Sensory profile of Italian salami with coriander (Coriandrum sativum L.) essential oil

The descriptive terminology and sensory prolife of four samples of Italian salami were determined using a methodology based on the Quantitative Descriptive Analysis (QDA). A sensory panel consensually defined sensory descriptors, their respective reference materials, and the descriptive evaluation ballot. Twelve individuals were selected as judges and properly trained. They used the following criteria: discriminating power, reproducibility, and individual consensus. Twelve descriptors were determined showing similarities and differences among the Italian salami samples. Each descriptor was evaluated using a 10 cm non-structured scale. The data were analyzed by ANOVA, Tukey test, and the Principal Component Analysis (PCA). The salami with coriander essential oil (T3) had lower rancid taste and rancid odor, whereas the control (T1) showed high values of these sensory attributes. Regarding brightness, T4 showed the best result. For the other attributes, T1, T2, T3, and T4 were similar.

Sensory profile of 'Douradão' peaches cold stored under controlled atmosphere

The sensory quality of 'Douradão' peaches cold stored in three different conditions of controlled atmosphere (CA1, CA2, CA3 and Control) was studied. After 14, 21 and 28 days of cold storage, samples were withdrawn from CA and kept for 4 days in ambient air for ripening. The sensory profile of the peaches and the descriptive terminology were developed by methodology based on the Quantitative Descriptive Analysis (QDA). The panelists consensually defined the sensory descriptors, their respective reference materials and the descriptive evaluation ballot. Fourteen panelists were selected based on their discrimination capacity and reproducibility. Seven descriptors were generated showing similarities and differences between samples. The data were analyzed by ANOVA, Tukey test and Principal Component Analysis (PCA). Results showed significant differences in the sensory profiles of the peaches. The PCA showed that CA2 and CA3 treatments were more characterized by the fresh peach flavor, fresh peach appearance, juiciness and flesh firmness, and were effective in keeping the good quality of the 'Douradão' peaches during the 28 days of cold storage. The Control and CA1 treatments were characterized by the mealiness and were ineffective for quality maintenance of the fruits during cold storage.

Modeling the water uptake by chicken carcasses during cooling by immersion

In this study, water uptake by poultry carcasses during cooling by water immersion was modeled using artificial neural networks. Data from twenty-five independent variables and the final mass of the carcass were collected in an industrial plant to train and validate the model. Different network structures with one hidden layer were tested, and the Downhill Simplex method was used to optimize the synaptic weights. In order to accelerate the optimization calculus, Principal Component Analysis (PCA) was used to preprocess the input data. The obtained results were: i) PCA reduced the number of input variables from twenty-five to ten; ii) the neural network structure 4-6-1 was the one with the best result; iii) PCA gave the following order of importance: parameters of mass transfer, heat transfer, and initial characteristics of the carcass. The main contributions of this work were to provide an accurate model for predicting the final content of water in the carcasses and a better understanding of the variables involved.

Monitoring of wine aging process by electrospray ionization mass spectrometry

The characterization of wine samples by direct insertion electrospray ionization mass spectrometry (ESI-MS), without pre-treatment or chromatographic separation, in a process denominated fingerprinting, has been applied to several samples of wine produced with grapes of the Pinot noir, Merlot and Cabernet Sauvignon varieties from the state o Rio Grande do Sul, in Brazil. The ESI-MS fingerprints of the samples detected changes which occurred during the aging process in the three grape varieties. Principal Component Analysis (PCA) of the negative ion mode fingerprints was used to group the samples, pinpoint the main changes in their composition, and indicate marker ions for each group of samples.

Analytical characteristics and discrimination of Brazilian commercial grape juice, nectar, and beverage

The production and commercialization of Brazilian grape juice is increasing annually, mainly due to its typicality, quality, and nutritional value. The present research was carried out in view of the great significance of Brazilian grape juice for the grape and wine industry. The purpose of this study, therefore, was to assess its composition as well as the discrimination between grape juice and other beverages. Twenty four samples of whole, sweetened, and reprocessed grape juices, grape nectar, and grape beverage were evaluated. Classical variables were analyzed by means of physicochemical methods; tartaric and malic acids, by HPLC; methanol, by gas chromatography; minerals, by atomic absorption spectrophotometry. These products were discriminated by the Principal Component Analysis (PCA). Results show that whole and sweetened grape juices were discriminated from other grape products because they featured higher values of total soluble solids, tartaric and malic acids, most minerals, phenolic compounds, and K/Na ratio, whereas grape nectar and grape beverage presented higher values of ºBrix/titratable acidity ratio. Reprocessed juice was discriminated due to its higher concentrations of Li and Na and lower hue.

