Mid-infrared Data Research Articles

FTIR and UV spectra for the prediction of triterpene acids in Macrohyporia cocos

The aim of this study was to exploit the potential of rapid and simple spectroscopic techniques combined with partial least squares (PLS) regression using for the content prediction of five triterpene acids in Macrohyporia cocos. Ultraviolet of 80% ethanol extract (UV1), ultraviolet of ethyl acetate extract (UV2), Fourier transform mid-infrared (FT-MIR) and Fourier transform near-infrared (FT-NIR) spectra of samples were collected. The data processing methods of spectral pretreatment, feature extraction and data fusion were adopted to improve prediction performance. The results showed that: (1) UV1, UV2 and FT-MIR did work for content prediction except FT-NIR. (2) Variable importance for the projection (VIP) outperformed interval PLS in feature extraction for the prediction of triterpene acids of M. cocos. Data fusion had no distinct advantage compared with single set analysis (3) Both UV1 and FT-MIR could achieve the prediction of poricoic acid A, and UV1 outperformed FT-MIR. (4) The UV1 subjected by second-order derivative pretreatment and VIP extraction was satisfactory for predicting poricoic acid A and dehydrotumulosic acid. The UV2 preprocessed by first-order derivative could achieve approximate prediction of dehydrotrametenolic acid. This study demonstrated that ultraviolet and mid-infrared spectral data were promising techniques for the rapid prediction of poricoic acid A (UV1 and FT-MIR), dehydrotumulosic acid (UV1) and dehydrotrametenolic acid (UV2) in M. cocos, and ultraviolet spectroscopy was superior to infrared one.

Atacama Compact Array observations of the pulsar-wind nebula of SNR 0540-69.3

ABSTRACT We present observations of the pulsar-wind nebula (PWN) region of SNR 0540-69.3. The observations were made with the Atacama Compact Array (ACA) in Bands 4 and 6. We also add radio observations from the Australia Compact Array at 3 cm. For 1.449–233.50 GHz, we obtain a synchrotron spectrum $F_{\nu } \propto \nu ^{-\alpha _{\nu }}$, with the spectral index αν = 0.17 ± 0.02. To conclude how this joins the synchrotron spectrum at higher frequencies, we include hitherto unpublished AKARI mid-infrared data, and evaluate published data in the ultraviolet (UV), optical, and infrared (IR). In particular, some broad-band filter data in the optical must be discarded from our analysis due to contamination by spectral line emission. For the UV/IR part of the synchrotron spectrum, we arrive at $\alpha _{\nu } = 0.87^{+0.08}_{-0.10}$. There is room for 2.5 × 10−3 M⊙ of dust with a temperature of ∼55 K if there are dual breaks in the synchrotron spectrum, one around ∼9 × 1010 Hz and another at ∼2 × 1013 Hz. The spectral index then changes at ∼9 × 1010 Hz from αν = 0.14 ± 0.07 in the radio to $\alpha _{\nu } = 0.35^{-0.07}_{+0.05}$ in the millimetre-to-far-IR range. The ACA Band 6 data marginally resolve the PWN. In particular, the strong emission $\text{$\sim$} 1\hbox{$.\!\!^{\prime \prime }$}5$ south-west of the pulsar, seen at other wavelengths, and resolved in the 3 cm data with its 0.″8 spatial resolution, is also strong in the millimetre range. The ACA data clearly reveal the supernova remnant shell ∼20–35 arcsec west of the pulsar, and for the shell we derive αν = 0.64 ± 0.05 for the range 8.6–145 GHz.

Genetic correlations of milk fatty acid contents predicted from milk mid-infrared spectra in New Zealand dairy cattle

