Classification and Identification of Three Vintage Designated Hungarian Spirits by Their Volatile Compounds

The quality of fruit based spirits varies year to year; therefore, the identification of the vintage of a distilled alcoholic beverage is necessary, but requires highly sensitive analytics. The interpretation of the gathered data requires a well-adapted chemometric method. In this study, Hungarian apple, sour cherry and plum distillates (pálinka’s) from different vintages were analyzed, classified and identified using volatile composition analyzed by GC-MS. The fruit’s origin, fermentation technique and distillation were the same at all the fruits; the only differences in the samples were their vintages (2010, 2011 and 2012). Analysis of variance (ANOVA) and Linear discriminant analysis (LDA) was applied for classification and components’ identification related to the vintage effect. The samples were successfully classified (correct classification rate ranging from 75 to 100%), three components are found to be related to the vintage effect regardless the fruit type: propanol, butanol and ethyl-propionate. GC-MS data proved to be a promising tool for classification of fruit distillate vintages.

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Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽

10.3390/s18124479 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4479 ◽

Cited By ~ 6

Author(s):

Xavier Cetó ◽

Núria Serrano ◽

Miriam Aragó ◽

Alejandro Gámez ◽

Miquel Esteban ◽

...

Keyword(s):

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Principal Component ◽

Reversed Phase ◽

Classification Rate ◽

Phenolic Profile ◽

Linear Discriminant ◽

Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

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Using Chemometric Analyses for Tracing the Regional Origin of Multifloral Honeys of Montenegro

Foods ◽

10.3390/foods9020210 ◽

2020 ◽

Vol 9 (2) ◽

pp. 210 ◽

Cited By ~ 1

Author(s):

Vesna Vukašinović-Pešić ◽

Nada Blagojević ◽

Snežana Brašanac-Vukanović ◽

Ana Savić ◽

Vladimir Pešić

Keyword(s):

Industrial Pollution ◽

Physicochemical Parameters ◽

Mineral Content ◽

Cross Validation ◽

Classification Rate ◽

Linear Discriminant ◽

Allowable Level ◽

Regional Origin ◽

Maximum Allowable Level

This is the first study of mineral content and basic physicochemical parameters of honeys of Montenegro. We examined honey samples from eight different micro-regions of Montenegro, and the results confirm that, with the exception of cadmium in samples from two regions exposed to industrial pollution, none of the 12 elements analyzed exceeded the maximum allowable level. The samples from areas exposed to industrial pollution were clearly distinguished from samples from other regions of Montenegro in the detectable contents of Pb, Cd, and Sr. This study showed that chemometric techniques might enhance the classification of Montenegrin honeys according to their micro-regional origin using the mineral content. Linear discriminant analysis revealed that the classification rate was 79.2% using the cross-validation method.

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Metabolomics Approach for Discrimination and Quality Control of Natural and Commercial Fallopia multiﬂora Products in Vietnam

International Journal of Analytical Chemistry ◽

10.1155/2020/8873614 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Nguyen Thi Thoa ◽

Nguyen Hai Dang ◽

Do Hoang Giang ◽

Nguyen Thi Thu Minh ◽

Nguyen Tien Dat

Keyword(s):

