Triacylglycerol stereospecific analysis and linear discriminant analysis for milk speciation

2012 ◽  
Vol 80 (2) ◽  
pp. 144-151 ◽  
Author(s):  
Francesca Blasi ◽  
Germana Lombardi ◽  
Pietro Damiani ◽  
Maria Stella Simonetti ◽  
Laura Giua ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Dairy Products ◽  
Stereospecific Analysis ◽  
Analysis Data ◽  
Cow Milk ◽  
Linear Discriminant ◽  
Public Consumption ◽  
Product Authenticity ◽  
Pure Milk

Product authenticity is an important topic in dairy sector. Dairy products sold for public consumption must be accurately labelled in accordance with the contained milk species. Linear discriminant analysis (LDA), a common chemometric procedure, has been applied to fatty acid% composition to classify pure milk samples (cow, ewe, buffalo, donkey, goat). All original grouped cases were correctly classified, while 90% of cross-validated grouped cases were correctly classified. Another objective of this research was the characterisation of cow-ewe milk mixtures in order to reveal a common fraud in dairy field, that is the addition of cow to ewe milk. Stereospecific analysis of triacylglycerols (TAG), a method based on chemical–enzymatic procedures coupled with chromatographic techniques, has been carried out to detect fraudulent milk additions, in particular 1, 3, 5% cow milk added to ewe milk. When only TAG composition data were used for the elaboration, 75% of original grouped cases were correctly classified, while totally correct classified samples were obtained when both total and intrapositional TAG data were used. Also the results of cross validation were better when TAG stereospecific analysis data were considered as LDA variables. In particular, 100% of cross-validated grouped cases were obtained when 5% cow milk mixtures were considered.

Detection of cow milk in donkey milk by chemometric procedures on triacylglycerol stereospecific analysis results

2011 ◽  
Vol 78 (3) ◽  
pp. 335-342 ◽  
Author(s):  
Lina Cossignani ◽  
Francesca Blasi ◽  
Ancilla Bosi ◽  
Gilda D'Arco ◽  
Silvia Maurelli ◽  
...  
Keyword(s):  
Lipid Fraction ◽  
Fatty Acid Distribution ◽  
Stereospecific Analysis ◽  
Cow Milk ◽  
Donkey Milk ◽  
Linear Discriminant ◽  
Milk Samples ◽  
Total Composition ◽  
Artificial Neural Network Ann ◽  
Pure Milk

Stereospecific analysis is an important tool for the characterization of lipid fraction of food matrices, and also of milk samples. The results of a chemical-enzymatic-chromatographic analytical method were elaborated by chemometric procedures such as linear discriminant analysis (LDA) and artificial neural network (ANN). According to the total composition and intrapositional fatty acid distribution in the triacylglycerol (TAG) backbone, the obtained results were able to characterize pure milk samples and milk mixtures with 1, 3, 5% cow milk added to donkey milk. The resulting score was very satisfactory. Totally correct classified samples were obtained when the TAG stereospecific results of all the considered milk mixtures (donkey-cow) were elaborated by LDA and ANN chemometric procedures.

scholarly journals Cross-Validation in Linear Discriminant Analysis of Triacylglycerol Structural Data from Istrian Olive Oils

1999 ◽  
Vol 82 (6) ◽  
pp. 1489-1494 ◽  
Author(s):  
Luciana Gabrielli Favretto ◽  
Barbara Campisi ◽  
Luciano Favretto ◽  
Maria Stella Simonetti ◽  
Lina Cossignani ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Olive Oil ◽  
Linear Discriminant Analysis ◽  
Cross Validation ◽  
Positional Distribution ◽  
Virgin Olive Oil ◽  
Stereospecific Analysis ◽  
Structural Data ◽  
Climatic Conditions ◽  
Linear Discriminant

Abstract This paper describes the differentiation and classification of olive oil samples produced in the Istrian peninsula in 3 areas characterized by different climatic conditions: Capodistria and Parenzo and Pola. The triacylglycerol (TAG) fraction of 41 samples of virgin olive oil was analyzed. In particular, TAG stereospecific analysis was performed to obtain the positional distribution of the fatty acids in the glycerol backbone. The quantitative data were evaluated by using Linear Discriminant Analysis, and the results obtained showed a differentiation of the olive oil samples according to their geographical origin. A cross-validation procedure, the Leave-One-Out classifier, was applied to test the adequacy of the discriminant model.

