Factorial Discriminant Analysis Research Articles

Analysis of PfEMP1—var Gene Sequences in Different Plasmodium falciparum Malarial Parasites

Plasmodium falciparum synthesizes P. falciparum erythrocyte membrane protein-1 (PfEMP-1), a product of the multicopy var gene family, which localizes on the surface of infected erythrocytes. This protein plays an important role in cytoadherence and immune evasion. Comparative analysis of the molecular sequences of the DBLα domain of the var gene from different isolates of the parasite reveals variations in the number of cysteines and presence of small conserved motifs like DGEA, RGD, GAG-binding motifs. Phylogenetic analysis while highlighting the extensive diversity leads to clustered them in separate clades far apart from each other. Discriminant factor analysis of physicochemical properties of amino acid sequences revealed that the aliphatic index, isoelectric point, and instability index have more effect in deciding the variance of different isolates sequences. The origin of diverse repertoire of the DBLα domain in the parasites highlights the complexity of host-parasite relationship in the context of parasite survival.

Rapid monitoring of the spoilage of minced beef stored under conventionally and active packaging conditions using Fourier transform infrared spectroscopy in tandem with chemometrics

Fourier transform infrared (FTIR) spectroscopy was exploited to measure biochemical changes within fresh minced beef in an attempt to rapidly monitor beef spoilage. Minced beef packaged either aerobically, under modified atmosphere and using an active packaging were held from freshness to spoilage at 0, 5, 10, and 15 °C. Frequent FTIR measurements were collected directly from the sample surface using attenuated total reflectance, in parallel the total viable counts of bacteria, the sensory quality and the pH were also determined. Principal components analysis allowed illuminating the wavenumbers potentially correlated with the spoilage process. Qualitative interpretation of spectral data was carried out using discriminant factorial analysis and used to corroborate sensory data and to accurately determine samples freshness and packaging. Partial least-squares regressions permitted estimates of bacterial loads and pH values from the spectral data with a fit of R 2 = 0.80 for total viable counts and fit of R 2 = 0.92 for the pH. Obtained results demonstrated that a FTIR spectrum may be considered as a metabolic fingerprint and that the method in tandem with chemometrics represents a powerful, rapid, economical and non-invasive method for monitoring minced beef freshness regardless the storage conditions (e.g. packaging and temperature).

Multivariate Analysis of Attenuated Total Reflection—Fourier Transform Infrared Spectroscopic Data to Confirm the Origin of Honeys

Fourier transform infrared (FT-IR) spectroscopy and chemometrics were used to verify the origin of honey samples (n=150) from Europe and South America. Authentic honey samples were collected from five sources, namely unfiltered samples from Mexico in 2004, commercially filtered samples from Ireland and Argentina in 2004, commercially filtered samples from the Czech Republic in 2005 and 2006, and commercially filtered samples from Hungary in 2006. Samples were diluted with distilled water to a standard solids content (70 degrees Brix) and their spectra (2500-12 500 nm) recorded at room temperature using an FT-IR spectrometer equipped with a germanium attenuated total reflection (ATR) accessory. First- and second-derivative and standard normal variate (SNV) data pretreatments were applied to the recorded spectra, which were analyzed using partial least squares (PLS) regression analysis, factorial discriminant analysis (FDA), and soft independent modeling of class analogy (SIMCA). In general, when an attenuated wavelength range (6800-11 500 nm) rather than the whole spectrum (2500-12 500 nm) was studied, higher correct classification rates were achieved. An overall correct classification of 93.3% was obtained for honeys by PLS discriminant analysis, while FDA techniques correctly classified 94.7% of honey samples. Correct classifications of up to 100% were achieved using SIMCA, but models describing some classes had very high false positive rates.

