The Experts below are selected from a list of 391479 Experts worldwide ranked by ideXlab platform

Colette C Fagan - One of the best experts on this subject based on the ideXlab platform.

  • Prediction of coagulation properties, titratable acidity, and pH of bovine milk using Mid-Infrared Spectroscopy.
    Journal of dairy science, 2009
    Co-Authors: Massimo De Marchi, R Dal Zotto, Colette C Fagan, Martino Cassandro, Colm P. O'donnell, Alessio Cecchinato, Mauro Penasa, Giovanni Bittante
    Abstract:

    This study investigated the potential application of Mid-Infrared Spectroscopy (MIR 4,000-900 cm(-1)) for the determination of milk coagulation properties (MCP), titratable acidity (TA), and pH in Brown Swiss milk samples (n = 1,064). Because MCP directly influence the efficiency of the cheese-making process, there is strong industrial interest in developing a rapid method for their assessment. Currently, the determination of MCP involves time-consuming laboratory-based measurements, and it is not feasible to carry out these measurements on the large numbers of milk samples associated with milk recording programs. Mid-Infrared Spectroscopy is an objective and nondestructive technique providing rapid real-time analysis of food compositional and quality parameters. Analysis of milk rennet coagulation time (RCT, min), curd firmness (a(30), mm), TA (SH degrees/50 mL; SH degrees = Soxhlet-Henkel degree), and pH was carried out, and MIR data were recorded over the spectral range of 4,000 to 900 cm(-1). Models were developed by partial least squares regression using untreated and pretreated spectra. The MCP, TA, and pH prediction models were improved by using the combined spectral ranges of 1,600 to 900 cm(-1), 3,040 to 1,700 cm(-1), and 4,000 to 3,470 cm(-1). The root mean square errors of cross-validation for the developed models were 2.36 min (RCT, range 24.9 min), 6.86 mm (a(30), range 58 mm), 0.25 SH degrees/50 mL (TA, range 3.58 SH degrees/50 mL), and 0.07 (pH, range 1.15). The most successfully predicted attributes were TA, RCT, and pH. The model for the prediction of TA provided approximate prediction (R(2) = 0.66), whereas the predictive models developed for RCT and pH could discriminate between high and low values (R(2) = 0.59 to 0.62). It was concluded that, although the models require further development to improve their accuracy before their application in industry, MIR Spectroscopy has potential application for the assessment of RCT, TA, and pH during routine milk analysis in the dairy industry. The implementation of such models could be a means of improving MCP through phenotypic-based selection programs and to amend milk payment systems to incorporate MCP into their payment criteria.

  • Prediction of processed cheese instrumental texture and meltability by Mid-Infrared Spectroscopy coupled with chemometric tools
    Journal of Food Engineering, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Colm D. Everard, Gerry Downey, E.m. Sheehan, Conor M. Delahunty, Donal J. O'callaghan, Vincent Howard
    Abstract:

    Abstract The objective of this study was to determine the potential of Mid-Infrared Spectroscopy coupled with multidimensional statistical analysis for the prediction of processed cheese instrumental texture and meltability attributes. Processed cheeses ( n  = 32) of varying composition were manufactured in a pilot plant. Following two and four weeks storage at 4 °C samples were analysed using texture profile analysis, two meltability tests (computer vision, Olson and Price) and Mid-Infrared Spectroscopy (4000–640 cm −1 ). Partial least squares regression was used to develop predictive models for all measured attributes. Five attributes were successfully modelled with varying degrees of accuracy. The computer vision meltability model allowed for discrimination between high and low melt values ( R 2  = 0.64). The hardness and springiness models gave approximate quantitative results ( R 2  = 0.77) and the cohesiveness ( R 2  = 0.81) and Olson and Price meltability ( R 2  = 0.88) models gave good prediction results.