Effect of whey protein concentrate on texture of fat-free desserts: sensory and instrumental measurements

It is important to understand how changes in the product formulation can modify its characteristics. Thus, the objective of this study was to investigate the effect of whey protein concentrate (WPC) on the texture of fat-free dairy desserts. The correlation between instrumental and sensory measurements was also investigated. Four formulations were prepared with different WPC concentrations (0, 1.5, 3.0, and 4.5 wt. (%)) and were evaluated using the texture profile analysis (TPA) and rheology. Thickness was evaluated by nine trained panelists. Formulations containing WPC showed higher firmness, elasticity, chewiness, and gumminess and clearly differed from the control as indicated by principal component analysis (PCA). Flow behavior was characterized as time-dependent and pseudoplastic. Formulation with 4.5% WPC at 10 °C showed the highest thixotropic behavior. Experimental data were fitted to Herschel-Bulkley model. The addition of WPC contributed to the texture of the fat-free dairy dessert. The yield stress, apparent viscosity, and perceived thickness in the dairy desserts increased with WPC concentration. The presence of WPC promotes the formation of a stronger gel structure as a result of protein-protein interactions. The correlation between instrumental parameters and thickness provided practical results for food industries.

Production of pizza dough with reduced fermentation time

The aim of this study was to reduce the fermentation time of pizza dough by evaluating the development of the dough during fermentation using a Chopin® rheofermentometer and verifying the influence of time and temperature using a 2² factorial design. The focus was to produce characteristic soft pizza dough with bubbles and crispy edges and soft in the center. These attributes were verified by the Quantitative Descriptive Analysis (QDA). The dough was prepared with the usual ingredients, fermented at a temperature range from 27 to 33 ºC for 30 to 42 minutes, enlarged, added with tomato sauce, baked, and frozen. The influence of the variables time and temperature on the release of carbon dioxide (H'm) was confirmed with positive and significant effect, using a rheofermentometer, which was not observed for the development or maximum height of the dough (Hm). The same fermentation conditions of the experimental design were used for the production of the pizza dough in the industrial process; it was submitted to Quantitative Descriptive Analysis (QDA), in which the samples were described by nine attributes. The results showed that some samples had the desired characteristics of pizza dough, demonstrated by the principal component analysis (PCA), indicating a 30 % fermentation time reduction when compared to the conventional process.

Sensory descriptors of cocoa beans from cultivated trees of Soconusco, Chiapas, Mexico

The odor and taste profile of cocoa bean samples obtained from trees cultivated in southern Mexico were evaluated by trained panelists. Seven representative samples (groups) of a total of 45 were analyzed. Four attributes of taste (sweetness, bitterness, acidity and astringency), and nine of odor (chocolate, nutty, hazelnut, sweet, acidity, roasted, spicy, musty and off-odor) were evaluated. A sample (G7) with higher scores in sweet taste and sweet and nutty odors was detected, as well as a high association between these descriptors and the sample, analyzed through principal component analysis (PCA). Similarly, samples that showed high scores for non-desired odors in cocoas such as off-odor and musty were identified and related by PCA to roasted odor and astringent taste (G2 and G4). Based on this scores, the samples were listed in descending order by their sensory quality as G7> G5> G6> G3> G1> G4> G2.

