Multivariate data analysis in classification of vegetable oils characterized by the content of fatty acids

Brodnjak-Vončina, Darinka; Kodba, Zdenka Cencič; Novič, Marjana

doi:10.1016/j.chemolab.2004.04.011

Cited by 114 publications

(50 citation statements)

References 27 publications

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“…Eventually, it helps to establish the authenticity of various kinds of foodstuffs and other agricultural produces. For instance, application of PCA to fatty acid data has been studied for authentication of commercial edible oils 8 and oils extracted from different peanut cultivars 7 . In a recent study, the fatty acid profiles of 119 oil samples were determined by GC and the correlation among peanut, soybean, rapeseed and palm oils were elucidated using PCA whereby computations proved that the samples form clusters according to the type of oil 9 .…”

Section: Introductionmentioning

confidence: 99%

Differentiation of Lard, Chicken Fat, Beef Fat and Mutton Fat by GCMS and EA-IRMS Techniques

2013

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Section: Introductionmentioning

confidence: 99%

Differentiation of Lard, Chicken Fat, Beef Fat and Mutton Fat by GCMS and EA-IRMS Techniques

2013

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“…However, application of multivariate data handling techniques such as PCA would enable researchers to extract more subtle information that could lead to the differentiation among them. PCA has already been recognized for its capability to identify patterns in data and expressing the data in such a way as to emphasize their similarities and differences (Brodnjak-Voncina et al 2005). For instance, this approach has been studied for authentication of commercial edible oils (Lee et al 1998), classification of…”

Section: Pca Of Fatty Acid Datamentioning

confidence: 99%

Differentiation of Fractionated Components of Lard from Other Animal Fats Using Different Analytical Techniques

Naquiah¹,

Marikkar²,

Mirghani³

et al. 2017

JSM

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A study was conducted to differentiate fractionated components of lard namely lard olein (LO) and lard stearin (LS) from other common animal fats. Lard fractions and animal fats were analyzed using differential scanning calorimetry (DSC), elemental analyzer-isotope ratio mass spectrometry (EA-IRMS), gas chromatography mass spectrometry (GC-MS) and Fourier transform infrared spectroscopy (FTIR Satu kajian telah dijalankan untuk membezakan komponen berperingkat lemak babi iaitu lemak babi olein (LO) dan lemak babi stearin (LS) daripada lemak haiwan biasa yang lain. Pecahan lemak babi dan lemak haiwan dianalisis menggunakan kalori pengimbasan kebezaan (DSC), unsur penganalisis-isotop nisbah jisim spektrometri (EA-IRMS), kromatografi gas spektrometri jisim (GC-MS) dan spektroskopi transformasi Fourier inframerah (FTIR

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“…To the aim some available literature data on FAME profiles of biodiesels from several types of oils were used [8][9][10][11]. In the investigations also our data of FAME of Bulgarian biodiesels and samples produced in our laboratory by transesterification of plant oils were utilized.…”

Section: Figure 1 Artificial Neuronmentioning

confidence: 99%

Upon the opportunity to apply ART2 Neural Network for clusterization of biodiesel fuels

Petkov

Mustafa

Sotirov

et al. 2016

Acta Scientifica Naturalis

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A chemometric approach using artificial neural network for clusterization of biodiesels was developed. It is based on artificial ART2 neural network. Gas chromatography (GC) and Gas Chromatography -mass spectrometry (GC-MS) were used for quantitative and qualitative analysis of biodiesels, produced from different feedstocks, and FAME (fatty acid methyl esters) profiles were determined. Totally 96 analytical results for 7 different classes of biofuel plants: sunflower, rapeseed, corn, soybean, palm, peanut, "unknown" were used as objects. The analysis of biodiesels showed the content of five major FAME (C16:0, C18:0, C18:1, C18:2, C18:3) and those components were used like inputs in the model. After training with 6 samples, for which the origin was known, ANN was verified and tested with ninety "unknown" samples. The present research demonstrated the successful application of neural network for recognition of biodiesels according to their feedstock which give information upon their properties and handling.

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Multivariate data analysis in classification of vegetable oils characterized by the content of fatty acids

Cited by 114 publications

References 27 publications

Differentiation of Lard, Chicken Fat, Beef Fat and Mutton Fat by GCMS and EA-IRMS Techniques

Differentiation of Lard, Chicken Fat, Beef Fat and Mutton Fat by GCMS and EA-IRMS Techniques

Differentiation of Fractionated Components of Lard from Other Animal Fats Using Different Analytical Techniques

Upon the opportunity to apply ART2 Neural Network for clusterization of biodiesel fuels

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