Exploring Chitosan Lactate As a Multifunctional Additive: Enhancing Quality and Extending Shelf Life of Whole Wheat Bread

Pratik Singh, Vikas Yadav,Deblu Sahu, Krishan Kumar,Doman Kim,Deng Yang,Sivaraman Jayaraman,Maciej Jarzebski,Marek Wieruszewski,Kunal Pal

Foods（2024）

Parul Univ | Natl Inst Technol Rourkela | Seoul Natl Univ | Qingdao Agr Univ | Poznan Univ Life Sci

Cited 0|Views22

Abstract

The shelf life of whole wheat bread (WWB) significantly impacts its freshness and overall quality. This research investigated the impact of chitosan lactate (CL) on various characteristics influencing the shelf life of WWB, including its physical, chemical, textural, antimicrobial, and sensory attributes. These characteristics were evaluated by conducting various experiments such as physical inspection, moisture, impedance, swelling, color, texture, FTIR, microbiological, and sensory analysis. CL with different concentrations was incorporated into WWB formulations: P0.0 (0.0% w/w CL, control), P0.5 (0.5% w/w CL), P1.0 (1.0% w/w CL), P2.0 (2.0% w/w CL), and P3.0 (3.0% w/w CL). The inclusion of CL promoted the Maillard reaction (MR) compared to P0.0. The promotion of MR resulted in the formation of a shinier crust, which increased as the CL content was increased. P0.5 comprised large-sized pores and exhibited increased loaf height. CL-containing WWB formulations showed an increased moisture content and decreased impedance values compared to the control. FTIR analysis of P0.5 demonstrated the enhanced interaction and bonding of water molecules. P0.5 demonstrated optimal textural, colorimetric, and antimicrobial properties compared to other formulations. The sensory attributes of WWBs remain unchanged despite CL addition. In conclusion, P0.5 exhibited optimal characteristics associated with better quality and prolonged shelf life.

Translated text

Key words

bakery,chitosan derivatives,overall quality,polysaccharides,storage life,texture

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper