Data reduction by randomization subsampling for the study of large hyperspectral datasets

Large amount of information in hyperspectral images (HSI) generally makes their analysis (e.g., principal component analysis, PCA) time consuming and often requires a lot of random access memory (RAM) and high computing power. This is particularly problematic for analysis of large images, containing millions of pixels, which can be created by augmenting series of single images (e.g., in time series analysis). This tutorial explores how data reduction can be used to analyze time series hyperspectral images much faster without losing crucial analytical information. Two of the most common data reduction methods have been chosen from the recent research. The first one uses a simple randomization method called randomized sub-sampling PCA (RSPCA). The second implies a more robust randomization method based on local-rank approximations (rPCA). This manuscript exposes the major benefits and drawbacks of both methods with the spirit of being as didactical as possible for a reader. A comprehensive comparison is made considering the amount of information retained by the PCA models at different compression degrees and the performance time. Extrapolation is also made to the case where the effect of time and any other factor are to be studied simultaneously.J.P Cruz-Tirado acknowledges scholarship funding from FAPESP, grant number 2020/09198–1

Cathodoluminescence of Recent biogenic carbonates: environmental and ontogenetic fingerprint

Cathodoluminescence (CL) examination of Recent biogenic carbonates shows that they are often luminescent regardless of their mineralogical composition (calcite v. aragonite), habitat (marine v. fresh water), way of life (sessile v. vagile) or environment (hyper- v. hyposaline water). Thus, the presence of luminescence in biogenic particles is not a reliable indicator of diagenetic alteration as some authors have suggested. In addition, CL can reveal variations in the mineralogy of shell material (e.g. regenerated calcitic v. primary aragonitic) and can highlight growth-related structures. Manganese (Mn2+) is the most likely activator of this luminescence, and its content in the shells of benthic organisms seems to be linked to growth rate, ontogeny, open sea conditions, bathymetry and salinity. In neritic environments the Mn2+ content and the CL of molluscs and foraminifera appear to increase with decreasing salinity. This study indicates that CL may be an important tool for the determination of environmental and ontogenetic parameters in biogenic carbonates in addition to its current use indiagenetic studie

Ionoluminescence: A New Tool for Nuclear Microprobes in Geology

When an ion beam in the energy range of a few MeV/amu impacts on a mineral, visible light can often be observed. This light, induced by energetic ions, is termed ionoluminescence (IL). The intensity and wavelength of the ionoluminescent light provide information concerning the nature of luminescence centers, such as trace substituents and structural defects, found in the mineral. This makes IL a useful complement to other methods of ion beam analysis (IBA), such as particle induced X-ray emission (PIXE) and Rutherford backscattering (RBS), in characterizing geological samples. In the present study, a proton or alpha particle beam was used for the IL excitation and IBA with a nuclear microprobe. The results obtained with IL were compared with those of cathodoluminescence (CL) and photoluminescence (PL)

Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy

Near Infrared (NIR) spectroscopy is an analytical technology widely used for the non-destructive characterisation of organic samples, considering both qualitative and quantitative attributes. In the present study, the combination of Multi-target (MT) prediction approaches and Machine Learning algorithms has been evaluated as an effective strategy to improve prediction performances of NIR data from wheat flour samples. Three different Multi-target approaches have been tested: Multi-target Regressor Stacking (MTRS), Ensemble of Regressor Chains (ERC) and Deep Structure for Tracking Asynchronous Regressor Stack (DSTARS). Each one of these techniques has been tested with different regression methods: Support Vector Machine (SVM), Random Forest (RF) and Linear Regression (LR), on a dataset composed of NIR spectra of bread wheat flours for the prediction of quality-related parameters. By combining all MT techniques and predictors, we obtained an improvement up to 7% in predictive performance, compared with the corresponding Single-target (ST) approaches. The results support the potential advantage of MT techniques over ST techniques for analysing NIR spectra

Deep computer vision system for cocoa classification

Cocoa hybridisation generates new varieties which are resistant to several plant diseases, but has individual chemical characteristics that affect chocolate production. Image analysis is a useful method for visual discrimination of cocoa beans, while deep learning (DL) has emerged as the de facto technique for image processing. However, these algorithms require a large amount of data and careful tuning of hyperparameters. Since it is necessary to acquire a large number of images to encompass the wide range of agricultural products, in this paper, we compare a Deep Computer Vision System (DCVS) and a traditional Computer Vision System (CVS) to classify cocoa beans into different varieties. For DCVS, we used a Resnet18 and Resnet50 as backbone, while for CVS, we experimented traditional machine learning algorithms, Support Vector Machine (SVM), and Random Forest (RF). All the algorithms were selected since they provide good classification performance and their potential application for food classification A dataset with 1,239 samples was used to evaluate both systems. The best accuracy was 96.82% for DCVS (ResNet 18), compared to 85.71% obtained by the CVS using SVM. The essential handcrafted features were reported and discussed regarding their influence on cocoa bean classification. Class Activation Maps was applied to DCVS’s predictions, providing a meaningful visualisation of the most important regions of the images in the model

Chia (Salvia hispanica) seeds degradation studied by fuzzy-c mean (FCM) and hyperspectral imaging and chemometrics - fatty acids quantification

Chia seeds are nutritious food because they have a high content of protein, polyunsaturated fatty acids (omega-3 and omega-6) and phenolic compounds. During storage, fatty acids are degraded, by oxidative and hydrolytic reactions, forming free fatty acids (FFA). In this work, we used Near Infrared Hyperspectral Imaging (NIR- HSI) and chemometrics to predict FFA acid value and fatty acids concentrations in chia seeds during storage. First, we explore the hyperspectral images by Fuzzy c-means (FCM), where it is possible to observe as chemical compounds are formed or degraded during storage. Second, PLSR models were developed to predict FFA value and fatty acids concentration. RPD values reached values higher then 2.0, indicating a good ability to estimate these chemical compounds, especially polyunsaturated fatty acids omega-3 and omega-6. Finally, NIR-hyperspectral imaging coupled with chemometrics allowed us to show the chemical degradation process of chia seeds during storage, mainly associated with polyunsaturated fatty acids degradation. Besides NIR-HSI showed to be a powerful technique to quantify the main fatty acids with high accuracy

Aplicação de reguladores de crescimento em figos produzidos fora da época normal

Fez-se a aplicação de giberelinas (50 e 100 ppm) e de clorofenoxipropionamida (250, 500 e 1000 ppm) sobre figos em vários estádios de desenvolvimento, de plantas podadas em dezembro, visando-se à produção de figos fora da época normal. Os resultados demostraram que as giberelinas a 50 ppm provocaram alongamento no comprimento dos figos. O tratamento com clorofenoxipropionamida (Fruitone CPA) a 1000 ppm resultou em frutos mais pesados, porém, estes valores não diferiram estatisticamente daqueles do tratamento controle. O peso médio dos figos foi de 50,0 gramas, para as primeiras oito semanas de colheita, período considerado no experimento.Gibberelins (50 and 100 ppm) and chlorophenoxy propionamide (250, 500 and 1000 ppm) were applied on figs growing on plants pruned in December, for an out of season production of fruits. The concentration of (1000 ppm) chlorophenoxy propionamide (Fruitone CPA) on treated figs induced on elongation of fruits, but the figs in this treatment did not differ from the control treatment in weight. The average weight of harvested figs at the end of 8 weeks (the time the experiment lasted) was 50.0 g