Evaluation of a linear spectral mixture model and vegetation indices (NDVI and EVI) in a study of schistosomiasis mansoni and Biomphalaria glabrata distribution in the state of Minas Gerais, Brazil

This paper analyses the associations between Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) on the prevalence of schistosomiasis and the presence of Biomphalaria glabrata in the state of Minas Gerais (MG), Brazil. Additionally, vegetation, soil and shade fraction images were created using a Linear Spectral Mixture Model (LSMM) from the blue, red and infrared channels of the Moderate Resolution Imaging Spectroradiometer spaceborne sensor and the relationship between these images and the prevalence of schistosomiasis and the presence of B. glabrata was analysed. First, we found a high correlation between the vegetation fraction image and EVI and second, a high correlation between soil fraction image and NDVI. The results also indicate that there was a positive correlation between prevalence and the vegetation fraction image (July 2002), a negative correlation between prevalence and the soil fraction image (July 2002) and a positive correlation between B. glabrata and the shade fraction image (July 2002). This paper demonstrates that the LSMM variables can be used as a substitute for the standard vegetation indices (EVI and NDVI) to determine and delimit risk areas for B. glabrata and schistosomiasis in MG, which can be used to improve the allocation of resources for disease control.

Chemical characterization and infrared spectroscopy of soil organic matter from two southern brazilian soils

Soil organic matter from the surface horizon of two Brazilian soils (a Latosol and a Chernosol), in bulk samples (in situ SOM) and in HF-treated samples (SOM), was characterized by elemental analyses, diffuse reflectance (DRIFT) and transmission Fourier transform infrared spectroscopy (T-FTIR). Humic acids (HA), fulvic acids (FA) and humin (HU) isolated from the SOM were characterized additionally by ultraviolet-visible spectroscopy (UV-VIS). After sample oxidation and alkaline treatment, the DRIFT technique proved to be more informative for the detection of "in situ SOM" and of residual organic matter than T-FTIR. The higher hydrophobicity index (HI) and H/C ratio obtained in the Chernosol samples indicate a stronger aliphatic character of the organic matter in this soil than the Latosol. In the latter, a pronounced HI decrease was observed after the removal of humic substances (HS). The weaker aliphatic character, the higher O/C ratio, and the T-FTIR spectrum obtained for the HU fraction in the Latosol suggest the occurrence of surface coordination of carboxylate ions. The Chernosol HU fraction was also oxygenated to a relatively high extent, but presented a stronger hydrophobic character in comparison with the Latosol HU. These differences in the chemical and functional group composition suggest a higher organic matter protection in the Latosol. After the HF treatment, decreases in the FA proportion and the A350/A550 ratio were observed. A possible loss of FA and condensation of organic molecules due to the highly acid medium should not be neglected.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Characterization and discrimination of soils by their reflected electromagnetic energy

The objective of this work was to verify if reflected energy of soils can characterize and discriminate them. A spectroradiometer (Spectral reflectance between: 400-2,500 nm) was utilized in laboratory. The soils evaluated are located in Bauru region, SP, Brazil, and are classified as Typic Argiudoll (TR), Typic Eutrorthox (LR), Typic Argiudoll (PE), Typic Haplortox (LE), Typic Paleudalf (PV) and Typic Quartzipsamment (AQ). They were characterized by their spectral reflectance as for descriptive conventional methods (Brazilian and International) according to the types of spectral curves. A method for the spectral descriptive evaluation of soils was established. It was possible to characterize and discriminate the soils by their spectral reflectance, with exception for LR and TR. The spectral differences were better identified by the general shape of spectral curves, by the intensity of band absorption and angle tendencies. These characteristics were mainly influenced by organic matter, iron, granulometry and mineralogy constituents. A reduction of iron and clay contents, which influenced higher reflectance intensity and shape variations, occurred on the soils LR/TR, PE, LE, PV and AQ, on that sequence. Soils of the same group with different clay textures could be discriminated. The conventional descriptive evaluation of spectral curves was less efficient on discriminating soils. Simulated orbital data discriminated soils mainly by bands 5 and 7.

Infrared thermometry to schedule irrigation of common bean

The objective of this work was to determine the critical irrigation time for common bean (Phaseolus vulgaris L. cv. Carioca) using infrared thermometry. Five treatments were analyzed. Canopy temperature differences between plants and a well-watered control about 1, 2, 3, 4, and 5±0.5ºC were tested. Physiological variables and plant growth were analyzed to establish the best time to irrigate. There was a significant linear correlation between the index and stomatal resistance, transpiration rate, and leaf water potential. Although significant linear correlation between the index and mean values of total dry matter, absolute growth rate, and leaf area index was found, no correlation was found with other growth index like relative growth rate, net assimilation rate, and leaf area ratio. Plants irrigated when their canopy temperature was 3±0.5ºC above the control had their relative growth rate mean value increased up to 59.7%, yielding 2,260.2 kg ha-1, with a reduction of 38.0% in the amount of water used. Plants irrigated when their canopy temperature was 4±0.5ºC yielded 1,907.6 kg ha-1, although their relative growth rate mean value was 4.0% below the control. These results show that the best moment to irrigate common bean is when their canopy temperature is between 3ºC and 4±0.5ºC above the control.

