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In recent decades, there has been a substantial increase in the consumption of fruits and vegetables due to their nutritional properties since they are known as sources of vitamins, minerals, fiber, and antioxidants. Moreover, a substantial growth in fresh-cut fruits and vegetables has been noticed because of their ease to use; in fact changes in human life styles have led consumers to move towards ready-to-eat products. In this context, product quality must be preserved at each step of product handling, processing, and storage, and therefore rapid methods should be available to provide useful information in process management. In this review an overview of the applications of widely used non-destructive techniques, namely, electronic nose and visible/near infrared spectroscopy, for measuring quality of fruits and vegetables is presented. A brief description of spectroscopic and electronic devices and a selection of applications are provided. Future perspectives about the simplification/application of these non-destructive techniques are finally explored.

The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers’ expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an “ad hoc” designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours’ algorithm and Partial Least Square–Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar’s test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values (p > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours’ model is considered the most convenient strategy to assess meat and fish freshness. [ABSTRACT FROM AUTHOR]