Food-scanner applications in the fruit and vegetable sector

To quantitatively measure the performance of portable NIR devices in testing the quality of fresh produce of FELIX INSTRUMENTS, a team of scientists from Applied Science, tested predictions by three scanners on the market: F-750 Produce Quality Meter, SCiO, and H-100F, and compared them with analysis from destructive standard methods.    They found that the dry matter content of avocado, table grape, tangerine, blueberry, and apple showed good cross-validation (r²) of 0.87, 0.92, 0.92, 0.94, and 0.95, respectively. The devices could predict the sugar content of persimmon, mango, kiwi, tomato, tangerine, table grape, and blueberry, with cross-validations (r²) of 0.75, 0.80, 0.84, 0.87, 0.93, 0.95, and 0.95, respectively. Relative water content of ginger was predicted with an accuracy of r² of 0.91.   Given the high degree of accuracy of the predictions by the three devices, the scientists concluded that the scanners were beneficial for the fresh produce supply chain, and suggested practical applications of the NIR tools to prevent food loss.