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Nondestructive intelligent and portable detection of postharvest translucency and internal browning in pineapples using visible/near-infrared spectroscopy

文献类型： 外文期刊

作者： Guo, Yinghua 1 ; Xu, Sai 2 ; Liang, Xin 2 ; Lu, Huazhong 3 ; Xiao, Boyi 1 ;

作者机构： 1.South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China

2.Guangdong Acad Agr Sci, Inst Facil Agr, Guangzhou 510640, Peoples R China

3.Guangdong Acad Agr Sci, Guangzhou 510640, Peoples R China

关键词： Pineapple internal browning; Pineapple translucency; Nondestructive detection; Visible/near infrared spectroscopy; Postharvest management

期刊名称：LWT-FOOD SCIENCE AND TECHNOLOGY （ 影响因子：6.6； 五年影响因子：6.9 ）

ISSN： 0023-6438

年卷期： 2025 年 229 卷

页码：

收录情况： SCI

摘要： Pineapple internal browning manifests as darkened translucent spots in the central tissue, with the number and area of these spots progressively increasing during storage. Translucency is characterized by excessive water accumulation in the flesh, leading to tissue softening which increases susceptibility to mechanical damage. This study innovatively utilizes the penetration characteristics of visible/near-infrared spectroscopy to achieve realtime detection and onset time prediction of postharvest internal disorders in pineapples by comparing different preprocessing methods and modeling strategies. Furthermore, we propose incorporating local spectral feature data as a key indicator for translucency detection, combined with feature-extracted data to enhance detection accuracy. To address systematic batch variations, we employ direct orthogonal signal correction to eliminate irrelevant spectral information, thereby improving model generalizability. Experimental results show that the maximum accuracy of the pineapple translucency detection model reached 95.2 % (training set) and 94.3 % (validation set), respectively. The dual-batch detection model for internal browning achieved an accuracy exceeding 90 % in both the training and validation sets. Meanwhile, the prediction model for the onset time of internal browning achieved a maximum accuracy of 93.7 % (training set) and 90.4 % (validation set). This work establishes a novel nondestructive detection method for postharvest pineapple disorders.

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