Mass spectrometry is an essential analytical technique for high-throughput analysis in proteomics and metabolomics. The development of new separation techniques, precise mass analyzers and ...experimental protocols is a very active field of research. This leads to more complex experimental setups yielding ever increasing amounts of data. Consequently, analysis of the data is currently often the bottleneck for experimental studies. Although software tools for many data analysis tasks are available today, they are often hard to combine with each other or not flexible enough to allow for rapid prototyping of a new analysis workflow.
We present OpenMS, a software framework for rapid application development in mass spectrometry. OpenMS has been designed to be portable, easy-to-use and robust while offering a rich functionality ranging from basic data structures to sophisticated algorithms for data analysis. This has already been demonstrated in several studies.
OpenMS is available under the Lesser GNU Public License (LGPL) from the project website at http://www.openms.de.
Visual monitoring of behaviour on-farm is mostly challenging due to the number of animals to be observed and the time required. However, behavioural problems such as cannibalism in turkeys may be ...preceded by subtle changes in behaviour. Machine learning techniques allow automatic behavioural monitoring of livestock to be carried out under different farming conditions. The aim of this study was to develop and test a novel pecking activity detection tool for potential use on turkey farms by means of acoustic data and a convolutional neural networks (CNN) model. Under near to commercial conditions, two metallic balls were used as pecking objects and suspended from the ceiling. Each pecking object was equipped with a microphone connected via a cable to a top view camera positioned on the ceiling. The recorded sound data were sampled in slots of 1 s and high pass filtering was performed to eliminate background noises. A total of 9200 filtered sound files were used for training and validation, and 3900 for testing set. They were labelled manually as peck or non-peck, using 7360 (80%) for training and 1840 (20%) for validation files, and fed into the CNN model. An additional 3900 new filtered sound clips were used to test the detection phase of the trained model. The experimental results illustrate that the deep learning-based detection method achieved high overall accuracy, precision, recall and F1-score of 96.8, 89.6, 92.0 and 90.8% in the detection phase. This indicates that the proposed technique could be used as a precise method for the detection of pecking activity levels in turkeys.
•Automatic turkeys pecking activity detection technique is developed.•Log-mel spectrogram of sound files was calculated and fed into to a CNN model.•The CNN model was trained and validated using filtered sound.•High overall accuracy, precision, recall and F1-score obtained by the model.
Celeriac is a good source of fibre, trace minerals, and phenolic compounds; it has a pleasant aroma but is a perishable material, prone to discolouration. This research investigated the optimisation ...of the quality and energy demand in hot-air dried celeriac slices. The experiment utilised the I-optimal design of response surface methodology with 30 experiment runs. Pre-drying treatments (blanching at 85 °C, three minutes; dipping in 1% citric acid solution, three minutes; no pre-drying treatment), drying temperatures (50, 60, and 70 °C), air velocities (1.5, 2.2, and 2.9 m/s), and thickness (three-, five, and seven-mm) were applied. The drying conditions affected drying time significantly (p < 0.0001). The model by Midilli and others and the logarithmic model fitted best with celeriac slices drying kinetics. Blanched samples had a higher ΔE*ab (total colour difference) and BI (browning index) but lower WI (whiteness index) than samples with other pre-drying treatments. The rehydration ratio decreased with the increase of sample thickness and blanching (p < 0.0001). A quadratic model described the specific energy consumption (Es) best. The dried samples compared with fresh samples had increased antioxidant activity but decreased total phenolic compound value. The optimisation solution chosen was 58 °C drying temperature, 2.9 m/s air velocity, and 4.6 mm sample thickness with acid pre-drying treatment.
Optimisation of processing time and pre-treatments are crucial factors prior to apple drying to produce a high-quality product. The purpose of the present study was to test the utility of physical ...(hot-water, HWB and steam blanching, SB) and chemical (1% ascorbic acid, AA; and 1% citric acid, CA) treatments, alone or in combination in reducing surface discolouration as well as oxidative enzyme activity in apple slices (cv. Golden Delicious and Elstar) exposed to air at room temperature for 0, 30 and 60 min. The total colour change (ΔE) for Golden Delicious was equal to 2.38, 2.68, and 4.05 after 0, 30 and 60 min of air exposure, respectively. Dipping in AA solution (1% w/v) was found to be the best treatment to limit surface discolouration of both apple cultivars. The best heat treatments to inhibit polyphenol oxidase/peroxidase enzymes activity were 70 °C HWB for Golden Delicious and 60 °C HWB for Elstar slices, both in combination with a solution of 1% AA and 1% CA. The tested apple cultivars were found to require different treatments at minimum ambient air exposure to obtain the best surface colour condition.
