NIR-Spektroskopie - Neueste Forschungsartikel der Fachverlage
Aktuelle Fachartikel zur NIR-Spektroskopie [Nahinfrarot-Spektroskopie (NIRS)], sortiert nach Erscheinungsdatum.
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beruecksichtige naturwissenschaftliche Journale:
Journal of Near Infrared Spectroscopy - published by
NIR Publications -
JNIRS is a bimonthly, peer review journal dedicated to all aspects of NIR spectroscopy and technology with the publication of original research papers, short communications, review articles and letters.
Aktuelle wissenschaftliche Fachartikel der
genannten Journale:
Biochars result from the pyrolytic processing of organic materials. There is an increasing interest in the production and use of
biochars from agricultural wastes, to sequester C in the soil and improve soil quality. Near-infrared spectroscopy, which has been used for decades to determine the
composition of the varying agricultural materials used in making biochars would thus appear to be an obvious method for analysing these materials. However, previous
work on charred cellulose, lignin, pine bark and wood showed that, while near-infrared spectroscopy using a Fourier transform spectrometer could be used for quantitative
analysis, spectral interpretation beyond the bands found in the 4000 cm–1 to 5000 cm–1 (2500–2000 nm) region was
difficult, to near impossible, due to the nature of the char spectra produced, high degree of baseline curvature and tendency to be very noisy. The objective of this work
was to re-examine the question of near infrared (NIR) spectroscopy and chars using biochars made from wheat straw charred at various temperatures for 3 h. Results
indicated that using spectrometer settings which work well for other materials such as soils or forages, especially with a Fourier Transform spectrometer, are not satisfactory
for working with biochars or similar materials such as coals. Results obtained with a scanning monochromator, which also scanned a larger sample area (25× to
100 × as large), were far superior to those obtained with a Fourier spectrometer with a praying mantis-style diffuse reflectance device and similar scanning
conditions. Although previous work has shown that quantitative analysis can be carried out using NIR spectra collected on the same Fourier transform instrument, under the
same conditions, these results and those show that qualitative analysis or spectral interpretation in the NIR require a different setup/procedure to be used. Collecting more
scans, and at a lower resolution, greatly improved the spectra, although heating the sample could be a problem. Using a different DRIFTS device which allowed a
significantly larger sample area to be scanned could also be useful, but was not tested.
Various studies, mainly
from temperate areas, have reported calibrations developed from the near infrared (NIR) spectra of faeces (F.NIRS) for predicting diet digestibility in ruminants and there
has been substantial variation in predictive accuracy as indicated by calibration and validation statistics. The present study was conducted to develop and examine the
reliability and robustness of F.NIRS calibration equations to estimate dry matter digestibility (DMD) of forage diets ingested by cattle grazing in the rangelands of northern
Australia. A large and diverse calibration data set of matched diet– aecal pairs was obtained over 10 years using three sampling methods: (1) grazed pasture with
diet samples collected from oesophageal fistulated steers and faeces collected from resident cattle; (2) in vivo digestibility experiments with penned cattle fed
forage hays; and (3) penned cattle fed pasture freshly harvested from the field (FR-FEED). Estimated in vivo DMD reference values were determined using
pepsin–cellulase in vitro analysis of diet samples. The final calibration set of 1052 samples represented 264 diets with DMD ranging from 38% to 75%. Calibration
statistics for DMD% were: standard error of calibration?+?1.87, standard error of cross validation + 1.91, and the coefficient of determination, R2,+ 0.90.
Factors of particular importance, with regard to the accuracy of DMD reference values, are identified and discussed and recommendations made for minimising reference
errors. A comprehensive series of independent validation tests was conducted by selecting validation sample sets from the entire sample set according to a range of
criteria. Each validation sub-set was tested using the calibration calculated from the remainder of the sample set. These tests showed that sampling method and
experimental site often had important effects on calibration statistics and performance and also that the standard error of performance of the overall calibration would
likely be <2.5 DMD percentage units when applied to samples sourced from regions and pasture types represented in the calibration. Despite the large size and diversity
of the calibration data set it was concluded that robustness would likely be improved by expansion of the calibration data set.