Delineation of within-site terroir effects using soil and vine water measurement. Investigation of Cabernet Franc

. The influence of vine water status was studied in commercial vineyard blocks of Vilis vinifera L. cv. Cabernet Franc in Niagara Peninsula, Ontario from 2005 to 2007. Vine performance, fruit composition and vine size of non-irrigated grapevines were compared within ten vineyard blocks containing different soil and vine water status. Results showed that within each vineyard block water status zones could be identified on GIS-generated maps using leaf water potential and soil moisture measurements. Some yield and fruit composition variables correlated with the intensity of vine water status. Chemical and descriptive sensory analysis was performed on nine (2005) and eight (2006) pairs of experimental wines to illustrate differences between wines made from high and low water status winegrapes at each vineyard block. Twelve trained judges evaluated six aroma and flavor (red fruit, black cherry, black current, black pepper, bell pepper, and green bean), thr~e mouthfeel (astringency, bitterness and acidity) sensory attributes as well as color intensity. Each pair of high and low water status wine was compared using t-test. In 2005, low water status (L WS) wines from Buis, Harbour Estate, Henry of Pelham (HOP), and Vieni had higher color intensity; those form Chateau des Charmes (CDC) had high black cherry flavor; those at RiefEstates were high in red fruit flavor and at those from George site was high in red fruit aroma. In 2006, low water status (L WS) wines from George, Cave Spring and Morrison sites were high in color intensity. L WS wines from CDC, George and Morrison were more intense in black cherry aroma; LWS wines from Hernder site were high in red fruit aroma and flavor. No significant differences were found from one year to the next between the wines produced from the same vineyard, indicating that the attributes of these wines were maintained almost constant despite markedly different conditions in 2005 and 2006 vintages. Partial ii Least Square (PLS) analysis showed that leaf \}' was associated with red fruit aroma and flavor, berry and wine color intensity, total phenols, Brix and anthocyanins while soil moisture was explained with acidity, green bean aroma and flavor as well as bell pepper aroma and flavor. In another study chemical and descriptive sensory analysis was conducted on nine (2005) and eight (2006) medium water status (MWS) experimental wines to illustrate differences that might support the sub-appellation system in Niagara. The judges evaluated the same aroma, flavor, and mouthfeel sensory attributes as well as color intensity. Data were analyzed using analysis of variance (ANOVA), principal component analysis (PCA) and discriminate analysis (DA). ANOV A of sensory data showed regional differences for all sensory attributes. In 2005, wines from CDC, HOP, and Hemder sites showed highest. r ed fruit aroma and flavor. Lakeshore and Niagara River sites (Harbour, Reif, George, and Buis) wines showed higher bell pepper and green bean aroma and flavor due to proximity to the large bodies of water and less heat unit accumulation. In 2006, all sensory attributes except black pepper aroma were different. PCA revealed that wines from HOP and CDC sites were higher in red fruit, black currant and black cherry aroma and flavor as well as black pepper flavor, while wines from Hemder, Morrison and George sites were high in green bean aroma and flavor. ANOV A of chemical data in 2005 indicated that hue, color intensity, and titratable acidity (TA) were different across the sites, while in 2006, hue, color intensity and ethanol were different across the sites. These data indicate that there is the likelihood of substantial chemical and sensory differences between clusters of sub-appellations within the Niagara Peninsula iii

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).

Towards the identification of a neighbourhood park typology : a conceptual and methodological exploration

Peu d’études ont évalué les caractéristiques des parcs pouvant encourager l’activité physique spécifiquement chez les jeunes. Cette étude vise à estimer la fiabilité d’un outil d’observation des parcs orienté vers les jeunes, à identifier les domaines conceptuels des parcs capturés par cet outil à l’aide d’une opérationnalisation du modèle conceptuel des parcs et de l’activité physique et à identifier différents types de parcs. Un total de 576 parcs ont été évalués en utilisant un outil d’évaluation des parcs. La fiabilité intra-juges et la fiabilité inter-juges de cet outil ont été estimées. Une analyse exploratoire par composantes principales (ACP) a été effectuée en utilisant une rotation orthogonale varimax et les variables étaient retenues si elles saturaient à ≥0.3 sur une composante. Une analyse par grappes (AG) à l’aide de la méthode de Ward a ensuite été réalisée en utilisant les composantes principales et une mesure de l’aire des parcs. L’outil était généralement fiable et l’ACP a permis d'identifier dix composantes principales qui expliquaient 60% de la variance totale. L’AG a donné un résultat de neuf grappes qui expliquaient 40% de la variance totale. Les méthodes de l’ACP et l’AG sont donc faisables avec des données de parcs. Les résultats ont été interprétés en utilisant l’opérationnalisation du modèle conceptuel.

