after artificial insemination (AI) from blood plasma. Samples were collected using jugular puncture at 18 and 25 days after AI from 188 Rasa Aragonesa and Ansotana ewes. Plasma samples were analyzed for pregnancy-associated glycoprotein (PAG) and progesterone (P4) using ELISA commercial kits. The spectra of plasma samples were recorded in the visible and near-infrared ranges. The performance of these tests were compared, using as criterion standard the pregnancy status determined using transabdominal ultrasonography at 45 days after AI. Pregnancy rate was 47.9% (90/188). At Day 18, sensitivity was similar in NIRS and P4 tests (98.9% vs. 100%; not significant) and greater than PAG (32.2%; both P < 0.001). Specificity was similar in NIRS and PAG tests (both 100%) and greater than that of P4 (84.7%; P < 0.001). At Day 25, sensitivity and specificity of NIRS and PAG were both 100%. It can be concluded that NIRS was an accurate method of diagnosis of pregnancy at Days 18 and 25 after AI in ewes.     

近红外反射光谱分析技术在植物育种与种质资源研究中的应用

近年来近红外反射光谱分析技术(NIRS)在植物育种与种质资源研究中的应用已成为一个活跃的研究领域,本从NIRS分析植物的种类和品质类型、种质资源品质分析和评价、加速品质育种进程等方面作一综述,并对NIRS在作物育种中的应用作了探讨。     

Fast and Inexpensive Detection of Total and Extractable Element Concentrations in Aquatic Sediments Using Near-Infrared Reflectance Spectroscopy (NIRS)

Adequate biogeochemical characterization and monitoring of aquatic ecosystems, both for scientific purposes and for water management, pose high demands on spatial and temporal replication of chemical analyses. Near-infrared reflectance spectroscopy (NIRS) may offer a rapid, low-cost and reproducible alternative to standard analytical sample processing (digestion or extraction) and measuring techniques used for the chemical characterization of aquatic sediments. We analyzed a total of 191 sediment samples for total and NaCl-extractable concentrations of Al, Ca, Fe, K, Mg, Mn, N, Na, P, S, Si, and Zn as well as oxalate- extractable concentrations of Al, Fe, Mn and P. Based on the NIR spectral data and the reference values, calibration models for the prediction of element concentrations in unknown samples were developed and tested with an external validation procedure. Except Mn, all prediction models of total element concentrations were found to be acceptable to excellent (ratio of performance deviation: RPD 1.8–3.1). For extractable element fractions, viable model precision could be achieved for NaCl-extractable Ca, K, Mg, NH₄ ⁺-N, S and Si (RPD 1.7–2.2) and oxalate-extractable Al, Fe and P (RPD 1.9–2.3). For those elements that showed maximum total values below 3 g kg⁻¹ prediction models were found to become increasingly critical (RPD <2.0). Low concentrations also limited the performance of NIRS calibrations for extracted elements, with critical concentration thresholds <0.1 g kg⁻¹ and 3.3 g kg⁻¹ for NaCl and oxalate extractions, respectively. Thus, reliable NIRS measurements of trace metals are restricted to sediments with high metal content. Nevertheless, we demonstrated the suitability of NIRS measurements to determine a large array of chemical properties of aquatic sediments. The results indicate great potential of this fast technique as an analytical tool to better understand the large spatial and temporal variation of sediment characteristics in an economically viable way.     

近红外光谱分析法测定东北黑土有机碳和全氮含量

以我国东北黑土为研究对象,分析了2004-2005年采集的136个土壤样品在3699～12000 cm^-1范围的近红外光谱,利用偏最小二乘法建立了原始光谱吸光度与土壤有机碳、全氮和碳氮比之间的定量分析模型.结果表明：土壤有机碳和全氮的模型拟合效果良好,决定系数R²分别为0.92和0.91（P<0.001）,相对分析误差RPD分别为3.45和3.36,利用该模型对验证样本土壤有机碳和全氮的预测值与实测值之间的相关系数分别为0.94和0.93（P<0.001）,表明可以用近红外光谱分析法对黑土有机碳和全氮含量进行测定.但是利用近红外光谱分析法对土壤碳氮比的预测并不理想,虽然验证样本集黑土碳氮比模型预测值与实测值呈显著相关（r＝0.74,P<0.001）,但是校正模型的R²为0.61,RPD仅为1.61,建立的模型不能对黑土碳氮比做出合理的估测.     

