Effect of Freezing Rate and Microwave Thawing on Texture and Microstructural Properties of Potato (Solanum tuberosum)

Food freezing is a preservation process that works by lowering temperature while simultaneously decreasing water activity. It is accepted that although freezing preserves foods, it generally has a negative effect on textural quality. This research investigated the texture response of potatoes (Solanum tuberosum) as a function of time to freeze (defined as the time for the center temperature to reach −20 °C) and thawing process. Potatoes slices (6 mm) were blanched then frozen in an ethanol/carbon dioxide bath, a pilot scale high velocity air freezer (HVAF) and a still air freezer to achieve various times to freeze. Slices were stabilized at –20 °C and thawed by 2 methods; room temperature air and microwave. Afterwards, samples were allowed to come to room temperature prior to texture profile analysis (TPA). Results indicate a maximum texture loss of the potato was reached at a time to freeze of approximately 8 min (corresponding to the HVAF). The texture difference between room temperature and microwave thawing methods was not shown to be significant (P = 0.05). SEM images showed the cellular structure of the potato in a HVAF to be similar to that of the still air freezer, validating that the matrix was maximally damaged in both conditions. This work created a continuous quality loss model for the potato as a function of time to freeze and showed no textural benefit to high velocity over still air freezing. Practical Application Making sure that a freezing process is designed to create a product of target quality maximizes energy use. The research performed here shows that maximum texture loss of this particular product is achieved even under conditions that are thought to maintain texture quality of foods (high velocity air freezing). This suggests that slower (and therefore less energetically intense) freezing could achieve a product of similar quality. This research shows a predictable loss in texture quality for a single ingredient. Extending this work to other ingredients would generate a database of prediction models that would allow processors to optimize freezing conditions for blends of several different ingredients.