Predicting rapeseed oil content with near-infrared spectroscopy

The objective of this work was to establish a calibration equation and to estimate the efficiency of near-infrared reflectance (NIR) spectroscopy for evaluating rapeseed oil content in Southern Brazil. Spectral data from 124 half-sib families were correlated with oil contents determined by the chemical method. The accuracy of the equation was verified by coefficient of determination (R²) of 0.92, error of calibration (SEC) of 0.78, and error of performance (SEP) of 1.22. The oil content of ten genotypes, which were not included in the calibration with NIR, was similar to the one obtained by the standard chemical method. NIR spectroscopy is adequate to differentiate oil content of rapeseed genotypes.

Discrimination of native wood charcoal by infrared spectroscopy

Brazil is one of the largest producers and consumers of charcoal in the world. About 50% of its charcoal comes from native forests, with a large part coming from unsustainable operations. The anatomic identification of charcoal is subjective; an instrumental technique would facilitate the monitoring of forests. This study aimed to verify the feasibility of using medium and near infrared reflectance spectroscopy to discriminate native (ipê) from plantation charcoals (eucalyptus). Principal Components Analysis, followed by Discriminant Factorial Analysis formed two different groups indicated by Mahalanobis distances of 40.6 and 80.3 for near and mid infrared, respectively. Validation of the model showed 100% efficacy.

Fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy and chemometric techniques for the determination of adulteration in petrodiesel/biodiesel blends

We propose an analytical method based on fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy to detect the adulteration of petrodiesel and petrodiesel/palm biodiesel blends with African crude palm oil. The infrared spectral fingerprints from the sample analysis were used to perform principal components analysis (PCA) and to construct a prediction model using partial least squares (PLS) regression. The PCA results separated the samples into three groups, allowing identification of those subjected to adulteration with palm oil. The obtained model shows a good predictive capacity for determining the concentration of palm oil in petrodiesel/biodiesel blends. Advantages of the proposed method include cost-effectiveness and speed; it is also environmentally friendly.

Application of fourier transform infrared spectroscopy, chemical and chemometrics analyses to the characterization of agro-industrial waste

Agroindustrial waste in general presents significant levels of nutrients and organic matter and has therefore been frequently put to agricultural use. In this context, the objective of this study was to determine the chemical composition, nitrogen, phosphorus, potassium, calcium, magnesium and carbon content, as well as the qualitative characteristics through Fourier transform infrared spectroscopy of four samples of poultry litter and one sample of cattle manure, from the southwestern region of Paraná, Brazil. Results revealed that, in general, the poultry litter presented higher amount of nutrients and carbon than the cattle manure. The infrared spectra allowed identification of the functional groups present and the differences in degree of sample humification. The statistical treatment confirmed the quantitative and qualitative differences revealed.

Influence of data acquisition geometry on soybean spectral response simulated by the prosail model

View angle and directional effects significantly affect reflectance and vegetation indices, especially when daily images collected by large field-of-view (FOV) sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) are used. In this study, the PROSAIL radiative transfer model was chosen to evaluate the impact of the geometry of data acquisition on soybean reflectance and two vegetation indices (Normalized Difference Vegetation Index - NDVI and Enhanced Vegetation Index -EVI) by varying biochemical and biophysical parameters of the crop. Input values for PROSAIL simulation were based on the literature and were adjusted by the comparison between simulated and real satellite soybean spectra acquired by the MODIS/Terra and hyperspectral Hyperion/Earth Observing-One (EO-1). Results showed that the influence of the view angle and view direction on reflectance was stronger with decreasing leaf area index (LAI) and chlorophyll concentration. Because of the greater dependence on the near-infrared reflectance, the EVI was much more sensitive to viewing geometry than NDVI presenting larger values in the backscattering direction. The contrary was observed for NDVI in the forward scattering direction. In relation to the LAI, NDVI was much more isotropic for closed soybean canopies than for incomplete canopies and a contrary behavior was verified for EVI.