Measuring carbon dynamics in field soils using soil spectral reflectance: prediction of maize root density, soil organic carbon and nitrogen content

This paper reports the development of a proximal sensing technique used to predict maize root density, soil carbon (C) and nitrogen (N) content from the visible and near-infrared (Vis-NIR) spectral reflectance of soil cores. Eighteen soil cores (0?C60?cm depth with a 4.6?cm diameter) were collected from two sites within a field of 90-day-old maize silage; Kairanga silt loam and Kairanga fine sandy loam (Gley Soils). At each site, three replicate soil cores were taken at 0, 15 and 30?cm distance from the row of maize plants (rows were 60?cm apart). Each soil core was sectioned at 5 depths (7.5, 15, 30, 45, and 60?cm) and soil reflectance spectra were acquired from the freshly cut surface at each depth. A 1.5?cm soil slice was taken at each surface to obtain root mass and total soil C and N reference (measured) data. Root densities decreased with depth and distance from plant and were lower in the silt loam, which had the higher total C and N contents. Calibration models, developed using partial least squares regression (PLSR) between the first derivative of soil reflectance and the reference data, were able to predict with moderate accuracy the soil profile root density (r ²?=?0.75; ratio of prediction to deviation [RPD]?=?2.03; root mean square error of cross-validation [RMSECV]?=?1.68?mg/cm³), soil% C (r ²?=?0.86; RPD?=?2.66; RMSECV?=?0.48%) and soil% N (r ²?=?0.81; RPD?=?2.32; RMSECV?=?0.05%) distribution patterns. The important wavelengths chosen by the PLSR model to predict root density were different to those chosen to predict soil C or N. In addition, predicted root densities were not strongly autocorrelated to soil C (r?=?0.60) or N (r?=?0.53) values, indicating that root density can be predicted independently from soil C. This research has identified a potential method for assessing root densities in field soils enabling study of their role in soil organic matter synthesis.     

Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy

Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n = 291) of different ages and colours were scanned and their visible–near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R²CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R²CV = 0.90 and 0.87; RMSECV = 1.01 and 1.08 μm and RPD = 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R²CV < 0.65 and RPD < 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R² of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 μm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.     

NIRS meets Ellenberg's indicator values: Prediction of moisture and nitrogen values of agricultural grassland vegetation by means of near-infrared spectral characteristics

Ellenberg indicator values are widely used ecological tools to elucidate relationships between vegetation and environment in ecological research and environmental planning. However, they are mainly deduced from expert knowledge on plant species and are thus subject of ongoing discussion. We researched if Ellenberg indicator values can be directly extracted from the vegetation biomass itself. Mean Ellenberg “moisture” (mF) and “nitrogen” (mN) values of 141 grassland plots were related to nutrient concentrations, fibre fractions and spectral information of the aboveground biomass. We developed calibration models for the prediction of mF and mN using spectral characteristics of biomass samples with near-infrared reflectance spectroscopy (NIRS). Prediction goodness was evaluated with internal cross-validations and with an external validation data set. NIRS could accurately predict Ellenberg mN, and with less accuracy Ellenberg mF. Predictions were not more precise for cover-weighted Ellenberg values compared with un-weighted values. Both Ellenberg mN and mF showed significant and strong correlations with some of the nutrient and fibre concentrations in the biomass. Against expectations, Ellenberg mN was more closely related to phosphorus than to nitrogen concentrations, suggesting that this value rather indicates productivity than solely nitrogen. To our knowledge we showed for the first time that mean Ellenberg indicator values could be directly predicted from the aboveground biomass, which underlines the usefulness of the NIRS technology for ecological studies, especially in grasslands ecosystems.     

