The prediction of intake potential and organic matter digestibility of grass silages by near infrared spectroscopy analysis of undried samples

A study was undertaken to examine a range of sample preparation and near infrared reflectance spectroscopy (NIPS) methodologies, using undried samples, for predicting organic matter digestibility (OMD g kg(-1)) and ad libitum intake (g kg(-1) W-0.75) of grass silages. A total of eight sample preparation/NIRS scanning methods were examined involving three extents of silage comminution, two liquid extracts and scanning via either external probe (1100-2200 nm) or internal cell (1100-2500 nm). The spectral data (log 1/R) for each of the eight methods were examined by three regression techniques each with a range of data transformations. The 136 silages used in the study were obtained from farms across Northern Ireland, over a two year period, and had in vivo OMD (sheep) and ad libitum intake (cattle) determined under uniform conditions. In the comparisons of the eight sample preparation/scanning methods, and the differing mathematical treatments of the spectral data, the sample population was divided into calibration (n = 91) and validation (n = 45) sets. The standard error of performance (SEP) on the validation set was used in comparisons of prediction accuracy. Across all 8 sample preparation/scanning methods, the modified partial least squares (MPLS) technique, generally minimized SEP's for both OMD and intake. The accuracy of prediction also increased with degree of comminution of the forage and with scanning by internal cell rather than external probe. The system providing the lowest SEP used the MPLS regression technique on spectra from the finely milled material scanned through the internal cell. This resulted in SEP and R-2 (variance accounted for in validation set) values of 24 (g/kg OM) and 0.88 (OMD) and 5.37 (g/kg W-0.75) and 0.77 (intake) respectively. These data indicate that with appropriate techniques NIRS scanning of undried samples of grass silage can produce predictions of intake and digestibility with accuracies similar to those achieved previously using NIRS with dried samples. (C) 1998 Elsevier Science B.V.

The application of Near-Infrared Reflectance Spectroscopy (NIRS) to detect melamine adulteration of soya bean meal

Soya bean products are used widely in the animal feed industry as a protein based feed ingredient and
have been found to be adulterated with melamine. This was highlighted in the Chinese scandal of
2008. Dehulled soya (GM and non-GM), soya hulls and toasted soya were contaminated with melamine
and spectra were generated using Near Infrared Reflectance Spectroscopy (NIRS). By applying chemometrics
to the spectral data, excellent calibration models and prediction statistics were obtained. The coefficients
of determination (R2) were found to be 0.89–0.99 depending on the mathematical algorithm used,
the data pre-processing applied and the sample type used. The corresponding values for the root mean
square error of calibration and prediction were found to be 0.081–0.276% and 0.134–0.368%, respectively,
again depending on the chemometric treatment applied to the data and sample type. In addition, adopting
a qualitative approach with the spectral data and applying PCA, it was possible to discriminate
between the four samples types and also, by generation of Cooman’s plots, possible to distinguish
between adulterated and non-adulterated samples.

Interpreting the near-infrared spectra of the 'golden standard' Type Ia supernova 2005cf

We present nine near-infrared (NIR) spectra of supernova (SN) 2005cf at epochs from -10 to +42d with respect to B-band maximum, complementing the existing excellent data sets available for this prototypical Type Ia SN at other wavelengths. The spectra show a time evolution and spectral features characteristic of normal Type Ia SNe, as illustrated by a comparison with SNe 1999ee, 2002bo and 2003du. The broad-band spectral energy distribution (SED) of SN 2005cf is studied in combined ultraviolet (UV), optical and NIR spectra at five epochs between ~8d before and ~10d after maximum light. We also present synthetic spectra of the hydrodynamic explosion model W7, which reproduce the key properties of SN 2005cf not only at UV-optical as previously reported, but also at NIR wavelengths. From the radiative-transfer calculations we infer that fluorescence is the driving mechanism that shapes the SED of SNe Ia. In particular, the NIR part of the spectrum is almost devoid of absorption features, and instead dominated by fluorescent emission of both iron-group material and intermediate-mass elements at pre-maximum epochs, and pure iron-group material after maximum light. A single P-Cygni feature of Mgii at early epochs and a series of relatively unblended Coii lines at late phases allow us to constrain the regions of the ejecta in which the respective elements are abundant. © 2012 The Authors Monthly Notices of the Royal Astronomical Society © 2012 RAS.