The objective of this study was to estimate genetic correlations among milk fatty acid (FA) concentrations in New Zealand dairy cattle. Concentrations of each of the most common FA, expressed as a percentage of the total FA, were determined by gas chromatography on a specific cohort of animals. Using this data set, prediction equations were derived using mid-infrared (MIR) spectroscopy data collected from the same samples. These prediction equations were applied to a large data set of MIR measurements in 34,141 milk samples from 3,445 Holstein-Friesian, 2,935 Jersey, and 3,609 crossbred Holstein-Friesian × Jersey cows, sampled an average of 3.42 times during the 2007-2008 season. Data were analyzed using univariate and bivariate repeatability animal models. Heritability of predicted FA concentration in milk fat ranged from 0.21 to 0.42, indicating that genetic selection could be used to change the FA composition of milk. The de novo synthesized FA (C6:0, C8:0, C10:0, C12:0, and C14:0) showed strong positive genetic correlations with each other, ranging from 0.24 to 0.99. Saturated FA were negatively correlated with unsaturated (-0.93) and polyunsaturated (-0.84) FA. The saturated FA were positively correlated with milk fat yield and fat percentage, whereas the unsaturated FA were negatively associated with fat yield and fat percentage. Our results indicate that bovine milk FA composition can be changed through genetic selection using MIR as a phenotypic proxy.

Short communication: Potential prediction of vitamin B12 concentration based on mid-infrared spectral data using Holstein Dairy Herd Improvement milk samples

The purpose of this study was (1) to predict the quantitative concentration of vitamin B12 in milk using mid-infrared (MIR) spectrometry, and (2) to evaluate the potential of MIR spectra to discriminate different clusters of records based on their B12 concentration. Milk samples were collected from 4,340 Holstein cows between 3 and 592 d in milk and located in 100 herds. Samples were taken using in-line milk meters and divided into 2 aliquots: one for MIR spectrometry and the other for B12 concentration reference analyses by radioassay. Analyses were performed on 311 selected spectral wavelengths. A partial least squares regression model was built to quantify B12 concentration. Discriminant analysis was used to isolate B12 concentration clusters. A B12 concentration threshold was set at 442 ng/dL, because this represents the cutoff value for a 250-mL glass of milk to fulfill 46% of the daily vitamin B12 recommended dietary allowance for individuals 14 yr or older. For each analysis, records coming from two-thirds of herds were used to calibrate prediction equations, and the remaining records (one-third of herds for validation) were used to assess the prediction performance. In the case of discriminant analysis, validation sets were divided into evaluation sets (one-third of herds) to obtain alternate probability cutoffs and in test sets (two-thirds of herds) to validate equations. Spectral and B12 concentration outliers were identified by calculating standardized Mahalanobis distance and with a residual analysis, respectively (n = 3,154). Regarding quantitative B12 concentration, cross-validation and validation coefficients of determination averaged 0.51 and 0.46, respectively, which are relatively low, which would limit the potential use of the developed quantitative equations. In addition, root mean square errors of prediction of cross validation and validation sets averaged 88.9 and 94.7 ng/dL, respectively. Area under the receiver operating characteristic curve of test sets averaged 0.81 based on the 442 ng/dL threshold, which could be considered to represent good accuracy of classification. However, the false discovery rate averaged 36%. In summary, models predicting quantitative B12 concentration had low cross-validation and validation coefficients of determination, limiting their use, but the proposed discriminant models could be used to identify milk samples with naturally high B12.

A Mid Infrared (MIR) Spectroscopy Study of the Composition of Edible Australian Green Ants (Oecophylla smaragdina)—a Qualitative Study

Alternative food sources are of importance in order to replace traditional and less sustainable ingredients for human consumption. In recent years, edible insects have been found to be an alternative source of proteins, lipids and fibre. The aim of this study was to evaluate the use of attenuated total reflectance–mid infrared (MIR) spectroscopy as a rapid tool to qualitatively characterise the chemical composition of edible green ant samples from Australia. Edible green ant anatomical parts (whole, abdomen and upper body plus legs), pre-processing (powdered and non-destructive) and material freshness (old and fresh) were analysed using attenuated total reflectance (ATR) MIR spectroscopy. Principal component analysis (PCA) of the MIR data allowed clustering samples according to body part. The main separation was driven by the composition of the abdomen, irrespective if the ants were analysed in a powdered form or as a whole. The MIR method was also able to identify a mild effect of storage conditions (e.g. freshness of the sample). It was concluded that MIR spectroscopy might be a promising tool to pre-screen edible insect composition and to evaluate the effect of storage.

