Leonardo da Vinci (1452-1519) is a key artistic and scientific figure of the Renaissance. He is renowned for his science of art, taking advantage of his acute observations of nature to achieve ...striking pictorial results. This study describes the analysis of an exceptional sample from one of Leonardo's final masterpieces: The Virgin and Child with St. Anne (Musée du Louvre, Paris, France). The sample was analyzed at the microscale by synchrotron-based hyperspectral photoluminescence imaging and high-angular X-ray diffraction. The results demonstrate Leonardo's use of two subtypes of lead white pigment, thus revealing how he must have possessed a precise knowledge of his materials; carefully selecting them according to the aesthetical results he aimed at achieving in each painting. This work provides insights on how Leonardo obtained these grades of pigment and proposes new clues regarding the optical and/or working properties he may have tried to achieve.
The increasing complexity of greenhouse production systems requires the step to a more data-driven approach that is based on robust, intelligent sensors. Whereas climate sensors are widely available ...in greenhouse systems, control based on crop data is still in its infancy. There is a clear need among growers to monitor the status of the crop in the greenhouse in real time and non-destructively compared to current practice of cumbersome, expensive and slow laboratory measurements. This project aimed to use visible and near infrared imaging spectroscopy to determine the contents of sugars and starch, dry matter percentage, chlorophyll, and nutrient composition in both leaves and fruits of tomato plants. The results were promising, with Q2 values up to 0.97 for dry matter content in leaves, 0.86 for sugar and 0.99 for chlorophyll-a. The results also showed that pigments have a better correlation to the visible spectrum, while water content, sugars and nutrients showed a better correlation in the near infrared part of the spectrum.
•Imaging spectroscopy can be used to replace costly laboratory analysis for monitoring crop status.•Good correlation (Q2> 0.90) with sugars, dry matter, leaf thickness, and pigments.•Correlations with starch, total nitrogen, and calcium are lower, but substantial (Q2> 0.79).•NIR range performs best for water, sugars, and nutrients, VNIR range for pigments.
X‐ray linear dichroism (XLD) is a fundamental property of many ordered materials that can for instance provide information on the origin of magnetic properties and the existence of differently ...ordered domains. Conventionally, measurements of XLD are performed on single crystals, crystalline thin films, or highly ordered nanostructure arrays. Here, it is demonstrated how quantitative measurements of XLD can be performed on powders, relying on the random orientation of many particles instead of the controlled orientation of a single ordered structure. The technique is based on a scanning X‐ray transmission microscope operated in the soft X‐ray regime. The use of a Fresnel zone plate allows X‐ray absorption features to be probed at ∼40 nm lateral resolution – a scale small enough to probe the individual crystallites in most powders. Quantitative XLD parameters were then retrieved by determining the intensity distributions of certain diagnostic dichroic absorption features, estimating the angle between their transition dipole moments, and fitting the distributions with four‐parameter dichroic models. Analysis of several differently produced ZnO powders shows that the experimentally obtained distributions indeed follow the theoretical model for XLD. Making use of Monte Carlo simulations to estimate uncertainties in the calculated dichroic model parameters, it was established that longer X‐ray exposure times lead to a decrease in the amplitude of the XLD effect of ZnO.
It is shown how soft X‐ray nanospectroscopy maps of powders (particle size ∼200 nm) can be analyzed to quantitatively retrieve X‐ray linear dichroism (XLD) parameters. A computational modeling procedure is described that can be used in conjunction with Monte Carlo simulations to prove statistical dissimilarity of XLD parameters between different samples.
Cultivation and breeding of legumes as protein sources in the human or animal diet could benefit from accurate, rapid, and non-invasive measurements of protein content. A study was conducted into the ...feasibility of a fully non-invasive, in vivo protein measurement methodology applied to soybean (Glycine max. L.). The proposed methodology works by recording spectral images of the soybean pods in the visible and near-infrared (Vis-NIR), a rule-based segmentation approach, and partial least squares (PLS) regression to predict the crude protein content of the beans contained within the imaged pods. Using all 150 channels of the spectral camera, a model could be calibrated with a mean absolute precision error (MAPE) of 4.8 % (R2 = 0.92). Applying a tailored feature elimination approach to select only eight spectral bands and degrading the spectral resolution to 25 nm yields a model with a MAPE of 6.0 % (R2 = 0.88), indicating the potential for multispectral cameras in this application.
•Crude protein content in soy bean can be inferred from optical spectrum of the pod.•A regression model was calibrated with an average prediction error of 4.8 %.•Feature elimination and data degradation shows potential of multispectral cameras.
X‐ray absorption and optical luminescence can both provide valuable but very different information on the chemical and physical properties of materials. Although it is known that the spectral ...characteristics of many materials are highly heterogeneous on the micro‐ and/or nanoscale, no methodology has so far been shown to be capable of spatially resolving both full X‐ray absorption and X‐ray excited optical luminescence (XEOL) spectra on the nanoscale in a correlative manner. For this purpose, the scanning transmission X‐ray microscope at the HERMES beamline of the SOLEIL synchrotron was equipped with an optical detection system capable of recording high‐resolution XEOL spectra using a 40 nm soft X‐ray probe. The functionality of the system was demonstrated by analyzing ZnO powder dispersions — showing simultaneously the X‐ray linear dichroism and XEOL behavior of individual submicrometric ZnO crystallites.
A multimodal spectral imaging technique is demonstrated that allows simultaneous acquisition of soft X‐ray transmission and X‐ray excited optical luminescence. The use of a Fresnel zone plate ensures a lateral resolution of the correlative spectral images of approximately 40 nm.
Apple scab is a fungal disease caused by Venturia inaequalis. Disease is of particular concern for growers, as it causes significant damage to fruit and leaves, leading to loss of fruit and yield. ...This article examines the ability of deep learning and hyperspectral imaging to accurately identify an apple symptom infection in apple trees. In total, 168 image scenes were collected using conventional RGB and Visible to Near-infrared (VIS-NIR) spectral imaging (8 channels) in infected orchards. Spectral data were preprocessed with an Artificial Neural Network (ANN) trained in segmentation to detect scab pixels based on spectral information. Linear Discriminant Analysis (LDA) was used to find the most discriminating channels in spectral data based on the healthy leaf and scab infested leaf spectra. Five combinations of false-colour images were created from the spectral data and the segmentation net results. The images were trained and evaluated with a modified version of the YOLOv5 network. Despite the promising results of deep learning using RGB images (P=0.8, mAP@50=0.73), the detection of apple scab in apple trees using multispectral imaging proved to be a difficult task. The high-light environment of the open field made it difficult to collect a balanced spectrum from the multispectral camera, since the infrared channel and the visible channels needed to be constantly balanced so that they did not overexpose in the images.
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