Evaluation of thermorregulatory capacity of dairy buffaloes using infrared thermography

ABSTRACT The objective of this study was to evaluate the thermoregulatory response of dairy buffaloes in pre-milking and post-milking. To identify animal thermoregulatory capacity, skin surface temperatures were taken by an infrared thermometer (SST), a thermographic camera (MTBP) as well as respiratory rate records (RR). Black Globe and Humidity Index (BGHI), radiating thermal load (RTL) and enthalpy (H) were used to characterize the thermal environment. Artificial Neural Networks analyzed those indices as well as animal physiological data, using a single layer trained with the least mean square (LMS) algorithm. The results indicated that pre-milking and post-milking environments reached BGHI, RR, SST and MTBP values above thermal neutrality zone for buffaloes. In addition, limits of surface skin temperatures were mostly influenced by changing ambient conditions to the detriment of respiratory rates. It follows that buffaloes are sensitive to environmental changes and their skin temperatures are the best indicators of thermal comfort in relation to respiratory rate.

The use of infrared thermography in the identification of pododermatitis in broilers

ABSTRACT International trade in broiler’ feet, mainly to Asian markets, has demanded better quality control. The objective of this research was to study the suitability of using chicken footpad surface temperature to determine early lesions of pododermatitis. The project was conducted in two houses A1 and A2) in a commercial farm during one production flock. A1 had reused litter of wood shavings and rice hulls, and A2 had a new litter of sawdust. Both houses had positive pressure ventilation. The inner area of the poultry was virtually divided into three quadrants. The footpads were checked for the feet quality, and a degree of pododermatitis was awarded. Thermal images were made to test the surface temperature of the foot and identify inflammation in a total of 30 birds per house, at ages 5, 19, 29, 28 and 40 days of grow-out. Conditions of the rearing environment as well as the surface temperature of the litter, litter moisture, and degree of compression, were recorded. The environment within the houses did not differ. The surface temperatures of the footpad did not differ between the groups. The minimum footpad surface temperatures within the scores were similar, except for the score 3, which did not occur in A1. There was a prevalence of severe injury in the house with a new litter.

Hot tool temperature analysis though infrared images

Textile manufacture occupies a prominent place in the national economy. Because of its importance researches have been made on the development of new materials, equipment and methods used in the production process. The cutting of textiles starts in the basic stage, to be followed by the process of the making of clothes and other articles. In the hot cutting of fabric, one of the variables of great importance in the control of the process is the contact temperature between the tool and the fabric. This work presents a technique for the measurement of the temperature based on the processing of infrared images. With this purpose, it was developed a system which is composed of an infrared camera, a framegrabber PC board and a software which analyses the punctual temperature in the cut area enabling the operator to achieve the necessary control of other variables involved in the process.

Petri Net approach for modelling system integration in intelligent buildings

In this paper, a Petri Net approach is introduced for modelling and simulation of control strategies in Intelligent Building. In this context, it is claimed that integration with other building systems can be achieved in a more systematic way considering a mechatronic approach (i.e. multidisciplinary concepts applied to the development of systems). The case study is the Ambulatory Building of Medical School Hospital of University of São Paulo. Particularly, the developed methodology is applied to the elevator system and to the HVAC (Heating, Ventilation and Air Conditioning) system. It is shown that using this approach, the control systems could be integrated, improving performance.

Application of isotope-selective non-dispersive infrared spectrometry for the evaluation of the 13C-urea breath test: comparison with three concordant methods

The aim of this work was to compare the performance of isotope-selective non-dispersive infrared spectrometry (IRIS) for the 13C-urea breath test with the combination of the 14C-urea breath test (14C-UBT), urease test and histologic examination for the diagnosis of H. pylori (HP) infection. Fifty-three duodenal ulcer patients were studied. All patients were submitted to gastroscopy to detect HP by the urease test, histologic examination and 14C-UBT. To be included in the study the results of the 3 tests had to be concordant. Within one month after admission to the study the patients were submitted to IRIS with breath samples collected before and 30 min after the ingestion of 75 mg 13C-urea dissolved in 200 ml of orange juice. The samples were mailed and analyzed 11.5 (4-21) days after collection. Data were analyzed statistically by the chi-square and Mann-Whitney test and by the Spearman correlation coefficient. Twenty-six patients were HP positive and 27 negative. There was 100% agreement between the IRIS results and the HP status determined by the other three methods. Using a cutoff value of delta-over-baseline (DOB) above 4.0 the IRIS showed a mean value of 19.38 (minimum = 4.2, maximum = 41.3, SD = 10.9) for HP-positive patients and a mean value of 0.88 (minimum = 0.10, maximum = 2.5, SD = 0.71) for negative patients. Using a cutoff value corresponding to 0.800% CO2/weight (kg), the 14C-UBT showed a mean value of 2.78 (minimum = 0.89, maximum = 5.22, SD = 1.18) in HP-positive patients. HP-negative patients showed a mean value of 0.37 (minimum = 0.13, maximum = 0.77, SD = 0.17). IRIS is a low-cost, easy to manage, highly sensitive and specific test for H. pylori detection. Storing and mailing the samples did not interfere with the performance of the test.