Abstract
Cheese quality is affected by seasonal variations. These variations can influence several aspects of cheese, including its flavor, texture, nutritional content, and overall sensory qualities. The aim of this study was to assess the performance of near-infrared (NIR) instrumentation in terms of its ability to detect seasonal variations in Halloumi cheese samples when applying limited sample preparation compared to traditional protocols. Therefore, the use of NIR spectroscopy was examined for the determination of seasonal variations in Halloumi cheese samples from Cyprus in combination with chemometrics. Partial Least Squares Discriminant Analysis (PLS-DA) was applied. We found that NIR and chemometrics successfully discriminated the Halloumi cheese samples based on different climate conditions, the four seasons in the year when the milk collection took place. To externally validate the model, the dataset was divided into training and test sets. The innovation of this study is that Halloumi cheese was studied regarding seasonal variations by applying NIR for the first time. The outcome of this preliminary study is positive in terms of the capability of NIR to distinguish seasonal variations in Halloumi cheese, especially those due to differences in fatty acid molecules throughout the year. Future studies will include more samples to increase the current database.