A COMPARISON OF THREE INSTRUMENTAL TESTS FOR PREDICTING SENSORY TEXTURE PROFILES OF CHEESE

Multivariate analysis techniques were used to seek correlations between texture sensory attributes assessed by a trained professional panel and instrumental measurements (compression, puncture and penetration) carried out on various types of cheeses. Twenty-nine cheeses were assessed by the panel and instruments. Correlation was sought using Partial Least Squares regression. Hardness (R=0.87), springiness (R=0.98) and cohesiveness of mass (R=0.89) were best predicted by instrumental data from a cone penetration test. The prediction of cohesiveness was acceptable using any of the three instrumental tests performed (0.76相似文献   

PREDICTION OF SENSORY TEXTURE OF FETA CHEESE MADE FROM ULTRAFILTERED MILK BY UNIAXIAL COMPRESSION AND SHEAR TESTING

The ability to predict sensory texture properties of Feta cheese made from ultrafiltered milk (UF-Feta) from uniaxial compression, small shear deformation measurements (frequency sweep, strain sweep, relaxation) and indices of proteolysis was studied. In principal component analysis (PCA) some of the instrumental variables were highly correlated, e.g. the moduli from uniaxial compression and shear measurements; and strain at fracture from uniaxial compression and indices of proteolysis. PCA of the six sensory attributes determined by a trained panel showed that mainly one type of information was present in the sensory results. Partial Least Squares regression (PLS) of all results revealed that stress at fracture from uniaxial compression was the individual instrumental parameter having the highest correlation with the sensory texture attributes. Of these, the three firmness attributes were best predicted by the instrumental parameters. As the shear measurements were not very useful for prediction of sensory texture properties by themselves, and as the increase in prediction precision by inclusion of these measurements was marginal, it is suggested that either stress at fracture alone, or together with three other parameters from uniaxial compression should be used to describe texture properties of UF-Feta cheese.     

Vane Rheometry for Textural Characterization of Cheddar Cheeses: Correlation with Other Instrumental and Sensory Measurements

The relationship between instrumental (vane method, texture profile analysis (TPA), uniaxial compression) and sensory texture measurements of Cheddar cheeses was investigated. A Haake VT 550 viscotester equipped with a four-bladed vane rotor was used for the vane test. Instrumental TPA was performed with a TA.XT2 Texture Analyser, and compression variables were calculated from TPA data. Vane parameters were significantly correlated with respective variables of compression and TPA (r=0.56-0.91), and sensory tests (r=0.54-0.88). Multivariate analysis indicated that seven sensory attributes of ten commercial Cheddar cheeses were satisfactorily predicted (calibration regression coefficient,R_cal >0.62) by variables of the vane, uniaxial compression and TPA tests. In particular, cheese firmness and cohesiveness evaluated by sensory panel were well described by vane stress and apparent strain. The results validate the vane method as an alternative to the existing cheese testing methods for rapid evaluation of cheese texture.     

Comparison of Sensory and Instrumental Texture Profile Techniques for the Evaluation of Beef and Beef-Soy Loaves

Relationships among sensory texture profile parameters, among values for textural parameters obtained through different methods of interpreting instrumental texture profile analysis (TPA) curves obtained with an Instron, and between sensory and instrumental TPA values were examined for beef and beef-soy loaves. The only singificant relationship among sensory parameters was between cohesiveness and springiness. Although a number of significant relationships existed between the various methods of interpreting instrumental parameters, a number of these were associated with secondary parameters having common primary parameters. A strong relationship was found between sensory springiness and adjusted downstroke cohesiveness. Sensory cohesiveness was related both to this instrumental parameter and to downstroke cohesiveness. Chewiness values calculated for these two methods for obtaining cohesiveness values showed strong negative associations with sensory fracturability scores.     