Effect of Topography on Maize Grains Elemental Profile: A Chemometric Approach

2020 ◽  
Vol 16 (8) ◽  
pp. 1079-1087
Author(s):  
Jorgelina Z. Heredia ◽  
Carlos A. Moldes ◽  
Raúl A. Gil ◽  
José M. Camiña
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Microwave Plasma ◽  
Emission Spectrometry ◽  
Linear Discriminant ◽  
Maize Seeds ◽  
Elemental Profile ◽  
Mineral Profile ◽  
Topographic Characteristics

Background: The elemental composition of maize grains depends on the soil, land and environment characteristics where the crop grows. These effects are important to evaluate the availability of nutrients with complex dynamics, such as the concentration of macro and micronutrients in soils, which can vary according to different topographies. There is available scarce information about the influence of topographic characteristics (upland and lowland) where culture is developed with the mineral composition of crop products, in the present case, maize seeds. On the other hand, the study of the topographic effect on crops using multivariate analysis tools has not been reported. Objective: This paper assesses the effect of topographic conditions on plants, analyzing the mineral profiles in maize seeds obtained in two land conditions: uplands and lowlands. Materials and Methods: The mineral profile was studied by microwave plasma atomic emission spectrometry. Samples were collected from lowlands and uplands of cultivable lands of the north-east of La Pampa province, Argentina. Results: Differentiation of maize seeds collected from both topographical areas was achieved by principal components analysis (PCA), cluster analysis (CA) and linear discriminant analysis (LDA). PCA model based on mineral profile allowed to differentiate seeds from upland and lowlands by the influence of Cr and Mg variables. A significant accumulation of Cr and Mg in seeds from lowlands was observed. Cluster analysis confirmed such grouping but also, linear discriminant analysis achieved a correct classification of both the crops, showing the effect of topography on elemental profile. Conclusions: Multi-elemental analysis combined with chemometric tools proved useful to assess the effect of topographic characteristics on crops.

Colorectal Cancer Classification and Survival Analysis Based on an Integrated RNA and DNA molecular signature

2020 ◽  
Vol 15 ◽  
Author(s):  
Mohanad Mohammed ◽  
Henry Mwambi ◽  
Bernard Omolo
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Negative Binomial ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Mutation Status ◽  
Linear Discriminant ◽  
Rnaseq Data

Background: Colorectal cancer (CRC) is the third most common cancer among women and men in the USA, and recent studies have shown an increasing incidence in less developed regions, including Sub-Saharan Africa (SSA). We developed a hybrid (DNA mutation and RNA expression) signature and assessed its predictive properties for the mutation status and survival of CRC patients. Methods: Publicly-available microarray and RNASeq data from 54 matched formalin-fixed paraffin-embedded (FFPE) samples from the Affymetrix GeneChip and RNASeq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. We applied the support-vector machines, artificial neural networks, random forests, k-nearest neighbor, naïve Bayes, negative binomial linear discriminant analysis, and the Poisson linear discriminant analysis algorithms for classification. Cox proportional hazards model was used for survival analysis. Results: Compared to the genelist from each of the individual platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity, and AUC for mutation status, across all the classifiers and is prognostic for survival in patients with CRC. NBLDA method was the best performer on the RNASeq data while the SVM method was the most suitable classifier for CRC across the two data types. Nine genes were found to be predictive of survival. Conclusion: This signature could be useful in clinical practice, especially for colorectal cancer diagnosis and therapy. Future studies should determine the effectiveness of integration in cancer survival analysis and the application on unbalanced data, where the classes are of different sizes, as well as on data with multiple classes.

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