Abstract Near-infrared (NIR) hyperspectral imaging was used to evaluate soluble solids content (SSC) in ‘Fuji’ apples [Malus sylvestris (L.) Mill. var. domestica (Borkh. Mansf.)]. Eighty ‘Fuji’ apples were analyzed by collecting four small block samples from each one (approximately 2.0 cm × 2.0 cm × 1.5 cm). Partial least squares (PLS) regression analysis was performed to determine the relation between SSC reference data and NIR spectral data measured from each sample. The cross-validation coefficient of determination (r2) between predicted and measured SSC values is 0.89 with a root mean squared error of cross-validation (RMSECV) of 0.55%. Then, we successfully mapped SSC at a high spatial resolution (375 μm per pixel). In addition, the absorption and reduced scattering coefficients of the measured samples were determined based on a diffusion theory model. The absorption coefficients are positively correlated to the SSC values (chemical information), whereas water cored tissue content (physical information) causes a characteristic change in light scattering coefficients. The fitting results were validated by Monte Carlo simulation, and the light penetration depth in ‘Fuji’ apples was estimated to be around 0.33 cm at 1198 nm and 0.17 cm at 1450 nm, respectively.

中文翻译：

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近红外高光谱成像对苹果可溶性固形物含量的非接触评价

摘要 近红外 (NIR) 高光谱成像用于评估“富士”苹果 [Malus sylvestris (L.) Mill. 中的可溶性固形物含量 (SSC)。变种 家养 (Borkh. Mansf.)]。通过从每个苹果中收集四个小块样品（约 2.0 cm × 2.0 cm × 1.5 cm）对 80 个“富士”苹果进行了分析。进行偏最小二乘 (PLS) 回归分析以确定 SSC 参考数据与从每个样品测量的 NIR 光谱数据之间的关系。预测和测量 SSC 值之间的交叉验证确定系数 (r2) 为 0.89，交叉验证的均方根误差 (RMSECV) 为 0.55%。然后，我们成功地以高空间分辨率（每像素 375 μm）绘制了 SSC。此外，基于扩散理论模型确定测量样品的吸收和减少的散射系数。吸收系数与 SSC 值（化学信息）正相关，而水芯组织含量（物理信息）会导致光散射系数的特征变化。拟合结果通过蒙特卡罗模拟验证，“富士”苹果的光穿透深度估计分别在 1198 nm 和 1450 nm 处约为 0.33 cm 和 0.17 cm。