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.

Short communication: Estimation of the financial benefit of using Jersey milk at different inclusion rates for Cheddar cheese production using partial budgeting.

Partial budgeting was used to estimate the net benefit of blending Jersey milk in Holstein-Friesian milk for Cheddar cheese production. Jersey milk increases Cheddar cheese yield. However, the cost of Jersey milk is also higher; thus, determining the balance of profitability is necessary, including consideration of seasonal effects. Input variables were based on a pilot plant experiment run from 2012 to 2013 and industry milk and cheese prices during this period. When Jersey milk was used at an increasing rate with Holstein-Friesian milk (25, 50, 75, and 100% Jersey milk), it resulted in an increase of average net profit of 3.41, 6.44, 8.57, and 11.18 pence per kilogram of milk, respectively, and this additional profit was constant throughout the year. Sensitivity analysis showed that the most influential input on additional profit was cheese yield, whereas cheese price and milk price had a small effect. The minimum increase in yield, which was necessary for the use of Jersey milk to be profitable, was 2.63, 7.28, 9.95, and 12.37% at 25, 50, 75, and 100% Jersey milk, respectively. Including Jersey milk did not affect the quantity of whey butter and powder produced. Although further research is needed to ascertain the amount of additional profit that would be found on a commercial scale, the results indicate that using Jersey milk for Cheddar cheese making would lead to an improvement in profit for the cheese makers, especially at higher inclusion rates.

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.

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.

Application of image texture analysis for online determination of curd moisture and whey solids in a laboratory-scale stirred cheese vat.

A noninvasive technology, which could be employed online to monitor syneresis, would facilitate the production of higher quality and more consistent cheese products. Computer vision techniques such as image texture analysis have been successfully established as rapid, consistent, and nondestructive tools for determining the quality of food products. In this study, the potential of image texture analysis to monitor syneresis of cheese curd in a stirred vat was studied. A fully randomized 2-factor (milk pH and stirring speed), 2-level factorial design was carried out in triplicate. During syneresis, images of the surface of the stirred curd-whey mixture were captured using a computer vision system. The images were subjected to 5 image texture analysis methods by which 109 image texture features were extracted. Significant correlations were observed between a number of image texture features and curd moisture and whey solids. Multiscale analysis techniques of fractal dimension and wavelet transform were demonstrated to be the most useful for predicting syneresis indices. Fractal dimension features predicted curd moisture and whey solids during syneresis with standard errors of prediction of 1.03% (w/w) and 0.58 g/kg, respectively. It was concluded that syneresis indices were most closely related to the image texture features of multiscale representation. The results of this study indicate that image texture analysis has potential for monitoring syneresis.

Application of mid-infrared spectroscopy to the prediction of maturity and sensory texture attributes of cheddar cheese.

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.

Effect of cutting time, temperature, and calcium on curd moisture, whey fat losses, and curd yield by response surface methodology.

Response surface methodology was used to study the effect of temperature, cutting time, and calcium chloride addition level on curd moisture content, whey fat losses, and curd yield. Coagulation and syneresis were continuously monitored using 2 optical sensors detecting light backscatter. The effect of the factors on the sensors' response was also examined. Retention of fat during cheese making was found to be a function of cutting time and temperature, whereas curd yield was found to be a function of those 2 factors and the level of calcium chloride addition. The main effect of temperature on curd moisture was to increase the rate at which whey was expelled. Temperature and calcium chloride addition level were also found to affect the light backscatter profile during coagulation whereas the light backscatter profile during syneresis was a function of temperature and cutting time. The results of this study suggest that there is an optimum firmness at which the gel should be cut to achieve maximum retention of fat and an optimum curd moisture content to maximize product yield and quality. It was determined that to maximize curd yield and quality, it is necessary to maximize firmness while avoiding rapid coarsening of the gel network and microsyneresis. These results could contribute to the optimization of the cheese-making process.

Computer vision and color measurement techniques for inline monitoring of cheese curd syneresis.

Optical characteristics of stirred curd were simultaneously monitored during syneresis in a 10-L cheese vat using computer vision and colorimetric measurements. Curd syneresis kinetic conditions were varied using 2 levels of milk pH (6.0 and 6.5) and 2 agitation speeds (12.1 and 27.2 rpm). Measured optical parameters were compared with gravimetric measurements of syneresis, taken simultaneously. The results showed that computer vision and colorimeter measurements have potential for monitoring syneresis. The 2 different phases, curd and whey, were distinguished by means of color differences. As syneresis progressed, the backscattered light became increasingly yellow in hue for circa 20 min for the higher stirring speed and circa 30 min for the lower stirring speed. Syneresis-related gravimetric measurements of importance to cheese making (e.g., curd moisture content, total solids in whey, and yield of whey) correlated significantly with computer vision and colorimetric measurements.

Evaluating mid-infrared spectroscopy as a new technique for predicting sensory texture attributes of processed cheese.

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 degrees C, mid-infrared spectra (640 to 4,000 cm(-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 1,767 cm(-1)). The remaining attributes were most successfully modeled using a combined range (930 to 1,767 cm(-1) and 2,839 to 4,000 cm(-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.