Factorial Discriminant Analysis Research Articles

Assessing Worldview-3 multispectral imaging abilities to map the tree diversity in semi-arid parklands

Semi-arid parkland agrosystems are strongly sensitive to climate change and anthropic pressure. In the context of sustainability research, trees are considered critical for various ecosystem services covering environment quality as well as food security and health. But their actual ecological impact on both cropland and natural vegetation is not well understood yet, and collecting spatial and structural information around agroforestry systems is becoming an important issue. Tree mapping in semi-arid parklands could be one of these prerequisites. While for obtaining an exhaustive inventory of individual trees and for analysing their spatial distribution, remote sensing is the ideal tool. However, it has been noted that depending on the spatial resolution and sensor spectral characteristics, tree species cannot be distinguished clearly, even in the sparsely vegetated semi-arid ecosystems of West Africa. Thus, this work focuses on assessing the capabilities of Worldview-3 imagery, acquired in 8 spectral bands, to detect, delineate, and identify certain key tree species in the Faidherbia albida parkland in Bambey, Senegal, based on a ground-truth database corresponding to 5000 trees. The tree crowns are delineated through NDVI thresholding and consecutive filtering to provide object-based radiometric signatures, radiometric indices, and textural information. A factorial discriminant analysis is then performed, which indicates that only four out of the seven most abundant species in the study area can be discriminated: “Faidherbia albida”,” Azadirachta indica”, “Balanites aegyptiaca” and “Tamarindus indica”. Next, random forest and support vector machine classifiers are employed to identify the optimal combination of classifier parameters to discriminate these classes with a high accuracy, robustness, and stability. The linear support vector machine with cost=1 and gamma=0.01 provides the optimal results with a global accuracy of 88 % and kappa of 0.71. This classifier is applied to the whole study area to map all the trees with crowns larger than 2 m, sorted in four identified species and a fifth common group of unidentified species. This map thus enables analysing the variability in tree density and the spatial distribution of different species. Such information can afterwards be correlated to the ecological functioning of the parkland and local practices, and offers promising opportunities to help future sustainability initiatives in different socio-ecological contexts.

Seasonal variations of phytoplankton assemblages in relation to environmental factors in Mediterranean coastal waters of Morocco, a focus on HABs species

Studies on phytoplankton and in particular Harmful Algal Blooms (HABs) species in southern Mediterranean waters are scarce. We performed from April 2008 to June 2009 weekly investigations on microphytoplankton community structure and abundance in two contrasted marine ecosystems located in the western Moroccan Mediterranean coast, M'diq Bay and Oued Laou Estuary. Simultaneously, we measured the main physico-chemical parameters. Globally, the two studied areas showed comparable values of the assessed abiotic environmental factors. Temperature and salinity followed seasonal variation with values ranging from 13.5 °C to 21.4 °C and 31 to 36.8, respectively. Average nutrient values in surface water ranged from 0.7 to 45.76 μM for dissolved inorganic nitrogen, 0.02–2.10 μM for PO4 and 0.23–17.46 μM for SiO4 in the study areas. A total of 92 taxa belonging to 8 taxonomic classes were found. The highest number of microphytoplankton abundance reached 1.2 × 106 cells L−1 with diatoms being the most abundant taxa. Factorial Discriminant Analysis (FDA) and Spearman correlation test showed a significant seasonal discrimination of dominant microphytoplankton species. These micro-organisms were associated with different environmental variables, in particular temperature and salinity. Numerous HABs species were encountered regularly along the year. Although Dinophysis species and Prorocentrum lima were present in both sites, no Lipophilic Shellfish Poisoning was detected for the analyzed bivalve mollusks. Domoic acid (DA), produced by toxic species of Pseudo-nitzschia was found with concentrations up to 18 µg DA g−1 in the smooth clam Callista chione. Data showed that the observed persistent and dramatic Paralytic Shellfish Poisoning (PSP) intoxication of mollusks resulted probably of Gymnodinium catenatum proliferations in both studied areas. Contrary to C. chione, the cockle Achanthocardia tuberculatum showed a permanent and extremely high toxicity level during the 15 months survey with up to 7545 µg Equivalent Saxitoxin kg−1 flesh (ten times higher than the sanitary threshold of 800 µg eqSTX Kg−1flesh). The present work highlights for the first time the dynamic of microphytoplankton including HABs species and their associated toxin accumulation in the commercially exploited shellfish in the southern western Mediterranean waters of Morocco. Furthermore, the acquired data will help us to improve the monitoring of HABs species and related toxins in these coastal marine systems.