Prediction of Phytochemical Composition, In Vitro Antioxidant Activity and Individual Phenolic Compounds of Common Beans Using MIR and NIR Spectroscopy

The aim of the present study was the evaluation of the performance of analytical models developed with both mid-infrared (MIR) and near-infrared (NIR) spectral data, to assess the phytochemical composition and in vitro antioxidant activity, besides individual phenolic compounds determined by HPLC-DAD, of flours from 21 distinct cultivars of Phaseolus vulgaris L. Partial least squares (PLS) regression was used to develop the analytical models, which were validated with an external set of samples. In MIR, the best prediction models were developed using the first derivative after normalization (R2c 0.86–0.99 and R2v 0.75–0.94), while for NIR, the use of the first derivative of the spectra after normalization led to the best results (R2c 0.94–0.99 and R2v 0.85–0.97). Both techniques allowed to ascertain the prediction models to ensure an accurate evaluation of the individual phenolic compounds in concentrations as low as ~ 5 μg g−1 and in vitro antioxidant capacity until the lower limit of 2.1 μmol g−1 dw. Therefore, this study revealed that the spectroscopic methodologies may represent an accurate and rapid method for quantification of phytochemical composition, in vitro antioxidant activity and individual phenolic compounds of bean flours; thus, their applicability in the food industry is representing an alternative to the traditional approaches.

Control chart and data fusion for varietal origin discrimination: Application to olive oil

Combining data from different analytical sources could be a way to improve the performances of chemometric models by extracting the relevant and complementary information for food authentication. In this study, several data fusion strategies including concatenation (low-level), multiblock and hierarchical models (mid-level), and majority vote (high-level) are applied to near- and mid-infrared (NIR and MIR) spectral data for the varietal discrimination of olive oils from six French cultivars by partial least square discriminant analysis (PLS1-DA). The performances of the data fusion models are compared to each other and to the results obtained with NIR or MIR data alone, with a choice of chemometric pre-treatments and either an arbitrarily fixed limit or a control chart decision rule. Concatenation and hierarchical PLS1-DA fail to improve the prediction results compared to individual models, whereas weighted multiblock PLS1-DA models with the control chart approach provide a more efficient differentiation for most, but not all, of the cultivars. The high-level models using a majority vote with the control chart decision rule benefit from the complementary results of the individual NIR and MIR models leading to more consistently improved results for all cultivars.

Understanding the learning mechanism of convolutional neural networks in spectral analysis

Deep learning approaches, especially convolutional neural network (CNN) models, have achieved excellent performances in vibrational spectral analysis. The critical drawback of the CNN approach is the lack of interpretation, and it is regarded as a black box. Interpreting the learning mechanism of chemometric models is critical for intuitive understanding and further application. In this study, an interpretable CNN model with a global average pooling layer is presented for Raman and mid-infrared spectral data analysis. A class activation mapping (CAM)-based approach is leveraged to visualize the active variables in the whole spectrum. The visualization of active variables shows a discriminative pattern in which the most contributed variables peaked around theoretical chemical characteristic bands. The visualization of the feature maps by three convolutional layers demonstrates the data transformation pipeline and how the CNN model hierarchically extracts informative spectral features. The first layer acts as a Savitzky-Golay filter and learns spectral shape characteristics, while the second layer learns enhanced patterns from typical spectral peaks on a few correlated variables. The third layer shows stable activations on critical spectral peaks. A partial least squares - linear discriminant analysis (PLS-LDA) model is presented for comparison on classification accuracy and model interpretation. The CNN model yields mean classification accuracies of 99.01 and 100% for E. coli and meat datasets on the test set, while the PLS-LDA models obtain accuracies of 98.83 and 100%. Both the CNN and PLS-LDA models demonstrate stable patterns on active variables while CNN models are more stable than PLS-LDA models on classification performances for various dataset partitions with Monte-Carlo cross-validation.

Detection of adulteration in Chinese honey using NIR and ATR-FTIR spectral data fusion.

