Food Quality Assessed by Chemometrics

Abstract

Food market globalization, food security as well as increasing consumer demand for safe, minimally processed and healthy food impose the need to establish new approaches for identifying and assessing food quality markers. Nowadays, food industry stakeholders are challenged to assure food quality and safety without compromising several prerequisites such as sustainable and ecologically resilient food production, prolonged shelf life, satisfactory sensory quality, enhanced nutritional value and health-promoting properties. In addition, food fraud related to deliberate product mislabeling or economically intended adulteration is of major concern for both industry and regulatory authorities due to cost and public health implications. Notwithstanding the great number of state-of-the-art analytical tools available for quantifying food quality markers, their implementation results in highly complex and big datasets, which are not easily interpretable. In this context, chemometrics e.g., supervised and unsupervised multivariate exploratory analyses, design-of-experiment methodology, univariate or multivariate regression modelling etc., are commonly implemented as part of food process optimization and food quality assessment. In this Special Issue, we aimed to publish innovative research and perspective papers on chemometric-assisted case studies relating to food quality assessment, food authenticity, mathematical modelling and optimization of processes involved in food manufacturing.

Keywords: chemometrics; data mining; food authenticity; food product development; food quality; foodomics; process optimization and modelling.