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Home > Studies on Production of Pectin from Fruit Wastes Available in Pakistan. It’S Bio- Characterization and Utilization in the Development of Pharmaceutical and Food Products

Studies on Production of Pectin from Fruit Wastes Available in Pakistan. It’S Bio- Characterization and Utilization in the Development of Pharmaceutical and Food Products

Thesis Info

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Author

Siddiqui, Nausheen Hameed

Program

PhD

Institute

University of Karachi

City

Karachi

Province

Sindh

Country

Pakistan

Thesis Completing Year

2016

Thesis Completion Status

Completed

Subject

Applied Sciences

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/8975/1/Nausheen%20Hameed%20siddiqiee%20phrama...2017%20Uok.pdf

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676727312263

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With the rising awareness and use of plant based functional fibers there is a growing market demand of pectin throughout the world. However, this is mainly linked with the price and quality of the products availability in the market for different purposes. The market price and current production of pectin has been influenced by many factors among which the provision of raw material for the extraction of pectin is of main concern. The purpose of this research investigations was not only to explore new source of pectin which can aid production of pectin but also to extract it in a simpler way as to minimize or reduce the cost of production. A systematic literature search was also carried out to evaluate the present findings comprising the earlier results in term of its application in developing food and pharmaceutical products. This thesis is based on work to find out the effective extraction of pectin from various fruit wastes available in Pakistan, its extraction in a most simple and effective way and then after extracting its usage in both pharmaceutical and food product. For this purpose numerous seasonal fruits available in Pakistan were studied in their respective time of availability as a screening program.The present research was focused on the isolation, physicochemical characterization and functional properties of pectin extracted from a new source. The extraction process for effective extraction was developed after using different solvents (organic and inorganic acids of different strengths), five extracting pH (1,3 ,5 6 and 7), five mechanical procedure (homogenizing , grinding, hammering, cutting and chopping) , two different boiling techniques, (Bunsen burner and microwave heating) . Sapodilla fruits when selected finally was subjected to other affecting parameters for better yield, like time of boiling (10, 20, 40 and 60 min respectively) with two different 69 strength of acid (0.1 and 1N HCl) strength of inorganic acid (0.1, 0.5 and 1N HCl respectively), and effect of organic acid (critic, tartaric and acetic acid) Initially five fruits, sapodilla, banana, muskmelon, apple and orange were selected for studying the physic mechanical effects among which two fruits (Apple and orange) were purely used for comparative study purpose. After this study three fruits (banana sapodilla and muskmelon) were selected for further evaluation. . Out of three fruits, highest pectin yield was recorded from banana (10.5%) followed by sapodilla (4.7%) and muskmelon (4.4%) respectively. In comparison orange peel indicated 22.7% and apple peel 4.85%. Identification and jelly grade tests were performed which supported sapodilla as most appropriate against banana and muskmelon pectin for further investigation to find out a new source of pectin which can be chemically stable, easily available, and pharmaceutically useful in nature. Hence sapodilla peel was used to extract pectin after using different phyico-mechanical processes. It was observed that the best extraction parameter to obtain maximum yield of pectin was after 10 min of boiling of chopped peels of sapodilla maintaining the pH with 1NHCl to be at pH 5. Present research was also concerned with the bio characterization of extracted sapodilla pectin. With the equivalent weight of 1700, degree of esterification 73.63% and jelly grade 100, sapodilla pectin was evaluated as high methoxyl pectin. Water binding, water holding and fat binding capacity was also assessed which also showed promising results. FTIR spectroscopy performed found that the pectin at pH5 was of best quality as compared to the other extracted pectin at different pH. Dynamic light scattering studies (DLS) were also performed to determine the partial side and molecular weight of extracted pectin and was compared with the commercial food grade pectin. The DLS 70 studies showed similarity in the particle size and molecular weight between standard and extracted pectin which also proved that the extraction performed was effective. Optimization of process of extraction of pectin was a studied after applying statistical software Box Behnken design. According to response surface methodology the best yield of pectin (3.7%) from sapodilla fruits can be achieved by keeping the pH 5 at 61.11 oC for 90 min of heating time. The verification of predicted model also gave similar results, 3.5% pectin was extracted on the predicted pH, temperature and time of boiling. The extracted pectin was also used in the formulation of two types of solid pharmaceutical dosage form and an oral liquid preparation (suspension). The tablets were first tested for its micromeritics properties of the granules. After the formulation of the desired tablets the tablets were compressed and were tested for hardness, thickness, loss on drying and dissolution properties. It was found that the concentration of added pectin has influence on both the micrometric properties of granules as well as on the dissolution profile of formulated tablets. Both the tablets showed increased in hardness and lowering of dissolution rate with the addition of increased amount of pectin. However the best formulation for paracetamol was F4 and F5 with 40 and 50 mg of pectin respectively while for ibuprofen R1 with 50mg of pectin concentration was best. The antidiarrheal preparation also exibited similar results in terms of its evaluation as suspension. The extracted sapodilla pectin was then used in preparation of food and pharmaceutical products. Two types of food preparation, jam and pudding were made using the extracted sapodilla pectin. The jam was also evaluated for its chemical and sensory attributes and found sapodilla pectin can be used in making of jam with a slightly higher concentration (10mg) than the food grade pectin while pudding was evaluated for its textural properties 71 mainly and found that the addition of sapodilla pectin (10 and 15g) has no significant impact on the textural properties of pudding and the pudding was equally acceptable as pudding made from equal amount of food grade pectin
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Computer Vision Based Pattern Matching and Displacement Detection During Fabric Printing

