Calorific Ash Carbon And Nitrogen-Content In Relation To Length And Dry-Weight Of Parathemisto-Gaudichaudi (Amphipoda Hyperiidea) In The North-East Atlantic Ocean

The calorific, ash, carbon and nitrogen content, length and dry weight were determined for the hyperiid Parathemisto gaudichaudi (Guerin). Regression equations for all these variables were determined so that they can be estimated by calculation from measurements of length of the hyperiid. Mean values for total nitrogen and carbon were 7.79±0.85% and 36.80±4.18% of the dry weight, respectively. The carbon to calorific equivalent for P. gaudichaudi was 10.37 kcal g-1 carbon (9.13 kcal g-1 when corrected for nitrogen). The calorific value for ash-free adult P. gaudichaudi was 5.138 kcal g-1±1.309 (4.510 kcal g-1 when corrected for nitrogen). This large variation in the calorific content (coefficient of variation of 25.84%) can be accounted for largely by variation in the ash content (coefficient of variation of 21.84%). The calorific value determined for P. gaudichaudi is similar to that measured for other carnivorous crustaceans and adds support to the hypothesis that animals with high calorific content have a low fecundity and an energy-rich store which can be used as a buffer during unfavourable periods in their life.

Evaluation of Serpa cheese proteolysis by nitrogen content and capillary zone electrophoresis

Proteolysis of Serpa cheese produced traditionally (B) and semi-industrially (C) was evaluated for the first time by determination of nitrogen content and capillary zone electrophoresis (CZE). A citrate dispersion of cheese was fractionated to determine the nitrogen in pH 4.4, trichloroacetic and phosphotungstic acid soluble fractions (pH 4.4-SN, TCA-SN and PTA-SN, respectively). The pH 4.4-SN was significantly higher for B ( P <0.001), while TCA-SN was significantly higher for C ( P <0.001). PTA-SN was also higher for C but at 60 days ripening no significant difference was found between B and C. Degradation of alpha(s1) - and beta-caseins evaluated by CZE was in good agreement with the maturation index (pH 4.4-SN/TN).

Evaluation of Serpa cheese proteolysis by nitrogen content and capillary zone electrophoresis

Proteolysis of Serpa cheese produced traditionally (B) and semi-industrially (C) was evaluated for the first time by determination of nitrogen content and capillary zone electrophoresis (CZE). A citrate dispersion of cheese was fractionated to determine the nitrogen in pH 4.4, trichloroacetic and phosphotungstic acid soluble fractions (pH 4.4-SN, TCA-SN and PTA-SN, respectively). The pH 4.4-SN was significantly higher for B ( P < 0.001), while TCA-SN was significantly higher for C ( P < 0.001). PTA-SN was also higher for C but at 60 days ripening no significant difference was found between B and C. Degradation of alpha(s1) - and beta-caseins evaluated by CZE was in good agreement with the maturation index (pH 4.4-SN/TN).

A novel intelligent system to nitrogen content prediction in plants using indirect chlorophyll measurements

The accurate identification of the nitrogen content in plants is extremely important since it involves economic aspects and environmental impacts, Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification in plants involves a lot of non-linear parameters and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought SPAD index using artificial neural networks (ANN). The network acts as identifier of relationships among, crop varieties, fertilizer treatments, type of leaf and nitrogen content in the plants (target). So, nitrogen content can be generalized and estimated and from an input parameter set. This approach can form the basis for development of an accurate real time system to predict nitrogen content in plants.

Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks

The accurate identification of the nitrogen content in crop plants is extremely important since it involves economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification involves a lot of nonlinear parametes and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can be form the basis for development of an accurate real time nitrogen applicator.