Virtual fencing (VF) is an emerging technology that creates virtual boundaries for livestock. Collars equipped with positioning systems, such as GPS, emit acoustic warning signals if an animal ...approaches the virtual fence and an electric impulse if it continues to move forward, deterring it from crossing the virtual fence. Compared to physical fences, virtual fences, combined with positioning systems, enable precise tracking of individual animals and fencing out small areas within pastures at high spatio-temporal resolutions and low cost. VF has the potential to enhance agri-environment schemes (AES) aimed at conserving biodiversity in three ways. (1) Many existing grassland AES focus on limiting livestock density and/or regulating the timing of grazing. Monitoring compliance with these contract conditions is costly, which puts compliance at risk. GPS tracking can help overcome compliance issues by continuously monitoring grazing animals at low cost. (2) Grazing on pastures at even and high livestock densities leads to low levels of biodiversity. Applying VF to exclude small areas from grazing provides structural and associated organismic diversity. AES could incentivise farmers to fence out such small areas to enhance biodiversity. (3) Grazing on patches with endangered plants or nests of meadow birds may negatively affect small-scale populations of endangered grassland species. Unmanned aerial vehicles and automated picture analyses could be used to detect valuable patches, transmit the information to VF systems, and AES could remunerate farmers for fencing them out. The article will explore these ideas on a conceptual level and discuss their potential benefits and drawbacks.
•Virtual fencing provides opportunities for improving AES.•Virtual fencing may improve legal compliance of existing AES.•AES may incentivise fencing out patches from grazing for more heterogeneity.•AES may incentivise fencing out patches with endangered species.
We present Herschel photometry and spectroscopy, carried out as part of the Herschel ultraluminous infrared galaxy (ULIRG) survey, and a model for the infrared to submillimetre emission of the ULIRG ...IRAS 08572+3915. This source shows one of the deepest known silicate absorption features and no polycyclic aromatic hydrocarbon emission. The model suggests that this object is powered by an active galactic nucleus (AGN) with a fairly smooth torus viewed almost edge-on and a very young starburst. According to our model, the AGN contributes about 90 per cent of the total luminosity of 1.1 × 1013 L, which is about a factor of 5 higher than previous estimates. The large correction of the luminosity is due to the anisotropy of the emission of the best-fitting torus. Similar corrections may be necessary for other local and high-z analogues. This correction implies that IRAS 08572+3915 at a redshift of 0.058 35 may be the nearest hyperluminous infrared galaxy and probably the most luminous infrared galaxy in the local (z < 0.2) Universe. IRAS 08572+3915 shows a low ratio of C ii to IR luminosity (log L
Cii/L
IR < −3.8) and a O i63 μm to C ii158 μm line ratio of about 1 that supports the model presented in this Letter.
Hyperspectral images (400–1700 nm) of apple slices during the hot-air drying were acquired. The fusion of spectral data and Gaussian Process Regression (GPR) successfully predicted vitamin C ...(R-squared = 0.93 and RMSE = 0.57 mg/100 g fresh-weigh), SSC (R-squared ≈ 0.99 and RMSE ≈ 2.47%), moisture content (R-squared ≈ 1 and RMSE = 0.89%), and shrinkage (R-squared ≈ 0.98 and RMSE = 3.66% for shrinkage). The chromaticity was likewise well predicted, however, GPR models failed to predict rehydration ratio and total phenolic content. As hyperspectral systems are expensive and computationally intensive, their possible substitution with multispectral systems was investigated by finding optimal wavelengths. In this context, 1450 nm and 980 nm were singled out by using a combination of filter-based, wrapper-based, and embedded wavelength selection algorithms. The corresponding prediction accuracies for vitamin C, SSC, moisture content, and shrinkage were almost as good as those of the full spectrum. In the case of chromaticity, it is suggested to use a color camera as most of the efficient wavelengths laid in the visible range. These results indicate potential replacement of hyperspectral imaging by much simpler and lower-cost imaging sensors by which the way will be paved towards the appearance of smart dryers.
