Rapid detection of soluble solids content in strawberry coated with chitosan based on hyperspectral imaging

Strawberries are popular fruit for their tender texture, juice and sweet taste. Prior on shelves, the harvesting and storage have always been the problems due to its fragility as well as susceptibility to rot. Chitosan coating has been widely used in fruit preservation, which can delay the storage time of fruits and has good preservation effect. The quality of chitosan-coated fruits is mostly detected by the typical conventional methods of physical or chemical testing. Since such methods need to deal with a large number of samples, which are time-consuming, laborious and destructive for detecting coated fruits. Therefore, in order to explore the possibility of detecting the soluble solids content (SSC) of strawberry coated with chitosan nondestructively and rapidly, hyperspectral imaging technology was employed to estimate the SSC of strawberry coated with chitosan in this study. Strawberry samples coated with 0, 0.5% and 1% chitosan acetic acid which were stored in 3 periods (1, 2 , 4 d). Outliers were eliminated by monte carlo-partial least squares (MCPLS) method, and the number of outliers was 10, 3 and 5 for the above respect treatments. Sample partitioning based on joint X-Y distance (SPXY) was used to split the data after eliminating outliers. After the partition of sample set, the modeling set contains the maximum and minimum SSC values in the three-concentration data, and the range of SSC values in the calibration set and validation set is large and the partition is reasonable. To find out the best model effect, Savitzky-Golay, baseline correction, De-trending, moving average smoothing (MA), multiplicative scatter correction (MSC) and standard normal variate (SNV) were used to pre-process the spectral data after eliminating the outliers. It was found that the strawberry sample data coated with 0 chitosan acetic acid solution pretreated by MSC had the best effect, while the strawberry sample data coated with 0.5% and 1% chitosan acetic acid solution without pretreatment had the best effect. R2 c was the largest and RMSECV was the smallest. Competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) method were applied to select the effective wavelengths, which were helpful for enhancing computer velocity and reducing data dimension. The number of effective wavelengths selected by CARS and SPA for the three concentrations was 32, 30, 20 and 11, 8, 16, respectively. Finally, partial least square method (PLS) and support vector regression (SVR) were used to build regression models. The final results showed that the PLS regression model was less effective than the SVR model, while the full spectrum data and the data of characteristic bands selected by CARS are less effective in the SVR model, and the SPA-SVR model was the best. The value of R2 c reached to 0.865 for strawberry samples coated with 0 chitosan acetic acid solution, and value of R2 v reached to 0.835; for the strawberries coated with 0.5% chitosan acetic acid solution R2 c was 0.808 and R2 v was 0.799; and the R2 c and R2 v were 0.834 and 0.875 for strawberries coated with 1% chitosan acetic acid solution, respectively. These results validated the applicability of hyperspectral imaging technology on rapid detection of SSC in strawberry coated with chitosan.