  • Evaluating Mid-Infrared Spectroscopy as a new technique for predicting sensory texture attributes of processed cheese.
    Journal of dairy science, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Colm D. Everard, Gerry Downey, E.m. Sheehan, Conor M. Delahunty, Donal J. O'callaghan
    Abstract:

    Abstract The objective of this study was to investigate the potential application of Mid-Infrared Spectroscopy for determination of selected sensory attributes in a range of experimentally manufactured processed cheese samples. This study also evaluates Mid-Infrared Spectroscopy against other recently proposed techniques for predicting sensory texture attributes. Processed cheeses (n=32) of varying compositions were manufactured on a pilot scale. After 2 and 4 wk of storage at 4°C, Mid-Infrared spectra (640 to 4,000cm −1 ) were recorded and samples were scored on a scale of 0 to 100 for 9 attributes using descriptive sensory analysis. Models were developed by partial least squares regression using raw and pretreated spectra. The mouth-coating and mass-forming models were improved by using a reduced spectral range (930 to 1,767cm −1 ). The remaining attributes were most successfully modeled using a combined range (930 to 1,767cm −1 and 2,839 to 4,000cm −1 ). The root mean square errors of cross-validation for the models were 7.4 (firmness; range 65.3), 4.6 (rubbery; range 41.7), 7.1 (creamy; range 60.9), 5.1 (chewy; range 43.3), 5.2 (mouth-coating; range 37.4), 5.3 (fragmentable; range 51.0), 7.4 (melting; range 69.3), and 3.1 (mass-forming; range 23.6). These models had a good practical utility. Model accuracy ranged from approximate quantitative predictions to excellent predictions (range error ratio=9.6). In general, the models compared favorably with previously reported instrumental texture models and near-infrared models, although the creamy, chewy, and melting models were slightly weaker than the previously reported near-infrared models. We concluded that Mid-Infrared Spectroscopy could be successfully used for the nondestructive and objective assessment of processed cheese sensory quality.

  • Application of Mid-Infrared Spectroscopy to the Prediction of Maturity and Sensory Texture Attributes of Cheddar Cheese
    Journal of food science, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Gerard Downey, Colm D. Everard, E.m. Sheehan, Donal J. O'callaghan, C. M. Delahunty, T. P. Guinee, V. Howard
    Abstract:

    The objective of this study was to determine the potential of Mid-Infrared Spectroscopy in conjunction with partial least squares (PLS) regression to predict various quality parameters in cheddar cheese. Cheddar cheeses (n = 24) were manufactured and stored at 8 degrees C for 12 mo. Mid-Infrared spectra (640 to 4000/cm) were recorded after 4, 6, 9, and 12 mo storage. At 4, 6, and 9 mo, the water-soluble nitrogen (WSN) content of the samples was determined and the samples were also evaluated for 11 sensory texture attributes using descriptive sensory analysis. The Mid-Infrared spectra were subjected to a number of pretreatments, and predictive models were developed for all parameters. Age was predicted using scatter-corrected, 1st derivative spectra with a root mean square error of cross-validation (RMSECV) of 1 mo, while WSN was predicted using 1st derivative spectra (RMSECV = 2.6%). The sensory texture attributes most successfully predicted were rubbery, crumbly, chewy, and massforming. These attributes were modeled using 2nd derivative spectra and had, corresponding RMSECV values in the range of 2.5 to 4.2 on a scale of 0 to 100. It was concluded that Mid-Infrared Spectroscopy has the potential to predict age, WSN, and several sensory texture attributes of cheddar cheese..

Donal J. O'callaghan - One of the best experts on this subject based on the ideXlab platform.

  • Prediction of processed cheese instrumental texture and meltability by Mid-Infrared Spectroscopy coupled with chemometric tools
    Journal of Food Engineering, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Colm D. Everard, Gerry Downey, E.m. Sheehan, Conor M. Delahunty, Donal J. O'callaghan, Vincent Howard
    Abstract:

    Abstract The objective of this study was to determine the potential of Mid-Infrared Spectroscopy coupled with multidimensional statistical analysis for the prediction of processed cheese instrumental texture and meltability attributes. Processed cheeses ( n  = 32) of varying composition were manufactured in a pilot plant. Following two and four weeks storage at 4 °C samples were analysed using texture profile analysis, two meltability tests (computer vision, Olson and Price) and Mid-Infrared Spectroscopy (4000–640 cm −1 ). Partial least squares regression was used to develop predictive models for all measured attributes. Five attributes were successfully modelled with varying degrees of accuracy. The computer vision meltability model allowed for discrimination between high and low melt values ( R 2  = 0.64). The hardness and springiness models gave approximate quantitative results ( R 2  = 0.77) and the cohesiveness ( R 2  = 0.81) and Olson and Price meltability ( R 2  = 0.88) models gave good prediction results.

