1,720,982 research outputs found
Coupling randomisation and sparse modelling for the exploratory analysis of large hyperspectral datasets
Full text linkSustainable innovation: sensory study of coffee-flavoured liqueur from spent coffee grounds
No full textComparison of colour and texture feature extraction methods to predict anthocyanins content in Sangiovese grapes
Full text linkColour and texture are the two main sources of information contained in RGB images of food products. Different image-level approaches are available to analyse the image properties based on the extraction of colour and texture features, and the selection of the most appropriate method is a critical point, since it could significantly impact the outcomes. The present study has three main objectives. Firstly, we propose an innovative data dimensionality reduction method to extract and codify the texture features of an RGB image into a one-dimensional signal, named texturegram (TXG). Then, TXG approach is compared with different image-level feature extraction methods, such as colourgrams (CLG), Soft Colour Texture Descriptors (SCTD) and Grey Level Co-occurrence Matrices (GLCM). These techniques were used to analyse a benchmark dataset of RGB images already considered in a previous study to build Partial Least Squares (PLS) models and relate the image features with anthocyanins content of red grape samples. We also investigated the possible advantages of combining the colour and texture information brought by the different image-level techniques using data fusion. PLS models were calculated considering different partitions of the RGB image dataset into training and test set. The performances of the different models were statistically evaluated by means of Analysis of Variance (ANOVA) and Principal Component Analysis (PCA). Overall, the results suggested an interesting, even if slight, improvement of the model performances when fusing CLG and TXG, but also highlighted the hybrid nature of TXG to simultaneously explore colour and texture properties
Photodegradation Kinetics of Alkyd Paints: The Influence of Varying Amounts of Inorganic Pigments on the Stability of the Synthetic Binder
Full text linkAs the effects of climate change pose an increasing risk of damaging outdoor modern and contemporary artworks’ aesthetic appearance by affecting their mechanical properties and chemical-physical stability, understanding the degradation processes attacking these objects is becoming more and more essential to their conservation. For this purpose, the kinetics of photo-oxidation processes occurring in alkyd paints and their stability in mixtures with different inorganic pigments were investigated. The aim was to characterize the different degradation reactions over time and study the photodegradation kinetics according to different pigments and pigment/binder ratios. This paper describes the degradation behavior of artificial ultramarine blue, hydrated chromium oxide green, and cadmium sulfate yellow pigments mixed with alkyd resin and aged under simulated sunlight exposure for a total of 1,008 h. The analytical techniques used offer complementary information on the characterization of the samples and their aging. Specifically, 3D Optical Microscopy allowed studying morphological and color changes. These results were supported by Scanning Electron Microscopy and Colorimetry analyses, also focused on studying the physical and granulometric characteristics of the pigments in relation to the binder degradation. Finally, qualitative and quantitative analysis was performed by Attenuated Total Reflection Infrared Spectroscopy. To support the obtained results, Multivariate Analysis of microscopic images was carried out with the aim of studying the degradation effects linked to color and texture changes. The obtained results demonstrate that the degradation processes of alkyd resin are influenced by the presence of the different inorganic pigments used and their concentration in the mixtures. This study should contribute as support to the field of conservation-restoration to find suitable protection strategies for paint surfaces against degradation agents
Colourgrams GUI: A graphical user-friendly interface for the analysis of large datasets of RGB images
Full text linkColourgrams GUI is a graphical user-friendly interface developed in order to facilitate the analysis of large datasets of RGB images through the colourgrams approach. Briefly, the colourgrams approach consists in converting a dataset of RGB images into a matrix of one-dimensional signals, the colourgrams, each one codifying the colour content of the corresponding original image. This matrix of signals can be in turn analysed by means of common multivariate statistical methods, such as Principal Component Analysis (PCA) for exploratory analysis of the image dataset, or Partial Least Squares (PLS) regression for the quantification of colour-related properties of interest. Colourgrams GUI allows to easily convert the dataset of RGB images into the colourgrams matrix, to interactively visualize the signals coloured according to qualitative and/or quantitative properties of the corresponding samples and to visualize the colour features corresponding to selected colourgram regions into the image domain. In addition, the software also allows to analyse the colourgrams matrix by means of PCA and PLS
Evaluation of the effect of factors related to preparation and composition of grated Parmigiano Reggiano cheese using NIR hyperspectral imaging
Full text linkThe present study is focused on the evaluation of the effect of grater type and fat content of the pulp on the spectral response obtained by near infrared hyperspectral imaging (NIR-HSI), when this technique is used to determine the rind percentage in Parmigiano Reggiano (P-R) cheese. To this aim, grated P-R cheese samples were prepared considering all the possible combinations between three levels of rind amount (8%, 18% and 28%), two levels of fat content of the pulp and two different grater types, and the corresponding hyperspectral images were acquired in the 900–1700 nm spectral range. In a first step, the average spectrum (AS) was calculated from each hyperspectral image, and the corresponding dataset was analysed by means of Analysis of Variance Simultaneous Component Analysis (ASCA) to assess the effect of the three considered factors and their two-way interactions on the spectral response. Then, the hyperspectral images were converted into Common Space Hyperspectrograms (CSH), which are signals obtained by merging in sequence the frequency distribution curves of quantities calculated from a Principal Component Analysis (PCA) model common to the whole hyperspectral image dataset. ASCA was also applied to the CSH dataset, in order to evaluate the effect of the considered factors on this kind of signals. Generally, all the three factors resulted to have a significant effect, but with a different extent according to the method used to analyse the hyperspectral images. Indeed, while fat content of the pulp and rind percentage showed a comparable effect on the spectral response of AS dataset, in the case of CSH signals rind percentage had a greater effect compared to the other main factors. However, CSH were also more sensitive to differences ascribable to the natural variability between diverse Parmigiano Reggiano cheese samples
