Psychophysical estimation of acid intensity and determination of sweet-acid interaction in a fish paste containing glycerol

In a fish paste made with cooked Brazilian flathead (Percophis brasiliensis), glycerol (17%), sodium chloride (1.5%) and potassium sorbate (0.1%) the following acid percentages: 0.2; 0.4; 0.6; 0.8, 1 and 1.5% w/w were incorporated to determine the relationship between added acetic acid and the sensorially perceived intensity, and the effects of the combination of sweet-acid tastes. Tests for paired comparison, ranking and structured verbal scales for sweet and acid attributes and psychophysical test were carried out. There was a perceptible difference among samples for differences of 0.4 units of acid concentration. Samples indicated as sweeter by 89.47% of the judges were those containing a lesser acid concentration. A reduction in glycerol sweetness when increasing acid levels was observed. Acetic acid reduced the sweetness of glycerol and inversely glycerol reduced the acidity of acetic acid. The data obtained with the magnitude estimation test agree with Steven's law.

Study of the volatile compounds from plum (Prunus domestica L. cv. Horvin) and estimation of their contribution to the fruit aroma

Simultaneous Distillation-Extraction (SDE) and headspace-solid phase microextraction (HS-SPME) combined with GC-FID and GC-MS were used to analyze volatile compounds from plum (Prunus domestica L. cv. Horvin) and to estimate the most odor-active compounds by application of the Odor Activity Values (OAV). The analyses led to the identification of 148 components, including 58 esters, 23 terpenoids, 14 aldehydes, 11 alcohols, 10 ketones, 9 alkanes, 7 acids, 4 lactones, 3 phenols, and other 9 compounds of different structures. According to the results of SDE-GC-MS, SPME-GC-MS and OAV, ethyl 2-methylbutanoate, hexyl acetate, (E)-2-nonenal, ethyl butanoate, (E)-2-decenal, ethyl hexanoate, nonanal, decanal, (E)-β-ionone, Γ-dodecalactone, (Z)-3-hexenyl acetate, pentyl acetate, linalool, Γ-decalactone, butyl acetate, limonene, propyl acetate, Δ-decalactone, diethyl sulfide, (E)-2-hexenyl acetate, ethyl heptanoate, (Z)-3-hexenol, (Z)-3-hexenyl hexanoate, eugenol, (E)-2-hexenal, ethyl pentanoate, hexyl 2-methylbutanoate, isopentyl hexanoate, 1-hexanol, Γ-nonalactone, myrcene, octyl acetate, phenylacetaldehyde, 1-butanol, isobutyl acetate, (E)-2-heptenal, octadecanal, and nerol are characteristic odor active compounds in fresh plums since they showed concentrations far above their odor thresholds.

Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil

Species distribution modeling has relevant implications for the studies of biodiversity, decision making about conservation and knowledge about ecological requirements of the species. The aim of this study was to evaluate if the use of forest inventories can improve the estimation of occurrence probability, identify the limits of the potential distribution and habitat preference of a group of timber tree species. The environmental predictor variables were: elevation, slope, aspect, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). To estimate the distribution of species we used the maximum entropy method (Maxent). In comparison with a random distribution, using topographic variables and vegetation index as features, the Maxent method predicted with an average accuracy of 86% the geographical distribution of studied species. The altitude and NDVI were the most important variables. There were limitations to the interpolation of the models for non-sampled locations and that are outside of the elevation gradient associated with the occurrence data in approximately 7% of the basin area. Ceiba pentandra (samaúma), Castilla ulei (caucho) and Hura crepitans (assacu) is more likely to occur in nearby water course areas. Clarisia racemosa (guariúba), Amburana acreana (cerejeira), Aspidosperma macrocarpon (pereiro), Apuleia leiocarpa (cumaru cetim), Aspidosperma parvifolium (amarelão) and Astronium lecointei (aroeira) can also occur in upland forest and well drained soils. This modeling approach has potential for application on other tropical species still less studied, especially those that are under pressure from logging.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.