Fluorescence Spectroscopy as a Promising Tool for a Polyphasic Approach to Pseudomonad Taxonomy

Fluorescence spectroscopy is an emerging tool for the analysis of biomolecules from complex matrices. We explored the potentialities of the method for the pseudomonad taxonomic purpose at the genus and species level. Emission spectra of three intrinsic fluorophores (namely, NADH, tryptophan, and the complex of aromatic amino acids and nucleic acid) were collected from whole bacterial cells. Their comparisons were performed through principal component analysis and factorial discriminant analysis. Reference strains from the Xanthomonas, Stenotrophomonas, Burkholderia, and Pseudomonas genera were well separated, with sensitivity and selectivity higher than 90%. At the species level, P. lundensis, P. taetrolens, P. fragi, P. chlororaphis, and P. stutzeri were also well separated, in a distant group, from P. putida, P. pseudoalcaligenes, and P. fluorescens. These results are in agreement with the generally admitted rRNA and DNA bacterial homology grouping but they also provide additional information about strain relatedness. In the case of environmental isolates, the method allows good discrimination, even for strains for which ambiguity still remained after PCR and API 20NE identification. Rapid, easy to perform, and low cost, fluorescence spectroscopy provides substantial information on cell components. Statistical analysis of collected data allows in-depth comparison of strains. Our results strongly support the view that fluorescence spectroscopy fingerprinting can be used as a powerful tool in a polyphasic approach to pseudomonad taxonomy.

Geographical traceability of Italian white truffle (Tuber magnatum Pico) by the analysis of volatile organic compounds

Results are presented that were obtained on the geographic traceability of the white truffle Tuber magnatum Pico. Solid-phase microextraction coupled to gas chromatography/mass spectrometry (SPME-GC/MS) was employed to characterize the volatile profile of T. magnatum white truffle produced in seven geographical areas of Italy. The main components of the volatile fraction were identified using SPME-GC/MS. Significant differences in the proportion of volatile constituents from truffles of different geographical areas were detected. The results suggest that, besides genetic factors, environmental conditions influence the formation of volatile organic compounds. The mass spectra of the volatile fraction of the samples were used as fingerprints to characterize the geographical origin. Next, stepwise factorial discriminant analysis afforded a limited number of characteristic fragment ions that allowed a geographical classification of the truffles studied.

Pregnancy-Associated Glycoprotein Secretion in North Moroccan Goats

The plasma profile of pregnancy-associated glycoprotein (PAG) and its relationship with fetal number were studied in 14 North Moroccan goats using a semi-heterologous radioimmunoassay (RIA). Peripheral blood was collected every day during the first month of pregnancy, afterward the blood samples were collected trice a week. The PAG were first detected at day 20 of pregnancy, their levels increase to week 8 of gestation then decrease slightly until parturition. Statistical differences between goats carrying one or two foetuses are observed from week 5 of pregnancy until parturition. Factorial Discriminant Analysis provides mathematical models for the discrimination between the litter size using the PAG level and the week of pregnancy. Using only one blood sample per week, high sensitivity, specificity and overall accuracy (74%, 88% and 81%) were obtained using these models. It is concluded that the PAG RIA is an effective tool for early diagnosis of pregnancy and for discrimination between the litter sizes in North Moroccan goats.

SYMBOLIC FACTORIAL DISCRIMINANT ANALYSIS FOR ILLUMINATION INVARIANT FACE RECOGNITION

In this paper, a new appearance-based technique called symbolic factorial discriminant analysis (symbolic FDA) is explored for face representation and recognition under varying illumination conditions. In the past few years, many appearance-based methods have been proposed to model image variations of human faces under different lighting conditions using single valued variables to represent the facial features. In the proposed symbolic factorial discriminant analysis method, we extract interval type discriminating features, which are robust to illumination changes. The minimum distance classifier with symbolic dissimilarity measure is used for classification. The proposed method has been successfully tested for face recognition using three databases, namely, Yale Face database B, CMU PIE database and Harvard database. The experimental results have demonstrated the effective performance of this method.

Rapid GIS-based profiling of West Nile virus transmission: defining environmental factors associated with an urban-suburban outbreak in Northeast Ohio, USA.

Human West Nile virus (WNV) infection was first detected in Cuyahoga county, Ohio, USA, in 2002. During that year's extensive epidemic/epizootic among non-immune human and bird populations, the county experienced 155 cases of severe human West Nile neurological disease (WNND, incidence = 11.1 cases/100,000), with 11 fatalities. Structured serosurveys indicated that 1.9%, or approximately 26,000 of county residents (population = 1,372,303) were infected that year. In early 2003, in order to better focus monitoring and control efforts, we used a geographical information system (GIS) approach and spatial statistical analysis to identify the association of environmental factors and human population structure with the observed local risk for WNV transmission. Within the varied range of urban/suburban/ rural habitats across the 1186 km2 county, exploratory analysis indicated significant clustering of WNND risk in inner-ring suburbs. Subsequent discriminant factor analysis based on inputs of census and land-use/land cover data was found to effectively classify sub-areas of the county having low, medium and high WNV risk. On a 1036 ha quadrat scale of resolution, higher risk of human infection was significantly associated with higher-income areas, increased fractionation of habitat and older housing, while it was negatively associated with areas of agricultural land, wetland or forest. The areal classification of WNV transmission risk has been validated over time through detection of increased local Culex spp. mosquito density (2002-2006), and increased frequency of WNV positive mosquito pools within the medium- and high-risk quadrats. This timely working identification of the transmission scale effectively focused control interventions against newly invasive WNV in a complex North American habitat.