Mid infrared spectroscopy coupled with chemometric tools for qualitative analysis of canned tuna with sunflower medium

The most common frauds of tuna cans supply chain concern the substitution or mixing of valuable tuna species with cheaper ones, which is strictly prohibited by the European regulation. The aim of this work was to investigate the ability of mid-infrared (MIR) spectroscopy as a rapid and non-destructive tool to identify canned bigeye, yellowfin and skipjack tunas and detect their presence in a binary mixture of canned tunas. The MIR spectra were acquired on 232 canned tunas produced at the pilot scale. The factorial discriminant analysis (FDA) was applied to the first 5 principal components (PCs) of the principal component analysis (PCA) of the three spectral regions: 3000–2800 cm−1, 1700–1500 cm−1 and 1500–900 cm−1. A robust model was created with correct classification amounting to 90.38% that was tested on 30 commercial canned tuna. A total (100%) of correct classification was obtained for skipjack tuna, while misclassification was observed for cans labelled as bigeye and yellowfin suggesting an adulteration action or mislabelling during process technology.

Toward the implementation of mid-infrared spectroscopy along the processing chain to improve quality of the tomato based products

The mid-infrared spectroscopy (MIRS) was investigated as a tool to improve the quality of tomato products considering its implementation at different steps along the processing chain.Models have been developed using partial least square (PLS) regression to predict the quality of raw and processed tomatoes. A relevant method (Multi-year Combining models) consisting in adding early-season tomatoes data within models developed using data of previous years, was shown as the most efficient and adapted to realistic industry conditions. MIRS predicted, in external validation, soluble solids content (R2 0.95), titratable acidity (R2 0.88) and dry matter content (R2 0.81) with a high accuracy of 0.1°Brix, 2.8 mmol H+/kg and 0.4% respectively.Secondly, MIRS was used to classify tomato products depending on processing methods (hot- or cold-break) or varieties using factorial discriminant analysis (FDA) based only on spectral data. MIRS was assessed as an efficient tool to classify processed tomato purees according to process, year and variety, more accurately than the classification obtained with the reference data.A possible implementation of MIRS was suggested at three strategic steps along the processing chain to i) characterize the incoming raw material, ii) monitor the matrix changes during processing and iii) control the final products.

Effective discrimination of flavours and tastes of Chinese traditional fish soups made from different regions of the silver carp using an electronic nose and electronic tongue

Silver carp is a one of the most important freshwater fish species in China, and is popular when making soup in the Chinese dietary culture. In order to investigate the profile of fish soup tastes and flavours cooked using different regions of the same fish, the silver carp was cut into four different regions: head, back, abdomen, and tail. The differences in taste and flavour of the four kinds of homemade fish soup were investigated by an electronic nose and electronic tongue. The basic chemical components of the different fish regions and the SDS-PAGE profile of the fish soup samples were investigated. Two chemometrics methods (principal component analysis and discriminant factor analysis) were used to classify the odour and taste of the fish soup samples. The results showed that the electronic tongue and nose performed outstandingly in discriminating the four fish soups even though the samples were made from different regions of the same fish. The taste and flavour information of different regions of the silver carp fish could provide the theoretical basis for food intensive processing.<br /><br />

Multi-Partitions Subspace Clustering

In model based clustering, it is often supposed that only one clustering latent variable explains the heterogeneity of the whole dataset. However, in many cases several latent variables could explain the heterogeneity of the data at hand. Finding such class variables could result in a richer interpretation of the data. In the continuous data setting, a multi-partition model based clustering is proposed. It assumes the existence of several latent clustering variables, each one explaining the heterogeneity of the data with respect to some clustering subspace. It allows to simultaneously find the multi-partitions and the related subspaces. Parameters of the model are estimated through an EM algorithm relying on a probabilistic reinterpretation of the factorial discriminant analysis. A model choice strategy relying on the BIC criterion is proposed to select to number of subspaces and the number of clusters by subspace. The obtained results are thus several projections of the data, each one conveying its own clustering of the data. Model’s behavior is illustrated on simulated and real data.