The aim of this study is to find a fast, accurate, and effective method for the detection of adulteration in honey circulating in the market. Near-infrared spectroscopy and mid-infrared spectroscopy data on natural honey and syrup-adulterated honey were integrated in the experiment. A method for identifying natural honey and syrup-adulterated honey was established by incorporating these data into a Support Vector Machine (SVM). In this experiment, 112 natural pure honey samples of 20 common honey types from 10 provinces in China were collected, and 112 adulterated honey samples with different percentages of syrup (10, 20, 30, 40, 50, and 60%) were prepared using six types of syrup commonly used in industry. The total number of samples was 224. The near- and mid-infrared spectral data were obtained for all samples. The raw spectra were pre-processed by First Derivative (FD) transform, Second Derivative (SD) transform, Multiple Scattering Correction (MSC), and Standard Normal Variate Transformation (SNVT). The above-corrected data underwent low-level and intermediate-level data fusion. In the last step, Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were employed as the optimization algorithms to find the optimal penalty parameter c and the optimal kernel parameter g for the SVM, and to establish the best SVM-based detection model for natural honey and syrup-adulterated honey. The results reveal that, compared to low-level data fusion, intermediate-level data fusion significantly improves the detection model. With the latter, the accuracy, sensitivity and specificity of the optimal SVM model all reach 100%, which makes it ideal for the identification of natural honey and syrup-adulterated honey.

Revealing the Double Nucleus of NGC 4490

Abstract NGC 4490/85 (UGC 7651/48) or Arp 269 is well known for being one of the closest interacting/merging galactic systems. NGC 4490 has a high star formation rate (SFR) and is surrounded by an enormous H i feature stretching about 60 kpc north and south of the optically visible galaxies. Both the driver for the high SFR in NGC 4490 and the formation mechanism of the H i structure are puzzling aspects of this system. We have used mid-infrared Spitzer data to show that NGC 4490 has a double nucleus morphology. One nucleus is visible in the optical, while the other is only visible at infrared and radio wavelengths. We find the optical nucleus and the potential infrared visible nucleus have similar sizes, masses, and luminosities. Both are comparable in mass and luminosity to other nuclei found in interacting galaxy pairs and much more massive and luminous compared with typical nonnuclear star-forming complexes. We examine possible origin scenarios for the infrared feature, and conclude that it is likely that NGC 4490 is itself a merger remnant, which is now interacting with NGC 4485. This earlier encounter provides both a possible driver for extended star formation in NGC 4490, and multiple pathways for the formation of the extended H i plume.

Short communication: Investigation of the temporal relationships between milk mid-infrared predicted biomarkers and lameness events in later lactation

This study reports on the exploration of temporal relationships between milk mid-infrared predicted biomarkers and lameness events. Lameness in dairy cows is an issue that can vary greatly in severity and is of concern for both producers and consumers. Metabolic disorders are often associated with lameness. However, lameness can arise weeks or even months after the metabolic disorder, making the detection of causality difficult. We already use mid-infrared technology to predict major milk components, such as fat or protein, during routine milk recording and for milk payment. It was recently shown that this technology can also be used to predict novel biomarkers linked to metabolic disorders in cows, such as oleic acid (18:1 cis-9), β-hydroxybutyrate, acetone, and citrate in milk. We used these novel biomarkers as proxies for metabolic issues. Other studies have explored the possibility of using mid-infrared spectra to predict metabolic diseases and found it (potentially) usable for indicating classes of metabolic problems. We wanted to explore the possible relationship between mid-infrared-based metabolites and lameness over the course of lactation. In total, data were recorded from 6,292 cows on 161 farms in Austria. Lameness data were recorded between March 2014 and March 2015 and consisted of 37,555 records. Mid-infrared data were recorded between July and December 2014 and consisted of 9,152 records. Our approach consisted of fitting preadjustments to the data using fixed effects, computing pair-wise correlations, and finally applying polynomial smoothing of the correlations for a given biomarker at a certain month in lactation and the lameness events scored on severity scale from sound or non-lame (lameness score of 1) to severely lame (lameness score of 5) throughout the lactation. The final correlations between biomarkers and lameness scores were significant, but not high. However, for the results of the present study, we should not look at the correlations in terms of absolute values, but rather as indicators of a relationship through time. When doing so, we can see that metabolic problems occurring in mo 1 and 3 seem more linked to long-term effects on hoof and leg health than those in mo 2. However, the quantity (only 1 pair-wise correlation exceeded 1,000 observations) and the quality (due to limited data, no separation according to more metabolic-related diseases could be done) of the data should be improved.

