PurposeThis paper seeks to estimate importance of various factors affecting the choice of fast food outlets by Indian young consumers.Design/methodology/approachThe study applies multivariate statistical tools to estimate importance of various factors affecting the choice of fast food outlets by Indian young consumers. In addition, the authors analysed the consumption patterns, impact of hygiene and nutritional values, and rating of various attributes of McDonald's and Nirula's.FindingsResults indicate that the young Indian consumer has passion for visiting fast food outlets for fun and change but home food is their first choice. They feel homemade food is much better than food served at fast food outlets. They have the highest value for taste and quality (nutritional values) followed by ambience and hygiene. Three dimensions (service and delivery dimension, product dimension, and quality dimension) of fast food outlets' attributes are identified based on factor analysis results. The two fast food outlets' rating differs significantly on the seven attributes. McDonald's scores are higher on all attributes except “variety”. Further, consumers feel that fast food outlets must provide additional information on nutritional values and hygiene conditions inside kitchen.Practical implicationsFast food providers need to focus on quality and variety of food besides other service parameters. There is need to communicate the information about hygiene and nutrition value of fast food which will help in building trust in the food provided by fast food players.Originality/valueEstimates importance of various factors affecting the choice of fast food outlets by Indian young consumers.
The primary objectives of modern agriculture includes the environmental sustainability, low production costs, improved plants’ resilience to various biotic and abiotic stresses, and high sowing seed value. Delayed and inconsistent field emergence poses a significant threat in the production of agri-crop, especially during drought and adverse weather conditions. To open new routes of nutrients’ acquisition and revolutionizing the adapted solutions, stewardship plans will be needed to address these questions. One approach is the identification of plant based bioactive molecules capable of altering plant metabolism pathways which may enhance plant performance in a brief period of time and in a cost-effective manner. A biostimulant is a plant material, microorganism, or any other organic compound that not only improves the nutritional aspects, vitality, general health but also enhances the seed quality performance. They may be effectively utilized in both horticultural and cereal crops. The biologically active substances in biostimulant biopreparations are protein hydrolysates (PHs), seaweed extracts, fulvic acids, humic acids, nitrogenous compounds, beneficial bacterial, and fungal agents. In this review, the state of the art and future prospects for biostimulant seedlings are reported and discussed. Biostimulants have been gaining interest as they stimulate crop physiology and biochemistry such as the ratio of leaf photosynthetic pigments (carotenoids and chlorophyll), enhanced antioxidant potential, tremendous root growth, improved nutrient use efficiency (NUE), and reduced fertilizers consumption. Thus, all these properties make the biostimulants fit for internal market operations. Furthermore, a special consideration has been given to the application of biostimulants in intensive agricultural systems that minimize the fertilizers’ usage without affecting quality and yield along with the limits imposed by European Union (EU) regulations.
Food-based components represent major sources of functional bioactive compounds. Milk is a rich source of multiple bioactive peptides that not only help to fulfill consumers ‘nutritional requirements but also play a significant role in preventing several health disorders. Understanding the chemical composition of milk and its products is critical for producing consistent and high-quality dairy products and functional dairy ingredients. Over the last two decades, peptides have gained significant attention by scientific evidence for its beneficial health impacts besides their established nutrient value. Increasing awareness of essential milk proteins has facilitated the development of novel milk protein products that are progressively required for nutritional benefits. The need to better understand the beneficial effects of milk-protein derived peptides has, therefore, led to the development of analytical approaches for the isolation, separation and identification of bioactive peptides in complex dairy products. Continuous emphasis is on the biological function and nutritional characteristics of milk constituents using several powerful techniques, namely omics, model cell lines, gut microbiome analysis and imaging techniques. This review briefly describes the state-of-the-art approach of peptidomics and lipidomics profiling approaches for the identification and detection of milk-derived bioactive peptides while taking into account recent progress in their analysis and emphasizing the difficulty of analysis of these functional and endogenous peptides.
Hybrid models based on feature selection and machine learning techniques have significantly enhanced the accuracy of standalone models. This paper presents a feature selection‐based hybrid‐bagging algorithm (FS‐HB) for improved credit risk evaluation. The 2 feature selection methods chi‐square and principal component analysis were used for ranking and selecting the important features from the datasets. The classifiers were built on 5 training and test data partitions of the input data set. The performance of the hybrid algorithm was compared with that of the standalone classifiers: feature selection‐based classifiers and bagging. The hybrid FS‐HB algorithm performed best for qualitative dataset with less features and tree‐based unstable base classifier. Its performance on numeric data was also better than other standalone classifiers, whereas comparable to bagging with only selected features. Its performance was found better on 70:30 data partition and the type II error, which is very significant in risk evaluation was also reduced significantly. The improved performance of FS‐HB is attributed to the important features used for developing the classifier thereby reducing the complexity of the algorithm and the use of ensemble methodology, which added to the classical bias variance trade‐off and performed better than standalone classifiers.
Credit scoring methods are widely used for evaluating loan applications in financial and banking institutions. Credit score identifies if applicant customers belong to good risk applicant group or a bad risk applicant group. These decisions are based on the demographic data of the
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