Prediction of process cheese instrumental texture and melting characteristics using dielectric spectroscopy and chemometrics

Abstract

This study evaluated the potentiality of dielectric spectroscopy as a tool to predict the functional properties of process cheese. Dielectric properties of process cheese were collected over the frequency range 0.2 to 3.2GHz at 25°C. Dielectric spectra of process cheese were collected using a high-temperature, open-ended dielectric probe connected to a vector network analyzer. The present study was conducted using 2 sets of commercial process cheese formulations and a set of specially formulated process cheese. For the all the process cheese samples analyzed, a decrease in dielectric constant and dielectric loss factor was observed as the incident frequency increased. Partial least square regression (PLSR) and multilayer perceptron neural network models were developed using the dielectric spectra of process cheese to predict the hardness (gf), melting point (°C), and modified Schreiber melt diameter (mm) of process cheese. The prediction models were validated using the full cross-validation method. The ratio of prediction error to deviation was greater than 2 for melt diameter and hardness, indicating a good practical utility of the PLSR prediction models. The predictability of multilayer perceptron neural network was less than the PLSR models and could be due to the small number of training samples in the data sets. Dielectric spectroscopy coupled with PLSR could be a useful tool for the nondestructive measurement of functional properties of process cheese.

Application of front-face fluorescence spectroscopy as a tool for monitoring changes in milk protein concentrate powders during storage

“…The prediction accuracy of the model was assessed using the residual prediction deviation (RPD) value, defined as the relationship between the standard deviation of the solubility index and RDI measurements and that of prediction in the FFFS model (Williams and Sobering, 1996). In the present study, RPD values <1.5 indicated a very poor model; RPD between 2 and 2.5 indicated a fair model or predictions that may be used for approximate quantitative predictions; and RPD values between 2.5 and 3.0 and >3.0 indicated good and very good predictions, respectively (Amamcharla and Metzger, 2015).…”

“…This indicates that the developed model had good predictability and practical utility. The RPD value desired is greater than 2 for a good calibration, and a value less than 1.5 indicates incorrect predictions and an unstable model (Karoui et al, 2006;Amamcharla and Metzger, 2015). The higher correlation and more robust model for the FFFS spectral data and solubility index values support our theory that FFFS data may be used to measure the solubility index of MPC powders.…”

The effect of native and polymerised whey protein isolate addition on surface and microstructural properties of processed cheeses and their meltability determined by Turbiscan

“…f), which seems to be indifferent to temperature changes. A transition from solid‐like to liquid‐like behaviour is used to determine melting temperature of cheese (Amamcharla & Metzger, ). Fagan et al () claim that cheese meltability is difficult to determine as it is dependent on thermal and rheological properties of the cheese.…”

Technology of healthy processed cheese products without melting salts with the use of freezing and non-fermentative catalysis

“…This is due to their high taste properties, nutritional value and simple production technology. For the human organism they are a valuable source of important functional nutrients, fully fledged proteins, essential amino acids, lipids, mineral substances, vitamins and others [3,5,6,9]. However, they are charac-terized by the low content of biologically active substances and reduced terms of shelf life.…”