Broad-scale ecological research often suffers from insufficient spatial and temporal replication. Near infrared (NIR) reflectance
spectroscopy offers the opportunity for rapid and cheap measurements of many chemical constituents in organic materials. However, standard NIR instrumentation requires
a certain amount of sample material which strongly restricts the fields of application for the NIR technique. Therefore, we tested if reliable predictions from NIR spectra
can be obtained utilising a device that reduces the amount of required sample material by more than 95% compared to standard equipment. For large and small sample
quantities, we present two sets of calibration models for C, N, P, K, Ca and Mg concentrations as well as fibre components such as neutral detergent fibre (NDF), acid
detergent fibre (ADF) and acid detergent lignin (ADL) in above-ground grassland community biomass. Coefficients of multiple determination (R2) of
calibration models based on spectral data derived from standard equipment for C, N, P, K, Ca, Mg, NDF, ADF and ADL were 0.78, 0.98, 0.78, 0.92, 0.87, 0.89, 0.95, 0.94
and 0.87, respectively. Except for C and P, the ratio of standard deviation of the reference values to the standard error of cross validation and ratio of performance
deviation indicated acceptable to high model precision. The application of NIR spectroscopy for C and P measurements was limited due to low variation in concentrations
and/or low concentrations in the analysed above-ground grassland biomass. As compared to the deviation of duplicate reference measurements, the standard error of
prediction was less than two times higher for C, N, NDF, ADF, ADL and K and up to three times higher for P, Ca and Mg. Prediction models based on the spectral data
recorded with a small sample cell (volume of sample material less than 0.25 cm3) were of similar precision. The significant reduction of sample material
required for NIR analysis and, at the same time, maintaining (high) precision of calibration models is an important advance towards the wider adoption of the NIR
technique in ecological research.
Near infrared (NIR)
spectroscopy is a potentially valuable tool for estimating the process parameters of anaerobic digestion (AD) in agricultural biogas plants. In addition to precision and
accuracy, the evaluation of model robustness versus changes in the feedstock composition and process stages are needed to implement this analytical technology into
common practice. This paper reports the first step of a global modelling approach, addressing the need for increased calibration robustness for the estimation of the
process parameters volatile solids (VS), ammonium (NH4–N), total inorganic carbon (TIC), total volatile fatty acids (VFA), acetic acid and propionic
acid in the fresh matter (FM) of digester sludge. Spectra from samples in different training sets varying with respect to their origin and feedstock composition were assessed
using partial least square regression. The comparison of the offline calibration results among the training sets revealed that an increase in the heterogeneity of the sample
matrices did not result in a relevant performance loss of the NIR models. With a root mean square error of cross-validation (RMSECV) of 4.0 g
kg–1 and 0.16 g kg–1 FM, VS and NH4–N exhibited the highest potential for estimation via NIR
spectroscopy. While the strong X–Y relationship for the structurally NIR-inactive TIC (RMSECV = 0.80 g kg–1 FM) indicated a
satisfactory screening potential, further study is required before application. The volatile fatty acid parameters, which are useful for detecting short-term changes of the AD,
did not result in good NIR models. However, there appears to be a realistic potential for a global VFA model with an estimation error of 0.9 g kg–1
FM, which may support the use of NIR-based rapid screening of the dynamics of the acidity level in digester sludge.