X-Ray Spectral Shape Variation in Changing-look Seyfert Galaxy SDSS J155258+273728

Abstract We analyze the X-ray, optical, and mid-infrared data of a “changing-look” Seyfert galaxy SDSS J155258+273728 at z ≃ 0.086. Over a period of one decade (2009–2018), its broad Hα line intensity increased by a factor of ∼4. Meanwhile, the X-ray emission in 2014 as observed by Chandra was about five times brighter than that in 2010 by Suzaku, and the corresponding emissions in the V-band, mid-infrared W1 band brighten by ∼0.18, 0.32 mag, respectively. Moreover, the absorption in X-rays is moderate and stable, i.e., N H ∼ 10 21 cm − 2 , but the X-ray spectrum becomes harder in the 2014 Chandra bright state (i.e., photon index Γ = 1.52 − 0.06 + 0.06 ) than that of the 2010 Suzaku low state ( Γ = 2.03 − 0.21 + 0.22 ). With an Eddington ratio being lower than a few percent, the inner region of the accretion disk in SDSS J155258+273728 is likely a hot accretion flow. We then compile from literature the X-ray data of “changing-look” active galactic nuclei (AGNs), and find that they generally follow the well-established “V”-shaped correlation in AGNs, that is, above a critical turnover luminosity the X-ray spectra soften with the increasing luminosity, and below that luminosity the trend is reversed in the way of “harder when brighter.” This presents direct evidence that CL-AGNs have distinctive changes in not only the optical spectral type, but also the X-ray spectral shape. The similarity in the X-ray spectral evolution between CL-AGNs and black hole X-ray binaries indicates that the observed CL-AGNs phenomena may relate to the state transition in accretion physics.

Low-activity Main-belt Comet 133P/Elst-Pizarro: New Constraints on Its Albedo, Temperature, and Active Mechanism from a Thermophysical Perspective

Abstract 133P/Elst-Pizarro is the first recognized main-belt comet, but we still know little about its nucleus. First we use mid-infrared data of Spitzer MIPS, Spitzer IRS, and the Wide-field Infrared Survey Explorer to estimate its effective diameter, km, geometric albedo, , and mean Bond albedo . The albedo is used to compute 133P’s temperature distribution, which shows significant seasonal variation, especially polar regions, ranging from ∼40 to ∼200 K. Based on current activity observations, the maximum water gas production rate is estimated to be , being far weaker than of JFC 67P at a similar heliocentric distance of ∼2.7 au, indicating a thick dust mantle on the surface to lower down the gas production rate. The diameter of the sublimation area may be <∼200 m according to our model prediction. We thus propose that 133P’s activity is more likely to be caused by sublimation of a regional near-surface ice patch rather than a homogeneous buried ice layer. Such a small near-surface ice patch might be exposed by one impact event, before which 133P may be an extinct comet (or ice-rich asteroid) with an ice layer buried below ∼40 m depth. The proposed ice patch may be located somewhere within latitude −50 ∼ 50° by comparing theoretical variation of sublimation temperature to the constraints from observations. The timescale to form such a thick dust mantle is estimated to be >100 Myr, indicating that 133P may be more likely to be a relatively old planetesimals or a member of an old family than a recently formed fragment of some young family.

Species discrimination and total polyphenol prediction of porcini mushrooms by fourier transform mid-infrared (FT-MIR) spectrometry combined with multivariate statistical analysis.

The plateau specialty agricultural products, wild porcini mushrooms, have great value both as a superb cuisine and as a potential medication. Due to quality different between species added with the fraud behavior in sales process, make poor quality or poisonous sample inflow into the market, which pose a health risk for consumers, but also disrupted the mushroom market. Traditional analysis way is time‐consuming and laborious. Therefore, the aim of this study is to develop a way using fourier transform mid‐infrared (FT‐MIR) spectrometry and data fusion strategies for the fast and accurate species discrimination and predict amount of total polyphenol in four porcini mushrooms. The t‐distributed stochastic neighbor embedding based on mid‐level data fusion showed two species of Boletus edulis and B. umbriniporus have been identified. The order of correct rate of PLS‐DA models was mid‐level data fusionq (100%) > mid‐level data fusione (97.06%) = mid‐level data fusionv (97.06%) = stipes (97.06%) > low‐level data fusion (94.12%) > caps (91.18%). The order of correct rate of grid‐search support vector machine models was low‐level data fusion (100%) > caps (94.12%) > stipes (91.18%), and the order of particle swarm optimization support vector machine was low‐level data fusion (100%) > caps (97.06%) > stipes (88.24%). The mid‐level data fusionq and low‐level data fusion had best discrimination accuracy (100%) allowing each mushroom classed into its real species, which could be used for accurate discrimination of samples. B. edulis mushrooms had highest total polyphenol, with 14.76 mg/g dw and 17.33 in caps and stipes mg/g dw, respectively. The phenols were easier to accumulate in the caps in Leccinum rugosiceps (1.03) and B. tomentipes (1.19), and the opposite phenomenon is observed in B. edulis (0.85) and B. umbriniporus (0.95). The correlation coefficient and residual predictive deviation of best prediction model were 86.76% and 2.40%, respectively, indicating that that there is good relevance between FT‐MIR and total polyphenol content, which could be used to predict roughly polyphenols content in mushrooms.