Multivariate Statistical Methods Used in Population Genetics

Several multivariate statistical methods are used in population genetics but there are very few studies that have revealed the strengths and weaknesses of different methods. Thus, this study aims to reveal the strengths and weaknesses of the different multivariate statistical methods used in population genetics through the world. This synthesis is carried out according to the methodology "Preferred Reporting Items for Systematic Reviews and Meta-Analyzes" (PRISMA). This study shown that various statistical methods or combination of multivariate statistical methods are used in population genetics. It emerges that there is no a priori a better method, so it is necessary to determine the method adapted to both the data collected and the research objective. This study identified the most commonly used multivariate statistical methods in genetics such as: Ordination methods (52.50%) are methods that summarize the information contained in the data matrix by minimizing wastage. This are: principal components analysis (by 32.0% of the articles), principal coordinates analysis (by 7.50% of the articles), discriminant analysis of principal component, factorial correspondence analysis, factorial discriminant analysis, factorial analysis on distance table. Clustering methods (35%) that aim to form groups of individuals that are as similar as possible, including the hierarchical ascending clustering (17.50% of articles), neighbor-joining, and Bayesian clustering model (by 15% of the articles). The analysis of the molecular variance (7.50%) which consists of studying the intra and inter-population variation and the Mantel test (5%) which aims to test the correlation between the matrix of genetic distances and other distance matrices (environmental causes of genetic variability).

The Risk of Bark and Ambrosia Beetles Associated with Imported Non-Coniferous Wood and Potential Horizontal Phytosanitary Measures

Many bark and ambrosia beetle species (Coleoptera: Scolytinae and Platypodinae) are known to have spread worldwide in relation to international trade. Concerns have been expressed within the European and Mediterranean Plant Protection Organization (EPPO) about recent introductions of non-indigenous species of these groups. Regulation of the non-coniferous wood trade into many EPPO member countries is currently not sufficient to cover such risks. In 2018–2019, an EPPO study on the risk of bark and ambrosia beetles associated with imported non-coniferous wood was carried out, and the key characteristics contributing to the pest risk from introduced species were determined using expert consensus. This paper summarizes the key findings of the study, which are available in full detail on the EPPO website. The study identified biological and other risk factors and illustrated them with examples from 26 beetle species or groups of species known to be invasive or posing a threat to plant health. These representative species were classified into three categories based on known damage and level of uncertainty. In the present article, factorial discriminant analyses were used to identify features of bark and ambrosia beetle biology associated with damage caused and invasiveness. Based on the information assembled and consideration of the risk factors, it was recommended that in order to prevent the introduction of new bark and ambrosia beetles via non-coniferous wood commodities, horizontal phytosanitary measures should be adopted, irrespective of the host plant species and the origin (i.e., for all genera of non-coniferous woody plants and from all origins). Phytosanitary measures are presented here for various wood commodities.

Machine Learning Model for Cancer Diagnosis based on RNAseq Microarray

Microarray technology is one of the most important recent breakthrough in experimental molecular biology. This novel technology for thousands of genes concurrently allows the supervising of expression levels in cells and has been increasingly used in cancer research to understand more of the molecular variations among tumors so that a more reliable classification becomes attainable. Machine learning techniques are loosely used to create substantial and precise classification models. In this paper, a function called Feature Reduction Classification Optimization (FeRCO) is proposed. FeRCO function uses machine learning techniques applied upon RNAseq microarray data for predicting whether the patient is diseased or not. The main purpose of FeRCO function is to define the minimum number of features using the most fitting reduction method along with classification technique that give the highest classification accuracy. These techniques include Support Vector Machine (SVM) both linear and kernel, Decision Trees, Random Forest, K-Nearest-Neighbors (KNN) and Naive Bayes (NB). Principle Component Analysis (PCA) both linear and kernel, Linear Discriminant Analysis (LDA) and Factor Analysis (FA) along with different machine learning techniques were used to find a low-dimensional subspace with better discriminative features for better classification. The major outcomes of this research can be considered as a roadmap for interesting researchers in that field to be able to choose the most suitable machine learning algorithm whatever classification or reduction.

Identification of milk types using two different fluorescence spectroscopy techniques

Fluorescence spectroscopy is a technique used to determine fluorescence spectrum that emits by fluorescent compounds. Milk had more than one fluorescent compounds such as tryptophan, vitamin A and etc. Quick identification of milk types is needed in dairy plants for milk quality control and safety. The ability of two different fluorescence spectroscopy techniques (front face fluorescence spectroscopy and synchronous scanning fluorescence spectroscopy) was determined to discriminate 40 milk samples according to milk species (10 samples for each of buffalo's, cow's, goat's and sheep's milk). The statistical methods, principal component analysis PCA and factorial discriminant analysis FDA, were used for more illustrating the obtained results. FDA was applied separately on the first five principal components obtained from PCA which performed on the two different fluorescence techniques. Results obtained from FDA showed that 100% of correct classification was obtained for data sets from the two different fluorescence techniques. The obtained results confirmed that the front face fluorescence spectroscopy and synchronous scanning fluorescence spectroscopy were capable of differentiate milk species.

