Colette C. Fagan

Effect of blending Jersey and Holstein-Friesian milk on Cheddar cheese processing, composition, and quality

The effect of Jersey milk use solely or at different inclusion rates in Holstein-Friesian milk on Cheddar cheese production was investigated. Cheese was produced every month over a year using nonstandardized milk consisting of 0, 25, 50, 75, and 100% Jersey milk in Holstein-Friesian milk in a 100-L vat. Actual, theoretical, and moisture-adjusted yield increased linearly with percentage of Jersey milk. This was also associated with increased fat and protein recoveries and lower yield of whey. The composition of whey was also affected by the percentage of Jersey milk, with lower whey protein and higher whey lactose and solids. Cutting time was lower when Jersey milk was used, but the cutting to milling time was higher because of slower acidity development. Hence, overall cheesemaking time was not affected by the use of Jersey milk. Using Jersey milk increased cheese fat content in autumn, winter, and spring and decreased cheese moisture in spring and summer. Cheese protein, salt, and pH levels were not affected. Cheese was analyzed for texture and color, and it was professionally graded at 3 and 8mo. The effect of Jersey on cheese sensory quality was an increase in cheese yellowness during summer and a higher total grading score at 3mo in winter; no other difference in cheese quality was found. The study indicates that using Jersey milk is a valid method of improving Cheddar cheese yield.

Prediction of coagulation properties, titratable acidity, and pH of bovine milk using mid-infrared spectroscopy

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 moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy

The potential of near infrared spectroscopy in conjunction with partial least squares regression to predict Miscanthus xgiganteus and short rotation coppice willow quality indices was examined. Moisture, calorific value, ash and carbon content were predicted with a root mean square error of cross validation of 0.90% (R(2) = 0.99), 0.13 MJ/kg (R(2) = 0.99), 0.42% (R(2) = 0.58), and 0.57% (R(2) = 0.88), respectively. The moisture and calorific value prediction models had excellent accuracy while the carbon and ash models were fair and poor, respectively. The results indicate that near infrared spectroscopy has the potential to predict quality indices of dedicated energy crops, however the models must be further validated on a wider range of samples prior to implementation. The utilization of such models would assist in the optimal use of the feedstock based on its biomass properties.

Prediction of Inorganic Salt and Moisture Content of Process Cheese Using Dielectric Spectroscopy

The development of on-line sensors for compositional analysis during cheese manufacture is desirable for improved quality control. Dielectric properties of a food product are principally determined by its moisture and salt content. This indicates that dielectric spectroscopy may offer a rapid, on-line and non-destructive method for the determination of moisture and salt content of process cheese. However limited information is available in the literature on the dielectric properties of process cheese. Therefore the aims of this study are to investigate the dielectric properties of process cheese samples over a range of compositional parameters and to assess the potential of dielectric spectroscopy to improve process control during process cheese manufacture. Dielectric spectra of process cheese samples were measured using a coaxial line probe between 300 MHz and 3 GHz. A clear tend was observed between higher moisture content and increases in the dielectric constant. Inorganic salt content was found to have a major influence on the loss factor. The dielectric data obtained was used to develop chemometric models for the prediction of moisture and inorganic salt content of two experimental sets of process cheese samples (exp A and exp B). The root mean square error of prediction (RMSEP) for the models developed to predict moisture content were 0.524% (w/w) (exp A), and 0.423% (w/w) (exp B), while the RMSEP of the inorganic salt models were 0.220% (w/w) (exp A), and 0.263% (w/w) (exp B). It was concluded that dielectric spectroscopy has potential application for compositional analysis in process cheese manufacture.

Visible-near infrared spectral sensing coupled with chemometric analysis as a method for on-line prediction of milled biomass composition pre-pelletising.

Chemical composition of biomass is critical to conversion efficiency during pelletisation. Visible-near infrared (vis–NIR) spectral sensing is a rapid and non-destructive sensing technology. The potential of on-line vis–NIR spectral sensing, in conjunction with chemometrics, to predict moisture, carbon and ash contents of milled Miscanthus and two short rotation coppice willow varieties was assessed. Spectroscopic information within the vis–NIR waveband of 400–1000 nm was analysed. Principal component analysis was successfully used to distinguish between the three varieties of biomass. Partial least squares regression validation models for moisture prediction over a range of 1.9–37.0% gave a coefficient of determination (r2) of 0.95 with a root mean square error of prediction of 2.5%. Carbon and ash cross-validation models achieved r2 = 0.85 and 0.50, respectively. These results were for a multiple biomass variety sample set. Results demonstrate on-line vis–NIR spectral sensing combined with chemometrics has the potential to be employed in an integrated pelletising management system.