The young stellar content of the giant H II regions M 8, G333.6−0.2, and NGC 6357 with VLT/KMOS

Context.The identification and characterisation of populations of young massive stars in (giant) H IIregions provides important constraints on (i) the formation process of massive stars and their early feedback on the environment, and (ii) the initial conditions for population synthesis models predicting the evolution of ensembles of stars.Aims.We identify and characterise the stellar populations of the following young giant H IIregions: M 8, G333.6−0.2, and NGC 6357.Methods.We have acquiredH- andK-band spectra of around 200 stars using theK-band Multi Object Spectrograph on the ESO Very Large Telescope. The targets for M 8 and NGC 6357 were selected from the Massive Young Star-Forming Complex Study in Infrared and X-ray (MYStIX), which combines X-ray observations with near-infrared (NIR) and mid-infrared data. For G333.6−0.2, the sample selection is based on the NIR colours combined with X-ray data. We introduce an automatic spectral classification method in order to obtain temperatures and luminosities for the observed stars. We analysed the stellar populations using their photometric, astrometric, and spectroscopic properties and compared the position of the stars in the Hertzprung-Russell diagram with stellar evolution models to constrain their ages and mass ranges.Results.We confirm the presence of candidate ionising sources in the three regions and report new ones, including the first spectroscopically identified O stars in G333.6−0.2. In M 8 and NGC 6357, two populations are identified: (i) OB main-sequence stars (M > 5M⊙) and (ii) pre-main sequence stars (M ≈ 0.5 − 5 M⊙). The ages of the clusters are ∼1−3 Myr, < 3 Myr, and ∼0.5−3 Myr for M 8, G333.6−0.2, and NGC 6357, respectively. We show that MYStIX selected targets have > 90% probability of being members of the H IIregion, whereas a selection based on NIR colours leads to a membership probability of only ∼70%.

Mid-infrared spectroscopy of zodiacal emission with AKARI/IRC

Abstract Interplanetary dust (IPD) is thought to be recently supplied from asteroids and comets. Grain properties of the IPD can give us information about the environment in the proto-solar system, and can be traced from the shapes of silicate features around 10$\, \mu$m seen in the zodiacal emission spectra. We analyzed mid-infrared slit-spectroscopic data of the zodiacal emission in various sky directions obtained with the Infrared Camera on board the Japanese AKARI satellite. After we subtracted the contamination due to instrumental artifacts, we successfully obtained high signal-to-noise spectra and have determined detailed shapes of excess emission features in the 9–12$\, \mu$m range in all sky directions. According to a comparison between the feature shapes averaged over all directions and the absorption coefficients of candidate minerals, the IPD was found to typically include small silicate crystals, especially enstatite grains. We also found variations in the feature shapes and the related grain properties among the different sky directions. From investigations of the correlation between feature shapes and the brightness contributions from dust bands, the IPD in dust bands seems to have a size frequency distribution biased toward large grains and shows indications of hydrated minerals. The spectra at higher ecliptic latitudes showed a stronger excess, which indicates an increase in the fraction of small grains included in the line of sight at higher ecliptic latitudes. If we focus on the dependence of detailed feature shapes on ecliptic latitudes, the IPD at higher ecliptic latitudes was found to have a lower olivine/(olivine + pyroxene) ratio for small amorphous grains. The variation of the mineral composition of the IPD in different sky directions may imply different properties of the IPD from different types of parent bodies, because the spatial distribution of the IPD depends on the type of the parent body.