Modelling Pedestrian Perspectives in Evaluating Satisfaction Levels of Urban Roadway Walking Facilities

Pedestrian Level of Service (PLOS) is a multifaceted expression which represents the working condition of walking facilities and satisfaction level of pedestrian experience while using these facilities. In this regard this study aimed at developing PLOS models for three pedestrian facilities such as sidewalk, signalized intersection and un-signalized intersection by using qualitative data sets. Total 2730 pedestrian’s real time sense of satisfaction data were collected from ten cities of India. Behavioral responses were taken in order to assess variation with different age and gender from study areas having miscellaneous activities. Taking sets of questions, different variable scores were estimated to provide a wide variation of independent variable that will be more reliable for model fitting. Before model development degree of dependency has been examined with the help of discriminant factor analysis. Implementing ridge regression technique three different PLOS models have been developed for three pedestrian facilities. PLOS scores found by using the proposed models were classified by applying GA-Fuzzy clustering technique in order to get six PLOS ranges (A-F). At signalized intersection 3% of participants were extremely dissatisfied with the facility and they gave rating of >5 for PLOS score. This high score was because of pedestrian speed was severely restricted at some places due to the left and permissible right turning vehicles. Results also revealed that for un-signalized intersection nearly 23% of participants were having PLOS score of >4 as waiting time was more than expected and also pedestrian had to face conflict with vehicles while crossing.

Tea geographical origin explained by LIBS elemental profile combined to isotopic information

The combined LIBS and ICP HRMS analysis of 13 tea samples are studied in view of identification of tea geographical origin. The elemental signature provided by LIBS spectra is treated by principal component analysis followed by partial least square discriminant analysis and factorial discriminant analysis. Selected element lines are found efficient to discriminate most sample groups. Data analysis model is improved by variable selection and the isotopic ratio 11B/10B was employed to improve the prediction capacity of the model. The alkaline earth: Ba, Ca, Mg, Sr and alkaline Rb, Na are easily detected by the LIBS system and these elements are important to classify sample according to their geographical origin. Minor elements like P, S, Fe, B … also bring discriminant information. A five clusters model gave best correct identification in a cross validation test (94.2%). This method also allowed to identify the origin of four unknown teas. In this study the use of FDA or PLS DA after the PCA examination of the LIBS/ICP MS data led to similar conclusions for fast classification of the tea samples and identification of the geographical origin of the four unknown teas.

Organization of agricultural production unit seen as a family business in mountainous South Kivu in the Democratic Republic of Congo

Agricultural extension actors in mountainous South Kivu in eastern DRC have been supporting producer’s increased efforts for decades. The adoption impact is still weak. This study determined and confirms the socio-economic factors for widespread adoption of the bean, cassava, maize and potato intensification technologies in this portion of the country. The qualitative and quantitative data from 360 file Managed were Households in Burhinyi Kabare Kaziba, Luhwinja, and Walungu Chiefdoms. The results of the discriminant factor analysis show that There are factors common to the wide-scale adoption of the use of improved varieties, the use of organic fertilization method, the use of the fertilizer, the fertilization method with GIFS, the use of the tractor thesis oven for crops in mountainous South Kivu.

Effect of salicylic acid treatment on sensory quality, flavor-related chemicals and gene expression in peach fruit after cold storage