Identification of different alcoholic beverages by electronic nose coupled to GC

In this work, 21 different alcoholic beverages (beer, wines and spirits) were analyzed by an electronic nose after a dehydration and dealcoholization procedure. Principal component analysis (PCA) and discriminant factorial analysis (DFA) allowed to clearly identify differences between these various alcoholic beverages and to classify them independent of their ethanol content. The PCA showed that the sample discrimination was carried out according to the content in aroma compounds. The discrimination quality was evaluated using the leave-one out method, which allowed to quantify the percentage of samples classed correctly for a determined number of sensors.

Feasibility Study on Using Visible-Near Infrared Spectroscopy Coupled with a Factorial Discriminant Analysis Technique to Identify Sheep Milk from Different Genotypes and Feeding Systems

Quick detection of differences in raw milk samples due to feeding and genotype is needed for quality control of milk and milk products. This study was undertaken to evaluate the potential of visible (vis) and near infrared (NIR) spectroscopy to discriminate between differences in sheep milk that are attributed to different feedings and genotypes. A mobile, fibre-type, vis-NIR spectrophotometer (Zeiss Corona 45 visnir fibre, Jena, Germany) with a light reflectance measurement range of 306.5–1710.9 nm was used to measure the light reflectance from 50 raw milk samples obtained from two experimental trials in Tunisia. Out of these 50 samples, 30 samples were from the first trial and were obtained equally from two different genotypes and 20 samples were from the second trial and were obtained equally from two different feedings of the same genotype. Milk samples were classified into four different classes, namely, Comisana genotype with pasture feeding (CO), Sicilo–Sarde genotype with pasture feeding (SS), Sicilo–Sarde genotype feeding on Scotch horse bean meal (SSB) and Sicilo–Sarde genotype feeding on soybean meal (SSS). Another milk sample classification based on three classes for three different feedings of one genotype (Sicilo–Sarde) was considered by removing CO. The factorial discriminant analysis (FDA) was applied on the first five principal components obtained from the principal component analysis performed on the vis-NIR spectra in order to classify milk samples into four and three assigned groups. Based on a four-group classification, correct classification (CC) of 92.5% and 95% was observed for the calibration and validation data sets, respectively. For a three-group classification, CC was 100% and 88.9% for the calibration and validation data sets, respectively. Even though the number of samples in the second trial (different feedings) was smaller (20 samples) compared with those in the first trial (30 samples), the FDA provided better discrimination of differences attributed to feeding compared to those attributed to genotypes. It was concluded that vis-NIR spectroscopy coupled with the FDA classification technique is a powerful tool to discriminate between genotypes and feeding differences that exist in raw sheep milk samples.

Discrimination of mango fruit maturity by volatiles using the electronic nose and gas chromatography

Mango fruit ( Mangifera indica L.), cv. ‘Cogshall’, ‘Kent’ and ‘Keitt’ were harvested at different maturities (61–115 d past flowering and 80–307 average g fresh weight for ‘Cogshall’) and at different sizes (364–1563 and 276–894 average g fresh weight for ‘Keitt’ and ‘Kent’, respectively). Immediately after harvest (green) or after 1 week of ripening at room temperature (ripe), fruit were homogenized or left intact and evaluated by electronic nose (enose) or by gas chromatography (GC) for aroma and other volatiles as well as for soluble solids and acids. Volatile data from the different harvest maturities and ripening stages were discriminated by using multivariate statistics (discriminant factor analysis). Both the enose and GC were able, in most cases, to separate fruit from different harvest maturities, especially for ‘Cogshall’ mangoes, at both the green and ripe stages as well as discriminate green from ripe fruit and fruit from the different varieties within a maturity stage. Solids and acids data indicated that later harvest maturities resulted in sweeter fruit and later-harvested fruit had a different volatile profile from earlier-harvested fruit. Mango fruit volatiles may be useful as maturity markers to determine optimal harvest maturity for mango fruit that results in full quality upon ripening.

