Nondestructive measurement and fingerprint analysis of apple texture quality based on NIR spectra

A rapid and nondestructive way to measure texture of apple was put forward based on NIR spectra and the relationships between NIR spectra and firmness and crunchiness were developed. The NIR spectra were acquired from 240 samples of apples with the wavelength from 800 to 2500nm. The multivariable analyses including partial least squares (PLS) and multiple linear regressions (MLR) were conducted to build the regression models and select the fingerprint spectra of firmness and crunchiness. The excellent models with high coefficient of determination（R2: 96.52%; 97.15 %）and low RMSEP (0.226 kg/cm2; 0.243 kg/cm2) were obtained by PLS+MSC models based on wavelength from 1300 to 2500 nm. The loading weights from PLS model were found to be the sensitive firmness wavelengths (1657, 1725, 1790, 2455, 1929 and 2304 nm) and crunchiness wavelengths (1613, 1725, 1895, 2304, 2058, 2087 and 2396 nm). These wavelengths were strongly related with apple’s texture(r: 0.921, 0.957) by MLR models evaluated. The results indicate that the PLS models and the fingerprint spectra can predict apple texture quality accurately. A new method which can evaluate apple texture quality rapidly, visually, simply and feasibly was developed.