Biogeochemistry of the shelf sediments of south eastern Arabian sea: Effect on benthic bacterial heterotrophs

The composition and variability of heterotrophic bacteria along the shelf sediments of south west coast of India and its relationship with the sediment biogeochemistry was investigated. The bacterial abundance ranged from 1.12 x 103 – 1.88 x 106 CFU g-1 dry wt. of sediment. The population showed significant positive correlation with silt (r = 0.529, p< 0.05), organic carbon (OC) (r = 0.679, p< 0.05), total nitrogen (TN) (r = 0.638, p< 0.05), total protein (TPRT) (r = 0.615, p< 0.05) and total carbohydrate (TCHO) (r = 0.675, p< 0.05) and significant negative correlation with sand (r = -0.488, p< 0.05). Community was mainly composed of Bacillus, Alteromonas, Vibrio, Coryneforms, Micrococcus, Planococcus, Staphylococcus, Moraxella, Alcaligenes, Enterobacteriaceae, Pseudomonas, Acinetobacter, Flavobacterium and Aeromonas. BIOENV analysis explained the best possible environmental parameters i.e., carbohydrate, total nitrogen, temperature, pH and sand at 50m depth and organic matter, BPC, protein, lipid and temperature at 200m depth controlling the distribution pattern of heterotrophic bacterial population in shelf sediments. The Principal Component Analysis (PCA) of the environmental variables showed that the first and second principal component accounted for 65% and 30.6% of the data variance respectively. Canonical Correspondence Analysis (CCA) revealed a strong correspondence between bacterial distribution and environmental variables in the study area. Moreover, non-metric MDS (Multidimensional Scaling) analysis demarcated the northern and southern latitudes of the study area based on the bioavailable organic matter

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Towards the development of a new wavelet for ECG classification

In this paper an attempt has been made to determine the number of Premature Ventricular Contraction (PVC) cycles accurately from a given Electrocardiogram (ECG) using a wavelet constructed from multiple Gaussian functions. It is difficult to assess the ECGs of patients who are continuously monitored over a long period of time. Hence the proposed method of classification will be helpful to doctors to determine the severity of PVC in a patient. Principal Component Analysis (PCA) and a simple classifier have been used in addition to the specially developed wavelet transform. The proposed wavelet has been designed using multiple Gaussian functions which when summed up looks similar to that of a normal ECG. The number of Gaussians used depends on the number of peaks present in a normal ECG. The developed wavelet satisfied all the properties of a traditional continuous wavelet. The new wavelet was optimized using genetic algorithm (GA). ECG records from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database have been used for validation. Out of the 8694 ECG cycles used for evaluation, the classification algorithm responded with an accuracy of 97.77%. In order to compare the performance of the new wavelet, classification was also performed using the standard wavelets like morlet, meyer, bior3.9, db5, db3, sym3 and haar. The new wavelet outperforms the rest

Toxic Metal Distribution in the Core Sediment of Cochin Estuarine System (CES)

This article present the result from a study of two sediment cores collected from the environmentally distinct zones of CES. Accumulation status of five toxic metals: Cadmium (Cd), Chromium (Cr), Cobalt (Co), Copper (Cu) and Lead (Pb) were analyzed. Besides texture and CHNS were determined to understand the composition of the sediment. Enrichment Factor (EF) and Anthropogenic Factor (AF) were used to differentiate the typical metal sources. Metal enrichment in the cores revealed heavy load at the northern (NS1 ) region compared with the southern zone (SS1). Elevation of metal content in core NS1 showed the industrial input. Statistical analyses were employed to understand the origin of metals in the sediment samples. Principal Component Analysis (PCA) distinguishes the two zones with different metal accumulation capacity: highest at NS1 and lowest at SS1. Correlation analysis revealed positive significant relation only in core NS1, adhering to the exposition of the intensified industrial pollution