Determining anthracnose-infested mango can involve laborious and time-consuming assays, resulting in delayed postharvest management and decreased fruit marketability. Near infrared spectroscopy (NIRS) is proposed to detect the fungus in fully matured ‘Namdokmai Sithong’ mango. Inoculation of Colletotrichum gloeosporioides (1 × 10⁶ conidia/mL) was artificially made onto one side of the fruit’s peel at the center of mango fruit while the other side was left intact. Interactance measurements were conducted at both inoculated and intact locations for 104 mango samples every 24 h until anthracnose symptoms visibly appeared. The classification approaches included a partial least squares discriminant analysis (PLS-DA) and a conventional artificial neural network (ANN). Results of our study revealed increased absorbance values corresponding with days after inoculation. Relatively high classification accuracies were obtained from all chemometrics approaches (˃ 89%). In the early hours after inoculation (24 h), the best classification result was obtained from the ANN model (98.1%), confirming that early detection was possible. Applications of PLS-DA and ANN are discussed.

确定受炭疽病感染的芒果可能需要费力且耗时的检测，导致采后管理延迟并降低水果的适销性。建议使用近红外光谱 (NIRS) 来检测完全成熟的“Namdokmai Sithong”芒果中的真菌。将炭疽菌（Colletotrichum gloeosporioides ） （1×10 ⁶分生孢子/mL）人工接种到芒果果实中心的果皮的一侧，而另一侧保持完整。每 24 小时在接种地点和完整地点对 104 个芒果样品进行相互作用测量，直到明显出现炭疽病症状。分类方法包括偏最小二乘判别分析（PLS-DA）和传统的人工神经网络（ANN）。我们的研究结果显示，吸光度值随接种后天数的增加而增加。所有化学计量学方法都获得了相对较高的分类精度（˃ 89%）。在接种后的最初几个小时（24小时），ANN模型获得了最好的分类结果（98.1％），证实了早期检测是可能的。讨论了 PLS-DA 和 ANN 的应用。