1,646 research outputs found

    Extraction and characterisation of pectin from dragon fruit (hylocereus polyrhizus) peels

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    Pectins are complex carbohydrate molecules that are used in numerous food applications as a gelling agent, thickener, stabiliser, and emulsifier. Dragon fruit (Hylocereus polyrhizus) is one of the tropical fruits that belong to the cactus family, Cactaceae. Since the peels of dragon fruit are often discarded as waste, it would be an advantage to convert it into a value-added product such as pectin. The objective of this study was to investigate the extraction of pectin from dragon fruit peels under different extraction time using hot water extraction method. The dragon fruit peels were extracted using distilled water at 80 °C with different extraction time of 20, 40, 60 and 80 min. The extracted pectin was characterised by its yield, moisture and ash content, degree of esterification and antioxidant activity. Determination of moisture and ash content was conducted using AOAC standard method. The determination of the degree of esterification of pectin was performed using Fourier Transform Infrared Spectroscopy (FTIR). DPPH assay was used to determine the antioxidant activity of the pectin extract. Based on the result, the yield of pectin decreases (20.34 to 16.20 %) with the increase of extraction time, moisture contents were between 4 to 6 % while ash contents were between 7 to 10 %. Pectin from dragon fruit peels was determined as low methoxyl pectin and has high percentage of antioxidant activity with low value of inhibition concentration (IC50) (0.0063 to 0.0080 mg/mL). 60 min extraction sample exhibits the highest antioxidant activity (81.91 % at 40 μg/mL), followed by 80 min extraction (81.68 % at 40 μg/mL), 40 min extraction (81.38 % at 40 μg/mL) and 20 min extraction (81.31 % at 40 μg/mL)

    Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization

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    Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies.Sponsored by: IEEE Geoscience and Remote Sensing SocietyRitrýnt tímaritPeer reviewedPre prin

    Fuzzy geometry, entropy, and image information

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    Presented here are various uncertainty measures arising from grayness ambiguity and spatial ambiguity in an image, and their possible applications as image information measures. Definitions are given of an image in the light of fuzzy set theory, and of information measures and tools relevant for processing/analysis e.g., fuzzy geometrical properties, correlation, bound functions and entropy measures. Also given is a formulation of algorithms along with management of uncertainties for segmentation and object extraction, and edge detection. The output obtained here is both fuzzy and nonfuzzy. Ambiguity in evaluation and assessment of membership function are also described

    A New Denoising System for SONAR Images

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    The Impact of Different Image Thresholding based Mammogram Image Segmentation- A Review

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    Images are examined and discretized numerical capacities. The goal of computerized image processing is to enhance the nature of pictorial data and to encourage programmed machine elucidation. A computerized imaging framework ought to have fundamental segments for picture procurement, exceptional equipment for encouraging picture applications, and a tremendous measure of memory for capacity and info/yield gadgets. Picture segmentation is the field broadly scrutinized particularly in numerous restorative applications and still offers different difficulties for the specialists. Segmentation is a critical errand to recognize districts suspicious of tumor in computerized mammograms. Every last picture have distinctive sorts of edges and diverse levels of limits. In picture transforming, the most regularly utilized strategy as a part of extricating articles from a picture is "thresholding". Thresholding is a prevalent device for picture segmentation for its straightforwardness, particularly in the fields where ongoing handling is required

    Image Segmentation Techniques: A Survey

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    Segmenting an image utilizing diverse strategies is the primary technique of Image Processing. The technique is broadly utilized in clinical image handling, face acknowledgment, walker location, and so on. Various objects in an image can be recognized using image segmentation methods. Researchers have come up with various image segmentation methods for effective analysis. This paper presents a survey and sums up the designs process of essential image segmentation methods broadly utilized with their advantages and weaknesses

    Image Segmentation Techniques: A Survey

    Get PDF
    Segmenting an image utilizing diverse strategies is the primary technique of Image Processing. The technique is broadly utilized in clinical image handling, face acknowledgment, walker location, and so on. Various objects in an image can be recognized using image segmentation methods. Researchers have come up with various image segmentation methods for effective analysis. This paper presents a survey and sums up the designs process of essential image segmentation methods broadly utilized with their advantages and weaknesses

    The Impact of Different Image Thresholding based Mammogram Image Segmentation- A Review

    Get PDF
    Images are examined and discretized numerical capacities. The goal of computerized image processing is to enhance the nature of pictorial data and to encourage programmed machine elucidation. A computerized imaging framework ought to have fundamental segments for picture procurement, exceptional equipment for encouraging picture applications, and a tremendous measure of memory for capacity and info/yield gadgets. Picture segmentation is the field broadly scrutinized particularly in numerous restorative applications and still offers different difficulties for the specialists. Segmentation is a critical errand to recognize districts suspicious of tumor in computerized mammograms. Every last picture have distinctive sorts of edges and diverse levels of limits. In picture transforming, the most regularly utilized strategy as a part of extricating articles from a picture is "thresholding". Thresholding is a prevalent device for picture segmentation for its straightforwardness, particularly in the fields where ongoing handling is required
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