Classification Of Different Samples Research Articles

The Visibility of Changes in the Antioxidant Compound Profiles of Strawberry and Raspberry Fruits Subjected to Different Storage Conditions Using ATR-FTIR and Chemometrics.

Strawberry cultivars Portola and Enduro, as well as raspberry cultivars Enrosadira and Kwazi, were evaluated for their antioxidant potential after treatment with gaseous ozone and different refrigeration storage conditions. Their antioxidant capacity was investigated with ABTS and DPPH methods, and the chemical composition was determined by measuring the total phenolic (TPC) and flavonoid (TFC) compounds. The classification of different samples of berry puree was influenced significantly by both the cultivars and the refrigeration storage method. Moreover, FTIR spectroscopy coupled with chemometrics was used as an alternative technique to conventional methods to determine the chemical composition of strawberries and raspberries. The chemometric discrimination of samples was achieved using principal component analysis (PCA), hierarchical clustering analysis (HCA) and linear discriminant analysis (LDA) modelling procedures performed on the FTIR preprocessed spectral data for the fingerprint region (1800-500 cm-1). The fingerprint range between 1500 and 500 cm-1, corresponding to deformation vibrations from polysaccharides, pectin and organic acid content, had a significant impact on the grouping of samples. The results obtained by PCA-LDA scores revealed a clear separation between four classes of samples and demonstrated a high overall classification rate of 97.5% in differentiating between the raspberry and strawberry cultivars.

Classification of Honey Powder Composition by FTIR Spectroscopy Coupled with Chemometric Analysis

Fourier transform infrared spectroscopy (FTIR) in connection with chemometric analysis were used as a fast and direct approach to classify spray dried honey powder compositions in terms of honey content, the type of diluent (water or skim milk), and carrier (maltodextrin or skim milk powder) used for the preparation of feed solutions before spray drying. Eleven variants of honey powders containing different amounts of honey, the type of carrier, and the diluent were investigated and compared to pure honey and carrier materials. Chemometric discrimination of samples was achieved by principal component analysis (PCA), hierarchical clustering analysis (HCA), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA) modelling procedures performed on the FTIR preprocessed spectral data for the fingerprint region (1800–750 cm−1) and the extended region (3600–750 cm−1). As a result, it was noticed that the type of carrier is a significant factor during the classification of different samples of powdered multifloral honey. PCA divided the samples based on the type of carrier, and additionally among maltodextrin-honey powders it was possible to distinguish the type of diluent. The result obtained by PCA-LDA and PLS-DA scores yielded a clear separation between four classes of samples and showed a very good discrimination between the different honey powder with a 100.0% correct overall classification rate of the samples.

Chemometrics in analytical chemistry

Chemometrics is a scientific discipline closely connected with statistics and mathematics. It has an important role in analytical chemistry. Modern analytical methods provide opportunity to collect large amounts of data for various samples. For handling analytical results different chemometric methods are employed, such as basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, the principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA). The objectives of chemometrics in analytical chemistry are focused on characterization and chemometrical classification of different samples. The quality of environmental samples such as water, sediment, soil, air samples etc. can be determined according to measured physical and chemical parameters, which represent the individual samples. Chemometric methods give information regarding measured parameters about similarity between sampling locations, sources of pollution, seasonal behavior and time trends. Monitoring of general pollution of environmental samples and following measuring parameters which are above permitted level given by legislation can be used for searching of pollution source and for planning
 prevention measures from pollution. Food samples can also be characterized by chemometrical methods. Chemometrics can be used for fast and efficient determination of food sample categories, such as edible oils, wines, fruits and fruit juices etc. Classification can also be performed according to the origin, source or season. From all these facts it is evident that the aim of chemometrics in analytical chemistry is high and extensive.

Measurement Strategies for the Classification of Edible Oils Using Low-Cost Miniaturised Portable NIR Instruments.

Miniaturised near-infrared (NIR) instruments have been increasingly used in the last few years, and they have become useful tools for many applications on different types of samples. The market already offers a wide variety of these instruments, each one having specific requirements for the correct acquisition of the instrumental signal. This paper presents the development and optimisation of different measuring strategies for two miniaturised NIR instruments in order to find the best measuring conditions for the rapid and low-cost analysis of olive oils. The developed strategies have been applied to the classification of different samples of olive oils, obtaining good results in all cases.