Correlation Between Process Cheese Meltability Determined by Sensory Analysis, Computer Vision Method and Olson and Price Test

The meltabilities of 14 process cheese samples were determined at 2 and 4 weeks after manufacture using sensory analysis, a computer vision method, and the Olson and Price test. Sensory analysis meltability correlated with both computer vision meltability (R2 = 0.71, P < 0.001) and Olson and Price meltability (R2 = 0.69, P < 0.001). There was a marked lack of correlation between the computer vision method and the Olson and Price test. This study showed that the Olson and Price test gave greater repeatability than the computer vision method. Results showed process cheese meltability decreased with increasing inorganic salt content and with lower moisture/fat ratios. There was very little evidence in this study to show that process cheese meltability changed between 2 and 4 weeks after manufacture.

Evaluation of milk compositional variables on coagulation properties using partial least squares.

The aim of this study was to investigate the effects of numerous milk compositional factors on milk coagulation properties using Partial Least Squares (PLS). Milk from herds of Jersey and Holstein-Friesian cattle was collected across the year and blended (n=55), to maximise variation in composition and coagulation. The milk was analysed for casein, protein, fat, titratable acidity, lactose, Ca2+, urea content, micelles size, fat globule size, somatic cell count and pH. Milk coagulation properties were defined as coagulation time, curd firmness and curd firmness rate measured by a controlled strain rheometer. The models derived from PLS had higher predictive power than previous models demonstrating the value of measuring more milk components. In addition to the well-established relationships with casein and protein levels, CMS and fat globule size were found to have as strong impact on all of the three models. The study also found a positive impact of fat on milk coagulation properties and a strong relationship between lactose and curd firmness, and urea and curd firmness rate, all of which warrant further investigation due to current lack of knowledge of the underlying mechanism. These findings demonstrate the importance of using a wider range of milk compositional variables for the prediction of the milk coagulation properties, and hence as indicators of milk suitability for cheese making.

Benefits and Challenges of Adopting PAT for the Food Industry

This chapter introduces the process analytical technology (PAT) framework and outlines the evolution of PAT over the last few decades. The adoption of PAT in the food and pharmaceutical industries is compared. Specific drivers for adopting PAT in the food industry are reviewed. Recent advances in PAT tools are highlighted together with the main challenges to adopting PAT in the food industry.

Effect of feeding high-oleic sunflower oil to dairy cows on the milk fatty acid profile – RESET study

O. Markey, K. E. Kliem, D. J. Humphries, R. Morgan, D. Vasilopoulou, A. Grandison, C. Fagan, S. Todd, K. G. Jackson, J. A. Lovegrove and D. I. Givens Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research, Department of Food and Nutritional Sciences, Food Production and Quality Division and Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AP

Preliminary evaluation of endogenous milk fluorophores as tracer molecules for curd syneresis

A front-face fluorescence spectroscopy probe was installed in the wall of a laboratory-scale cheese vat. Excitation and emission filters were chosen for the selective detection of vitamin A, tryptophan, and riboflavin fluorescence. The evolution of the fluorescence of each fluorophore during milk coagulation and syneresis was monitored to determine if they had the potential to act as intrinsic tracers of syneresis and also coagulation. The fluorescence profiles for 2 of the fluorophores during coagulation could be divided into 3 sections relating to enzymatic hydrolysis of κ-casein, aggregation of casein micelles, and crosslinking. A parameter relating to coagulation kinetics was derived from the tryptophan and riboflavin profiles but this was not possible for the vitamin A response. The study also indicated that tryptophan and riboflavin may act as tracer molecules for syneresis, but this was not shown for vitamin A. The evolution of tryptophan and riboflavin fluorescence during syneresis followed a first-order reaction and had strong relationships with curd moisture and whey total solids content (r=0.86-0.96). Simple 1- and 2-parameter models were developed to predict curd moisture content, curd yield, and whey total solids using parameters derived from the sensor profiles (standard error of prediction=0.0005-0.394%; R(2)=0.963-0.999). The results of this study highlight the potential of tryptophan and riboflavin to act as intrinsic tracer molecules for noninvasive inline monitoring of milk coagulation and curd syneresis. Further work is required to validate these findings under a wider range of processing conditions.