Modeling of pH and acidity for industrial cheese production

Comparative study of ANN and conventional methods in forecasting first lactation milk yield in Murrah buffalo

Present investigation was undertaken to predict first lactation 305-day milk yield (FL305DMY) using monthly test day milk records. Under this study, multiple linear regression (MLR) and artificial neural network (ANN) approach were used. Effectiveness of both methods was also compared for prediction of FL305DMY in Murrah buffalo. The data on 3336 monthly test day milk yields records of first lactation pertaining to 556 Murrah buffaloes maintained at National Dairy Research Institute, Karnal; Central Institute for research on buffalo; Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana and Choudhary Charan Singh Haryana Agricultural University (CCSHAU), Hisar were used in this study. In MLR study, it was observed that model 14 having four independent variable, i.e. FSP, TD2, TD4 and TD6 fulfilled most criteria such as highest R2, lowest MSE, lowest RMSE, lowest CP, lowest MAE, lowest MAPE, and lowest U value. In the present investigation, the accuracy of prediction obtained from ANN was almost similar to MLR for prediction of FL305DMY using monthly test day milk records in Murrah buffalo. The best ANN algorithm achieved 76.8% accuracy of prediction for optimum model, whereas the MLR explained 76.9% of accuracy of prediction of FL305DMY in Murrah buffalo. MLR method is simple as compared to ANN, hence MLR method could be preferred.

A Model Describing Debaryomyces hansenii Growth and Substrate Consumption During a Smear Soft Cheese Deacidification and Ripening

A mechanistic model for Debaryomyces hansenii growth and substrate consumption, lactose conversion into lactate by lactic acid bacteria, as well as lactose and lactate transfer from the core toward the rind was established. The model described the first step (14 d) of the ripening of a smear soft cheese and included the effects of temperature and relative humidity of the ripening chamber on the kinetic parameters. Experimental data were collected from experiments carried out in an aseptic pilot scale ripening chamber under 9 different combinations of temperature (8, 12, and 16 degrees C) and relative humidity (85, 93, and 99%) according to a complete experimental design. The model considered the cheese as a system with 2 compartments (rind and core) and included 5 state evolution equations and 16 parameters. The model succeeded in predicting D. hansenii growth and lactose and lactate concentrations during the first step of ripening (curd deacidification) in core and rind. The nonlinear data-fitting method allowed the determination of tight confidence intervals for the model parameters. The residual standard error (RSE) between model predictions and experimental data was close to the experimental standard deviation between repeated experiments.

Statistical data validation methods for large cheese plant database

Production data of the cheesemaking process are used to monitor milk fat and protein recoveries in cheese, cheese yield, and composition and eventually to predict these parameters. Due to the large impact of these factors on cheese quality and plant profitability, it is very important to use reliable data for analysis, modeling, and control of the process. This paper tested six methods for detecting erroneous data in industrial cheesemaking databases. The data analyzed came from 4 yr of stirred-curd Cheddar cheese production in an industrial cheesemaking facility, comprising over 10,000 vats. Single vat outliers were detected using a simple statistical criterion of mean +/- 3.6 SD on single variable distributions, Fourier series modeling of seasonal variables (fat, protein, lactose, and total solids in milk, and protein in whey), and the multivariate Mahalanobis outlier analysis. Detection of outlier productions (corresponding to several vats) was done by applying the mean +/- 3.6 SD criterion to variables obtained through calculating the fat mass balance, fat retention coefficient, and yield efficiency. Data treatment enabled the detection of outlier data, but also pinpointed variables with a low reliability (manually registered times). Single variable and multivariable methods proved complementary, and the use of both types of methods is recommended when validating an existing database.

ADSA Foundation Scholar Award. Formation and physical properties of milk protein gels

Gelation of milk proteins is the crucial first step in both cheese and yogurt manufacture. Several types of milk gels are discussed, with an emphasis on recent developments in our understanding of how these gels are formed and some of their key physical properties. Areas discussed include the latest dual-binding model for casein micelles; some recent developments in rennet-induced gelation; review of the methods that have been used to monitor milk coagulation; and a discussion of some of the possible causes for the wheying-off defect in yogurts. Casein micelles are the primary building blocks of casein-based gels; however, controversy about its structure continues. The latest model proposed for the formation of casein micelles is the dual-binding model proposed by Horne, 1998, which suggests that casein micelles are formed as a result of two binding mechanisms, namely hydrophobic attraction and colloidal calcium phosphate (CCP) bridging. Most previous models for the casein micelle have treated milk gelation from the viewpoint of simple particle destabilization and aggregation, but they have not been able to explain several unusual rheological properties of milk gels. Although there have been many techniques used to monitor the milk gelation process over the past few decades, only a few appear attractive as possible in-vat coagulation sensors. Another important aspect of milk gels is the defect in yogurts called wheying-off, which is the appearance of whey on the gel surface. The factors responsible for its occurrence are still unclear, but they have been investigated in model acid gel systems.