Classification and quantification of minced mutton adulteration with pork using thermal imaging and convolutional neural network

A novel and reliable method to classify and quantify the adulterated minced mutton is proposed in this study based on thermal imaging combined with convolutional neural network (CNN). Firstly, thermal videos of 35 pure mutton samples, 35 pure pork samples, and 175 adulterated mutton samples (minced mutton mixed with pork at different levels: 10-20-30-40-50%) during continuous heating were collected. Secondly, thermal images at the faster heating rate stage were extracted, and their information at regions of interest was obtained for building the qualitative and quantitative CNN models. When the models were developed, learning rate and mini batch of models were selected by parameter comparison. Finally, the optimal qualitative classification model using Softmax classifier was determined to classify pure mutton samples, adulterated mutton samples and pure pig samples, and the optimal quantitative prediction model using regression function was determined to predict the pork proportion of adulterated mutton mixed with minced pork. The results showed that, the accuracy of validation and test sets of the qualitative CNN model was 99.97% and 99.99%, respectively; the determination coefficient (R2), root mean square error (RMSE) and relative prediction deviation (RPD) of validation and test sets of the quantitative CNN model were 0.9933, 0.0251, 12.2487 and 0.9933, 0.0252, 12.2387, respectively. Therefore, thermal imaging combined with CNN has achieved good results in qualitative classification of different samples and quantitative prediction of adulterated proportion. Due to its economy and convenience, this method has great application potential in the detection and supervision of adulterated food.

Potential of image processing, dielectric spectroscopy and intelligence methods in order to authentication of microalgae biodiesel

Due to increasing the consumption and limited access of fossil fuels, alternative and clean energy sources is required. On the other hand, the production of biodiesel from some plants, such as microalgae, is costly and leading to high prices. Hence, some of the Profiteers, add cheap biodiesels such as canola and waste cooking oil biodiesel to expensive biodiesel with various percentages. In this research, a combination of non-destructive capacitive sensors and machine vision techniques were used to classify adulterated microalgae biodiesel samples. To evaluate the obtained data from the dielectric system, discriminant analysis (DA) and support vector machine (SVM) methods were used. In the next step, the color features that were extracted from the three color spaces of RGB, HSV and CMY were evaluated by an artificial neural network method. The fusion of the two techniques of image processing and dielectric technique can play a significant role in the classification of different samples.

Fuzzy Removing Redundancy Restricted Boltzmann Machine: improving learning speed and classification accuracy

To improve the feature extraction ability and shorten the learning time, fuzzy removing redundancy restricted Boltzmann machine (F3RBM) is developed. The features extracted by F3RBM with unsupervised learning are imported into support vector machine (SVM) to establish F3RBM-SVM model, which achieves fast and high-precision automatic classification of different samples. To expand the feature extraction capability of restricted Boltzmann machine (RBM), the deterministic parameters of control model are replaced by fuzzy numbers in view of the superiority of fuzzy idea and the redundancy removal mechanism is introduced. Comparing the feature similarity of hidden units with the threshold value, if the similarity is greater than the threshold value, they are considered to be redundant units with the same features. The redundant units are removed to achieve further dimension reduction. Finally, the learning speed, feature extraction ability, and classification accuracy of different models are compared in MINIST handwritten dataset, Fashion MNIST dataset, and Olivetti Face dataset. The experimental results show that the feature extraction capability of FRBM and F3RBM is better than that of RBM. When there are a large number of hidden units, the learning speed of F3RBM is obviously faster than that of FRBM. The features extracted from F3RBM are imported into the SVM to build F3RBM-SVM model, which improves the classification accuracy and learning speed than general classifier. When adding other noises, F3RBM-SVM has better robustness than other models.

Rapid discrimination of different Apiaceae species based on HPTLC fingerprints and targeted flavonoids determination using multivariate image analysis.

Species of Apiaceae are used in folk medicine as spices and in officinal medicinal preparations of drugs. They are an excellent source of phenolics exhibiting antioxidant activity, which are of great benefit to human health. Discrimination among Apiaceae medicinal herbs remains an intricate challenge due to their morphological similarity. In this study, a combined "untargeted" and "targeted" approach to investigate different Apiaceae plants species was proposed by using the merging of high-performance thin layer chromatography (HPTLC)-image analysis and pattern recognition methods which were used for fingerprinting and classification of 42 different Apiaceae samples collected from Egypt. Software for image processing was applied for fingerprinting and data acquisition. HPTLC fingerprint assisted by principal component analysis (PCA) and hierarchical cluster analysis (HCA)-heat maps resulted in a reliable untargeted approach for discrimination and classification of different samples. The "targeted" approach was performed by developing and validating an HPTLC method allowing the quantification of eight flavonoids. The combination of quantitative data with PCA and HCA-heat-maps allowed the different samples to be discriminated from each other. The use of chemometrics tools for evaluation of fingerprints reduced expense and analysis time. The proposed method can be adopted for routine discrimination and evaluation of the phytochemical variability in different Apiaceae species extracts.