  • Evaluating Mid-Infrared Spectroscopy as a new technique for predicting sensory texture attributes of processed cheese.
    Journal of dairy science, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Colm D. Everard, Gerry Downey, E.m. Sheehan, Conor M. Delahunty, Donal J. O'callaghan
    Abstract:

    Abstract The objective of this study was to investigate the potential application of Mid-Infrared Spectroscopy for determination of selected sensory attributes in a range of experimentally manufactured processed cheese samples. This study also evaluates Mid-Infrared Spectroscopy against other recently proposed techniques for predicting sensory texture attributes. Processed cheeses (n=32) of varying compositions were manufactured on a pilot scale. After 2 and 4 wk of storage at 4°C, Mid-Infrared spectra (640 to 4,000cm −1 ) were recorded and samples were scored on a scale of 0 to 100 for 9 attributes using descriptive sensory analysis. Models were developed by partial least squares regression using raw and pretreated spectra. The mouth-coating and mass-forming models were improved by using a reduced spectral range (930 to 1,767cm −1 ). The remaining attributes were most successfully modeled using a combined range (930 to 1,767cm −1 and 2,839 to 4,000cm −1 ). The root mean square errors of cross-validation for the models were 7.4 (firmness; range 65.3), 4.6 (rubbery; range 41.7), 7.1 (creamy; range 60.9), 5.1 (chewy; range 43.3), 5.2 (mouth-coating; range 37.4), 5.3 (fragmentable; range 51.0), 7.4 (melting; range 69.3), and 3.1 (mass-forming; range 23.6). These models had a good practical utility. Model accuracy ranged from approximate quantitative predictions to excellent predictions (range error ratio=9.6). In general, the models compared favorably with previously reported instrumental texture models and near-infrared models, although the creamy, chewy, and melting models were slightly weaker than the previously reported near-infrared models. We concluded that Mid-Infrared Spectroscopy could be successfully used for the nondestructive and objective assessment of processed cheese sensory quality.

  • Application of Mid-Infrared Spectroscopy to the Prediction of Maturity and Sensory Texture Attributes of Cheddar Cheese
    Journal of food science, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Gerard Downey, Colm D. Everard, E.m. Sheehan, Donal J. O'callaghan, C. M. Delahunty, T. P. Guinee, V. Howard
    Abstract:

    The objective of this study was to determine the potential of Mid-Infrared Spectroscopy in conjunction with partial least squares (PLS) regression to predict various quality parameters in cheddar cheese. Cheddar cheeses (n = 24) were manufactured and stored at 8 degrees C for 12 mo. Mid-Infrared spectra (640 to 4000/cm) were recorded after 4, 6, 9, and 12 mo storage. At 4, 6, and 9 mo, the water-soluble nitrogen (WSN) content of the samples was determined and the samples were also evaluated for 11 sensory texture attributes using descriptive sensory analysis. The Mid-Infrared spectra were subjected to a number of pretreatments, and predictive models were developed for all parameters. Age was predicted using scatter-corrected, 1st derivative spectra with a root mean square error of cross-validation (RMSECV) of 1 mo, while WSN was predicted using 1st derivative spectra (RMSECV = 2.6%). The sensory texture attributes most successfully predicted were rubbery, crumbly, chewy, and massforming. These attributes were modeled using 2nd derivative spectra and had, corresponding RMSECV values in the range of 2.5 to 4.2 on a scale of 0 to 100. It was concluded that Mid-Infrared Spectroscopy has the potential to predict age, WSN, and several sensory texture attributes of cheddar cheese..

Colm P. O'donnell - One of the best experts on this subject based on the ideXlab platform.