Analysis of mebendazole polymorphs by Fourier transform IR spectrometry using chemometric methods

A diffuse reflectance IR Fourier transform IR spectrometry (DRIFTS) method was developed for the rapid, direct measurement of mebendazole in drugs. Conventional KBr spectra and DRIFTS spectra were compared for the best determination of the active substance in the drug formulations. Two chemometric approaches were used in the data processing: multicomponent partial least squares (PLS2) and principal component regression. The best results were obtained with the PLS2 method.     

Differentiation between carcasses from suckling lambs reared with ewe milk or milk replacers by near infrared reflectance spectroscopy of perirenal fat

Near infrared reflectance spectroscopy (NIRS) was used to discriminate between carcasses from Churra breed suckling lambs reared with ewe milk or milk replacers. Samples were scanned over the NIR spectral range (1100–2500 nm). The results showed that NIRS could be used successfully to discriminate the suckling lambs depending on milk source, with a 100% of correctly classified samples. NIRS technology would be a good method, since it is a rapid, economical and little complex method.     

Prediction of CP and starch concentrations in ruminal in situ studies and ruminal degradation of cereal grains using NIRS

Ruminal in situ incubations are widely used to assess the nutritional value of feedstuffs for ruminants. In in situ methods, feed samples are ruminally incubated in indigestible bags over a predefined timespan and the disappearance of nutrients from the bags is recorded. To describe the degradation of specific nutrients, information on the concentration of feed samples and undegraded feed after in situ incubation (‘bag residues’) is needed. For cereal and pea grains, CP and starch (ST) analyses are of interest. The numerous analyses of residues following ruminal incubation contribute greatly to the substantial investments in labour and money, and faster methods would be beneficial. Therefore, calibrations were developed to estimate CP and ST concentrations in grains and bag residues following in situ incubations by using their near-infrared spectra recorded from 680 to 2500 nm. The samples comprised rye, triticale, barley, wheat, and maize grains (20 genotypes each), and 15 durum wheat and 13 pea grains. In addition, residues after ruminal incubation were included (at least from four samples per species for various incubation times). To establish CP and ST calibrations, 620 and 610 samples (grains and bag residues after incubation, respectively) were chemically analysed for their CP and ST concentration. Calibrations using wavelengths from 1250 to 2450 nm and the first derivative of the spectra produced the best results (R²_Validation=0.99 for CP and ST; standard error of prediction=0.47 and 2.10% DM for CP and ST, respectively). Hence, CP and ST concentration in cereal grains and peas and their bag residues could be predicted with high precision by NIRS for use in in situ studies. No differences were found between the effective ruminal degradation calculated from NIRS estimations and those calculated from chemical analyses (P>0.70). Calibrations were also calculated to predict ruminal degradation kinetics of cereal grains from the spectra of ground grains. Estimation of the effective ruminal degradation of CP and ST from the near-infrared spectra of cereal grains showed promising results (R²>0.90), but the database needs to be extended to obtain more stable calibrations for routine use.     

THE USE OF NEAR INFRARED REFLECTANCE SPECTROMETRY FOR CHARACTERIZATION OF BROWN ALGAL TISSUE1

Measuring qualitative traits of plant tissue is important to understand how plants respond to environmental change and biotic interactions. Near infrared reflectance spectrometry (NIRS) is a cost‐, time‐, and sample‐effective method of measuring chemical components in organic samples commonly used in the agricultural and pharmaceutical industries. To assess the applicability of NIRS to measure the ecologically important tissue traits of carbon, nitrogen, and phlorotannins (secondary metabolites) in brown algae, we developed NIRS calibration models for these constituents in dried Sargassum flavicans (F. K. Mertens) C. Agardh tissue. We then tested if the developed NIRS models could detect changes in the tissue composition of S. flavicans induced by experimental manipulation of temperature and nutrient availability. To develop the NIRS models, we used partial least squares regression to determine the statistical relationship between trait values determined in laboratory assays and the NIRS spectral data of S. flavicans calibration samples. Models with high predictive power were developed for all three constituents that successfully detected changes in carbon, nitrogen, and phlorotannin content in the experimentally manipulated S. flavicans tissue. Phlorotannin content in S. flavicans was inversely related to nitrogen availability, and nitrogen, temperature, and tissue age interacted to significantly affect phlorotannin content, demonstrating the importance of studies that investigate these three variables simultaneously. Given the speed of analysis, accuracy, small tissue requirements, and ability to measure multiple traits simultaneously without consuming the sample tissue, NIRS is a valuable alternative to traditional methods for determining algal tissue traits, especially in studies where tissue is limited.     