•HSI-based GPR predicted vitamin C with R2 = 0.93 and RMSE = 0.57 mg/100 g.•Moisture content, SSC, and shrinkage were predicted with R2 > 0.98 and RMSE <3.66%.•Full spectrum (400–1700 nm) was reduced to optimal wavelengths of 980 and 1450 nm.
Although counterproductive work behavior (CWB) has long been established as a broad domain of job behaviors, little agreement exists about its internal structure. The present research addressed ...alternative models of broadly defined CWB according to which specific behaviors can be grouped into (a) one general factor, or into (b) two, (c) five, or (d) eleven narrower facets, and a number of possible integrations of these models. First, conceptual differences between these models (including the nature of overall CWB as implying a reflective or formative model, boundaries of the domain, and relations among specific facets) are reviewed with regard to theoretical and practical implications. In Study 1, structural meta-analysis was then used to test whether a reflective higher-order factor underlies meta-analytically constructed correlation matrices of five CWB facets. Analyses supported a general factor model. For Study 2, a primary data set (N = 1,237 employees) was collected in order to test alternative structural models and possible integrations of these models. Confirmatory factor analyses revealed that the best fit was for a bimodal (nonhierarchical) model in which individual CWBs simultaneously load on one of the eleven facets describing their content (e.g., theft, absenteeism) and on one of three factors describing the target primarily harmed (organization, other persons, self). Less support was found for hierarchical models and for models involving fewer content factors. These findings suggest that CWB is best described by a reflective higher-order factor at the general level and by a complex set of bimodal facets at the more specific level.
In this study, partial least square (PLS) regression models were developed to predict moisture content (MC) (model 1), CIELAB color (model 2) or all four parameters (model 3) of beef slices during ...drying. Model development was based on data from two measurement campaigns of MC (%), CIELAB L*, a* and b*values and hyperspectral data in the range of 500–1009 nm. To increase the robustness of the models, the beef samples varied dependent on cattle breed, cut and pre-treatment. With low-cost, non-invasive continuous monitoring systems in mind, the models were simplified by wavelengths selection. The Deming and Passing-Bablok regression and the Bland-Altman plot revealed high model performances. Mean differences (full/reduced model) of −0.64/-0.64 for MC, −0.14/-0.15 for CIELAB L*, 0.05/0.04 for a* and 0.08/0.06 for b* values were achieved for model 3, which shows the high potential for simple real-time monitoring applications combining all investigated factors and parameters.
•Robust models for spectral measurements of moisture and color during drying of beef.•High accordance between spectral and laboratory measurements.•Simplified high performance models by selection of maximum ten wavelengths.•High potential of simple non-invasive spectral monitoring systems for beef drying.
•Automatic mounting events detection among pigs is presented.•Ellipse fitting algorithms were used to localize each pig in the image.•The Euclidean distances between head, tail and sides of pigs were ...obtained.•Major and minor axis lengths were altered during mounting events.
Excessive mounting behaviours amongst pigs cause a high risk of poor welfare, arising from skin lesions, lameness and stress, and economic losses from reduced performance. The aim of this study was to develop a method for automatic detection of mounting events amongst pigs under commercial farm conditions by means of image processing. Two pens were selected for the study and were monitored for 20days by means of top view cameras. The recorded video was then visually analysed for selecting mounting behaviours, and extracted images from the video files were subsequently used for image processing. An ellipse fitting technique was applied to localize pigs in the image. The intersection points between the major and minor axis of each fitted ellipse and the ellipse shape were used for defining the head, tail and sides of each pig. The Euclidean distances between head and tail, head and sides, the major and minor axis length of the fitted ellipse during the mounting were utilized for development of an algorithm to automatically identify a mounting event. The proposed method could detect mounting events with high level of sensitivity, specificity and accuracy, 94.5%, 88.6% and 92.7%, respectively. The results show that it is possible to use machine vision techniques in order to automatically detect mounting behaviours among pigs under commercial farm conditions.
PDF