Maize (Zea mays L.) is
the most commonly used substrate for methane production through anaerobic fermentation and is gaining further importance in Germany. Laboratory assays used for the
determination of methane fermentation yield (MFY), i.e. the amount of methane produced per unit of dry matter, are complex and costly. Thus, the adoption of near
infrared (NIR) spectroscopy, which is already successfully used for fast and cost-effective examination of animal feeds, would remedy this problem. The objectives of this
study were to examine the potential of employing NIR spectroscopy to predict MFY as measured in a discontinuous fermenter, investigate the reliability of prediction of
parameters related to the kinetics of MFY and compare models based on NIR spectroscopy with that on chemical composition for reliable prediction of MFY. Samples of
dried whole plant material from 55 maize genotypes, grown in six environments, were analysed for their MFY using a discontinuous fermentation assay for different
fermentation times. Further, chemical composition of the samples was analysed and NIR spectra were measured. Calibration models were developed to predict MFY and
related traits based on NIR spectroscopy or chemical composition. Prediction of MFY after a short fermentation time (R2 = 0.88 after five days) was
better than after complete fermentation (R2 = 0.77 after 35 days). Chemical composition models were always inferior to NIR spectroscopy models and
showed a strong decrease in performance to predict MFY with ongoing fermentation time. The superiority of NIR spectroscopy is most likely attributable to higher
information content in the NIR spectra. The fast determination of MFY by NIR spectroscopy will enable the examination of a larger number of samples and, therefore,
allow for the use of MFY in maize breeding for biogas production.
The effects of the number of seeds in a training sample set on the ability to predict the viability of cabbage or radish
seeds are presented and discussed. The supervised classification method extended canonical variates analysis (ECVA) was used to develop a classification model.
Calibration sub-sets of different sizes were chosen randomly with several iterations and using the spectral-based sample selection algorithms DUPLEX and CADEX. An
independent test set was used to validate the developed classification models. The results showed that 200 seeds were optimal in a calibration set for both cabbage and
radish data. The misclassification rates at optimal sample size were 8%, 6% and 7% for cabbage and 3%, 3% and 2% for radish respectively for random method (averaged
for 10 iterations), DUPLEX and CADEX algorithms. This was similar to the misclassification rate of 6% and 2% for cabbage and radish obtained using all 600 seeds in the
calibration set. Thus, the number of seeds in the calibration set can be reduced by up to 67% without significant loss of classification accuracy, which will effectively
enhance the cost-effectiveness of NIR spectral analysis. Wavelength regions important for the discrimination between viable and non-viable seeds were identified using
interval ECVA (iECVA) models, ECVA weight plots and the mean difference spectrum for viable and no-viable seeds.
A modification of ensemble Monte Carlo uninformative
variable elimination (EMCUVE) is proposed, which does not involve the use of random variables, with the aim of improving the performance of partial least squares (PLS)
regression models, increasing the consistency of results and reducing processing time by selecting the most informative variables in a spectral dataset. The proposed
method (ensemble Monte Carlo variable selection—EMCVS) and the robust version (REMCVS) were compared to PLS models and with the existing EMCUVE
method using three near infrared (NIR) datasets, i.e. prediction of n-butanol in a five-solvent mixture, moisture in corn and glucosinolates in rapeseed. The
proposed methods were more consistent, produced models with better predictive accuracy (lower root mean squared error of prediction) and required lower computation
time than the conventional EMCUVE method on these datasets. In this application, the proposed method was applied to PLS regression coefficients but it may, in
principle, be used on any regression vector.
Hyperspectral images of curved surfaces contain undesirable artefacts that are a consequence of the morphology, or shape of the
sample. A software correction was developed to remove the variation in pixel intensity in hyperspectral images of spherical samples generated on a linescan type imaging
system. The correction is based directly on well known physical effects involving light reflection and intensity. The three predominant principles investigated are the
behaviour of light reflected from Lambertian surfaces, the 1/x2 relationship between light intensity and distance from the source, and the variation in arc
length along a circle as seen from the detectors. The algorithm was tested using hyperspectral images of a uniform spherical Teflon sample. Pixel intensity profiles and
histograms were generated for the corrected images and evaluated to determine the effectiveness of the algorithm based on the fact that the ideal result would be a
uniform image (as is appropriate for a uniform sample). Results indicate that the algorithm effectively improves pixel intensity uniformity, although some variability
remains. Contributing factors to the remaining pixel intensity variation in the corrected images include non-uniformity of sample illumination, specular reflection,
unintended ambient light and reflections from surfaces. The same principle can be applied to samples with circular cross sections along a particular axis, which includes
many agricultural commodities.
