The effect of milk fat substitution on the rheological properties of Edam-type cheese

Fermentation for Designing Innovative Plant-Based Meat and Dairy Alternatives

Fermentation was traditionally used all over the world, having the preservation of plant and animal foods as a primary role. Owing to the rise of dairy and meat alternatives, fermentation is booming as an effective technology to improve the sensory, nutritional, and functional profiles of the new generation of plant-based products. This article intends to review the market landscape of fermented plant-based products with a focus on dairy and meat alternatives. Fermentation contributes to improving the organoleptic properties and nutritional profile of dairy and meat alternatives. Precision fermentation provides more opportunities for plant-based meat and dairy manufacturers to deliver a meat/dairy-like experience. Seizing the opportunities that the progress of digitalization is offering would boost the production of high-value ingredients such as enzymes, fats, proteins, and vitamins. Innovative technologies such as 3D printing could be an effective post-processing solution following fermentation in order to mimic the structure and texture of conventional products.

Walnut Oil Oleogels as Milk Fat Replacing System for Commercially Available Chocolate Butter

A breakfast spread named chocolate butter exists on the market. For economic and technological reasons, cream in the original recipe is replaced with vegetable oils such as palm oil or by partially hydrogenated sunflower oil. The study aims to reformulate chocolate flavor butter, using cold pressed walnut oil (WO) oleogels (OGs) structured with 10% waxes and monoglyceride (MG), as a milk fat replacing system. The rheological, textural and microscopic characteristics of the oleogels and the spreads were compared. Oil binding capacity (OBC) and colorimetry were also assessed. Fourier transform infrared studies were used to monitor the composition of the samples. Oleogels and oleogel based chocolate butter behaved like strong gels (G’ > G”). The use of candelilla wax (CW) led to the formation of a much firmer spread (S-CW), with a hardness of 3521 g and G’LVR of 139,920 Pa, while the monoglyceride-based spread (S-MG) registered a hardness of 1136 g and G’LVR 89,952 Pa. In the spreadability test, S-CW registered a hardness of 3376 g and hardness work of 113 mJ, comparable to the commercially available chocolate butter. The formulated spreads exhibited shear thinning effects, and increased viscosity with decreasing temperature. A large round peak at 3340 cm−1 was present in the spectra of the candelilla wax-based oleogel (OG-CW) and the reference spreads due to hydrogen bonding, but was absent in S-CW or S-MG. The FTIR spectra of the alternative spreads exhibited the same peaks as the WO and the oleogels, but with differences in the intensities. S-CW exhibited a dense crystal network, with spherulitic crystals of 0.66−1.73 µm, which were statistically similar to those of the reference made from cream (S-cream). S-MG exhibited the lowest stability upon centrifugation, with an OBC of 99.76%. Overall, both oleogel-based chocolate spreads can mimic the properties of the commercially existing chocolate butter references.

Prediction of tenderness of chicken by using viscoelasticity based on airflow and optical technique

Tenderness is an index for evaluating meat quality. A prediction model of tenderness was established based on the chicken deformation, which was determined by a viscoelasticity system combined with airflow and optical technique. Different preprocessing methods were used to preprocess the deformation. The interval variables that represent the viscoelasticity of the chicken in deformation, were screen by synergy interval partial least squares algorithm (Si-PLS) and moving window partial least squares algorithm (Mw-PLS). The prediction model was established by principal component regression (PCR) and partial least squares regression (PLSR). The optimum PLSR prediction model was established when Mw-PLS was used to screen the interval variables of Savitzy-Golay (S-G) smoothing data. The correlation coefficient and the root mean square error of the calibration set were 0.965 and 0.874 kg, respectively. The corresponding value of the prediction set was 0.943 and 1.005 kg. This research provides a new method to assess the quality of poultry meat that conducts on airflow and optical techniques.