Improving accuracy of prediction model for soluble solids content of watermelon by variable selection based on near-infrared spectroscopy

Abstract: Fruit internal quality is an important indicator in the fruit grading, preservation and storage stage; rapid non-destructive detection of fruit internal quality can improve the marketable value of watermelon. Near infrared spectroscopy (NIRS) is a powerful technology with the virtue of the convenience and accuracy. This work focused on soluble solids content (SSC) determination of the Qilin watermelon. On basis of the NIRS, we adopted a near-infrared diffuse transmittance technique. We used the home-built measurement system with a fiber optic spectrometer to acquire the spectra. Partial least squares regression (PLSR), multiple linear regression (MLR) and principal component regression (PCR) were used to establish mathematical models. In order to improve the predictive models, firstly, variables were reduced by the interval spectral average method and the interval spectral extraction method, respectively.Further model optimization was carried out by a successive projections algorithm (SPA). The results showed that the PLSR models with 115 variables obtained by an interval of 5 in 574 variables were the best one in first step. Different pretreatments were employed using the 115 variables. By comparison of the results of the PLSR models with different pretreatment, we adopted the normalization pretreatment as the input of SPA algorithm, six optimal wavelengths (702.32, 713.68, 732.58, 770.23, 863.53 and 904.21 nm) were picked out. In a comparison of the predictive results of PLSR, MLR and PCR, the performance of the PLSR model for SSC prediction was better, the correlation coefficient of prediction (rpre) was 0.828, root mean square error of calibration (RMSEC) was 0.589, and root mean square error of prediction (RMSEP) was 0.611. The results revealed that this model took less time for modeling and had reliable predictive ability. This study showed that the home-built measurement system was stable, and provided a theoretical basis for on-line nondestructive detection of the internal quality of watermelon.