Clustering Analysis ◽

Principal Component ◽

Multivariate Statistical Methods ◽

Multivariate Statistical ◽

Fda Guidance ◽

Promising Tool ◽

Precision And Accuracy ◽

And Control

A precise HPLC-DAD-based quantification together with the metabolomics statistical method was developed to distinguish and control the quality of Fallopia multiflora, a popular medicinal material in Vietnam. Multivariate statistical methods such as hierarchical clustering analysis and principal component analysis were utilized to compare and discriminate six natural and twelve commercial samples. 2,3,4′,5-Tetrahydroxystilbene 2-O-β-D-glucopyranoside (THSG) (1), emodin (4), and the new compound 6-hydroxymusizin 8-O-α-D-apiofuranosyl-(1⟶6)-β-D-glucopyranoside (5) could be considered as important markers for classification of F. multiﬂora. Furthermore, seven phenolics were quantified that the variation in the contents of selected metabolites revealed the differences in the quality of natural and commercial samples. Recovery of the compounds from the analytes was more than 98%, while the limits of detection (LOD) and the limits of quantitation (LOQ) ranged from 0.5 to 6.6 μg/ml and 1.5 to 19.8 μg/ml, respectively. The linearity, LOD, LOQ, precision, and accuracy satisfied the criteria FDA guidance on bioanalytical methods. Overall, this method is a promising tool for discrimination and quality assurance of F. multiflora products.

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Classification of Chinese Herbal Medicine Based on Improved LDA Algorithm Using Machine Olfaction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.1532 ◽

2012 ◽

Vol 239-240 ◽

pp. 1532-1536 ◽

Cited By ~ 1

Author(s):

De Han Luo ◽

Ya Wen Shao

Keyword(s):

Small Sample Size ◽

Herbal Medicines ◽

Small Sample ◽

Fisher Criterion ◽

Classification Rate ◽

Scatter Matrix ◽

Linear Discriminant ◽

Chinese Herbal ◽

Machine Olfaction

Linear discriminant analysis (LDA) is a popular method among pattern recognition algorithms of machine olfaction. However, “Small Sample Size” (SSS) problem would occur while using LDA algorithm with traditional Fisher criterion if the within-class scatter matrix is singular. In this paper, maximum scatter difference (MSD) criterion and LDA were combined to solve SSS problem, so that three kinds of Chinese herbal medicines from different growing areas were accurately classified. At the same time, the classification result was enhanced. It works out that only a few samples of Anhui Atractylodes are classified incorrectly, however, the classification rate reaches 97.8%.

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Navel Orange Maturity Classification by Multispectral Indexes Based on Hyperspectral Diffuse Transmittance Imaging

Journal of Food Quality ◽

10.1155/2017/1023498 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7

Author(s):

Xuan Wei ◽

Jin-Cheng He ◽

Da-Peng Ye ◽

Deng-Fei Jie

Keyword(s):

Multispectral Imaging ◽

Nearest Neighbor ◽

Hyperspectral Image ◽

Low Cost ◽

Imaging Systems ◽

Correct Classification Rate ◽

Discriminate Analysis ◽

Classification Rate

Maturity grading is important for the quality of fruits. Nondestructive maturity detection can be greatly beneficial to the consumer and fruit industry. In this paper, a hyperspectral image of navel oranges was obtained using a diffuse transmittance imaging based system. Multispectral indexes were built to identify the maturity with the hyperspectral technique. Five indexes were proposed to combine the spectra at wavelengths of 640, 760 nm (red edges), and 670 nm (for chlorophyll content) to grade the navel oranges into three maturity stages. The index of (T670+T760-T640)/(T670+T760+T640) seemed to be more appropriate to classify maturity, especially to distinguish immature oranges that can be straightly identified in accordance with the value of this index ((T670+T760-T640)/(T670+T760+T640)). Different indexes were used as the input of linear discriminate analysis (LDA) and of k-nearest neighbor (k-NN) algorithm to identify the maturity, and it was found that k-NN with (T670+T760-T640)/(T670+T760+T640) could reach the highest correct classification rate of 96.0%. The results showed that the built index was feasible and accurate in the nondestructive classification of oranges based on the hyperspectral diffuse transmittance imaging. It will greatly help to develop low-cost and real-time multispectral imaging systems for the nondestructive detection of fruit quality in the industry.