Abstract Salicylic acid (SA) has been used in reducing chilling injury of horticultural crops caused by postharvest cold storage. However, effect of SA on fruit flavor quality in response to chilling need to be further investigated. In the present study, SA treated peach fruit (Prunus persica L. Batsch., cv. Hujingmilu) were stored at 0 °C for 7, 14, 21 and 28 d followed by a subsequent shelf-life at 20 °C, respectively. SA treatment (1 mM) alleviated development of flesh browning and maintained softening ability of peach fruit after cold storage. Electronic nose (e-nose) and electronic tongue (e-tongue) analysis showed separation of SA treated fruit and controls based on discriminant factor analysis (DFA) plots, particular for peaches during 3 d shelf-life after 28 d cold storage (C28dS3). Reduced content of fruity note volatile esters and lactones was observed for peach fruit with extended cold storage, SA treatment maintained significant higher volatiles than controls. Transcript levels of genes derived from volatile ester biosynthesis pathway, including lipoxygenase PpLOX1, hyperoxide lyase PpHPL1, alcohol dehydrase PpADH1 and alcohol acyltransferase PpAAT1, were analyzed using real-time quantitative PCR. For SA-treated peach fruit after cold storage, significant higher transcript levels was detected for the PpLOX1 which encodes the first enzymatic step of the pathway. Regrading to soluble sugars, high sucrose content and low content of fructose and glucose was observed for SA-treated peach fruit. Gene expression analysis revealed higher transcript abundance of sucrose synthase PpSUS4, neutral invertase PpNINV8 and tonoplastic monosaccharide transporter PpTMT2 in peach fruit treated with SA. No significant difference in contents were observed for citric acid, malic acid and quinic acid between SA-treated samples and controls. This study showed that SA treatment alleviated the cold storage-induced reduction of a number of volatiles and sugars, and thereby maintained flavor quality of peach fruit during shelf-life after cold storage.

Vitamin K antagonist has a higher impact than heparin in preventing circuit clotting in chronic haemodialysis patients.

BackgroundIn dialysis sessions, some data suggest that decreasing or even avoiding additional anticoagulation by heparin is possible among patients already treated with oral anticoagulation. However, the required dose of heparin may actually depend on the pre-dialysis international normalized ratio (INR), which varies from one session to another. The aim of our study was to determine the respective role of INR and heparin dosing in the risk of circuit clotting during chronic haemodialysis.Methods From early 2012 to July 2016, we analysed the totality of dialysis sessions performed at Brest University Hospital among haemodialysis patients treated by vitamin K antagonists (VKA). We established a prediction of circuit clotting on the basis of a simplified score obtained by combining INR and heparin dosing.ResultsIn total, 7184 dialysis sessions among chronic haemodialysis patients under VKA were identified, including 233 with clotting events. The mean INR without clotting events was 2.5 versus 1.8 with clotting events (P < 0.001). Frequencies of circuit clotting were different according to INR group (INR <2.0, INR 2.0–3.0, INR >3.0; P < 0.0001). The protective role of VKA was higher than heparin, as shown by discriminant factor analysis (P < 0.0001). Conclusion. Our study established a predictive model of thrombosis risk of dialysis circuits in patients treated by VKA for a given heparin dose and a given INR. This model shows a marginal contribution of heparin to protect against the risk of thrombosis compared with VKA. Moreover, heparin would not appear to be necessary for patients with an INR >2.2.

Phenotypic variability of natural and cultivated populations of Oreochromis niloticus from Haut Sassandra region (Côte d'Ivoire)

1 ABSTRACT The phenotypic characterization of four populations of Oreochromis niloticus (Linnaeus, 1758) from the Haut-Sassandra region was carried out using 147 specimens. Among them, 39 were from the natural environment (Sassandra River) and 108 from three fish farms in the town of Daloa. The body weight, sixteen (16) metric characters and fifteen (15) meristic characters recorded on each individual made it possible to analyze the phenotypic variability of the four populations. Twenty-eight (28) on the 31 morphometric characters studied differentiated populations (P &lt;0.001). Based on these 28 characters, Principal Correspondence Analysis (PCA) and Hierarchical Classification Analysis (HCA) grouped the individuals into two groups, a group consisting of Guessabo and PK 12(Kilometer 12 fish farm located at the end of the city of Daloa) specimens and the other group consisting of APDRACI and Quartier Manioc (two fish farms in the city) individuals. Six morphometric characters (PrAL. IOD. LDF. DAL. AFL and SsL) were the most relevant in the discrimination of these two groups. Discriminant factor analysis (DFA) has also classified these populations into two clusters identical to those of the PCA and the HCA. The brood stocks of PK 12 farm is morphologically identical to specimens of the natural environment (Guessabo). The waters of the PK 12 farm have similar characteristics to the natural environment. The PK 12 farm compared to the others (APDRACI and Quartier Manioc) would be better managed.

