Liu Sanqing, Huang Wenqian, Lin Lin, Fan Shuxiang
College of Mechanical Engineering, Guangxi University, Nanning 530004, China.
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Foods. 2022 May 21;11(10):1502. doi: 10.3390/foods11101502.
Predicting the soluble solid content (SSC) of peaches based on visible/near infrared spectroscopy has attracted widespread attention. Due to the anisotropic structure of peach fruit, spectra collected from different orientations and regions of peach fruit will bring variations in the performance of SSC prediction models. In this study, the effects of spectra collection orientations and regions on online SSC prediction models for peaches were investigated. Full transmittance spectra were collected in two orientations: stem-calyx axis vertical (Orientation1) and stem-calyx axis horizontal (Orientation2). A partial least squares (PLS) method was used to evaluate the spectra collected in the two orientations. Then, each peach fruit was divided into three parts. PLS was used to evaluate the corresponding spectra of combinations of these three parts. Finally, effective wavelengths were selected using the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS). Both orientations were ideal for spectra acquisition. Regions without peach pit were ideal for modeling, and the effective wavelengths selected by the SPA led to better performance. The correlation coefficient and root mean square error of validation of the optimal models were 0.90 and 0.65%, respectively, indicating that the optimal model has potential for online prediction of peach SSC.
基于可见/近红外光谱预测桃的可溶性固形物含量（SSC）已引起广泛关注。由于桃果实的各向异性结构，从桃果实不同方向和区域采集的光谱会使SSC预测模型的性能产生差异。本研究考察了光谱采集方向和区域对桃在线SSC预测模型的影响。在两个方向上采集全透射光谱：果梗 - 萼片轴垂直（方向1）和果梗 - 萼片轴水平（方向2）。采用偏最小二乘法（PLS）评估在这两个方向上采集的光谱。然后，将每个桃果实分为三部分。使用PLS评估这三部分组合的相应光谱。最后，采用连续投影算法（SPA）和竞争性自适应重加权采样（CARS）选择有效波长。两个方向都适合光谱采集。无桃核的区域适合建模，且SPA选择的有效波长能带来更好的性能。最优模型的验证相关系数和均方根误差分别为0.90和0.65%，表明该最优模型具有在线预测桃SSC的潜力。
