Abstract Moldy core is an internal disease afflicting otherwise healthy apples. This paper reports the results of a study on the effect of fruit orientation on the online detection of moldy core in Fuji apples using Vis/NIR transmission spectroscopy. Using a self-designed online spectral measurement system, transmission spectra in the range 550−900 nm were collected from apples positioned in three orientations: vertical calyx−stem axis with stem upward (T1), horizontal calyx−stem axis (T2), and vertical calyx−stem axis with stem downward (T3). In order to evaluate the spectral stability of the system in different orientations, the area change rate (ACR) and signal-to-noise ratio (SNR) were introduced. The propagation of light inside the fruit was also analyzed. After excluding outliers, 244 apple samples were divided into a training set (113 healthy apples and 70 diseased apples) and a test set (38 healthy apples and 23 diseased apples) at a ratio of 3:1. After spectral pretreatment, a support vector machine (SVM) was used to develop compensation models for the detection of moldy core in apples at all orientations (global model) and each separate orientation (local models). The results show that T2 provides the best performance in our system, and that the measurement orientation significantly affects the recognition accuracy of moldy core in apples. In contrast, the global model is insensitive to changes in fruit orientation, and the recognition accuracy of the global SVM model is 100 % in all three orientations. Thus, it is considered that the global SVM model is a promising candidate for the online detection of moldy core in apples using transmission spectroscopy, regardless of the spectral measurement orientation.

中文翻译：

---

在线检测发霉苹果核的测量取向补偿与透射光谱对比

摘要 霉核是一种影响其他健康苹果的内部疾病。本文报告了使用 Vis/NIR 透射光谱法对果实取向对富士苹果霉核在线检测影响的研究结果。使用自行设计的在线光谱测量系统，从位于三个方向的苹果收集了 550-900 nm 范围内的透射光谱：垂直花萼-茎轴向上（T1），水平花萼-茎轴（T2）和垂直花萼-茎轴，茎向下（T3）。为了评估系统在不同方向的光谱稳定性，引入了面积变化率（ACR）和信噪比（SNR）。还分析了光在果实内的传播。排除异常值后，244个苹果样本按3:1的比例分为训练集（113个健康苹果和70个病苹果）和一个测试集（38个健康苹果和23个病苹果）。光谱预处理后，使用支持向量机（SVM）开发补偿模型，用于检测苹果在所有方向（全局模型）和每个单独方向（局部模型）发霉的核。结果表明，T2在我们的系统中提供了最好的性能，并且测量方向显着影响了苹果霉核的识别精度。相比之下，全局模型对水果方向的变化不敏感，全局SVM模型在三个方向的识别准确率都是100%。因此，