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高光谱成像在红薯可溶性固形物含量空间预测中的应用
食品检测
2026/09/21
摘要：可见光和近红外 (Vis-NIR) 高光谱成像用于快速检测和可视化‘北京 553’和‘红蕉’红薯中的可溶性固形物含量 (SSC)。从每个品种切片红薯的 420 个 DOI 获取高光谱图像。通过蒙特卡洛偏最小二乘法 (MCPLS) 从北京 553’和‘红 蕉’红薯中去除了 8 个和 10 个异常值。确定了最佳光谱预处理以提高预测模型的性能。采用连续投影算法 (SPA) 和竞 争自适应重加权采样 (CARS) 来选择特征波长。使用偏最小二乘回归 (PLSR)、支持向量回归 (SVR) 和多元线性回归 (MLR) 开发了 SSC 预测模型。 SPA-SVR 模型的预测效果更佳，对于‘北京 553’红薯，其 Rp 2 为 0.8581，RMSEP 为 0.2951，RPDp 为2.56；对于‘红蕉’红薯，其 CARS-MLR 模型的 Rp 2 为 0.8153，RMSEP 为 0.2744，RPDp 为 2.09。 使用 SPA-SVR 和 CARS-MLR 模型以像素为单位获取 SSC 的空间分布图，以简单的方式量化 SSC 水平。总体结果表 明，Vis-NIR 高光谱成像是红薯 SSC 空间预测的有力工具。
期刊：ROYAL SOCIETY OF CHEMISTRY
DOI：10.1039/c9ra10630h
设备：GaiaSorter
Application of hyperspectral imaging for spatial prediction of soluble solid content in sweet potato
Abstract：Visible and near infrared (Vis-NIR) hyperspectral imaging was used for fast detection and visualization of soluble solid content (SSC) in ‘Beijing 553’ and ‘Red Banana’ sweet potatoes. Hyperspectral images were acquired from 420 DOIs of each cultivar of sliced sweet potatoes. There were 8 and 10 outliers removed from ‘Beijing 553’ and ‘Red Banana’ sweet potatoes by Monte Carlo partial least squares (MCPLS). The optimal spectral pretreatments were determined to enhance the performance of the prediction model. Successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were employed to select characteristic wavelengths. SSC prediction models were developed using partial least squares regression (PLSR), support vector regression (SVR) and multivariate linear regression (MLR). The more effective prediction performances emerged from the SPA–SVR model with Rp 2 of 0.8581, RMSEP of 0.2951 and RPDp of 2.56 for ‘Beijing 553’ sweet potato, and the CARS–MLR model with Rp 2 of 0.8153, RMSEP of 0.2744 and RPDp of 2.09 for ‘Red Banana’ sweet potato. Spatial distribution maps of SSC were obtained in a pixel-wise manner using SPA–SVR and CARS–MLR models for quantifying the SSC level in a simple way. The overall results illustrated that Vis-NIR hyperspectral imaging was a powerful tool for spatial prediction of SSC in sweet potatoes.
