Application of Raman Spectroscopy for Non-destructive and Rapid Detection of the Freshness of Green Bean

This study proposed a non-destructive method for rapid freshness evaluation of green bean based on Raman spectroscopy. The Raman spectra of green bean with different storage times were collected, its physicochemical indicators reflecting changes in freshness including mass loss rate, total color difference (∆E), hardness, ascorbic acid content and chlorophyll content were measured, and chemometrics methods were used to associate the spectral data with the physicochemical data. The effectiveness of different spectral pretreatments such as baseline correction (BL), Gaussian filter (GF), normalization (NL) and standard normal variable (SNV) was compared, and partial least squares regression (PLSR) and principal component regression (PCR) were used individually to establish freshness prediction models will all or selected wavenumbers. Moreover, the regression coefficient (RC) method was used to select the characteristic Raman wavenumbers. The simplified PLSR model for each freshness indicator showed a correlation coefficient of calibration set (r_c) of greater than 0.92, a correlation coefficient of prediction set (r_p) of greater than 0.89, and a residual predictive deviation (RPD) of greater than 2.0. The r_c and r_p of the PCR model were greater than 0.79 and 0.73, respectively. Therefore, the results of this study indicate that Raman spectroscopy allows non-destructive and rapid detection of the freshness of green bean.