COMPARISON OF TWO INSTRUMENTAL METHODS WITH SENSORY TEXTURE OF PROTEIN GELS2

The textural attributes of 8 different heat-induced protein gel preparations evaluated by torsion failure testing and Instron texture profile analysis (TPA) were compared to sensory ratings by a trained texture profile panel. The gels presented a wide range of textural properties as determined by the instrumental and sensory parameters. Among the instrumental parameters, true shear strain at failure was the most frequent and significant predictor of sensory notes. Initial shear modulus and 50% compression force had the poorest correlations with sensory notes. Comparison of the two instrumental tests produced high correlations between shear stress at failure and TPA hardness; true shear strain at failure and TPA cohesiveness; and, initial shear modulus and 50% compression force. High correlations were also observed among various panel notes. Canonical correlation analyses showed that sets of linear combinations of parameters from each one of the 3 tests (torsion, TPA or sensory) were highly correlated to sets from either of the other two. Regression equations relating each of the instrumental tests to sensory notes were developed. Of the torsion failure parameters, the logarithm of true shear strain most commonly appeared in the equations. Of the TPA parameters, cohesiveness and its logarithm were the terms that were most frequent. High R² values were obtained for regression equations developed for predicting torsion failure parameters based on TPA parameters.     

Effect of Thermal Treatment on Selected Cereals and Millets Flour Doughs and Their Baking Quality

Selected cereals (rice and sorghum) and millets (finger millet and pearl millet) were steamed for 20 min at ambient pressure. The rheological properties of doughs, made from these steamed as well raw grain flours, were characterized in addition to examining their baking quality. The two-cycle compression test was employed and instrumental values were correlated with sensory attributes (color, aroma, taste, stickiness, chewiness, tearing strength, cohesiveness, and rollability) using principal component analysis (PCA). Rice doughs made from both raw as well as thermally treated flour imparted maximum hardness (96.6–99.3 N) and least cohesiveness (0.05–0.09) with highest stickiness values (105–110°) among all the dough samples at the same moisture content. Pearl millet and raw sorghum flour doughs possessed the least instrumental hardness, adhesiveness, and stickiness and were the easiest to flatten. The PCA biplot showed that sensory and instrumental cohesiveness formed a cluster on the left side on the x axis while shear force, and sensory attributes like tearing strength, chewiness, stickiness, and rollability formed another cluster on the other side of the axis. Raw rice and finger millet doughs were associated with the high extent of instrumental and sensory stickiness. Thermally treated pearl millet and sorghum doughs were the best followed by treated rice and finger millet samples to give the desirable dough characteristics, and were quite close to wheat chapathi in texture.     

PREDICTING SENSORY COHESIVENESS, HARDNESS AND SPRINGINESS OF SOLID FOODS FROM INSTRUMENTAL MEASUREMENTS

ABSTRACT

The sensory evaluation of cohesiveness, hardness and springiness of 15 solid food samples was performed by eight trained assessors. The rheologic response of the 15 samples was estimated by performing cyclic compression tests and stress–relaxation tests. From the force–deformation curves of the first two cycles of the compression test, texture profile analysis parameters related to cohesiveness, hardness and springiness were calculated. Young's modulus (E), strain (d_i) and stress (s_i) at peak as well as irrecoverable strain (r_i) and irrecoverable work (L_i) were monitored during the first five cycles. From the stress–relaxation response, Peleg's linearization model parameters, K₁ and K₂, were estimated by best‐fit regression. These parameters were used for predicting sensory attributes. Hardness and springiness were both accurately predicted by rheologic properties, while cohesiveness prediction was less representative.

PRACTICAL APPLICATIONS

This study contributes to enhance the knowledge in the research area of sensory instrumental correlation. Also, the research allows to better understanding that no single instrument is able to measure all texture attributes adequately. In fact, the results demonstrate that both stress–relaxation and cyclic compression tests need to be performed for the correct prediction of sensory responses.     

