Comparative study of data mining techniques for the authentication of organic grape juice based on ICP-MS analysis

Maione, Camila; Paula, Eloísa Silva de; Gallimberti, Matheus; Batista, Bruno Lemos; Campiglia, Andrés D.; Barbosa, Fernando; Barbosa, Rommel M.

doi:10.1016/j.eswa.2015.11.024

Cited by 59 publications

(20 citation statements)

References 31 publications

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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%

Elemental fingerprint profiling with multivariate data analysis to classify organic chocolate samples

C.M.

Maione

Barbosa

et al. 2018

Journal of Chemometrics

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Chocolate is an appreciated food derived from cacao fruit. The flavonoids and minerals present in the chocolate have benefits to health, and some specific minerals are known to be toxic. Because of differences in their production systems, organic chocolate has a distinguishable pattern in mineral concentrations than conventional chocolate. Aiming for authenticity and study of the toxic elements in organic chocolate, we present in this work a simple method to classify organic chocolate samples based on elemental fingerprint profiling and multivariate data analysis. Thirty-eight elements (toxic and essential) were determined in 36 chocolate samples (organic and conventional) by using inductively coupled plasma mass spectrometry to establish reference ranges and to identify differences in patterns of elements in both type of samples.Our results showed that Al, Zn, Mn, Cu, and Ba are the most present components for both types of chocolate, and higher concentrations of essential elements Fe, Zn, and Mg are found in conventional type, opposing the idea that organic food is rich in essential elements. Principal component analysis and linear discriminant analysis were used for multivariate data analysis, and sample differentiation was possible with 83% accuracy.

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Section: Introductionmentioning

confidence: 99%

Elemental fingerprint profiling with multivariate data analysis to classify organic chocolate samples

C.M.

Maione

Barbosa

et al. 2018

Journal of Chemometrics

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“…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%

Estimation of the Weights of Almond Nuts Based on Physical Properties through Data Mining

Gürbüz

Demır

Eski

et al. 2018

Not Bot Horti Agrobo

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Quality attributes are the major parameters designating market values of the agricultural goods and commodities. Several practices are applied to improve quality parameters of the fruits and vegetables. Such quality attributes should also be estimated through various approaches before to design of equipment and tools used in handling and processing of these goods and to design storage facilities. Data mining is a novel approach used to estimate various attributes or quality parameters of the fruits from previously measured attributes. Different algorithms embedded into data mining operations may yield quite accurate and reliable equations for estimation of quality attributes. Almond is a significant cash crop for growers. Since almond is quite tolerant to droughts and salinity, it is preferred in various parts of the country by producers. Weight is the primary quality parameter designating market value of the almonds. This study was conducted to estimate nut weights of seven different almond varieties and to develop an equation for the estimation of nut weights. Data mining approach was used to estimate nut weights from physical fruit quality attributes (kernel length, width, thickness, arithmetic mean diameter, geometric mean diameter, sphericity, surface area, volume, shape index and aspect ratio). Present findings revealed quite significant, accurate and practicable rules to estimate the nut weights of different almond varieties. It was concluded that data mining could be used as a reliable tool to estimate the nut weights of different almond varieties from the physical attributes of the fruits.

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“…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).…”

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confidence: 99%

The use of data mining to classify Carménère and Merlot wines from Chile

et al. 2018

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The identity of the Carménère variety was lost in Chile and regarded as a Merlot grape. This grape disappeared in Bordeaux, France, the country of origin of the grape, because of the phylloxera plague. In the present paper, a study on the classification of Chilean Carménère and Merlot wine samples based on chemical parameters was carried out. A total of 64 samples were analysed, and 20 chemical parameters were determined. Forty‐five samples were labelled as “Carménère” and 19 samples as “Merlot” according to the wine label. The samples were preprocessed with sampling algorithms to double the number of Merlot samples to 38 and reduce the Carménère samples to 38. The dataset was analysed with the data mining techniques support vector machines and correlation‐based feature selection. The capability of classifying the samples with all 20 chemical parameters was 86.8% accurate, and when using only the variables a*, total anthocyanins, cyan‐3‐glu, malv‐3‐acetylglu, peon‐3‐acetylglu, and vitisin A, which were selected through correlation‐based feature selection, accuracy increased to 93.4%. Therefore, wine anthocyanins and the parameter a* of wine colour proved useful to discriminate the wines.

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Comparative study of data mining techniques for the authentication of organic grape juice based on ICP-MS analysis

Cited by 59 publications

References 31 publications

Elemental fingerprint profiling with multivariate data analysis to classify organic chocolate samples

Elemental fingerprint profiling with multivariate data analysis to classify organic chocolate samples

Estimation of the Weights of Almond Nuts Based on Physical Properties through Data Mining

The use of data mining to classify Carménère and Merlot wines from Chile

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