The present study was carried out to find the effect of fruit harvesting stage (October, November and December) on the physicochemical and antioxidant properties in five kiwi cultivars (Abbot, Bruno, Allison, Hayward, Monty). Results showed that soluble solid content (SSC) and pH increased while ascorbic acid (Vit C), titrated acidity (TAD) and SSC/TAD decreased in all the cultivars with delay in harvesting. Total polyphenols (TP) were decreased while total flavonoids (TF) increased in all tested cultivars with delay in harvesting. The highest concentration of TP (2.02 mg gallic acid equivalent/g fresh weight) and TF (51.12 mg catechin equivalent/100g FW) were found in cultivar 'Allison' in the month of October and December, respectively. Antioxidant activities (AA) were genotype depended and no trend was observed with month of harvesting. Principal component analysis (PCA) showed strong correlation between Vit C, TP and antioxidant activities. Two major clusters were computed using agglomerative hierarchical clustering (AHC). All the studied important traits may be used in the breeding programmes to increase the variability for different physiochemical and antioxidative characteristics and to make suitable selections that could be acceptable to consumers.

A neural network (NN) technique is used to obtain vertical profiles of temperature from NOAA-15 and 16 Advanced Microwave Sounding Unit-A (AMSU-A) measurements over the Indian region. The corresponding global analysis data generated by National Center for Environmental Prediction (NCEP) and AMSU-A data from July 2006 to April 2007 are used to build the NN training data-sets and the independent dataset of May 2007 to July 2007 divided randomly into two independent dataset for training (land) and testing (ocean). NOAA-15 and 16 satellite data has been obtained in the form of level 1b (instrument counts, navigation and calibration information appended) format and pre-processed by ATOVS (Advanced TIROS Operational Vertical Sounder) and AVHRR (Advanced Very High Resolution Radiometer) Processing Package (AAPP). The root mean square (RMS) error of temperature profile retrieved with the NN is compared with the errors from the International Advanced TOVS (ATOVS) Processing Package (IAPP). The over all results based on the analysis of the training and independent datasets show that the quality of retrievals with NN provide better results over the land and comparable over the ocean. The RMS errors of NN are found to be less than 3 °C at the surface, 0.9° to 2.2° between 700 and 300 hPa and less than 2 °C between 300 and 100 hPa. It has also been observed that the NN technique can yield remarkably better results than IAPP at the low levels and at about 200-hPa level. Finally, the network based AMSU-A 54.94-GHz (Channel-7) brightness temperature (maximum Tb) and its warm core anomaly near the center of the cyclone has been used for the analysis of Gonu cyclone formed over Arabian Sea during 31 May to 7 June 2007. Further, the anomalies are related to the intensification of the cyclone. It has been found that the single channel AMSU-A temperature anomaly at 200 hPa can be a good indicator of the intensity of tropical cyclone. Therefore it may be stated that optimized NN can be easily applied to AMSU-A retrieval operationally and it can also offer substantial opportunities for improvement in tropical cyclone studies.

Se utilizó la técnica de redes neuronales (NN, por sus siglas en inglés) para obtener los perfiles verticales de temperatura a partir de las mediciones de la Advanced Microwave Sounding Unit-A NOAA-15 y 16 (AMSU-A, por sus siglas en inglés) sobre la región de la India. Los datos del correspondiente análisis global generados por el National Center for Environmental Prediction (NCEP) y los del AMSU-A de mayo a julio de 2007 se utilizaron para construir el conjunto de datos de entrenamiento de la red neuronal; el conjunto de datos independiente de mayo a julio de 2007 se dividió al azar en dos para entrenamiento (tierra) y prueba (océano). Los datos de los satélites NOAA-15 y 16 se obtuvieron en el formato de nivel 1b (información de conteo instrumental, navegación y calibración incluida) y preprocesada por el Advanced TIROS Operational Vertical Sounder (ATVOS) y el Advanced Very High Resolution Radiometer (AVHRR)_Processing Package (IAPP). El error medio de la raíz cuadrada (RMS) de los datos del perfil de temperatura recuperados con la NN se compara con los errores del International Advanced TOVS (ATOVS) Processing Package (IAPP). Los resultados globales basados en el análisis del entrenamiento y los conjuntos de datos independientes muestran que la calidad de los datos recuperados con la NN proporciona mejores resultados sobre la tierra y comparables sobre el océano. Se encontró que los errores RMS de la NN son menores a 3° en la superficie, entre 0.9 2.2° a 700-300 hPa y menores de 2° a 300-100 hPa. También se observó que la técnica de NN puede producir resultados notablemente mejores que los del IAPP en los niveles bajos y alrededor del nivel de 200 hPa. Finalmente, el brillo de temperatura (Tb máxima) con base en la red correspondiente a 54.94 GHz (canal 7) de la AMSU-A y la anomalía del núcleo cálido cercana al núcleo del ciclón se utilizaron para el análisis del ciclón Gonu que se formó sobre el Mar de Arabia del 31 de mayo al 7 de junio de 2007. Estas anomalías están relacionadas con la intensificación del ciclón. Se encontró que la anomalía de temperatura del canal único de la AMSU-A a 200 hPa puede ser un buen indicador de la intensidad del ciclón tropical. Por lo tanto, se puede establecer que una red neuronal optimizada puede ser aplicada con facilidad para la recuperación operacional de datos de la AMSU-A y que también puede ofrecer oportunidades sustanciales para mejorar el estudio de los ciclones tropicales.