Abstract Near infrared (NIR) spectroscopy is widely used for non-destructive prediction of fruit traits. Common traits such as dry matter (DM) and soluble solids contents (SSC) can be predicted with reliable accuracy. However, the main problem with NIR spectroscopy is that a model developed on one batch may not perform very well when tested on other batches. Reasons for that are the physical, chemical and environmental differences between the experiments performed in different batches. To deal with these issues, approaches such as variables selection, dynamic orthogonal projection (DOP) and transfer component analysis (TCA) can be used. However, the techniques are known but it is rarely possible for a new user or non-specialist to implement them in the practical situations. To overcome this limitation, for the first time, a graphical user interface-based toolbox (FRUITNIR-GUI) for basic chemometric data processing (regression and variable selection) is developed and presented. The GUI allows performing model adaption and maintenance in the context of multi-batch NIR spectroscopic experiments related to fruit. Furthermore, a case-study demonstrating its effectiveness in correcting for seasonality when predicting DM in apples is presented. The toolbox provides a push-button approach to build chemometric models of varying complexity for the characterization of fruit quality. Moreover, approaches such as variable selection and batch correction with DOP and TCA can improve the model performances on new batches. FRUITNIR-GUI can be freely downloaded at https://github.com/puneetmishra2/FRUITNIR and run using the password “welovenirs” (without quotation marks).

中文翻译：

---

FRUITNIR-GUI：用于校正与水果质量预测相关的多批次近红外实验中的外部影响的图形用户界面

摘要 近红外（NIR）光谱被广泛用于水果性状的无损预测。可以可靠准确地预测干物质 (DM) 和可溶性固形物含量 (SSC) 等常见特性。然而，NIR 光谱的主要问题是，在一个批次上开发的模型在其他批次上测试时可能表现不佳。其原因是不同批次进行的实验之间的物理、化学和环境差异。为了解决这些问题，可以使用变量选择、动态正交投影 (DOP) 和传递分量分析 (TCA) 等方法。然而，这些技术是已知的，但对于新用户或非专家来说，在实际情况中很少可能实施它们。为了克服这个限制，第一次，开发并展示了用于基本化学计量数据处理（回归和变量选择）的基于图形用户界面的工具箱（FRUITNIR-GUI）。GUI 允许在与水果相关的多批次 NIR 光谱实验的背景下执行模型适应和维护。此外，还提供了一个案例研究，展示了它在预测苹果 DM 时校正季节性的有效性。该工具箱提供了一种一键式方法来构建具有不同复杂性的化学计量模型，以表征水果质量。此外，变量选择和使用 DOP 和 TCA 进行批次校正等方法可以提高新批次的模型性能。FRUITNIR-GUI 可以在 https://github.com/puneetmishra2/FRUITNIR 免费下载并使用密码“welovenirs”（不带引号）运行。