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MULTILEVEL KOHONEN NETWORK LEARNING FOR CLUSTERING PROBLEMS

Journal of Information and Communication Technology ◽

10.32890/jict.7.2008.8075 ◽

2008 ◽

Author(s):

Siti Mariyam Shamsuddin ◽

Anazida Zainal ◽

Norfadzila Mohd Yusof

Keyword(s):

Large Scale ◽

Pattern Separation ◽

Distance Measures ◽

Classification Rate ◽

Network Learning ◽

Large Scale Dataset ◽

Self Organising Map ◽

Clustering Problems

Clustering is the procedure of recognising classes of patterns that occur in the environment and assigning each pattern to its relevant. Unlike classical statistical methods, self-organising map (SOM) does not require any prior knowledge about the statistical distribution of the patterns in the environment. In this study, an alternative classification of self-organising neural networks, known as multilevel learning, was proposed to solve the task of pattern separation. The performance of standard SOM and multilevel SOM were evaluated with different distance or dissimilarity measures in retrieving similarity between patterns. The purpose of this analysis was to evaluate the quality of map produced by SOM learning using different distance measures in representing a given dataset. Based on the results obtained from both SOM methods, predictions can be made for the unknown samples. The results showed that multilevel SOM learning gives better classification rate for small and medium scale datasets, but not for large scale dataset.

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Classification of Riboswitch Families Using Block Location-Based Feature Extraction (BLBFE) Method

Advanced Pharmaceutical Bulletin ◽

10.15171/apb.2020.012 ◽

2019 ◽

Vol 10 (1) ◽

pp. 97-105

Author(s):

Faegheh Golabi ◽

Mousa Shamsi ◽

Mohammad Hosein Sedaaghi ◽

Abolfazl Barzegar ◽

Mohammad Saeid Hejazi

Keyword(s):

Feature Extraction ◽

Probabilistic Neural Network ◽

Nearest Neighbors ◽

Correct Classification Rate ◽

K Nearest Neighbors ◽

Classification Rate ◽

Regulate Gene Expression ◽

Linear Discriminant ◽

Sensitivity Specificity

Purpose: Riboswitches are special non-coding sequences usually located in mRNAs’ un-translated regions and regulate gene expression and consequently cellular function. Furthermore, their interaction with antibiotics has been recently implicated. This raises more interest in development of bioinformatics tools for riboswitch studies. Herein, we describe the development and employment of novel block location-based feature extraction (BLBFE) method for classification of riboswitches. Methods: We have already developed and reported a sequential block finding (SBF) algorithm which, without operating alignment methods, identifies family specific sequential blocks for riboswitch families. Herein, we employed this algorithm for 7 riboswitch families including lysine, cobalamin, glycine, SAM-alpha, SAM-IV, cyclic-di-GMP-I and SAH. Then the study was extended toward implementation of BLBFE method for feature extraction. The outcome features were applied in various classifiers including linear discriminant analysis (LDA), probabilistic neural network (PNN), decision tree and k-nearest neighbors (KNN) classifiers for classification of the riboswitch families. The performance of the classifiers was investigated according to performance measures such as correct classification rate (CCR), accuracy, sensitivity, specificity and f-score. Results: As a result, average CCR for classification of riboswitches was 87.87%. Furthermore, application of BLBFE method in 4 classifiers displayed average accuracies of 93.98% to 96.1%, average sensitivities of 76.76% to 83.61%, average specificities of 96.53% to 97.69% and average f-scores of 74.9% to 81.91%. Conclusion: Our results approved that the proposed method of feature extraction; i.e. BLBFE method; can be successfully used for classification and discrimination of the riboswitch families with high CCR, accuracy, sensitivity, specificity and f-score values.