Exploring the Mid-infrared SEDs of Six AGN Dusty Torus Models. I. Synthetic Spectra

Abstract At distances from the active galaxy nucleus where the ambient temperature falls below ∼1500–1800 K, dust is able to survive. It is thus possible to have a large dusty structure present that surrounds the active galaxy nucleus. This is the first of two papers aiming at comparing six dusty torus models with available spectral energy distributions, namely, Fritz et al., Nenkova et al., Hönig & Kishimoto, Siebenmorgen et al., Stalevski et al., and Hönig & Kishimoto. In this first paper we use synthetic spectra to explore the discrimination between these models and under which circumstances they allow us to restrict the torus parameters, while our second paper analyzes the best model to describe the mid-infrared spectroscopic data. We have produced synthetic spectra from current instruments GTC/CanariCam and Spitzer/IRS and future James Webb Space Telescope (JWST)/MIRI and JWST/NIRSpec instruments. We find that for a reasonable brightness (F 12 μm > 100 mJy) we can actually distinguish among models except for the two pairs of parent models. We show that these models can be distinguished based on the continuum slopes and the strength of the silicate features. Moreover, their parameters can be constrained within 15% of error, irrespective of the instrument used, for all the models except Hönig & Kishimoto. However, the parameter estimates are ruined when more than 50% of circumnuclear contributors are included. Therefore, future high spatial resolution spectra such as those expected from JWST will provide enough coverage and spatial resolution to tackle this topic.

Energy profiling of dairy cows from routine milk mid-infrared analysis

The balance of body energy within and across lactations can have health and fertility consequences for the dairy cow. This study aimed to create a large calibration data set of dairy cow body energy traits across the cow's productive life, with concurrent milk mid-infrared (MIR) spectral data, to generate a prediction tool for use in commercial dairy herds. Detailed phenotypic data from 1,101 Holstein Friesian cows from the Langhill research herd (SRUC, Scotland) were used to generate energy balance (EB) and effective energy intake (EI), both in megajoules per day. Pretreatment of spectral data involved standardization to account for drift over time and machine. Body energy estimates were aligned with their spectral data to generate a prediction of these traits based on milk MIR spectroscopy. After data edits, partial least squares analysis generated prediction equations with a coefficient of determination from split sample 10-fold cross validation of 0.77 and 0.75 for EB and EI, respectively. These prediction equations were applied to national milk MIR spectra on over 11 million animal test dates (January 2013 to December 2016) from 4,453 farms. The predictions generated from these were subject to phenotypic analyses with a fixed regression model highlighting differences between the main dairy breeds in terms of energy traits. Genetic analyses generated heritability estimates for EB and EI ranging from 0.12 to 0.17 and 0.13 to 0.15, respectively. This study shows that MIR-based predictions from routinely collected national data can be used to generate predictions of dairy cow energy turnover profiles for both animal management and genetic improvement of such difficult and expensive-to-record traits.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning.

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Constraining Stellar Parameters and Atmospheric Dynamics of the Carbon AGB star V Oph

Abstract Molecules and dust produced by the atmospheres of cool evolved stars contribute to a significant amount of the total material found in the interstellar medium. To understand the mechanism behind the mass loss of these stars, it is of pivotal importance to investigate the structure and dynamics of their atmospheres. Our goal is to verify if the extended molecular and dust layers of the carbon-rich asymptotic giant branch (AGB) star V Oph, and their time variations, can be explained by dust-driven winds triggered by stellar pulsation alone, or if other mechanisms are in play. We model V Oph mid-infrared interferometric VLTI-MIDI data (8–13 μm), at phases 0.18, 0.49, and 0.65, together with literature photometric data, using the latest-generation self-consistent dynamic atmosphere models for carbon-rich stars: DARWIN. We determine the fundamental stellar parameters: T eff = 2600 K, L bol = 3585 L ⊙, M = 1.5 M ⊙, C/O = 1.35, M ⊙ yr−1. We calculate the stellar photospheric radii at the three phases: 479, 494, 448 R ⊙; and the dust radii: 780, 853, 787 R ⊙. The dynamic models can fairly explain the observed N-band visibility and spectra, although there is some discrepancy between the data and the models, which is discussed in the text. We discuss the possible causes of the temporal variations of the outer atmosphere, deriving an estimate of the magnetic field strength, and computing upper limits for the Alfvén waves velocity. In addition, using period–luminosity sequences, and interferometric modeling, we suggest V Oph as a candidate to be reclassified as a semi-regular star.