Elephant search optimization combined with deep neural network for microarray data analysis

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, Elephant search (ESA) based optimization is proposed to select best gene expressions from the large volume of microarray data. Firefly search (FFS) is also used to understand the effectiveness of the Elephant search method in feature selection process. Stochastic gradient descent based Deep Neural Network as Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on ten most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning approach are compared with the most recent published article for its suitability in future Bioinformatics research. Finally, Statistical significance test by one-way ANOVA with post hoc Tukey’s test is conducted to deduce a number of insights on the selection of the best classification model.

A competing endogenous RNA network identifies novel mRNA, miRNA and lncRNA markers for the prognosis of diabetic pancreatic cancer.

Pancreatic cancer (PaC) is highly associated with diabetes mellitus (DM). However, the mechanisms are insufficient. The study aimed to uncover the underlying regulatory mechanism on diabetic PaC and find novel biomarkers for the disease prognosis. Two RNA-sequencing (RNA-seq) datasets, GSE74629 and GSE15932, as well as relevant data in TCGA were utilized. After pretreatment, differentially expressed genes (DEGs) or miRNAs (DEMs) or lncRNAs (DELs) between diabetic PaC and non-diabetic PaC patients were identified, and further examined for their correlations with clinical information. Prognostic RNAs were selected using KM curve. Optimal gene set for classification of different samples were recognized by support vector machine. Protein-protein interaction (PPI) network was constructed for DEGs based on protein databases. Interactions among three kinds of RNAs were revealed in the 'lncRNA-miRNA-mRNA' competing endogenous RNA (ceRNA) network. A group of 32 feature genes were identified that could classify diabetic PaC from non-diabetic PaC, such as CCDC33, CTLA4 and MAP4K1. This classifier had a high accuracy on the prediction. Seven lncRNAs were tied up with prognosis of diabetic PaC, especially UCA1. In addition, crucial DEMs were selected, such as hsa-miR-214 (predicted targets: MAP4K1 and CCDC33) and hsa-miR-429 (predicted targets: CTLA4). Notably, interactions of 'HOTAIR-hsa-miR-214-CCDC33' and 'CECR7-hsa-miR-429-CTLA4' were highlighted in the ceRNA network. Several biomarkers were identified for diagnosis of diabetic PaC, such as HOTAIR, CECR7, UCA1, hsa-miR-214, hsa-miR-429, CCDC33 and CTLA4. 'HOTAIR-hsa-miR-214-CCDC33' and 'CECR7-hsa-miR-429-CTLA4' regulations might be two important mechanisms for the disease progression.

A new approach to discriminate Rosmarinus officinalis L. plants with antioxidant activity, based on HPTLC fingerprint and targeted phenolic analysis combined with PCA

A new combined “untargeted” and “targeted” metabolomics approach to investigate Rosmarinus officinalis L. plants was proposed by using the merging of high-performance thin-layer chromatography (HPTLC) assay and liquid chromatography mass spectrometry (LC–MS) with principal component analysis (PCA). Rapid fingerprints of Rosmarinus extracts were obtained and compared by using HPTLC technique; targeted compounds were identified and quantified by LC–MS/MS (MRM), while the antioxidant capacity of extracts was evaluated by methods based on the ability to scavenge radicals such as ABTS+* and DPPH*, since the metabolite expression is generally correlated with the biological activity. By HPTLC fingerprints, a rapid visual comparison of secondary metabolites in Rosmarinus samples from different places of collection was obtained and PCA was performed providing an overview of the capacity of the HPTLC variables to discriminate samples. HPTLC fingerprint assisted by PCA results a reliable untargeted approach for the discrimination and classification of different samples on the basis of their geographical area, from west to east of Sardinia. The ‘targeted’ approach was performed by developing LC–MS/MS (MRM) method allowing the identification and quantification of 12 compounds belonging to the family of flavonoids and phenolic acids. The combination of quantitative data with PCA allowed to discriminate La Maddalena Rosmarinus sample from others, in a correlated way with antioxidant activities results, showing values significantly higher. A positive Pearson correlation between antioxidant activity measured with DPPH and ABTS and total phenolic contents, finally confirmed that antioxidant activity depends on phenolic amounts.