Modelling of sensory and instrumental texture parameters in processed cheese by near infrared reflectance spectroscopy

This study investigated the application of near infrared (NIR) reflectance spectroscopy to the measurement of texture (sensory and instrumental) in experimental processed cheese samples. Spectra (750 to 2498 nm) of cheeses were recorded after 2 and 4 weeks storage at 4 degrees C. Trained assessors evaluated 9 sensory properties, a texture profile analyser (TPA) was used to record 5 instrumental parameters and cheese 'meltability' was measured by computer vision. Predictive models for sensory and instrumental texture parameters were developed using partial least squares regression on raw or pre-treated spectral data. Sensory attributes and instrumental texture measurements were modelled with sufficient accuracy to recommend the use of NIR reflectance spectroscopy for routine quality assessment of processed cheese.     

INFLUENCE OF UNIAXIAL COMPRESSION RATE ON RHEOLOGICAL PARAMETERS AND SENSORY TEXTURE PREDICTION OF COOKED POTATOES

The effect of uniaxial compression rate (20–1000 mm/min) on the parameters: Stress (σ_ftrue), strain (ε_fHencky) and work to fracture (W_f), modulus of deformability (E_d), maximum slope before fracture (E_max) and work during 75% compression (W_total) was investigated for ten potato varieties. Multivariate data analysis was used to study the correlation between and within the sensory and nonsensory measurements by Principal Component Analysis (PCA) which showed σ_ftrue, E_max, W_f, and W_total to explain the same type of information in the data, and ε_fHencky versus E_d another type of information in the data. The deformation rate had a large effect on ε_fHencky. Nine sensory texture attributes covering the mechanical, geometrical and moistness attributes were evaluated. Relationships between uniaxial compression data at various deformation rates and the sensory texture attributes were studied by Partial Least Squares Regression (PLSR). A minor effect of deformation rate on the correlation with the sensory texture properties was obtained. Mechanical properties were predicted to a higher extent than the geometrical attributes and moistness. The prediction of the mechanical, geometrical and moistness attributes increased largely by using uniaxial compression supplemented by chemical measures such as dry matter and pectin methylesterase, but here no relevant effect of deformation rate was obtained.     

UNIAXIAL COMPRESSION OF UF-FETA CHEESE RELATED TO SENSORY TEXTURE ANALYSIS

Rheological characteristics of seven Feta cheeses with different textures and produced from ultrafiltered milk (UF-Feta cheeses) were evaluated by uniaxial compression and sensory texture analysis. The effect of uniaxial deformation rate (50–2500 mm/min) on four rheological parameters: Stress at fracture s?_f), Hencky strain at fracture (?_f), deformability modulus (E) and work to fracture (W_f) was examined. Three Principal Components (PC) described 76, 16 and 4% respectively, of the variation in the uniaxial compression data set (4 parameters at 12 deformation rates). Statistically α_f, E and W_f described the same type of information in the data set. Six sensory texture attributes of the UF-Feta cheeses were evaluated by a sensory texture panel: nonoral firmness, nonoral brittleness, nonoral spreadability, oral crumbliness, oral firmness and oral stickiness. One PC described 93% of the variation in the sensory texture data and grouped the sensory variables into two negatively correlated groups: nonoral firmness nonoral brittleness, oral firmness and oral crumbliness versus nonoral spreadability and oral stickiness. Correlations and Partial Least Squares regression (PLS) between instrumental and sensory texture variables showed that nonoral and oral firmness were the nonoral and oral sensory variables best predicted from instrumental measurements. α_f, E and W_f were all able to predict nonoral and oral firmness. Of the instrumental parameters, α_f generally gave the best correlation to nonoral firmness at all deformation rates. Above a deformation rate of 50 mm/min correlations between α_f and nonoral firmness were almost independent of deformation rate, and at any deformation rate correlations between α_f and oral firmness     

Prediction of sensory textural properties from rheological analysis for process cheeses varying in emulsifying salt,protein and moisture contents