Front face fluorescence spectroscopy and visible spectroscopy coupled with chemometrics have the potential to characterise ripening of Cabernet Franc grapes

The potential of front-face spectroscopy for grape ripening dates discrimination was investigated on Cabernet Franc grapes from three parcels located on the Loire Valley and for six ripening dates. The 18 batches were analysed by front-face fluorescence spectroscopy and visible spectroscopy. The excitation spectra (250–310 nm, emission wavelength = 350 nm) were characterised by a shoulder at 280 nm. Grapes spectra were classified by factorial discriminant analysis (FDA). Ripening dates were well predicted by fluorescence spectra: grapes before veraison were separated from grapes after veraison and almost every ripening date was identified. The common spectroscopic space obtained by CCSWA showed that wavelengths corresponding to anthocyanin absorption in the visible were correlated to fluorescence wavelengths around the starting and ending points of the shoulder (263 and at 292 nm). Then, regression models were investigated to predict total soluble solids (TSS), total acidity, malvidin-3G, total anthocyanins and total phenolics content from visible and fluorescence spectra. To predict technological indicators (TSS and total acidity), the PLS model with visible spectra (RMSECV = 0.82°Brix or 0.96 g L −1 H 2SO 4) was better than those with fluorescence one (RMSECV = 1.39°Brix or 2.06 g L −1 H 2SO 4). For malvidin-3G and total anthocyanins, all R c 2 and R cv 2 were superior to 0.90 and RMSECV were low. Visible and fluorescence spectroscopies succeeded in predicting anthocyanin content. Concerning total phenolic, the best prediction was provided by fluorescence spectroscopy.

Selection of variables using factorial discriminant analysis for the state identification of an anaerobic UASB–UAF hybrid pilot plant, fed with winery effluents

Anaerobic wastewater treatment has become a widely used method for wastewater depuration, and has been applied in a wide range of situations, from urban wastewater to highly toxic industrial wastewater. Particularly it has been successfully applied to the treatment of the beverage industries effluents. To avoid the destabilization of the system a monitoring diagnosis and control system of the depuration processes is necessary. The cost of this system is an important issue, that depends on the number of parameters that must be controlled for an adequate performance of a wastewater plant control system. This work shows how the classic statistical classification techniques can be applied to determine the number variables that must be monitored to achieve an adequate performance of anaerobic UASB-UAF hybrid Pilot Plant monitoring and control system. The obtained results had not been unique, so different combinations of variables can be selected for a good wastewater treatment process control. Economic or technical criteria may be considered to determine the final variables set in each particular situation.

Building of a metal oxide gas sensor-based electronic nose to assess the freshness of sardines under cold storage

Abstract We report on the building of a simple and reproducible electronic nose based on commercially available metal oxide gas sensors aimed at monitoring the freshness of sardines stored at 4 °C. Sample delivery is based on the dynamic headspace method and four features are extracted from the transient response of each sensor. By using an unsupervised method, namely principal component analysis (PCA), we found that sardine samples could be grouped into three categories (fresh, medium and aged), which corresponded to an increasing number of days that sardines had spent under cold storage. Then, supervised linear or non-linear pattern recognition methods (PARC) such as discriminant factor analysis (DFA) or fuzzy ARTMAP neural networks (FANN) were successfully applied to build classification models to sort sardine samples according to these three states of freshness. The success rate in classification was 96.88% for the neural network classifier. Additionally, 10 volatile species that indicated the evolution of sardines with the number of days of cold storage were identified by SPME/MS/GC.

