Development of fiber-enriched muffins using pomegranate peel powder and its effect on physico-chemical properties and shelf life of the muffins

BACKGROUND

Pomegranate peel is a by-product from the pomegranate processing industries and is a rich source of dietary fibers and bioactive compounds. It has good antioxidant and antimicrobial properties. In the present study, the effects of substitution of refined wheat flour with pomegranate peel powder (PPP) at a rate of 2%, 4%, 6%, 8% and 10% on the physico-chemical and sensorial properties as well as on the oxidative and microbial stability of muffins were investigated.

RESULTS

A significant reduction in specific volume (1.99 to 1.57 cm3  g-1 ), weight loss (11.73 to 10.14 g 100 g-1 ) and an increase in crumb hardness (633.06 to 2311.5 g) of muffins were observed on addition of PPP. Moreover, the nutritional value was improved by a significant increase in the fiber content (4.39 to 10.66%), total phenols (0.443 to 48.53 mg GAE 100 g-1 ), antioxidant activity (75.94% to 99.36%), calcium (200.33 to 294.33 mg 100 g-1 ), potassium (227.33 to 425.33 mg 100 g-1 ) and magnesium (96.33 to 288.33 mg 100 g-1 ). The pasting and rheological properties of muffin batter showed a significant decrease in the final and peak viscosity, as well as increase in storage, loss and complex modulus. The muffin samples were organoleptically acceptable up to a level of 8% PPP. Free fatty acid content, peroxide value and microbial count of the muffin with 8% PPP were significantly lower compared to the control sample and more oxidatively and microbially stable for a storage period of 21 and 28 days at ambient and refrigerated temperatures, respectively.

CONCLUSION

The present study provides the opportunity to use PPP as functional ingredients and natural preservative in the preparation of muffins. © 2023 Society of Chemical Industry.

Multiresidue analysis of pesticides in four different pomegranate cultivars: Investigating matrix effect variability by GC-MS/MS and LC-MS/MS

Matrix effect (ME) is unavoidable in multiresidue pesticide analysis, even when using highly advanced instruments, and differences in MEs can affect residue analytical accuracy due to pomegranate cultivar composition variations. However, literature to support this claim is limited.The study used GC-MS/MS and LC-MS/MS to investigate four different Indian pomegranate cultivar extracts and their MEs on multi-class pesticides.The whole fruit and arils of all cultivarswere tested for 22 GC-amenable and 21 LC-amenable pesticides. Principal component analysis of the data confirmed that each cultivar had unique MEs for each pesticide.The majority of pesticides showed acute variations in recovery rates with 95% confidence, while GC-MS/MS-amenablepesticides showed more variation. Although extrapolative dilution reduced the influence of MEs on analytical accuracy, a generalized matrix-matching for all cultivars was not possible to achieve.To reduce the variability in MEs, it is recommended that a cultivar-specific matrix-matched standard should be used.

Image dataset of pomegranate fruits (Punica granatum) for various machine vision applications

Dataset - an essential aspect and the requirement for any of the machine learning project. Collection/creation of dataset in the agriculture domain is highly challenging task because the domain itself is uncertain. Main objective of the present paper is to create an image dataset of pomegranate fruits of different grades. Accordingly, we have considered ‘Ruby’ cultivar of pomegranate and sincerely constructed the dataset. Fruits belonging to three grades are considered. The images for each fruit are covered from all the three angles. The dataset created also contains the weights of the fruits. The dataset consists of 12 folders named after their effective quality grades. The usage of this dataset is already proved in the works carried out by the authors in their previous studies. This dataset is highly helpful for the data science engineer / machine learning programmer or machine learning expert working in the field of precision agriculture.