Direct classification of related species of fungal endophytes (Epichloë spp.) using visible and near-infrared spectroscopy and multivariate analysis

The aim of this work was to investigate the potential of visible and near-infrared (Vis-NIR) reflectance spectroscopy for the classification of three morphologically similar species of fungal endophytes of grasses. Vis-NIR spectra (400–2498 nm) from 34 isolates of Epichloë sylvatica , 32 of Epichloë typhina and 38 of Epichloë festucae were recorded directly from fresh mycelium growing in potato dextrose agar plates. Multivariate procedures applied to the spectral data were discriminant modified partial least squares regression, soft independent modelling of class analogy and discriminant partial least squares regressions (PLS1, PLS2). Several types of data pretreatments were tested to develop the classification models. The best predictive models were achieved with PLS2 analysis; with this method, 90% of E. typhina and 100% of E. festucae and E. sylvatica external validation samples were successfully classified. These results show the potential of Vis-NIR spectroscopy combined with multivariate analysis as a rapid method for classifying morphologically similar species of filamentous fungi.     

Comparison of near infra-red spectroscopy, neutral detergent fibre assay and in-vitro organic matter digestibility assay for rapid determination of the biochemical methane potential of meadow grasses

This paper investigates near infra-red spectroscopy (NIRS) as an indirect and rapid method to assess the biochemical methane potential (BMP) of meadow grasses. Additionally analytical methods usually associated with forage analysis, namely, the neutral detergent fibre assay (NDF), and the in-vitro organic matter digestibility assay (IVOMD), were also tested on the meadow grass samples and the applicability of the models in predicting the BMP was studied. Based on these, regression models were obtained using the partial least squares (PLS) method. Various data pre-treatments were also applied to improve the models. Compared to the models based on the NDF and IVOMD predictions of BMP, the model based on the NIRS prediction of BMP gave the best results. This model, with data pre-processed by the mean normalisation method, had an R² value of 0.69, a root mean square error of prediction (RMSEP) of 37.4 and a residual prediction deviation (RPD) of 1.75.     

Predicting Soil Salinity with Vis–NIR Spectra after Removing the Effects of Soil Moisture Using External Parameter Orthogonalization

Robust models for predicting soil salinity that use visible and near-infrared (vis–NIR) reflectance spectroscopy are needed to better quantify soil salinity in agricultural fields. Currently available models are not sufficiently robust for variable soil moisture contents. Thus, we used external parameter orthogonalization (EPO), which effectively projects spectra onto the subspace orthogonal to unwanted variation, to remove the variations caused by an external factor, e.g., the influences of soil moisture on spectral reflectance. In this study, 570 spectra between 380 and 2400 nm were obtained from soils with various soil moisture contents and salt concentrations in the laboratory; 3 soil types × 10 salt concentrations × 19 soil moisture levels were used. To examine the effectiveness of EPO, we compared the partial least squares regression (PLSR) results established from spectra with and without EPO correction. The EPO method effectively removed the effects of moisture, and the accuracy and robustness of the soil salt contents (SSCs) prediction model, which was built using the EPO-corrected spectra under various soil moisture conditions, were significantly improved relative to the spectra without EPO correction. This study contributes to the removal of soil moisture effects from soil salinity estimations when using vis–NIR reflectance spectroscopy and can assist others in quantifying soil salinity in the future.