樱桃番茄的可溶性固体含量（Soluble Solids Content, SSC）是评价其品质和成熟状态的关键参数。该文搭建了光纤光谱透射检测系统采集了不同成熟度樱桃番茄样本的原始光谱信息后，通过理化实验测定样本的SSC指标经SPXY算法对样本进行划分；然后用标准正态变量变换等算法（Standard Normal Variable transformation, SNV）对采集到的原始光谱进行预处理；采用连续投影算法（Successive Projection Algorithm, SPA）和竞争性自适应加权算法（Competitive Adaptive Reweighted Sampling, CARS）进行特征波长提取；最后利用灰狼优化算法（Grey Wolf Optimization, GWO）优化支持向量回归模型（Support Vector Regression, SVR）建立了樱桃番茄SSC的最优预测模型。结果表明，使用SNV算法预处理后的光谱建立的预测模型的校正集和预测集的相关系数得到了明显改善。SNV-CARS-GWO-SVR模型是樱桃番茄的最佳预测模型，预测集均方根误差（Root Mean Square Error of Prediction set, RMSEP）为0.28，残差预测偏差（Residual Predictive Deviation, RPD）为2.75。利用自行搭建的搭建了光纤光谱透射检测系统完全可以实现樱桃番茄SSC的检测，为不同成熟度番茄的SSC在线快速、无损检测提供了一种新的方法。

The soluble solids content (SSC) is a key parameter for evaluating the quality and maturity of cherry tomatoes. In this study, a fiber optic spectral transmission detection system was built to collect the raw spectral information of cherry tomatoes at different maturity levels. The SSCs of the samples were determined using physicochemical experiments, and the values were classified using the SPXY algorithm. Next, the raw spectra collected were preprocessed using standard normal variable (SNV) transformation. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithm were used for characteristic wavelength extraction. Finally, the Grey Wolf optimization (GWO) algorithm was applied to optimize the support vector regression (SVR) model to build an optimal model for predicting the SSC of cherry tomatoes. The results showed significant improvement of the coefficients of the calibration and prediction sets of the established prediction model based on SNV-preprocessed spectra. The SNV-CARS-GWO-SVR model was the best model for predicting the SSC of cherry tomatoes, with a root-mean-square-error of the prediction set (RMSEP) value of 0.28 and residual predictive deviation (RPD) value of 2.75. In summary, this independently developed fiber optic spectral transmission detection system fully achieved the nondestructive detection of the SSC of cherry tomatoes, thus providing a new method for the rapid and nondestructive online detection of SSCs of cherry tomatoes at different maturity levels.

国家自然科学基金项目（31871863；32072302；32072348）；中央引导地方发展专项资金项目（YDZX2022176）；山东省自然基金项目（ZR2023QC114）；湖北省自然科学基金项目（2012FKB02910）；湖北省研究与开发计划项目（2011BHB016）；中央引导地方发展专项资金项目（YDZX2022176）；山东省科技型中小企业创新能力提升工程项目（2021TSGC1251；2023TSGC0389；2021TSGC0766）