Authors

Abstract

Soluble solid content (SSC) is one of the important evaluation indexes of the internal quality and taste of fresh jujube. In order to realize the online nondestructive detection of SSC of fresh jujube, this paper took Huping jujube as the research object, adopted self-constructed nondestructive online testing system to collect the spectral information of jujubes (350~2500 nm), and studied the influence of the rotational speed of 4 r/min on the online prediction model of SSC of jujube. Kennard-Stone (KS) algorithm was used to divide the sample into correction set and prediction set. Six commonly used preprocessing methods such as SG smoothing (S-G), multiplicative scatter correction (MSC), standard normal variate (SNV), orthogonal signal correction (OSC), first derivative (FD), and second derivative (SD) were applied to the spectral data, and the regression coefficient (RC) algorithm and the successive projections algorithm (SPA) were utilized to select informative wavelengths, and a quantitative prediction model for the SSC of Huping jujube was established using partial least squares regression (PLSR). The results indicate that the PLSR prediction model established by preprocessing the original spectrum with OSC and combining it with RC algorithm to select characteristic wavelengths was optimal. Therefore, when predicting the SSC of Huping jujube, the optimal model was OSC-RC-PLSR, and the correlation coefficients of the correction set and prediction set were 0.846 and 0.782, respectively, and the corrected root mean square error (RMSEC) and predicted root mean square error (RMSEP) were 1.962 and 2.247, respectively. The results show that non-destructive detection of soluble solid content of jujube can be achieved by combining visible-near-infrared spectroscopy and appropriate regression model, which provides an innovative way for online sorting and identifying fresh jujube.

Abstract in Chinese

可溶性固形物(Soluble Solid Content, SSC)是鲜枣内部品质与口感的重要评价指标之一。为实现鲜枣SSC的在线无损检测，本文以壶瓶枣为研究对象，采用自行搭建的无损在线检测系统采集壶瓶枣的光谱信息（350～2500nm），研究了旋转速度为4 r/min条件下对壶瓶枣SSC在线预测模型的影响。对120个完好壶瓶枣光谱分别采用不同预处理方法，并结合RC回归系数法和连续投影算法筛选特征波长，建立SSC的偏最小二乘回归（PLSR）定量预测模型。研究结果表明，原始光谱经OSC预处理，再结合RC算法筛选特征波长建立的壶瓶枣SSC偏最小二乘定量预测模型结果最优，其校正集和预测集相关系数分别为0.846和0.782，均方根误差分别为1.962和2.247。该研究有助于对鲜枣品质在线分选提供技术支持。