  • Prediction of coagulation properties, titratable acidity, and pH of bovine milk using Mid-Infrared Spectroscopy.
    Journal of dairy science, 2009
    Co-Authors: Massimo De Marchi, R Dal Zotto, Colette C Fagan, Martino Cassandro, Colm P. O'donnell, Alessio Cecchinato, Mauro Penasa, Giovanni Bittante
    Abstract:

    This study investigated the potential application of Mid-Infrared Spectroscopy (MIR 4,000-900 cm(-1)) for the determination of milk coagulation properties (MCP), titratable acidity (TA), and pH in Brown Swiss milk samples (n = 1,064). Because MCP directly influence the efficiency of the cheese-making process, there is strong industrial interest in developing a rapid method for their assessment. Currently, the determination of MCP involves time-consuming laboratory-based measurements, and it is not feasible to carry out these measurements on the large numbers of milk samples associated with milk recording programs. Mid-Infrared Spectroscopy is an objective and nondestructive technique providing rapid real-time analysis of food compositional and quality parameters. Analysis of milk rennet coagulation time (RCT, min), curd firmness (a(30), mm), TA (SH degrees/50 mL; SH degrees = Soxhlet-Henkel degree), and pH was carried out, and MIR data were recorded over the spectral range of 4,000 to 900 cm(-1). Models were developed by partial least squares regression using untreated and pretreated spectra. The MCP, TA, and pH prediction models were improved by using the combined spectral ranges of 1,600 to 900 cm(-1), 3,040 to 1,700 cm(-1), and 4,000 to 3,470 cm(-1). The root mean square errors of cross-validation for the developed models were 2.36 min (RCT, range 24.9 min), 6.86 mm (a(30), range 58 mm), 0.25 SH degrees/50 mL (TA, range 3.58 SH degrees/50 mL), and 0.07 (pH, range 1.15). The most successfully predicted attributes were TA, RCT, and pH. The model for the prediction of TA provided approximate prediction (R(2) = 0.66), whereas the predictive models developed for RCT and pH could discriminate between high and low values (R(2) = 0.59 to 0.62). It was concluded that, although the models require further development to improve their accuracy before their application in industry, MIR Spectroscopy has potential application for the assessment of RCT, TA, and pH during routine milk analysis in the dairy industry. The implementation of such models could be a means of improving MCP through phenotypic-based selection programs and to amend milk payment systems to incorporate MCP into their payment criteria.

  • Prediction of processed cheese instrumental texture and meltability by Mid-Infrared Spectroscopy coupled with chemometric tools
    Journal of Food Engineering, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Colm D. Everard, Gerry Downey, E.m. Sheehan, Conor M. Delahunty, Donal J. O'callaghan, Vincent Howard
    Abstract:

    Abstract The objective of this study was to determine the potential of Mid-Infrared Spectroscopy coupled with multidimensional statistical analysis for the prediction of processed cheese instrumental texture and meltability attributes. Processed cheeses ( n  = 32) of varying composition were manufactured in a pilot plant. Following two and four weeks storage at 4 °C samples were analysed using texture profile analysis, two meltability tests (computer vision, Olson and Price) and Mid-Infrared Spectroscopy (4000–640 cm −1 ). Partial least squares regression was used to develop predictive models for all measured attributes. Five attributes were successfully modelled with varying degrees of accuracy. The computer vision meltability model allowed for discrimination between high and low melt values ( R 2  = 0.64). The hardness and springiness models gave approximate quantitative results ( R 2  = 0.77) and the cohesiveness ( R 2  = 0.81) and Olson and Price meltability ( R 2  = 0.88) models gave good prediction results.

  • Evaluating Mid-Infrared Spectroscopy as a new technique for predicting sensory texture attributes of processed cheese.
    Journal of dairy science, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Colm D. Everard, Gerry Downey, E.m. Sheehan, Conor M. Delahunty, Donal J. O'callaghan
    Abstract:

