• Title/Summary/Keyword: Indian Processed Food

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The Impact of Reference Groups and Product Familiarity on Indian Consumers' Product Purchases

  • Yu, Jong-Pil;Dutta, Payal Kaishap;Pysarchik, Dawn Thorndike
    • Journal of Global Scholars of Marketing Science
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    • v.17 no.2
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    • pp.75-97
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    • 2007
  • Less than 3% of India's food basket, consists of processed food, therefore processed food can be viewed as an innovation or new product to Indian consumers. This research investigates the effects of product familiarity and reference groups on Indian consumers' attitudes and purchase behavior of new processed food products. For the study, the model is developed by modifying Cambel and Goodstein's (2001) "Moderate Incongruity Effect" to include important cross-cultural influences on attitudes and purchase decisions among Indian consumers. Empirical analysis was conducted through structural equation modeling (SEM). SEM results indicated that reference group influence has a stronger positive effect on consumers' attitudes and actual purchase behavior of more familiar processed foods than of less familiar processed food. In addition, attitudes have a stronger positive effect on consumers' actual purchase of more familiar than of less familiar processed foods.

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The importance of assessing heavy metals in medicinal herbs: a quantitative study

  • Behera, Bhagyashree;Bhattacharya, Sanjib
    • CELLMED
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    • v.6 no.1
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    • pp.3.1-3.4
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    • 2016
  • Consumption of herbal products from the medicinal plants contaminated with heavy metals can cause serious consequences on human health. This is a major concern for traditional and herbal medicine. The present study was carried out to analyze and quantify the levels of six potentially toxic heavy metals namely arsenic, lead, cadmium, mercury, chromium and nickel in ten important Indian medicinal herbs. The air dried raw herbs were processed by microwave assisted wet digestion and analyzed by using atomic absorption spectrophotometer equipped with graphite tube atomizer. Except the chromium content in three plants, all the levels of six heavy metals analyzed were found to be quite below the permissible limits in all the ten raw medicinal herbs analyzed. The present work implies that, regular and systematic screening of raw medicinal herbs is necessary to check the levels of the heavy metal contaminants before using them for consumption or preparation of herbal formulations so that the possible contamination cannot cumulate up to the finished products.

Effect of dietary mannanoligosaccharide supplementation on nutrient digestibility, hindgut fermentation, immune response and antioxidant indices in dogs

  • Pawar, Mahesh M.;Pattanaik, Ashok K.;Sinha, Dharmendra K.;Goswami, Tapas K.;Sharma, Kusumakar
    • Journal of Animal Science and Technology
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    • v.59 no.5
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    • pp.11.1-11.7
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    • 2017
  • Background: Use of prebiotics in companion animal nutrition is often considered advantageous over probiotics because of the ease of handling, ability to withstand processing and storage etc. While most of the studies on prebiotic use in dogs have been done with processed food as basal diet, the response in relation to homemade diet feeding is not very well explored. Methods: The study was conducted to evaluate the effects of dietary mannanoligosaccharide (MOS) supplementation on nutrient digestibility, hindgut fermentation, immune response and antioxidant indices in dogs. Ten Spitz pups were divided into two groups: control (CON) with no supplementation, and experimental (MOS) wherein the basal diet was supplemented with MOS at 15 g/kg diet. All dogs were fed on a home-prepared diet for a period of 150 days. The study protocol included a digestion trial, periodic blood collection and analysis for lipid profile and erythrocytic antioxidants. Immune response of the animals was assessed towards the end of the feeding period. Results: Results revealed no significant (P > 0.05) variations in palatability score, intake and apparent digestibility of nutrients between the groups. Faecal score, faeces voided, faecal pH, concentrations of ammonia, lactate and short-chain fatty acids were comparable (P > 0.05) between the two groups. Cell-mediated immune response, assessed as delayed-type of hypersensitivity response, was significantly higher (P < 0.05) in the MOS group. The percent of lymphocyte sub-populations CD4+ and ratio of CD4+:CD8+ were also significantly (P < 0.05) higher in MOS group. The serum IgG levels were similar (P > 0.05) in both the groups. Supplementation of MOS lowered (P < 0.05) serum total- and LDL- cholesterol levels, when compared with the control group. The erythrocytic antioxidant indices were similar (P > 0.05) between the two groups. Conclusions: The results indicated that supplementation of MOS at the rate of 15 g/kg in the diet of dog augmented the cell-mediated immune response and serum lipid profile without any influences on digestibility of nutrients, hindgut fermentation and antioxidants indices.

Crop Yield and Crop Production Predictions using Machine Learning

  • Divya Goel;Payal Gulati
    • International Journal of Computer Science & Network Security
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    • v.23 no.9
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    • pp.17-28
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    • 2023
  • Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.