Discrimination of Storage Duration of Apples Stored in a Cooled Room and Shelf-Life by Visible-Near Infrared Spectroscopy

Improved non-destructive instrumental approaches for grading fruit during post-harvest could be an efficient way to monitor stock in the apple industry. The objective of this study was to evaluate the ability of visible-near infrared (vis-NIR) spectroscopy in reflectance mode for classifying apples left on the shelf or stored in a cooled room. The ability of NIR spectroscopy to classify the duration of storage of three apple cultivars in two storage modalities was evaluated. A total of 450 fruit, sampled after 7, 14, 28, 60, 90 and 120 days of storage in a cooled room (CR) and 7, 14 and 28 days in shelflife (SL), has been studied. The classification of these modalities was analysed by factorial discriminant analysis (FDA) pooling the spectral data of all cultivars (global models) into a common data set. Then, the cultivar effect on the classification of the same modalities was analysed by processing data from each cultivar in separate factorial descriminant analyses. A preliminary analysis showed the genetic variability of spectral data due to the three apple cultivars. We show that vis-NIR spectroscopy allowed the correct classification of the fruits of each cultivar by more than 95%. The classification relied on both vis and NIR absorption bands: 500, 680, 1400 to 1700, 1850, 1950, 2200 and 2300 nm. We show that storage modalities of global models can be classified by more than 75% and 83% for fruits stored in a cooled room and shelf, respectively. Classification of the same storage modalities was improved by cultivar models with percentage of individuals correctly classified of 86% (Gala), 89% (Elstar) and 85% (Smoothee) for fruits stored in a cooled room and 95% (Gala), 98% (Elstar) and 95% (Smoothee) for fruits left in shelflife. We conclude that despite the slight increase of efficiency of the models when we considered each apple cultivar separately, global models applicable to a set of different cultivars presents a correct level of classification and could be usefull for some commercial applications.

Potential of visible and near-infrared spectroscopy to derive colour groups utilising the Munsell soil colour charts

A study was undertaken to investigate the potential of visible (VIS) and near-infrared (NIR) spectroscopy to derive soil colour groups as a new methodology that might assist in soil type classification. A fibre-optic VIS–NIR spectrophotometer (306.5 – 1710.9 nm) was used to measure the light properties in reflectance mode of 342 dry soil samples collected from fields in Belgium and Northern France. The colour of soil samples was measured with the Munsell soil colour charts, dividing soil samples into two groups of 236 and 106 samples belonging to Hue 2.5Y and Hue 10YR, respectively. Factorial Discriminant Analysis (FDA) was performed separately on each Hue data using the first five Principal Components (PCs) of a Principal Component Analysis (PCA) carried out on colour groups defined a priori. Considering the 2.5Y Hue group, soil samples were classified successfully into four colour groups with a correct classification (CC) of 87.3% and 81.8% for the calibration and validation spectra, respectively. Lower accuracy was observed for the 10YR Hue group for classification into three groups, since 79.6% and 75.0% of samples were correctly classified for calibration and validation sample sets, respectively. Establishing four colour groups by combining both Hue groups decreased the classification accuracy considerably. In order to improve the classification accuracy, the first 5 PCs and first 2 PCs of PCA performed on soil spectra and on the total carbon (Tot-C), extractable phosphorous (Ext-P) and texture index (Ti), respectively, were pooled (concatenated) into a signal matrix and analysed newly by the FDA. This led to improvement of the classification results of four Hue 2.5Y groups (CC of 82.1% and 89.1%) and three Hue 10YR groups (CC of 84.1% and 82.4%). However, best concatenation results of four groups for the combination of both Hue groups were obtained when only Tot-C and Ext-P were considered without Ti, obtaining a CC of 84.2% of the validation data sets. This suggests that the VIS-NIR spectroscopy combined with chemometric tools has the potential of identifying different soil colour groups for a large geographical area.

A comparison and joint use of mid infrared and fluorescence spectroscopic methods for differentiating between manufacturing processes and sampling zones of ripened soft cheeses

Ten traditional M1 (n = 5) and M2 (n = 5) soft cheeses produced from raw milk, and five other stabilised M3 (n = 5) cheeses manufactured from pasteurised milk, were studied using mid infrared (MIR) and front face fluorescence (FFFS) spectroscopies. MIR (3000–900 cm−1), tryptophan (excitation: 290 nm, emission: 305-450 nm), 400-640 emission spectra (excitation: 380 nm) and vitamin A (excitation: 280–350 nm, emission: 410 nm) spectra were recorded at two sampling zones (external (E) and central (C)) of the investigated cheeses. When the factorial discriminant analysis (FDA) was applied to the MIR spectra, the classification was not satisfactory. With tryptophan fluorescence spectra, correct classification of 94.4 and 69.4% was observed for the calibration and validation spectra, respectively. Better classification was obtained using vitamin A fluorescence spectra, since 91.8 and 80.6% of the calibration and validation spectra, respectively, were correctly classified. When the first five principal components (PCs) of the PCA extracted from each data set were pooled into a single matrix and analysed by FDA, the classification was considerably improved, obtaining a percentage of correct classification of 100 and 91.7% for the calibration and validation samples, respectively. It was concluded that concatenation of the physico-chemical and spectroscopic data sets is an efficient technique for the identification of soft cheese varieties.