    Abstract The objective of this study was to investigate the potential application of Mid-Infrared Spectroscopy for determination of selected sensory attributes in a range of experimentally manufactured processed cheese samples. This study also evaluates Mid-Infrared Spectroscopy against other recently proposed techniques for predicting sensory texture attributes. Processed cheeses (n=32) of varying compositions were manufactured on a pilot scale. After 2 and 4 wk of storage at 4°C, Mid-Infrared spectra (640 to 4,000cm −1 ) were recorded and samples were scored on a scale of 0 to 100 for 9 attributes using descriptive sensory analysis. Models were developed by partial least squares regression using raw and pretreated spectra. The mouth-coating and mass-forming models were improved by using a reduced spectral range (930 to 1,767cm −1 ). The remaining attributes were most successfully modeled using a combined range (930 to 1,767cm −1 and 2,839 to 4,000cm −1 ). The root mean square errors of cross-validation for the models were 7.4 (firmness; range 65.3), 4.6 (rubbery; range 41.7), 7.1 (creamy; range 60.9), 5.1 (chewy; range 43.3), 5.2 (mouth-coating; range 37.4), 5.3 (fragmentable; range 51.0), 7.4 (melting; range 69.3), and 3.1 (mass-forming; range 23.6). These models had a good practical utility. Model accuracy ranged from approximate quantitative predictions to excellent predictions (range error ratio=9.6). In general, the models compared favorably with previously reported instrumental texture models and near-infrared models, although the creamy, chewy, and melting models were slightly weaker than the previously reported near-infrared models. We concluded that Mid-Infrared Spectroscopy could be successfully used for the nondestructive and objective assessment of processed cheese sensory quality.

  • Application of Mid-Infrared Spectroscopy to the Prediction of Maturity and Sensory Texture Attributes of Cheddar Cheese
    Journal of food science, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Gerard Downey, Colm D. Everard, E.m. Sheehan, Donal J. O'callaghan, C. M. Delahunty, T. P. Guinee, V. Howard
    Abstract:

    The objective of this study was to determine the potential of Mid-Infrared Spectroscopy in conjunction with partial least squares (PLS) regression to predict various quality parameters in cheddar cheese. Cheddar cheeses (n = 24) were manufactured and stored at 8 degrees C for 12 mo. Mid-Infrared spectra (640 to 4000/cm) were recorded after 4, 6, 9, and 12 mo storage. At 4, 6, and 9 mo, the water-soluble nitrogen (WSN) content of the samples was determined and the samples were also evaluated for 11 sensory texture attributes using descriptive sensory analysis. The Mid-Infrared spectra were subjected to a number of pretreatments, and predictive models were developed for all parameters. Age was predicted using scatter-corrected, 1st derivative spectra with a root mean square error of cross-validation (RMSECV) of 1 mo, while WSN was predicted using 1st derivative spectra (RMSECV = 2.6%). The sensory texture attributes most successfully predicted were rubbery, crumbly, chewy, and massforming. These attributes were modeled using 2nd derivative spectra and had, corresponding RMSECV values in the range of 2.5 to 4.2 on a scale of 0 to 100. It was concluded that Mid-Infrared Spectroscopy has the potential to predict age, WSN, and several sensory texture attributes of cheddar cheese..

Colm D. Everard - One of the best experts on this subject based on the ideXlab platform.

  • Prediction of processed cheese instrumental texture and meltability by Mid-Infrared Spectroscopy coupled with chemometric tools
    Journal of Food Engineering, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Colm D. Everard, Gerry Downey, E.m. Sheehan, Conor M. Delahunty, Donal J. O'callaghan, Vincent Howard
    Abstract:

    Abstract The objective of this study was to determine the potential of Mid-Infrared Spectroscopy coupled with multidimensional statistical analysis for the prediction of processed cheese instrumental texture and meltability attributes. Processed cheeses ( n  = 32) of varying composition were manufactured in a pilot plant. Following two and four weeks storage at 4 °C samples were analysed using texture profile analysis, two meltability tests (computer vision, Olson and Price) and Mid-Infrared Spectroscopy (4000–640 cm −1 ). Partial least squares regression was used to develop predictive models for all measured attributes. Five attributes were successfully modelled with varying degrees of accuracy. The computer vision meltability model allowed for discrimination between high and low melt values ( R 2  = 0.64). The hardness and springiness models gave approximate quantitative results ( R 2  = 0.77) and the cohesiveness ( R 2  = 0.81) and Olson and Price meltability ( R 2  = 0.88) models gave good prediction results.