Textural characteristics of process cheeses varying in emulsifying salt (disodium phosphate), protein and moisture contents were evaluated by rheological compression using texture profile analysis and by sensory evaluation. The primary objective of this study was to predict sensory textural parameters using instrumental rheological parameters. All sensory parameters correlated with one or more instrumental parameters, e.g. rheological firmness versus sensory firmness (R = 0.98, P < 0.001), rheological chewiness versus sensory rubbery (R = 0.92, P < 0.001) and rheological chewiness versus sensory chewy (R = 0.86, P < 0.001). Partial least squares calibration models were developed for each of nine sensory parameters using instrumental parameters. Principal component analysis of instrumental and sensory parameters illustrated relationships among parameters. It was shown that instrumental parameters could be used to supplement sensory evaluation of process cheese texture. Increasing emulsifying salt content increased firmness, springiness and chewiness and decreased adhesiveness, mouthcoating and mass formation. Increasing protein content resulted in increased fracture strain and stress and chewiness and decreased melting. Increasing moisture content increased cohesiveness and decreased firmness and chewiness. Copyright © 2007 Society of Chemical Industry     

INSTRUMENTAL AND SENSORY TEXTURAL PROPERTIES OF FURA

ABSTRACT

A uniaxial compression test and sensory textural analysis was conducted of fura samples made from millet flour. Significant differences (p<0.05) existed among the samples for hardness (the force to compress the sample between molar teeth), cohesiveness (extent to which sample falls apart during chewing) and gumminess (denseness and cohesion persisting during mastication). Correlations between sensory and instrumental tests revealed that a significant relationship exists between modulus of deformability and cohesiveness (r=?0.93, p<0.05); gradient and springiness (r=?0.90, p<0.05); deformation at failure and chewiness (r=0.98, p<0.05); energy per unit mass and gumminess (r=?0.95, p<0.05). A fura quality scale was established based on the peak force; soft and poor quality fura have a peak force of <19 kN; acceptable fura has a peak force of 19–24 kN; a firm and good quality fura, 24–25 kN; very hard and very poor quality fura have a peak force of >25 kN.     

Correlation between instrumental and sensory ratings by evaluation of some texture reference scales

The objective of this study was to investigate instrumental–sensory relationships of some texture scales using argentine foods as references. Textural characteristics of these foods were instrumentally investigated by the texture profile analysis technique. Principal component analysis (PCA) was used to describe the main attributes of the food samples. High Pearson’s correlation coefficients were found between hardness and fracturability (r = 0.94; P < 0.0001), hardness and gumminess (r = 0.71; P < 0.0001) and springiness and cohesiveness (r = 0.85; P < 0.0001). PCA identified two significant principal components, which accounted for 81.2% of the variance in the instrumental data. Additionally, a trained panel described the texture characteristics of the food samples according to the standard reference scales. The correlation curves showed nonlinear relationships (R² between 85.6% and 99.9%) which were used to predict sensory attributes of other food samples. Some texture attributes like hardness and fracturability were accurately predicted by mechanical properties, while others like cohesiveness and adhesiveness were less representative.     

Instrumental and Expert Assessment of Mahon Cheese Texture

ABSTRACT: To improve Mahon cheese texture assessment, the relationship between instrumental and sensory measurements was sought. For that purpose 30 pieces of Mahon cheese from different batches and 2 different manufacturers were examined. Textural characteristics at different curing times were evaluated by uniaxial compression, puncture, and sensory analysis. Significant linear correlations were found between instrumental and sensory measurements. A logarithmic model (Weber-Fechner) fitted data better than a linear one. Only 1 factor was extracted when considering all the instrumental and sensory variables, thus indicating that both sets of measurements are related to the same phenomenon. The best predictors for Mahon cheese sensory attributes were found to be cheese moisture, deformability modulus, and slope in puncture.