2014
DOI: 10.1016/j.microc.2013.10.011
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Classification of edible vegetable oil using digital image and pattern recognition techniques

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Cited by 31 publications
(25 citation statements)
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“…In fact, lipid autoxidation and inadequate storage contribute significantly to the deterioration and reduction of the shelf-life of vegetable oils causing changes in colour, texture, odour and flavour, and loss of vitamins (Milanez & Pontes 2014). Sunflower oil, like most vegetable oils, is composed mainly of triacylglycerols (98-99%) and a small fraction of phospholipids, tocopherols, carotenoids, sterols, and waxes.…”
mentioning
confidence: 99%
“…In fact, lipid autoxidation and inadequate storage contribute significantly to the deterioration and reduction of the shelf-life of vegetable oils causing changes in colour, texture, odour and flavour, and loss of vitamins (Milanez & Pontes 2014). Sunflower oil, like most vegetable oils, is composed mainly of triacylglycerols (98-99%) and a small fraction of phospholipids, tocopherols, carotenoids, sterols, and waxes.…”
mentioning
confidence: 99%
“…Figure 2 shows the apparatus built for image acquisition of the bacteria cultures. A box with dimensions sized 30 cm× 22 cm×23 cm was built and internally covered with white office paper in order to avoid external light interferences and light reflection into the box, ensuring the uniformity to the captured images [24]. A Webcam, Microsoft® model Lifecam Cinema, with 7.1 megapixels, was set above the sample holder vertically.…”
Section: Samplesmentioning
confidence: 99%
“…On the other hand, color histograms describe the statistical distribution of the pixels in a digital image as a function of the recorded color component, and not a feature or a physicalchemical behavior directly [22]. Color histograms have been successfully used as input data for classification of teas [22], honeys [23], and edible vegetable oils [24].…”
Section: Introductionmentioning
confidence: 99%
“…The Internet of things is prompting the coming of the era of "Internet of meters" which unites the household gas meter, water meter and electric meter. In the large-scale data statistics, the digit recognition of the meter is a very important part of the whole intelligent "Internet of meters", so it has become a hot research topic for many years [1,2]. Digit recognition generally uses the traditional methods of feature matching and feature discrimination, but the recognition rate of these methods is not high [3][4][5].With the rapid development of neural network technology, it itself has a high degree of parallelism, strong self-organizing ability and fault tolerance, and better noise interference suppression ability to open up a new route for rapid and accurate digit recognition [6,7].…”
Section: Introductionmentioning
confidence: 99%