  • Evaluating Mid-Infrared Spectroscopy as a new technique for predicting sensory texture attributes of processed cheese.
    Journal of dairy science, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Colm D. Everard, Gerry Downey, E.m. Sheehan, Conor M. Delahunty, Donal J. O'callaghan
    Abstract:

    Abstract The objective of this study was to investigate the potential application of Mid-Infrared Spectroscopy for determination of selected sensory attributes in a range of experimentally manufactured processed cheese samples. This study also evaluates Mid-Infrared Spectroscopy against other recently proposed techniques for predicting sensory texture attributes. Processed cheeses (n=32) of varying compositions were manufactured on a pilot scale. After 2 and 4 wk of storage at 4°C, Mid-Infrared spectra (640 to 4,000cm −1 ) were recorded and samples were scored on a scale of 0 to 100 for 9 attributes using descriptive sensory analysis. Models were developed by partial least squares regression using raw and pretreated spectra. The mouth-coating and mass-forming models were improved by using a reduced spectral range (930 to 1,767cm −1 ). The remaining attributes were most successfully modeled using a combined range (930 to 1,767cm −1 and 2,839 to 4,000cm −1 ). The root mean square errors of cross-validation for the models were 7.4 (firmness; range 65.3), 4.6 (rubbery; range 41.7), 7.1 (creamy; range 60.9), 5.1 (chewy; range 43.3), 5.2 (mouth-coating; range 37.4), 5.3 (fragmentable; range 51.0), 7.4 (melting; range 69.3), and 3.1 (mass-forming; range 23.6). These models had a good practical utility. Model accuracy ranged from approximate quantitative predictions to excellent predictions (range error ratio=9.6). In general, the models compared favorably with previously reported instrumental texture models and near-infrared models, although the creamy, chewy, and melting models were slightly weaker than the previously reported near-infrared models. We concluded that Mid-Infrared Spectroscopy could be successfully used for the nondestructive and objective assessment of processed cheese sensory quality.

  • Application of Mid-Infrared Spectroscopy to the Prediction of Maturity and Sensory Texture Attributes of Cheddar Cheese
    Journal of food science, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Gerard Downey, Colm D. Everard, E.m. Sheehan, Donal J. O'callaghan, C. M. Delahunty, T. P. Guinee, V. Howard
    Abstract:

    The objective of this study was to determine the potential of Mid-Infrared Spectroscopy in conjunction with partial least squares (PLS) regression to predict various quality parameters in cheddar cheese. Cheddar cheeses (n = 24) were manufactured and stored at 8 degrees C for 12 mo. Mid-Infrared spectra (640 to 4000/cm) were recorded after 4, 6, 9, and 12 mo storage. At 4, 6, and 9 mo, the water-soluble nitrogen (WSN) content of the samples was determined and the samples were also evaluated for 11 sensory texture attributes using descriptive sensory analysis. The Mid-Infrared spectra were subjected to a number of pretreatments, and predictive models were developed for all parameters. Age was predicted using scatter-corrected, 1st derivative spectra with a root mean square error of cross-validation (RMSECV) of 1 mo, while WSN was predicted using 1st derivative spectra (RMSECV = 2.6%). The sensory texture attributes most successfully predicted were rubbery, crumbly, chewy, and massforming. These attributes were modeled using 2nd derivative spectra and had, corresponding RMSECV values in the range of 2.5 to 4.2 on a scale of 0 to 100. It was concluded that Mid-Infrared Spectroscopy has the potential to predict age, WSN, and several sensory texture attributes of cheddar cheese..

E.m. Sheehan - One of the best experts on this subject based on the ideXlab platform.

  • Prediction of processed cheese instrumental texture and meltability by Mid-Infrared Spectroscopy coupled with chemometric tools
    Journal of Food Engineering, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Colm D. Everard, Gerry Downey, E.m. Sheehan, Conor M. Delahunty, Donal J. O'callaghan, Vincent Howard
    Abstract:

    Abstract The objective of this study was to determine the potential of Mid-Infrared Spectroscopy coupled with multidimensional statistical analysis for the prediction of processed cheese instrumental texture and meltability attributes. Processed cheeses ( n  = 32) of varying composition were manufactured in a pilot plant. Following two and four weeks storage at 4 °C samples were analysed using texture profile analysis, two meltability tests (computer vision, Olson and Price) and Mid-Infrared Spectroscopy (4000–640 cm −1 ). Partial least squares regression was used to develop predictive models for all measured attributes. Five attributes were successfully modelled with varying degrees of accuracy. The computer vision meltability model allowed for discrimination between high and low melt values ( R 2  = 0.64). The hardness and springiness models gave approximate quantitative results ( R 2  = 0.77) and the cohesiveness ( R 2  = 0.81) and Olson and Price meltability ( R 2  = 0.88) models gave good prediction results.

