Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

以部分或全部透射几何和点光谱模式操作的短波近红外 (NIR) 光谱已越来越多地用于评估树上和包装线上的完整水果的质量。硬件的发展与所采用的建模技术的发展并行。这篇评论记录了用于该应用的光谱预处理和建模技术的范围。在过去的三十年里，已经从使用多元线性回归转向偏最小二乘回归。近年来，对跨季节和跨仪器的模型稳健性的关注推动了机器学习方法的转变，例如人工神经网络和深度学习，而这种转变得益于大量多样化的训练和测试集的可用性。