This study evaluated hyperspectral imaging (900–1700 nm) and the optimal binning strategy for the reduction of spatial data to obtain quantitative maps of some quality attributes in intact oranges. Artificial neural network (ANN) was used to develop prediction models using 198 oranges. Different levels of pixel binning were tested for predicting the samples of an external test set (N = 66). The best models obtained achieved root mean square error of cross validation (RMSECV) values of 0.87 %, 0.23 g L⁻¹, 2.78 and 1.11 for SSC, TA, MI and BrimA, respectively. Models were then applied to different spatial resolution sample images. The coefficients of determination (R²) and the root mean square error of prediction (RMSEP) values for the test set were then compared. A chemical image was developed to display the distribution of SSC, TA, MI and BrimA in the binned images of the orange fruit, demonstrating the potential of using a 10 × 10 spatial binning (corresponding to a 99 % reduction in the original dataset) to develop prediction models for quantifying taste attributes in intact oranges.

这项研究评估了高光谱成像（900-1700 nm）和用于减少空间数据以获得完整橙子中某些质量属性的定量图的最佳分箱策略。人工神经网络（ANN）用于开发使用198个橘子的预测模型。测试了不同级别的像素合并，以预测外部测试集的样本（N = 66）。最好模型中获得实现根交叉验证（RMSECV）的0.87％的值，0.23克L-的均方误差^-1，2.78和1.11 SSC，TA，分别MI和布里马。然后将模型应用于不同的空间分辨率样本图像。测定系数（R ²），然后比较测试集的预测均方根误差（RMSEP）值。已开发出一种化学图像来显示橙色水果的合并图像中SSC，TA，MI和BrimA的分布，证明了使用10×10空间合并（对应于原始数据集减少99％）的潜力。开发量化橘子口味特征的预测模型。