Sun Haixia, Zhang Shujuan, Ren Rui, Xue Jianxin, Zhao Huamin
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Foods. 2022 Aug 20;11(16):2522. doi: 10.3390/foods11162522.
To solve the failure problem of the visible/near infrared (VIS/NIR) spectroscopy model, soluble solids content (SSC) detection for fresh jujubes cultivated in different modes was carried out based on the method of variable optimization and model update. Iteratively retained informative variables (IRIV) and successive projections algorithm (SPA) algorithms were used to extract characteristic wavelengths, and least square support vector machine (LS-SVM) was used to establish detection models. Compared with IRIV, IRIV-SPA achieved better performance. Combined with the offset properties of the wavelength, repeated wavelengths were removed, and wavelength recombination was carried out to create a new combination of variables. Using these fused wavelengths, the model was recalibrated based on the Euclidean distance between samples. The LS-SVM detection model of SSC was established using the update method of wavelength fusion-Euclidean distance. Good prediction results were achieved using the proposed model. The determination coefficient (R), root mean square error (RMSE), and residual predictive deviation (RPD) of the test set on SSC of fresh jujubes cultivated in the open field were 0.82, 1.49%, and 2.18, respectively. The R, RMSE, and RPD of the test set on SSC of fresh jujubes cultivated in the rain shelter were 0.81, 1.44%, and 2.17, respectively. This study realized the SSC detection of fresh jujubes with different cultivation and provided a method for the establishment of a robust VIS/NIR detection model for fruit quality, effectively addressing the industry need for identifying jujubes grown in the open field.
为解决可见/近红外（VIS/NIR）光谱模型的失效问题，基于变量优化和模型更新方法，对不同种植模式下的鲜枣可溶性固形物含量（SSC）进行了检测。采用迭代保留信息变量（IRIV）和连续投影算法（SPA）提取特征波长，并用最小二乘支持向量机（LS-SVM）建立检测模型。与IRIV相比，IRIV-SPA性能更佳。结合波长的偏移特性，去除重复波长并进行波长重组以创建新的变量组合。利用这些融合波长，基于样本间的欧氏距离对模型进行重新校准。采用波长融合 - 欧氏距离更新方法建立了SSC的LS-SVM检测模型。所提模型取得了良好的预测结果。露地栽培鲜枣测试集在SSC上的决定系数（R）、均方根误差（RMSE）和残差预测偏差（RPD）分别为0.82、1.49%和2.18。雨棚栽培鲜枣测试集在SSC上的R、RMSE和RPD分别为0.81、1.44%和2.17。本研究实现了不同栽培方式下鲜枣的SSC检测，为建立稳健的水果品质VIS/NIR检测模型提供了方法，有效满足了行业对识别露地种植枣的需求。
