2016
DOI: 10.1016/j.eswa.2015.11.024
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Comparative study of data mining techniques for the authentication of organic grape juice based on ICP-MS analysis

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Cited by 62 publications
(21 citation statements)
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“…In recent years, the application of chemometric techniques in quality control of food products has gained considerable attention from several research groups worldwide . Classification of a specific sample or group of samples by its or their region of production, for instance, is made after analyzing the chemical components and establishing a pattern (ie, a chemical fingerprint) .…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, the application of chemometric techniques in quality control of food products has gained considerable attention from several research groups worldwide . Classification of a specific sample or group of samples by its or their region of production, for instance, is made after analyzing the chemical components and establishing a pattern (ie, a chemical fingerprint) .…”
Section: Introductionmentioning
confidence: 99%
“…Conventional statistics are not able to uncover the hidden information within data sets, thus data mining approaches have been developed to reveal such a hidden knowledge in data sets (Maione et al, 2016). Data mining approaches can extract quite much predictive information from large databases.…”
Section: Measurement and Calculation Of Seed Parametersmentioning
confidence: 99%
“…The reduction of the number of chemical components capable of classifying wines will possibly save time and economic resources in a new classification. Some research has used feature selection instead of feature extraction to successfully classify food products, as in the classification of Chilean and Brazilian Cabernet Sauvignon wines (da Costa et al, 2016), organic and conventional sugarcane (Barbosa et al, 2015), organic and conventional grape juice (Maione et al, 2016), organic and conventional orange juice (Turra et al, 2017), and rice (Maione & Barbosa, 2018), as well as in the classification of breast masses in mammogram images (Görgel, Sertbas, & Uçan, 2015) and inspection of dies attachment on printed circuit boards (Vafeiadis et al, 2018).…”
mentioning
confidence: 99%