Infrared non-detection of fomalhaut b: Implications for the planet interpretation

The nearby A4-type star Fomalhaut hosts a debris belt in the form of an eccentric ring, which is thought to be caused by dynamical influence from a giant planet companion. In 2008, a detection of a point source inside the inner edge of the ring was reported and was interpreted as a direct image of the planet, named Fomalhaut b. The detection was made at 600-800nm, but no corresponding signatures were found in the near-infrared range, where the bulk emission of such a planet should be expected. Here, we present deep observations of Fomalhaut with Spitzer/IRAC at 4.5 µm, using a novel point-spread function subtraction technique based on angular differential imaging and Locally Optimized Combination of Images, in order to substantially improve the Spitzer contrast at small separations. The results provide more than an order ofmagnitude improvement in the upper flux limit of Fomalhaut b and exclude the possibility that any flux from a giant planet surface contributes to the observed flux at visible wavelengths. This renders any direct connection between the observed light source and the dynamically inferred giant planet highly unlikely. We discuss several possible interpretations of the total body of observations of the Fomalhaut system and find that the interpretation that best matches the available data for the observed source is scattered light from a transient or semi-transient dust cloud. © 2012 The American Astronomical Society. All rights reserved.

The feasibility of using near infrared and Raman spectroscopic techniques to detect fraudulent adulteration of chili powders with Sudan dye

Chili powder is a globally traded commodity which has been found to be adulterated with Sudan dyes from 2003 onwards. In this study, chili powders were adulterated with varying quantities of Sudan I dye (0.1-5%) and spectra were generated using near infrared reflectance spectroscopy (NIRS) and Raman
spectroscopy (on a spectrometer with a sample compartment modified as part of the study). Chemometrics were applied to the spectral data to produce quantitative and qualitative calibration models and prediction statistics. For the quantitative models coefficients of determination (R2) were found to be
0.891-0.994 depending on which spectral data (NIRS/Raman) was processed, the mathematical algorithm used and the data pre-processing applied. The corresponding values for the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were found to be 0.208-0.851%
and 0.141-0.831% respectively, once again depending on the spectral data and the chemometric treatment applied to the data. Indications are that the NIR spectroscopy based models are superior to the models produced from Raman spectral data based on a comparison of the values of the chemometric
parameters. The limit of detection (LOD) based on analysis of 20 blank chili powders against each calibration model gave 0.25% and 0.88% for the NIR and Raman data, respectively. In addition, adopting a qualitative approach with the spectral data and applying PCA or PLS-DA, it was possible to discriminate
between adulterated chili powders from non-adulterated chili powders.

The use of handheld near-infrared reflectance spectroscopy (NIRS) for the proximate analysis of poultry feed and to detect melamine adulteration of soya bean meal

The use of handheld near infrared (NIR) instrumentation, as a tool for rapid analysis, has the potential to be used widely in the animal feed sector. A comparison was made between handheld NIR and benchtop instruments in terms of proximate analysis of poultry feed using off-the-shelf calibration models and including statistical analysis. Additionally, melamine adulterated soya bean products were used to develop qualitative and quantitative calibration models from the NIRS spectral data with excellent calibration models and prediction statistics obtained. With regards to the quantitative approach, the coefficients of determination (R2) were found to be 0.94-0.99 with the corresponding values for the root mean square error of calibration and prediction were found to be 0.081-0.215 % and 0.095-0.288 % respectively. In addition, cross validation was used to further validate the models with the root mean square error of cross validation found to be 0.101-0.212 %. Furthermore, by adopting a qualitative approach with the spectral data and applying Principal Component Analysis, it was possible to discriminate between adulterated and pure samples.

Optical and near-infrared observations of SN 2011dh - The first 100 days

We present optical and near-infrared (NIR) photometry and spectroscopy of the Type IIb supernova (SN) 2011dh for the first 100 days. We complement our extensive dataset with Swift ultra-violet (UV) and Spitzer mid-infrared (MIR) data to build a UV to MIR bolometric lightcurve using both photometric and spectroscopic data. Hydrodynamical modelling of the SN based on this bolometric lightcurve have been presented in Bersten et al. (2012, ApJ, 757, 31). We find that the absorption minimum for the hydrogen lines is never seen below ~11 000 km s-1 but approaches this value as the lines get weaker. This suggests that the interface between the helium core and hydrogen rich envelope is located near this velocity in agreement with the Bersten et al. (2012) He4R270 ejecta model. Spectral modelling of the hydrogen lines using this ejecta model supports the conclusion and we find a hydrogen mass of 0.01-0.04 M⊙ to be consistent with the observed spectral evolution. We estimate that the photosphere reaches the helium core at 5-7 days whereas the helium lines appear between ~10 and ~15 days, close to the photosphere and then move outward in velocity until ~40 days. This suggests that increasing non-thermal excitation due to decreasing optical depth for the γ-rays is driving the early evolution of these lines. The Spitzer 4.5 μm band shows a significant flux excess, which we attribute to CO fundamental band emission or a thermal dust echo although further work using late time data is needed. Thedistance and in particular the extinction, where we use spectral modelling to put further constraints, is discussed in some detail as well as the sensitivity of the hydrodynamical modelling to errors in these quantities. We also provide and discuss pre- and post-explosion observations of the SN site which shows a reduction by ~75 percent in flux at the position of the yellow supergiant coincident with SN 2011dh. The B, V and r band decline rates of 0.0073, 0.0090 and 0.0053 mag day-1 respectively are consistent with the remaining flux being emitted by the SN. Hence we find that the star was indeed the progenitor of SN 2011dh as previously suggested by Maund et al. (2011, ApJ, 739, L37) and which is also consistent with the results from the hydrodynamical modelling. Figures 2, 3, Tables 3-10, and Appendices are available in electronic form at http://www.aanda.orgThe photometric tables are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/562/A17