There is an absolute need for the robust quality inspection system, which can take on the new challenges and overcome the human limitations to inspect the fabric production consistently. To address this gap, computer vision based techniques are employed by enterprises. Some of the beneficent manufacturers include ceramic, circuit board printer, paper printer and textiles. Excellent work has been done in the yarn and weaving production sections of textile industry, however fabric printing monitoring is being explored by the researchers. The referential approach is mostly adopted to monitor fabric printing for defect detection. It works by acquiring an error-free image and registering this image with subsequent images captured during the production. In this method, the major time-consuming problem is to find the design repeat in the sample image before further processing. The researchers suggested some methods like distance matching in RGB space, photo encoders, genetic algorithm along with recursive splitting, Fuzzy C-means clustering, and cross correlation algorithm. There are certain limitations with these methods, for instance, sensitive to small deformation, required extra hardware prone to mechanical fault and it is expensive in calculations. In this research, a method for finding the design repeat is proposed. The method is tuned up according to the textile printing domain. Since the fabric moves in one direction during printing process, therefore the image registration can be confined to the same direction. To further speed up the process, the image registration method is applied using few initial pixel columns of the reference image with the sample image. Such bunch of selected columns of the reference image is matched with the same number of columns selected from sample image by moving this bunch on the sample image column by column. The maximum matching position is marked as the start of the design repeat. As the repeat size is always fixed, therefore complete design can be extracted from acquired image for defect detection. The experimental results on different fabric designs using the above-mentioned method are promising. Moreover, this technique improves the image alignment speed which enhances defect detection system performance. The most common defect is the displacement or misregistration of a colour. This defect is caused by the misalignment of cylindrical screens of fabric printing machine. As every screen is responsible for a colour, the misaligned screen registers the colour on the incorrect position. It leads to defective production and contributes to a substantial loss of the material and time. Few researchers proposed the feature based solution which are either computationally expensive or do not provide detail information about the defect like location and colour. Further, some techniques depend on the additional algorithms to find defects. Mostly, these methods depend on the accuracy of reference and sample image alignment. So there is always a chance of wrong notification. An improved algorithm for the detection of displacement of a colour during fabric printing is proposed in this thesis. The algorithm concatenates red, green and blue pixel values of the RGB image to represent a colour and later produces a frequency distribution of different colours for both sample and reference images. The colours with low frequencies are removed considering noise. In the next step, colours are grouped depending upon their distances from each other. These colour groups are later used to detect any new colour in the sample image which is actually created by the displacement defect and colour variation. The proposed algorithm successfully detects displacement and colour variation defects when it is tested using different flawed printed fabric images. The result shows that the proposed method has almost same accuracy as stat of the art algorithm and more helpful to detect the colour displacement, and it can also overcome the shortcoming of repeat finding process.