Dielectric properties of processed cheese

Dynamic studies of several processed cheeses were carried out for evaluation of their dielectric properties and their relaxation behavior. Impedance spectroscopy has been used to study the systems under discussion in low-frequency domain. A substantial number of impedance spectra will be given and discussed that relate the dielectric properties to food nutrients. Also influence of moisture on food quality might be evaluated.

Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis

Recently, hyperspectral-imaging (HSI), as a rapid and non-destructive technique, has generated much interest due to its unique potential to monitor food quality and safety. The specific aim of the study is to investigate the potential of the HSI (430-1010 nm) coupled with Linear Deep Neural Network (LDNN) to predict the TVB-N content of rainbow trout fillet during 12 days storage at 4 ± 2 °C. After the acquisition of hyperspectral images, the TVB-N content of fish fillets was obtained by a conventional method (micro-Kjeldahl distillation). To simplify the calibration models, nine optimal wavelengths were selected by the successive projections algorithm. A seven layers LDNN was designed to estimate the TVB-N content of samples. The LDNN model showed acceptable performance for prediction of TVB-N content of fish fillet (R²p = 0.853; RSMEP = 3.159 and RDP = 3.001). The performance of LDNN model was comparable with the results of previous works. Although, the results of the meta-analysis did not show any significant difference between various chemometric models. However, the least-squares support vector machine algorithm showed better prediction results as compared to the other models (RMSEP: 2.63 and R²_p = 0.897). Further studies are required to improve the prediction power of the deep learning model for prediction of rainbow-trout fish quality.

Prediction of various freshness indicators in fish fillets by one multispectral imaging system

In current study, a simple multispectral imaging (430–1010 nm) system along with linear and non-linear regressions were used to assess the various fish spoilage indicators during 12 days storage at 4 ± 2 °C. The indicators included Total-Volatile Basic Nitrogen (TVB-N) and Psychrotrophic Plate Count (PPC) and sensory score in fish fillets. immediately, after hyperspectral imaging, the reference values (TVB-N, PPC and sensory score) of samples were obtained by traditional method. To simplify the calibration models, nine optimal wavelengths were selected by genetic algorithm. The prediction performance of various chemometric models including partial least-squares regression (PLSR), multiple-linear regression (MLR), least-squares support vector machine (LS-SVM) and back-propagation artificial neural network (BP-ANN) were compared. All models showed acceptable performance for simultaneous predicting of PPC, TVB-N and sensory score (R²_P ≥ 0.853 and RPD ≥ 2.603). Non-linear models were considered better quantitative model to predict all of three freshness indicators in fish fillets. Among the three spoilage indices, the best predictive power was obtained for PPC value and the weakest one was acquired for TVB-N content prediction. The best model for prediction TVB-N (R²_p = 0.862; RMSEP = 3.542 and RPD = 2.678) and sensory score (R²_p = 0.912; RMSEP = 1.802 and RPD = 3.33) belonged to GA-LS-SVM and for prediction of PPC value was BP-ANN (R²_p = 0.921; RMSEP = 0.504 and RPD = 3.64). Therefore, developing multispectral imaging system based on LS-SVM model seems to be suitable for simultaneous prediction of all three indicators (R²_P > 0.862 and RPD > 2.678). Further studies needed to improve the accuracy and applicability of HSI system for predicting freshness of rainbow-trout fish.

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

This study investigated the feasibility of front-face fluorescence spectroscopy (FFFS) to predict the solubility index and relative dissolution index (RDI) of milk protein concentrate (MPC) powders during storage. Twenty MPC powders with varying protein contents from 4 different commercial manufacturers were used in this study. The MPC powders were stored at 2 temperatures (25 and 40°C) for 0, 1, 2, 4, 8, and 12 wk. The front-face fluorescence spectra of tryptophan and Maillard products were recorded and analyzed with chemometrics to predict solubility of MPC powders. The similarity maps showed clear discrimination of the MPC samples stored at 25 and 40°C. Partial least squares regression models were developed using the fluorescence spectra of tryptophan and Maillard products to predict the solubility index and RDI measurements of MPC powders, and the prediction models were validated using an independent test set. Coefficients of determination (R²) of 0.76, 0.84, and 0.68 were obtained between fluorescence spectra (tryptophan emission, Maillard emission, and Maillard excitation, respectively) and solubility index. The R² values for the RDI predictions were 0.58 and 0.60 for the data set of tryptophan emission and Maillard emission, respectively. The ratio of prediction error to standard deviation was >2 for Maillard emission fluorescence spectra and solubility index measurements, indicating good practical utility of the partial least squares regression prediction models. The results indicated that the solubility and dissolution behavior of MPC powders were related to their protein content and storage conditions that could be measured using FFFS. Hence, FFFS can be used as a rapid nondestructive analytical technique to predict the solubility and dissolution characteristics of MPC powders.

DEVELOPMENT OF THE NEW METHOD OF THE MELTED CHEESE PRODUCTS WITHOUT SALT-MELTERS USING CRYOMECHANOLYSIS

The aim of the work is elaboration of the principally new method of deep processing of rennet cheeses to the melting using the complex action of freezing and cryomechanolysis on the raw material that gives a possibility to destruct the hardly soluble biopolymers and to transform them into soluble form. The principally new method of the deep processing of rennet cheeses for receiving the melt cheese products without salts-smelters was elaborated. It differs from the traditional ones by the complete exclusion of the salts-smelters. This method is based on the use of the influence of freezing and fine-dispersed comminution on the raw material. It allows open biological potential of the rennet cheeses more fully and to extract the hidden (bound) protein forms from nanocomplexes of lipids and mineral substances. It allows destruct the proteins of rennet cheeses to the separate polymers and dipeptides and tripeptides. The used technological methods gave a possibility to exclude the salts-smelters at the rennet cheeses manufacturing. They favor the transformation of lipid-proteins paracaseinate calcium phosphate complexes to the separate amino acids and peptides and allow receive homogenous plastic mass. It was established, that at the complex action of freezing and fine-dispersed comminution on the rennet cheese the destruction of hardly soluble lipid-protein nanocomplexes and release of protein from the bound state into free one – nanoform (by 33,5…35 % more) takes place. The mechanisms of this process, connected with cryomechanodestruction of connections between lipids and proteins and non-fermented catalysis of nanocomplexes were described. It was established, that cryomechanodestruction and non-fermented catalysis of protein to the separate monomers – α-amino acids (by 55…60 %) takes place at freezing and fine-dispersed comminution of rennet cheese before melting. The mechanism of freezing and non-fermented analysis, connected with cryomechanocracking of protein molecules at the expanse of peptide protein connections destruction to the separate α-amino acids and their transformation into the free form was described. It was also demonstrated, that the conformational changes of protein molecules take place synchronously. The offered and elaborated nanotechnology of melt cheese products on the base of rennet cheeses without salts-smelters includes complex action of freezing and fine-dispersed comminution. The mechanisms of processes, connected with cryomechanodestruction of connections between lipids and protein to the separate α-amino acids are described. The cheese fillings for “Pancake” confectionary and cheese snacks – falafels were manufactured on the base of cheese mass, received using the new method and enriching vegetable nanoadditives. They exceed the well-known analogs by chemical composition and are remarkable for the storage life, increased in 2 times. At the same time the significant part of substances (BAS and biopolymers) in cheese filings is in nanodimensional form (55…60 % of protein), especially, free α-amino acids, easily assimilated by the human organism. The recipes and technologies of sauces-dressings, sauces-deeps, cheese snacks and so on are also elaborated on the base of cheese mass, received by the new method.