Chemotypes in Melaleuca quinquenervia (Cav.) S.T. Blake (Niaouli) from Benin Using Multivariate Statistical Analysis of Their Essential Oils

Forty compounds have been characterized by GC and GC/MS and relative retention times in 300 samples of Niaouli (Melaleuca quinquenervia) oils collected on 75 different trees during four seasons. Twenty-one compounds were submitted to multivariate statistical analyses using Principal Component Analysis (PCA). Factorial Discriminant Analysis (FDA) revealed the presence of three chemotypes. Chemotype I is already known (1,8-cineole; 50%), chemo-type II had an equal content in 1,8-cineole (33%) and viridiflorol (32%). The third (chemotype III) was characterized by a high content in viridiflorol (50%). The 1,8-cineole chemotype represented 58% of three populations. Seasonal differentiation does not occur, showing that Niaouli oil composition is stable all year.

Utilisation of mid-infrared spectroscopy for determination of the geographic origin of Gruyère PDO and L’Etivaz PDO Swiss cheeses

The potential of mid-infrared spectroscopy (MIR), using an attenuated total reflectance (ATR) cell, was evaluated for the authentication of 25 Gruyère PDO and L’Etivaz PDO cheeses produced at different altitudes in Switzerland. In order to test the ability of MIR to authenticate the investigated cheeses, chemometric tools, such as principal component analysis (PCA) and factorial discriminant analysis (FDA), were applied to the three spectral regions of the MIR (e.g. 3000–2800 cm −1, 1700–1500 cm −1, and 1500–900 cm −1). By applying the FDA to the first 10 principal components (PCs) of the PCA applied to each spectral regions, the best rate of correct classification was obtained in the 3000–2800 cm −1 and 1500–900 cm −1 spectral regions, since 90.5% and 90.9% were achieved, respectively. It can be concluded that these two spectral regions could be considered as valuable tools for the determination of the geographical origin of the investigated cheeses.

The analysis of psychometric properties of Human Figure Drawings’ Test

The present study investigated whether Human Figure Drawings (HFD) can be considered as valid and reliable devices to assess intellectual abilities as well as the emotional adjustment in the practice of child psychologists. HFD are most popular of all drawing techniques often included in test batteries as relatively short, easy-to-administer, friendly and non-threatening measures. Contradictory empirical evidence concerning their psychometric properties is presented with the aim to overcome the gap between large scale of practical use and a relatively small amount of scientific investigations. Draw-A-Person: A Quantitative Scoring System (DAP: QSS) and Draw-A-Person: Screening Procedure for Emotional Disturbance (DAP: SPED) created by Naglieri J. and colleagues were chosen as the most modern and objective scoring systems in regard to other HFD. Both techniques and WISC-IIILT were administered to a clinical sample consisting of 95 children aged 6–16 with emotional/behavioural disturbances and 70 children without clinical disturbances closely matched for the age and gender criteria. T-test, correlations, discriminant and exploratory factor analysis were applied for data analysis. Results suggest that DAP:QSS shortly meets standards of concurrent validity with WISC-IIILT as it was found no statistically significant correlations with the scores of Wechsler Intelligence Scale for Children-IIILT (p > 0.05). A additionally, noncognitive factors – liking to draw and clinical disturbance – can cause biased results of the assessment of intellectual abilities when using DAP: QSS: liking to draw correlates with the higher results as compared to WISC-IIILT scores, while intellectual abilities of the children with clinical disturbance were underestimated (p = 0.00). Standard error of estimation appeared to be higher when using DAP: QSS than WISC-IIILT. The total score of Draw-A-Person:Screening Procedure for Emotional Disturbance was significantly higher in the clinical sample (p = 0.00). It proves the validity of this instrument to screen the children for emotional maladjustment but the cases of false-negative screening cast doubt about its strength. Both devices showed good reliability (internal consistency as well as inter-scorer).