Electrospun nanofibers of polylactide (pla)/polyglutamic acid (?-pga) blends and their use as ammonia detecting kits in intelligent meat packaging

Name: Sdok, Soksreymeng

keyword: Gamma polyglutamic acid

; Polylactide

; Electrospinning

; Ammonia detection

; Meat spoilage process

; Freshness of meat products

Abstract: Freshness of meat products is possibly influent by many factors after harvest. Changes in physical, chemical, and microbiological properties of meat is reflected to shelf life or storage periods. As a result, from microorganism spoilage ‘s metabolites, volatile compounds such ammonia or amines can be considered as fingerprint to identify meat’s freshness level. In this study, sensor with high sensitivity is designed and fabricated by blending biocompatible polylactide (PLA) and gamma poly glutamic acids (γ-PGA) under electrospinning technique. The nanofiber materials are subsequently restrained γ-PGA by thermal cross linking of γ-PGA with ethylene glycol(EG) and glycerol(G). Results from FTIR surface characterization of different treated fibrous mats reveal that the carboxylic content of γ-PGA are the highest in blended fibrous mats, while the EG treated fibers depict much lower contents due to efficiency of this cross linker with carboxylic acid groups of γ-PGA. Under ammonia gas absorption study, the treated materials undergo degradation of polymer. This clearly indicates that fibrous the mats are workable with ammonia by scissoring of carbonyl ester and carboxylic acid groups. To develop a sensor for detection of spoilage compounds in meat spoilage such as ammonia, anthocyanin dye from red cabbage is applied by staining on the material’s surfaces. Under fish incubation, FTIR spectra of fish samples and dye staining materials are correlated, in terms of chemical structure and color dye staining as function of time. From series of FTIR spectra, 2D-FTIR were employed to understand the chemical mechanisms in meat spoilage process. The materials show distinct spectral change patterns with dye. The color change has high potential for evaluation of freshness by naked eyes. To increase the accuracy and practicability, artificial neural network model (ANN) is employed by inputting RGB data from images of color change as a result from low concentration ammonia standard. The experimental results and predicted results from the model are in good agreement, with R-square=0.9906. This clearly indicates that the coupled nano sensor material and ANN model can be an alternative method for analysis of amine volatiles in short analysis time

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Pakorn Opaprakasit

Role: advisor

Name: Atitsa Petchsuk

Role: co-advisor

Created: 2016

Modified: 2564-05-19

Issued: 2021-05-18

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2016.687

eng

DegreeName: Master of Science

Level: Master's Degree

Descipline: Engineering and Technology

Grantor: Thammasat University

©copyrights Thammasat University

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