Noninvasive method for internal quality evaluation of pear fruit using fiber-optic FT-NIR spectrometry

Abstract

An experimental study on noninvasive methods for simultaneous prediction of pears' internal quality was investigated. This method is based on Fourier transform near infrared (FT-NIR) spectrometry with fiber optics in the region between 800 and 2500 nm. A total of 248 pear samples were used to develop the calibration models. The quality indices included soluble solids content (SSC) and titratable acidity (TA). Partial least squares (PLS) regression and principle component regression (PCR) were carried out describing the relationships between the data sets of laboratory data and the FT-NIR spectra. Calibration models based on the different spectral ranges and with several data pre-processing techniques (smoothing, multiplicative signal correction, standard normal variate, etc) were also compared. Performance of different calibration models was assessed in terms of root mean square errors of prediction (RMSEP) and correlation coefficients (r) of validation set of samples. The best predictive models had a RMSEP of 0.320, 0.019, and correlation coefficient (r) equal to 0.93, 0.89 for SSC and TA, respectively. It is concluded that FT- NIR spectrometry could be an easy to facilitate, reliable, accurate and fast method for noninvasive prediction of pears' internal quality.