Determination of soluble solids content and firmness in plum using hyperspectral imaging and chemometric algorithms

Hyperspectral imaging technology coupled with chemometric algorithms was investigated to determine soluble solids content (SSC) and firmness in plum. A model of determining SSC and firmness in plum was established and optimized. Hyperspectral images of two varieties of "Red" and "Green" plums were acquired by hyperspectral imaging acquisition system. Two methods of partial least square regression (PLSR) and principal components regression (PCR) were employed to establish models based on full spectra. The multiple linear regression (MLR) and error back propagation (BP) network were applied to establish simplified models based on characteristic spectra selected by the methods of successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS). By comparing the results of different modeling methods, CARS-MLR model was the optimal models for predicting SSC and firmness with R2 P greater than 0.9 and 0.6, RPD above 3.7 and 1.8. Especially, for SSC (R2 P = 0.93, RMSEP = 0.57%, RPD = 3.73), for firmness (R2 P = 0.69, RMSEP = 0.63 kg/cm(2), RPD = 1.81). Results showed that hyperspectral imaging technology coupled with chemometric algorithms is feasible to determine SSC and firmness, CARS-MLR model was the optimal models. Practical applications Soluble solids content (SSC) and firmness are two significant quality attributes that determine fruit maturity and grade after harvest. Traditional methods for determining SSC and firmness are destructive, time-consuming, and extremely affected by subjective factors. Hyperspectral imaging technology has the advantages of nondestructive, rapid, non-pollution, and so on. The results demonstrated that hyperspectral imaging technology coupled with chemometric algorithms is feasible to determine SSC and firmness, and it can provide a theoretical basis to develop a real-time detection system to determine the quality of fruits.