Spitzer infrared spectrograph point source classification in the Small Magellanic Cloud

The Magellanic Clouds are uniquely placed to study the stellar contribution to dust emission. Individual stars can be resolved in these systems even in the mid-infrared, and they are close enough to allow detection of infrared excess caused by dust. We have searched the Spitzer Space Telescope data archive for all Infrared Spectrograph (IRS) staring-mode observations of the Small Magellanic Cloud (SMC) and found that 209 Infrared Array Camera (IRAC) point sources within the footprint of the Surveying the Agents of Galaxy Evolution in the Small Magellanic Cloud (SAGE-SMC) Spitzer Legacy programme were targeted, within a total of 311 staring-mode observations. We classify these point sources using a decision tree method of object classification, based on infrared spectral features, continuum and spectral energy distribution shape, bolometric luminosity, cluster membership and variability information. We find 58 asymptotic giant branch (AGB) stars, 51 young stellar objects, 4 post-AGB objects, 22 red supergiants, 27 stars (of which 23 are dusty OB stars), 24 planetary nebulae (PNe), 10 Wolf-Rayet stars, 3 H II regions, 3 R Coronae Borealis stars, 1 Blue Supergiant and 6 other objects, including 2 foreground AGB stars. We use these classifications to evaluate the success of photometric classification methods reported in the literature.

CSI 2264: Simultaneous Optical and Infrared Light Curves of Young Disk-bearing Stars in NGC 2264 with CoRoT and Spitzer—Evidence for Multiple Origins of Variability

We present the Coordinated Synoptic Investigation of NGC 2264, a continuous 30 day multi-wavelength photometric monitoring campaign on more than 1000 young cluster members using 16 telescopes. The unprecedented combination of multi-wavelength, high-precision, high-cadence, and long-duration data opens a new window into the time domain behavior of young stellar objects. Here we provide an overview of the observations, focusing on results from Spitzer and CoRoT. The highlight of this work is detailed analysis of 162 classical T Tauri stars for which we can probe optical and mid-infrared flux variations to 1% amplitudes and sub-hour timescales. We present a morphological variability census and then use metrics of periodicity, stochasticity, and symmetry to statistically separate the light curves into seven distinct classes, which we suggest represent different physical processes and geometric effects. We provide distributions of the characteristic timescales and amplitudes and assess the fractional representation within each class. The largest category (>20%) are optical "dippers" with discrete fading events lasting ~1-5 days. The degree of correlation between the optical and infrared light curves is positive but weak; notably, the independently assigned optical and infrared morphology classes tend to be different for the same object. Assessment of flux variation behavior with respect to (circum)stellar properties reveals correlations of variability parameters with Hα emission and with effective temperature. Overall, our results point to multiple origins of young star variability, including circumstellar obscuration events, hot spots on the star and/or disk, accretion bursts, and rapid structural changes in the inner disk. Based on data from the Spitzer and CoRoT missions. The CoRoT space mission was developed and is operated by the French space agency CNES, with participation of ESA's RSSD and Science Programmes, Austria, Belgium, Brazil, Germany, and Spain.

Young Stellar Object VARiability (YSOVAR): Long Timescale Variations in the Mid-infrared