Exploring the potential of NIR hyperspectral imaging for automated quantification of rind amount in grated Parmigiano Reggiano cheese
Full text linkParmigiano Reggiano (P-R) is one of the most important Italian food products labelled with Protected Designation of Origin (PDO). The PDO denomination is applied also to grated P-R cheese products meeting the requirements regulated by the Specifications of Parmigiano Reggiano Cheese. Different quality parameters are monitored, including the percentage of rind, which is edible and should not exceed the limit of 18% (w/w). The present study aims at evaluating the possibility of using near infrared hyperspectral imaging (NIR-HSI) to quantify the rind percentage in grated Parmigiano Reggiano cheese samples in a fast and non-destructive manner. Indeed, NIR-HSI allows the simultaneous acquisition of both spatial and spectral information from a sample, which is more suitable than classical single-point spectroscopy for the analysis of heterogeneous samples like grated cheese. Hyperspectral images of grated P-R cheese samples containing increasing levels of rind were acquired in the 900–1700 nm spectral range. Each hyperspectral image was firstly converted into a one-dimensional signal, named hyperspectrogram, which codifies the relevant information contained in the image. Then, the matrix of hyperspectrograms was used to calculate a calibration model for the prediction of the rind percentage using Partial Least Squares (PLS) regression. The calibration model was validated considering two external test sets of samples, confirming the effectiveness of the proposed approach
Mixture design and multivariate image analysis to monitor the colour of strawberry yoghurt purée
Full text linkFood colour is a commercial added value, since it represents the first appealing factor for consumers. In this context, this study was aimed at evaluating the effect of strawberry yoghurt purée (SYP) formulation on the corresponding colour and on its variation over time, which is mainly due to degradation and browning phenomena. To this aim, a combined approach was used that included mixture design and multivariate analysis of RGB images. Strawberry purée, sugar, lemon juice and two types of thickener were mixed in different proportions by I-optimal mixture design to obtain 44 SYP formulations. The samples were subjected to light and temperature stress conditions for five weeks; during this time the RGB images of the samples were acquired using a flatbed scanner, along with the images of the corresponding control samples. The dimensionality of the acquired images was reduced by two different approaches: i) the conversion of images into signals, namely colourgrams, which can be seen as the colour fingerprint of the imaged samples, and ii) the calculation of the median values of various colour-related parameters. The colourgrams dataset was then subjected to exploratory data analysis using Principal Component Analysis, while the median values of colour-related parameters were analysed using Response Surface Methodology and Partial Least Squares-Discriminant Analysis. The aim of data analysis was both to find the best colour parameters to describe colour variability over time, and to investigate the cause-effect relationship between mixture proportions and colour response. The results highlighted that, among the considered colour parameters, relative green (i.e., the ratio of green to lightness) and red could be used to monitor colour changes. Colour variation due to stress conditions was more pronounced for samples with a high percentage of strawberry purée, and the type of thickener also affected the colour degradation kinetics
SO2- and NOx- initiated atmospheric degradation of polymeric films: Morphological and chemical changes, influence of relative humidity and inorganic pigments
Full text linkThe influence of polluting gases on the stability of polymeric films has not been studied extensively. In fact, the chemical interactions of such materials in contact with the ambient atmosphere depend on different factors such as the environmental conditions, the manufacturing process of the product, the presence of additives or various pigments. In this study, accelerated artificial gas ageing was carried out. The experiments were performed in a gas chamber exposing the samples to sulphur dioxide (SO2) and nitrogen oxide (NOx) with a concentration of 15 ppm. The relative humidity (RH) content chosen was 50% and 80% for a total of 168 h of gas exposure. The paint samples under investigation were composed of three different binding media (acrylic, alkyd, and styrene-acrylic) with various inorganic pigments. The morphological changes on the sample surface due to the different impact of the pollutant gases were investigated using a 3D microscope. Moreover, qualitative and semi-quantitative analyses were performed by Fourier-Transform Infrared (ATR-FTIR) spectroscopy, focusing on the degradation reactions and surface mapping evaluation. In order to fully exploit the chemical information on these materials contained in the ATR-FTIR spectra, multivariate analysis was carried out. In particular, Principal Component Analysis (PCA) enabled verifying the main spectral differences between the unaged and aged samples. Moreover, Analysis-of-Variance-Simultaneous Component Analysis (ASCA) was carried out to understand the influence of gas type, relative humidity, and inorganic pigment type on the deterioration process of the binders
Data Fusion Approach to Simultaneously Evaluate the Degradation Process Caused by Ozone and Humidity on Modern Paint Materials
Full text linkThe knowledge of the atmospheric degradation reactions affecting the stability of modern materials is still of current interest. In fact, environmental parameters, such as relative humidity (RH), temperature, and pollutant agents, often fluctuate due to natural or anthropogenic climatic changes. This study focuses on evaluating analytical and statistical strategies to investigate the degradation processes of acrylic and styrene-acrylic paints after exposure to ozone (O3) and RH. A first comparison of FTIR and Py-GC/MS results allowed to obtain qualitative information on the degradation products and the influence of the pigments on the paints’ stability. The combination of these results represents a significant potential for the use of data fusion methods. Specifically, the datasets obtained by FTIR and Py-GC/MS were combined using a low-level data fusion approach and subsequently processed by principal component analysis (PCA). It allowed to evaluate the different chemical impact of the variables for the characterization of unaged and aged samples, understanding which paint is more prone to ozone degradation, and which aging variables most compromise their stability. The advantage of this method consists in simultaneously evaluating all the FTIR and Py-GC/MS variables and describing common degradation patterns. From these combined results, specific information was obtained for further suitable conservation practices for modern and contemporary painted films
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