Determination of Pear Internal Quality Attributes by Fourier Transform Near Infrared (FT-NIR) Spectroscopy and Multivariate Analysis

This paper attempted the feasibility to determine firmness and soluble solid content (SSC) in intact pears using Fourier transform near infrared (FT-NIR) spectroscopy coupled with multivariate analysis. Principal component analysis and independent component analysis were employed comparatively to extract latent vectors from the original spectra data. Extreme learning machine (ELM) was performed to calibrate regression model. Some parameters of ELM model were optimized according to the lowest root mean square error of cross-validation in the calibration set. Moreover, the root mean square error of prediction of the calibration model was finally corrected for making it more closed to the true prediction error due to the effect of reference measurement error existing in the pear sample attribute value on the prediction error of the model. Experimental results showed that the and ratio performance deviation (RPD) in the prediction set were achieved as follows: = 0.81 and RPD = 2.28 for the firmness model when ICs = 6 and = 0.91 and RPD = 3.43 for the SSC model when ICs = 5. This study demonstrates that the predictive precision of the calibration model can be effectively enhanced in measurement of firmness and SSC in intact pears by use of FT-NIR spectroscopy combined with appropriate chemometrics methods.