Prediction of sensory texture of cooked potatoes using uniaxial compression, near infrared spectroscopy and low field H-1 NMR spectroscopy

The present work evaluated the ability of uniaxial compression, near-infrared reflectance (NIR) and low field pulsed H-1 nuclear magnetic resonance (LF-NMR) in predicting the sensory texture quality of 24 samples of cooked potato by partial least squares regression (PLSR). The best predictions of the sensory texture profile were found for (1) LF-NMR measures (Carr-Purcell-Meiboom-Gill relaxation) on raw potatoes and (2) uniaxial compression on cooked potatoes combined with the chemical measure dry matter and pectin methylesterase activity. Among the sensory variables, the root mean square error of prediction indicated springiness, firmness, moistness and chewiness to be better predicted than the geometrical variables reflection from surface, mealiness and graininess. Transverse relaxation times, deter mixed according to bi-exponential fitting, resulted in a fast relaxing (T-21 of 100 ms) common water component for raw and cooked potatoes and a slower relaxing component with T-22 of 250 ms Sol cooked and 500 ms for raw potatoes. The only covariant NMR parameter was the amount of the slow relaxing component (T-22) which correlates negatively to dry matter (r = - 0.85) and to mealiness (r = - 0.77), and positively to,moistness (r = 0.75). This study clearly demonstrates that LF-NMR (CPMG) relaxation on raw potato samples can be applied as an alternative rapid method for detecting sensory texture of cooked potatoes.