  • Evaluating Mid-Infrared Spectroscopy as a new technique for predicting sensory texture attributes of processed cheese.
    Journal of dairy science, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Colm D. Everard, Gerry Downey, E.m. Sheehan, Conor M. Delahunty, Donal J. O'callaghan
    Abstract:

    Abstract The objective of this study was to investigate the potential application of Mid-Infrared Spectroscopy for determination of selected sensory attributes in a range of experimentally manufactured processed cheese samples. This study also evaluates Mid-Infrared Spectroscopy against other recently proposed techniques for predicting sensory texture attributes. Processed cheeses (n=32) of varying compositions were manufactured on a pilot scale. After 2 and 4 wk of storage at 4°C, Mid-Infrared spectra (640 to 4,000cm −1 ) were recorded and samples were scored on a scale of 0 to 100 for 9 attributes using descriptive sensory analysis. Models were developed by partial least squares regression using raw and pretreated spectra. The mouth-coating and mass-forming models were improved by using a reduced spectral range (930 to 1,767cm −1 ). The remaining attributes were most successfully modeled using a combined range (930 to 1,767cm −1 and 2,839 to 4,000cm −1 ). The root mean square errors of cross-validation for the models were 7.4 (firmness; range 65.3), 4.6 (rubbery; range 41.7), 7.1 (creamy; range 60.9), 5.1 (chewy; range 43.3), 5.2 (mouth-coating; range 37.4), 5.3 (fragmentable; range 51.0), 7.4 (melting; range 69.3), and 3.1 (mass-forming; range 23.6). These models had a good practical utility. Model accuracy ranged from approximate quantitative predictions to excellent predictions (range error ratio=9.6). In general, the models compared favorably with previously reported instrumental texture models and near-infrared models, although the creamy, chewy, and melting models were slightly weaker than the previously reported near-infrared models. We concluded that Mid-Infrared Spectroscopy could be successfully used for the nondestructive and objective assessment of processed cheese sensory quality.

  • Application of Mid-Infrared Spectroscopy to the Prediction of Maturity and Sensory Texture Attributes of Cheddar Cheese
    Journal of food science, 2007
    Co-Authors: Colette C Fagan, Colm P. O'donnell, Gerard Downey, Colm D. Everard, E.m. Sheehan, Donal J. O'callaghan, C. M. Delahunty, T. P. Guinee, V. Howard
    Abstract:

    The objective of this study was to determine the potential of Mid-Infrared Spectroscopy in conjunction with partial least squares (PLS) regression to predict various quality parameters in cheddar cheese. Cheddar cheeses (n = 24) were manufactured and stored at 8 degrees C for 12 mo. Mid-Infrared spectra (640 to 4000/cm) were recorded after 4, 6, 9, and 12 mo storage. At 4, 6, and 9 mo, the water-soluble nitrogen (WSN) content of the samples was determined and the samples were also evaluated for 11 sensory texture attributes using descriptive sensory analysis. The Mid-Infrared spectra were subjected to a number of pretreatments, and predictive models were developed for all parameters. Age was predicted using scatter-corrected, 1st derivative spectra with a root mean square error of cross-validation (RMSECV) of 1 mo, while WSN was predicted using 1st derivative spectra (RMSECV = 2.6%). The sensory texture attributes most successfully predicted were rubbery, crumbly, chewy, and massforming. These attributes were modeled using 2nd derivative spectra and had, corresponding RMSECV values in the range of 2.5 to 4.2 on a scale of 0 to 100. It was concluded that Mid-Infrared Spectroscopy has the potential to predict age, WSN, and several sensory texture attributes of cheddar cheese..