Comparative flavor analysis of eight varieties of Xinjiang flatbreads from the Xinjiang Region of China

AbstractBackground and objectivesThe volatile and nonvolatile compounds of eight types of Xinjiang baked flatbreads were measured to identify their characteristic components, with key odorants determined using the relative odor activity values (ROAVs).FindingsElectronic nose results indicated that Walnut Nang aroma differed significantly from that of other Xinjiang flatbreads. GC‐MS results demonstrated that the principal volatile compounds in flatbreads were mostly aldehydes, alcohols, and ketones, with volatile profiles of flatbreads varying greatly. Among the flavor components tested, aldehydes with ROAVs higher than 1 contributed most significantly to overall aroma. In Rose Nang, aldehydes and alcohols were key odorants, with (E,E)‐2,4‐decadienal playing a major role in most flatbreads. All tested flatbreads were distinguishable using the electric tongue and discriminant factor analysis. Sugar analysis revealed lower maltose but higher glucose, sucrose, and fructose levels in Rose Nang compared to other samples. Finally, umami taste amino acids were dominant in all tested Xinjiang flatbreads.ConclusionsOur comparative analysis of the flavor substances of different kinds of Xinjiang baked flatbreads lays a foundation for research on flavor formation.Significance and noveltyThe analysis could provide a useful basis in the development of processing methods for Xinjiang flatbreads.

Multisensory System Used for the Analysis of the Water in the Lower Area of River Danube.

The present paper describes the development of a multisensory system for the analysis of the natural water in the Danube, water collected in the neighboring area of Galati City. The multisensory system consists of a sensor array made up of six screen-printed sensors based on electroactive compounds (Cobalt phthalocyanine, Meldola’s Blue, Prussian Blue) and nanomaterials (Multi-Walled Carbon Nanotubes, Multi-Walled Graphene, Gold Nanoparticles). The measurements with the sensors array were performed by using cyclic voltammetry. The cyclic voltammograms recorded in the Danube natural water show redox processes related to the electrochemical activity of the compounds in the water samples or of the electro-active compounds in the sensors detector element. These processes are strongly influenced by the composition and physico-chemical properties of the water samples, such as the ionic strength or the pH. The multivariate data analysis was performed by using the principal component analysis (PCA) and the discriminant factor analysis (DFA), the water samples being discriminated according to the collection point. In order to confirm the observed classes, the partial least squares discriminant analysis (PLS-DA) method was used. The classification of the samples according to the collection point could be made accurately and with very few errors. The correlations established between the voltammetric data and the results of the physico-chemical analyses by using the PLS1 method were very good, the correlation coefficients exceeding 0.9. Moreover, the predictive capacity of the multisensory system is very good, the differences between the measured and the predicted values being less than 3%. The multisensory system based on voltammetric sensors and on multivariate data analysis methods is a viable and useful tool for natural water analysis.

Influential Factors towards Political Participation of Thai Local Senior Citizens

The purposes of this study were to investigate the levels of knowledge and political participation of local senior citizens and discriminant factors inhibiting the participation in local politics of senior citizens. The population of this quantitative research was senior citizens aged over 60 years old in Nakhon Pathom province. The sample size of 420 senior citizens was drawn by a multi-stage random sampling technique. A questionnaire was used as a research instrument. The findings revealed that a majority of senior citizens participated in a local politics at a low-to-moderate level (60.95%), followed by a high level (39.05%). Regarding their knowledge about political participation, the findings revealed that both groups of participants were most aware about rights in political participation. For political participation, both groups showed that they had low engagement in political auditing and local politics activities. Discriminant analysis of two groups revealed influencing predictors of local political participation among senior citizens in the following order: were (1) knowledge of duties, (2) knowledge of freedoms, (3) being informed about political participation, (4) knowledge of rights, and (5) income. The discriminant analysis of influential factors on local political participation of senior citizens had its discriminant power at a high level of 80.70%. The study revealed that the government must promote and prioritize political participation by providing knowledge of political rights, duties, and freedoms.

Identification of roselle varieties through simple discriminating physicochemical characteristics using multivariate analysis

The objective of this work is to study the feasibility of a more objective and rigorous classification of the calices of Hibiscus sabdariffa based on their physicochemical profile. To do so, 19 analyses were carried out on 4 varieties of calices cultivated in Senegal: Vimto, Koor, Thai and CLT92. Principal component analysis results showed that 15 physicochemical and biochemical parameters could be potentially used to discriminate the varieties of calices. Polyphenolic and anthocyanin contents were anti-correlated to protein content and could be used to differentiate the Vimto/CLT92 and the Koor/Thai varieties. Within these two clusters, pH and lipid content could discriminate each variety. Finally, factorial discriminant analysis showed that total anthocyanin content, lipid content and chromaticity C* were the 3 parameters enabling the most efficient classification of calices according to variety and led to 100% classification accuracy.