Browsing by Author "Kumari, A"

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    Effect of varieties and storage on the quality parameters of nectarine (Prunus persica)-based intermediate moisture food (IMF) products
    (2022-03) Shivani; Banyal, S; Kumari, A
    Fruits play an important role in maintaining a healthy life. Nectarine is a hybrid fruit of peach and plum, wherein efforts were made to develop intermediate moisture food products (jam and jelly) from nectarine varieties (May Fire, Snow Queen, and Sil ver King). The study aimed to determine the effect of storage on the nutritional (TSS, pH, acidity, ascorbic acid, and sugars) and sensory parameters (color, taste, flavor, texture, and overall acceptability) of jam and jelly at different storage intervals. Storage had a nonsignificant effect on the total soluble solids, with reported mean values of 69.670 Brix, while the pH content of jam varied significantly from 2.90-2.20 during 6 months of storage. The values for acidity and total sugars increased (P≤0.05) signifi cantly from 1.92-2.03 percent and 57.04 to 56.93 percent, respectively. However, the ascorbic acid content decreased signifi cantly from 4.64 - 1.66 mg/100 g. In the case of jelly, the total soluble solids and pH decreased from 67.78 – 67.440 Brix and 2.70 – 2.48, respectively, during storage for 6 months at ambient temperature. The ascorbic acid content decreased from 4.56 2.10 mg/100 g. Among cultivars, there was a nonsignificant difference in the nutritional parameters of jam, but in the case of jelly, different cultivars had a significant effect on TSS, pH, and ascorbic acid content. Organoleptically, the nectarine jam was rated as ‘liked very much’, while the jelly ‘liked slightly’, with good storage acceptance up to 6 months. Being nectarine as a superfood can be explored to develop speciality food products for vulnerable sections of society.
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    On estimation of P(Y < X) for inverse Pareto distribution based on progressively first failure censored data
    (2023-11) Alharb, R; Garg, R; Kumar, I; Kumari, A
    The stress-strength reliability (SSR) model ϕ = P(Y < X) is used in numerous disciplines like reliability engineering, quality control, medical studies, and many more to assess the strength and stresses of the systems. Here, we assume X and Y both are independent ran dom variables of progressively first failure censored (PFFC) data following inverse Pareto distribution (IPD) as stress and strength, respectively. This article deals with the estimation of SSR from both classical and Bayesian paradigms. In the case of a classical point of view, the SSR is computed using two estimation methods: maximum product spacing (MPS) and maximum likelihood (ML) estimators. Also, derived interval estimates of SSR based on ML estimate. The Bayes estimate of SSR is computed using the Markov chain Monte Carlo (MCMC) approximation procedure with a squared error loss function (SELF) based on gamma informative priors for the Bayesian paradigm. To demonstrate the relevance of the different estimates and the censoring schemes, an extensive simulation study and two pairs of real-data applications are discussed.