Guava (Psidium guajava L.) is one of the most important tropical fruits belonging to the genus Psidium and the Myrtaceae family and claim to have phenolic compounds that have been reported to possess strong antioxidant activity. This study was aimed to evaluate the bioactive constituents in guava cultivars at different ripening stages by HPLC. The five guava cultivars were selected at different ripening stages and the bioactive components were analysed by high-pressure liquid chromatography. The quantification of bioactive compounds revealed that the highest amount of bioactive compounds was found in cultivar Safeda at the unripe stage, while a minimum amount was found in ripe Apple Colour guava cultivar. The six bioactive compounds were quantified in the range of gallic acid (9.46-63.08 mg/100 g), quercetin (0.11-2.51 mg/100 g), myrcetin (0.09-0.034 mg/100 g), ascorbic acid (7.45-75.07 mg/100 g), apegenin (0.01-0.032 mg/100 g) and lycopene (0.34-0.92 mg/100 g). The exploratory evaluation of guava samples was performed through Principal Component Analysis (PCA), the bioactive compounds, lycopene, myricetin, and quercetin are dominant variables on this PC1 (61.52%) (Scores better than 0.7), thereby causing greater variability among these samples. The second principal component (PC2) represents 16.54% of the total variance and the ascorbic acid, gallic acid and apeginin (score better than 0.7), are the dominant variables on this PC.

While a lot of work is done on extracting sentiments and opinions in unstructured text, majority of it is focused on contextual sentiment mining and features that are more focused on sentiments. The team attempted to use contextual text analytics to identify product or service features that drives the sentiment of the user. This is done through application of cosine similarity and neural networks. Customers speak about product or service feature when it is important for the them. The second stage of the analysis is focused on supervised learning, that identifies key drivers of a product or service. It helps in deriving those elements which are subconsciously being evaluated by customers but not spoken. We also test the significant difference in views of people pre and post Covid in their reviews. We found that factors related to Covid have gone up by 30% but not statistically significant. Given the volume of data, the team has analyzed these on cloud to assess the cloud computing readiness for such analysis. Feedback around the post Covid topics helps us understand the issues that need to be addressed by restaurant industry.

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An experiment was conducted at Main Experimental Station of Department of Vegetable Scienec, ANDUA&T, Kumarganj, and Ayodhya (U.P.) during Rabi season, 2020-21. Experimental material for study was consisted of sixty two coriander genotypes including two checks (Hisar Anand and ND Cor-2). The experiment was conducted in Augmented Block Design six blocks spaced 30 cm with plant to plant spacing of 15 cm. Each genotypes were grown in the plot size 2.0 m ×0.90 m. Observation were noted on eleven characters viz. days to 50% flowering (days), plant height (cm), nodes per plant, branches per plant, umbels per plant, umbellates per umbel, fruits per umbellate, fruits per umbel, umbel diameter (cm), 1000 seed weight (g) and seed yield per plant (g). The mean sum of square (analysis of variance) due blocks were highly significant for all eleven characters representing the variability among the genotypes considered in this study due to diverse genetic makeup of the genotypes. The higher extent phenotypic and genotypic coefficient of variation were observed for seed yield per plant. High heritability along with high expected genetic advance in per cent of mean was observed for most of the characters that provide good scope for further improvement in advance generations.