Determination of fat, protein, moisture, and salt content of Cheddar cheese using mid-infrared transmittance spectroscopy.

The objective of our work was to develop and evaluate the performance of a rapid method for measuring fat, protein, moisture, and salt content of Cheddar cheese using a combination mid-infrared (MIR) transmittance analysis and an in-line conductivity sensor in an MIR milk analyzer. Cheddar cheese was blended with a dissolving solution containing pentasodium triphosphate and disodium metasilicate to achieve a uniform, particle-free dispersion of cheese, which had a fat and protein content similar to milk and could be analyzed using a MIR transmittance milk analyzer. Annatto-colored Cheddar cheese samples (34) from one cheese factory were analyzed using reference chemistry methods for fat (Mojonnier ether extraction), crude protein (Kjeldahl), moisture (oven-drying total solids), and salt (Volhard silver nitrate titration). The same 34 cheese samples were also dissolved using the cheese dissolver solution, and then run through the MIR and used for calibration. The reference testing for fat and crude protein was done on the cheese after dispersion in the dissolver solution. Validation was done using a total of 36 annatto-colored Cheddar cheese samples from 4 cheese factories. The 36 validation cheese samples were also analyzed using near-infrared spectroscopy for fat, moisture, and the coulometric method for salt in each factory where they were produced. The validation cheeses were also tested using the same chemical reference methods that were used for analysis of the calibration samples. Standard error of prediction (SEP) values for moisture and fat on the near-infrared spectroscopy were 0.30 and 0.45, respectively, whereas the MIR produced SEP values of 0.28 and 0.23 for moisture (mean 36.82%) and fat (mean 34.0%), respectively. The MIR also out-performed the coulometric method for salt determination with SEP values of 0.036 and 0.139 at a mean level of salt of 1.8%, respectively. The MIR had an SEP value of 0.19 for estimation at a mean level of 24.0% crude protein, which suggests that MIR could be an easy and effective way for cheese producers to measure protein to determine protein recovery in cheese making.

Effect of uncertainty in composition and weight measures in control of cheese yield and fat loss in large cheese factories.

Our objective was to develop a computer-based cheese yield, fat recovery, and composition control performance measurement system to provide quantitative performance records for a Cheddar and mozzarella cheese factory. The system can be used to track trends in performance of starter cultures and vats, as well as systematically calculate theoretical yield. Yield equations were built into the spreadsheet to evaluate cheese yield performance and fat losses in a cheese factory. Based on observations in commercial cheese factories, sensitivity analysis was done to demonstrate the sensitivity of cheese factory performance to analytical uncertainty of data used in the evaluation. Analytical uncertainty in the accuracy of milk weight and milk and cheese composition were identified as important factors that influence the ability to manage consistency of cheese quality and profitability. It was demonstrated that an uncertainty of ±0.1% milk fat or milk protein in the vat causes a range of theoretical Cheddar cheese yield from 10.05 to 10.37% and an uncertainty of yield efficiency of ±1.5%. This equates to ±1,451 kg (3,199 lb) of cheese per day in a factory processing 907,185 kg (2 million pounds) of milk per day. The same is true for uncertainty in cheese composition, where the effect of being 0.5% low on moisture or fat is about 484 kg (1,067 lb) of missed revenue opportunity from cheese for the day. Missing the moisture target causes other targets such as fat on a dry basis and salt in moisture to be missed. Similar impacts were demonstrated for mozzarella cheese. In analytical performance evaluations of commercial cheese quality assurance laboratories, we found that analytical uncertainty was typically a bias that was as large as 0.5% on fat and moisture. The effect of having a high bias of 0.5% moisture or fat will produce a missed opportunity of 484 kg of cheese per day for each component. More accurate rapid methods for determination of moisture, fat, and salt contents of cheese in large cheese factories will improve the accuracy of yield performance evaluation and control of consistency of cheese composition and quality.