Reduction of maillard browning in spray dried low-lactose milk powders due to protein polysaccharide interactions.

Lactose hydrolysed concentrated milk was prepared using ß-galactosidase enzyme (4.76U/mL) with a reaction period of 12 h at 4 °C. Addition of polysaccharides (5 % maltodextrin/ß-cyclodextrin) to concentrated milk either before or after lactose hydrolysis did not result in significant differences (p > 0.05) in degree of hydrolysis (% DH) of lactose and residual lactose content (%). Three different inlet temperatures (165 °C, 175 °C and 185 °C) were used for the preparation of powders which were later characterised based on physico-chemical and maillard browning characteristics. Moisture content, solubility and available lysine content of the powders decreased significantly, whereas, browning parameters i.e., browning index, 5-hydroxymethylfurfural, furosine content increased significantly (p < 0.05) with an increase in inlet air temperature. The powder was finally prepared with 5 % polysaccharide and an inlet air temperature of 185 °C which reduced maillard browning. Protein-polysaccharide interactions were identified using Fourier Transform infrared spectroscopy, fluorescence spectroscopy and determination of free amino groups in the powder samples. Maltodextrin and ß-cyclodextrin containing powder samples exhibited lower free amino groups and higher degree of graft value as compared to control sample which indicated protein-polysaccharide interactions. Results obtained from Fourier Transform infrared spectroscopy also confirmed strong protein-polysaccharide interactions, moreover a significant decrease in fluorescence intensity was also observed in the powder samples. These interactions between the proteins and polysaccharides reduced the maillard browning in powders.

A Metabolomics Approach to Establish the Relationship between the Techno-Functional Properties and Metabolome of Indian Goat Yoghurt.

INTRODUCTION: Goat milk has poorer fermentation characteristics due to the absence or only traces of αs1-casein, due to which goat yoghurt contains a less dense gel structure. Moreover, the fermentation characteristics of the milk vary between the breeds of the same species. Therefore, it becomes imperative to explore a few metabolites which could regulate the techno-functional properties of goat yoghurt. OBJECTIVES: This study was aimed at relating the metabolite profile of yoghurt prepared from milk of Barbari, an indigenous goat breed of India, and its techno-functional properties (firmness, whey syneresis, and flow behaviour) using multivariate data analysis and regression models. RESULTS: Goat yoghurt was prepared with two different total solids (TS) levels (12 and 16%) and cultures, namely, commercial culture comprising a thermophilic yoghurt culture (A) and NCDC-263 comprising a mixed yoghurt culture (B). Results demonstrated a significant difference (p < 0.05) in whey syneresis with the increase in the TS level. Flow behaviour of all yoghurt samples showed a decrease in viscosity with an increase in shear rate, which confirmed its non-Newtonian behaviour and shear thinning nature, whereas frequency sweep confirmed its viscoelastic nature. Firmness was the most affected under the influence of different TS and culture levels. It was higher (p < 0.05) for 16-A, followed by 16-3B, and minimum for 12-2B. GC-MS-based metabolomics of the yoghurt revealed a total of 102 metabolites, out of which 15 metabolites were differentially expressed (p < 0.05), including 2-hydroxyethyl palmitate, alpha-mannobiose, and myo-inositol. Multivariate data analysis revealed clear separation among groups using principal component analysis and several correlations using a correlation heat map. Further, regression analysis exhibited methylamine (0.669) and myo-inositol (0.947) with higher regression coefficients (R2 values) exceeding 0.6, thus demonstrating their significant influence on the techno-functional properties, mainly firmness, of the yogurt. CONCLUSION: In conclusion, A gas chromatography-based metabolomics approach could successfully establish a relationship between the metabolome and the techno-functional properties of the yoghurt.

Growth Characteristics Modeling of Mixed Culture of Bifidobacterium bifidum and Lactobacillus acidophilus using Response Surface Methodology and Artificial Neural Network

Different culture conditions viz. additional carbon and nitrogen content, inoculum size and age, temperature and pH of the mixed culture of Bifidobacterium bifidum and Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted for the cultivations using a Fractional Factorial (FF) design experiments for different variables. This novel concept of combining the optimization and modeling presented different optimal conditions for the mixture of B. bifidum and L. acidophilus growth from their one variable at-a-time (OVAT) optimization study. Through these statistical tools, the product yield (cell mass) of the mixture of B. bifidum and L. acidophilus was increased. Regression coefficients (R2) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.08 and 0.3%, respectively. The optimum conditions for the maximum biomass yield were at temperature 38°C, pH 6.5, inoculum volume 1.60 mL, inoculum age 30 h, carbon content 42.31% (w/v), and nitrogen content 14.20% (w/v). The results demonstrated a higher prediction accuracy of ANN compared to RSM.

Growth characteristics modeling of Lactobacillus acidophilus using RSM and ANN

The culture conditions viz. additional carbon and nitrogen content, inoculum size, age, temperature and pH of Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted to cultivations from a Box-Behnken Design (BBD) design experiments for different variables. This concept of combining the optimization and modeling presented different optimal conditions for L. acidophilus growth from their original optimization study. Through these statistical tools, the product yield (cell mass) of L. acidophilus was increased. Regression coefficients (R²) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.06 and 0.2%, respectively. The results demonstrated a higher prediction accuracy of ANN compared to RSM.