The YSOVAR (Young Stellar Object VARiability) Spitzer Space Telescope observing program obtained the first extensive mid-infrared (3.6 and 4.5 μm) time series photometry of the Orion Nebula Cluster plus smaller footprints in 11 other star-forming cores (AFGL 490, NGC 1333, Mon R2, GGD 12-15, NGC 2264, L1688, Serpens Main, Serpens South, IRAS 20050+2720, IC 1396A, and Ceph C). There are ~29,000 unique objects with light curves in either or both IRAC channels in the YSOVAR data set. We present the data collection and reduction for the Spitzer and ancillary data, and define the "standard sample" on which we calculate statistics, consisting of fast cadence data, with epochs roughly twice per day for ~40 days. We also define a "standard sample of members" consisting of all the IR-selected members and X-ray-selected members. We characterize the standard sample in terms of other properties, such as spectral energy distribution shape. We use three mechanisms to identify variables in the fast cadence data—the Stetson index, a χ2 fit to a flat light curve, and significant periodicity. We also identified variables on the longest timescales possible of six to seven years by comparing measurements taken early in the Spitzer mission with the mean from our YSOVAR campaign. The fraction of members in each cluster that are variable on these longest timescales is a function of the ratio of Class I/total members in each cluster, such that clusters with a higher fraction of Class I objects also have a higher fraction of long-term variables. For objects with a YSOVAR-determined period and a [3.6]-[8] color, we find that a star with a longer period is more likely than those with shorter periods to have an IR excess. We do not find any evidence for variability that causes [3.6]-[4.5] excesses to appear or vanish within our data set; out of members and field objects combined, at most 0.02% may have transient IR excesses.

YSOVAR: Mid-infrared Variability in the Star-forming Region Lynds 1688

The emission from young stellar objects (YSOs) in the mid-infrared (mid-IR) is dominated by the inner rim of their circumstellar disks. We present IR data from the Young Stellar Object VARiability (YSOVAR) survey of ~800 objects in the direction of the Lynds 1688 (L1688) star-forming region over four visibility windows spanning 1.6 yr using the Spitzer Space Telescope in its warm mission phase. Among all light curves, 57 sources are cluster members identified based on their spectral energy distribution and X-ray emission. Almost all cluster members show significant variability. The amplitude of the variability is larger in more embedded YSOs. Ten out of 57 cluster members have periodic variations in the light curves with periods typically between three and seven days, but even for those sources, significant variability in addition to the periodic signal can be seen. No period is stable over 1.6 yr. Nonperiodic light curves often still show a preferred timescale of variability that is longer for more embedded sources. About half of all sources exhibit redder colors in a fainter state. This is compatible with time-variable absorption toward the YSO. The other half becomes bluer when fainter. These colors can only be explained with significant changes in the structure of the inner disk. No relation between mid-IR variability and stellar effective temperature or X-ray spectrum is found.

Infrared and Raman screening of seized novel psychoactive substances: A large scale study of >200 samples

The potential of IR absorption and Raman spectroscopy for rapid identification of novel psychoactive substances (NPS) has been tested using a set of 221 unsorted seized samples suspected of containing NPS. Both IR and Raman spectra showed large variation between the different sub-classifications of NPS and smaller, but still distinguishable, differences between closely related compounds within the same class. In initial tests, screening the samples using spectral searching against a limited reference library allowed only 41% of the samples to be fully identified. The limiting factor in the identification was the large number of active compounds in the seized samples for which no reference vibrational data were available in the libraries rather than poor spectral quality. Therefore, when 33 of these compounds were independently identified by NMR and mass spectrometry and their spectra used to extend the libraries, the percentage of samples identified by IR and Raman screening alone increased to 76%, with only 7% of samples having no identifiable constituents. This study, which is the largest of its type ever carried out, therefore demonstrates that this approach of detecting non-matching samples and then identifying them using standard analytical methods has considerable potential in NPS screening since it allows rapid identification of the constituents of the majority of street quality samples. Only one complete feedback cycle was carried out in this study but there is clearly the potential to carry out continuous identification/updating when this system is used in operational settings.

Generating Data Augmented Spectroscopic Data For Performance Enhancement

The application of chemometrics in food science has revolutionized the field by allowing the creation of models able to automate a broad range of applications such as food authenticity and food fraud detection. In order to create effective and general models able to address the complexity of real life problems, a vast amount of varied training samples are required. Training dataset has to cover all possible types of sample and instrument variability. However, acquiring a varied amount of samples is a time consuming and costly process, in which collecting samples representative of the real world variation is not always possible, specially in some application fields. To address this problem, a novel framework for the application of data augmentation techniques to spectroscopic data has been designed and implemented. This is a carefully designed pipeline of four complementary and independent blocks which can be finely tuned depending on the desired variance for enhancing model's robustness: a) blending spectra, b) changing baseline, c) shifting along x axis, and d) adding random noise.
This novel data augmentation solution has been tested in order to obtain highly efficient generalised classification model based on spectroscopic data. Fourier transform mid-infrared (FT-IR) spectroscopic data of eleven pure vegetable oils (106 admixtures) for the rapid identification of vegetable oil species in mixtures of oils have been used as a case study to demonstrate the influence of this pioneering approach in chemometrics, obtaining a 10% improvement in classification which is crucial in some applications of food adulteration.