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The Potential Of Geomatics In The Realization Of A Map Of Desertification Sensitivity Southern Massif Belezma - Batna - (Algeria)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-751-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 751-756 ◽

Cited By ~ 2

Author(s):

H. Benmessaoud ◽

F. Chergui ◽

R. Sahnouni ◽

C. Chafai

Keyword(s):

Weather Conditions ◽

The North ◽

Sustained Reduction ◽

Highly Sensitive ◽

North Eastern ◽

Human Reality ◽

Restoration Work ◽

North Eastern Part

Desertification is the gradual and sustained reduction in the quantity and quality of the biological productivity of arid and semi-arid land. The study area is located in the North Eastern part of Algeria, it has a rich heritage in its biodiversity, however weather conditions and adverse human reality, induce a degradation of the physical environment in the form of a regression of vegetation cover. To assess desertification in our study area map of desertification sensitivity is a tool for decision support. For the realization of this Map we used the ArcGis software applied a methodology which is inspired by the concept MEDALUS (Mediterranean Desertification and Land Use, 1999) by crossing four thematic layers that may have an impact on the process of desertification. The results of Cartography and statistical analysis permit the classification of our region in terms of sensitivity to desertification in four very important classes. (Not affected, Insensitive, Sensitive and highly sensitive). More than 69.92% of the surface area were classified sensitive to very sensitive, For against 30.07% is classified in unallocated insensitive. Planning restoration work and the fight against desertification are expected to limit the risk of desertification in the study area perspectives.

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Detection and classification of Bacteria using Raman Spectroscopy Combined with Multivariate Analysis

Defence Life Science Journal ◽

10.14429/dlsj.2.12275 ◽

2017 ◽

Vol 2 (4) ◽

pp. 435 ◽

Cited By ~ 10

Author(s):

S. Sil ◽

R. Mukherjee ◽

N. S. Kumar ◽

Aravind S. ◽

J. Kingston ◽

...

Keyword(s):

Raman Spectroscopy ◽

Multivariate Analysis ◽

Principal Component ◽

Hierarchical Cluster ◽

Spectroscopic Techniques ◽

Hybrid Technique ◽

Chemometric Method ◽

Linear Discriminant ◽

Linear Discrimination

Vibrational spectroscopic techniques have advantages over conventional microbiological approaches towards identification & detection of pathogens. Since unique spectral fingerprint is obtained, one can identify very closely related bacteria using such methods. In this study Raman microspectroscopy in combination with chemometric method has been used to classify four strains of E. coli (two pathogenic & two non-pathogenic). Different multivariate approaches such as hierarchical cluster analysis, principal component analysis & linear discriminant analysis were explored to obtain efficient classification of the Raman signals obtained from the four strains of E.coli. It was observed that multivariate analysis was able to classify the bacteria at strain level. Linear discrimination analysis using PC scores (PC-LDA) was found to give very good result with as high as 100% accuracy. This hybrid technique (Raman spectroscopy & multivariate analysis) has tremendous potential to be developed as a tool for bacterial identification.

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ITERATIVE NON-SEQUENTIAL PROTEIN STRUCTURAL ALIGNMENT

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720009004205 ◽

2009 ◽

Vol 07 (03) ◽

pp. 571-596 ◽

Cited By ~ 7

Author(s):

SAEED SALEM ◽

MOHAMMED J. ZAKI ◽

CHRISTOPHER BYSTROFF

Keyword(s):

Sequence Similarity ◽

Structural Alignment ◽

Structural Similarity ◽

Initial Alignment ◽

Novel Approach ◽

Highly Sensitive ◽

Protein Structural Alignment ◽

Alignment Step

Structural similarity between proteins gives us insights into their evolutionary relationships when there is low sequence similarity. In this paper, we present a novel approach called SNAP for non-sequential pair-wise structural alignment. Starting from an initial alignment, our approach iterates over a two-step process consisting of a superposition step and an alignment step, until convergence. We propose a novel greedy algorithm to construct both sequential and non-sequential alignments. The quality of SNAP alignments were assessed by comparing against the manually curated reference alignments in the challenging SISY and RIPC datasets. Moreover, when applied to a dataset of 4410 protein pairs selected from the CATH database, SNAP produced longer alignments with lower rmsd than several state-of-the-art alignment methods. Classification of folds using SNAP alignments was both highly sensitive and highly selective. The SNAP software along with the datasets are available online at

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