Classification of geographic origin of rice by data mining and inductively coupled plasma mass spectrometry

Maione, Camila; Batista, Bruno Lemos; Campiglia, Andrés D.; Barbosa, Fernando; Barbosa, Rommel M.

doi:10.1016/j.compag.2015.11.009

Cited by 109 publications

(36 citation statements)

References 19 publications

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“…In addition, there have been many reports on using mineral element analysis to identify the origin of food and plants. [11][12][13][14][15] The mineral and trace elemental composition of crops reflects the cultivar and soil conditions where crops grows. [16,17] Stable isotope analysis has been used to trace the origin of cereal grains, such as rice, wheat, soybean and so on.…”

Section: Introductionmentioning

confidence: 99%

Geographical origin traceability of foxtail millet based on the combination of multi-element and chemical composition analysis

Liu¹,

Liang

Lu³

et al. 2018

International Journal of Food Properties

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Section: Introductionmentioning

confidence: 99%

Geographical origin traceability of foxtail millet based on the combination of multi-element and chemical composition analysis

Liu¹,

Liang

Lu³

et al. 2018

International Journal of Food Properties

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“…Subset #1 included C00, subset #2 included C00 and AE1, and the last #20 contained all the variable of E-tongue and the E-nose. When we gradually accumulated the number of variables, and observed the classification results of the models, then achieved the purpose of filtering redundant information was feasible [33]. In this way, we not only obtained the best subset to achieve the purpose of reducing redundant variables, but also observed the classification tendency of SVM when variable was added gradually.…”

Section: Extraction Of Cross-perception Sensor Characteristic Variablesmentioning

confidence: 97%

Discrimination of Beer Based on E-tongue and E-nose Combined with SVM: Comparison of Different Variable Selection Methods by PCA, GA-PLS and VIP

H¹,

Shi²,

Fu³

et al. 2017

Preprint

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Multi-sensor data fusion of E-tongue and E-nose can provide a more comprehensive and more accurate analysis results. However, it also brings some redundant information, it is a hot issue to reduce the feature dimension for pattern recognition. In this paper, the taste-olfactory data fusion based on E-tongue and E-nose combined with Support Vector Machine (SVM) was used to classify five different beers. First, the taste and olfactory feature information were obtained based on E-tongue and E-nose. Second, the original feature data of single system were fused, then Principal Component Analysis (PCA) was applied to extract principal components, Genetic Algorithm-Partial Least Squares (GA-PLS) was used to select the characteristic variables, 20 subsets were generated with those variables based on the best Variable Importance of Projection (VIP) score. Finally, the classification models based on SVM were established, also c and g of SVM were calculated by Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), the classification results of all subsets were obtained. The results showed that the classification accuracy using data fusion was much higher over single E-tongue and single E-nose, and the variable selection method by VIP had the best classification performance in #12 subset coupled with GA-SVM.

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“…Diantara berbagai parameter pendiskriminasi yang diperoleh dengan metode analitik, kandungan mineral merupakan salah satu parameter yang sering digunakan untuk mengidentifikasi asal geografis berbagai produk pangan, baik produk pangan segar seperti beras (Cheajesadagul, Arnaudguilhem, Shiowatana, Siripinyanond, & Szpunar, 2013;Chung, Kim, Lee, & Kim, 2015;Gonzálvez, Armenta, & De La Guardia, 2011;Li et al, 2013;Maione, Lemos, Dobal, Barbosa, & Melgaço, 2016;Suzuki, Chikaraishi, Ogawa, Ohkouchi, & Korenaga, 2008), kentang (Giacomo, Del Signore, & Giaccio, 2007;Keppler & Hamilton, 2008;Sukartiko, 2012a) dan biji kopi (Anderson & Smith, 2002;Weckerle, Richling, Heinrich, & Schreier, 2002), maupun pangan olahan seperti minuman anggur (Martin et al, 1999). Menurut Anderson, Magnuson, Tschirgi, & Smith (1999), komposisi mineral buah dan sayur merupakan refleksi komposisi mineral tanah dan lingkungan dimana tanaman tersebut tumbuh.…”

Section: Pendahuluanunclassified

Discrimination of Geographical Indication Products from Yogyakarta Special Region Using Mineral Content

2017

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AbstrakPenelitian ini bertujuan membedakan produk-produk berindikasi geografis dari Daerah Istimewa Yogyakarta menggunakan kandungan mineralnya. Kandungan dari empat elemen, yaitu Cu, Fe, Mn dan Zn, pada sampel salak pondoh dan nira kelapa yang merupakan produk berindikasi geografis dari Daerah Istimewa Yogyakarta diukur menggunakan Atomic Absorption Spectrometry (AAS PENDAHULUANProduk berindikasi geografis (IG) merupakan produk yang memiliki keterkaitan kuat dengan daerah dimana produk tersebut berasal sehingga ketika dipasarkan, daerah asal produk, yang memberikan ciri dan kualitas pada produk tersebut, digunakan sebagai bagian nama produknya. Meskipun jumlah produk yang didaftarkan sebagai produk IG di Indonesia semakin meningkat, namun jumlahnya masih relatif sedikit jika dibandingkan dengan negara lain di kawasan Eropa dan Asia. Sampai dengan Maret 2017, terdapat 39 produk yang telah mendapatkan sertifikat indikasi geografis dari Kementerian Hukum dan Hak Asasi Manusia, dengan komposisi 92% merupakan produk dalam negeri dan 8% merupakan produk asing (Direktorat Jenderal Hak Kekayaan Intelektual Kementerian Hukum dan Hak Asasi Manusia RI, 2017). Diantara produk-produk yang telah tersertifikasi tersebut, terdapat dua produk yang berasal dari Daerah Istimewa Yogyakarta, yaitu Gula Kelapa Kulon Progo dan Salak Pondoh Sleman. Menurut Sukartiko (2012b), banyaknya jumlah produk yang telah didaftarkan tersebut mengindikasikan setidaknya dua hal, adanya permintaan terhadap produk-produk IG, dan keinginan produsen untuk melindungi produkproduknya dari kemungkinan upaya pemalsuan. Selain itu, keberadaan produk hukum yang mengatur indikasi geografis juga menguatkan indikasi tersebut. Tingginya permintaan dan kebutuhan perlindungan terhadap pemalsuan tersebut, karenanya perlu dilengkapi dengan kajian parameter pendiskrimisasi yang dapat digunakan untuk mengotentikasi produk-produk berindikasi geografis dan membedakannya dari produk-produk non-IG atau produk-produk IG dari daerah asal yang berbeda.

show abstract

Classification of geographic origin of rice by data mining and inductively coupled plasma mass spectrometry

Cited by 109 publications

References 19 publications

Geographical origin traceability of foxtail millet based on the combination of multi-element and chemical composition analysis

Geographical origin traceability of foxtail millet based on the combination of multi-element and chemical composition analysis

Discrimination of Beer Based on E-tongue and E-nose Combined with SVM: Comparison of Different Variable Selection Methods by PCA, GA-PLS and VIP

Discrimination of Geographical Indication Products from Yogyakarta Special Region Using Mineral Content

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