Improving the accuracy of NIR detection of moldy core in apples using different diameter correction methods

IF 6.4 1区农林科学 Q1 AGRONOMY

Postharvest Biology and Technology Pub Date : 2024-10-23 DOI:10.1016/j.postharvbio.2024.113279

Hanlin Li, Jiajun Zan, Linxin Zhang, Binyan Hou, Tong Sun, Dong Hu

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引用次数: 0

Abstract

Moldy core in apples is a common disease, with early symptoms not visible on the fruit surface. When affected apples are mixed with healthy ones, overall fruit quality declines, leading to the decay of healthy apples. Therefore, there is an urgent need for a rapid, non-destructive detection method. However, variations in apple diameter significantly affect the intensity of NIR transmission spectra, impacting the accuracy of detecting moldy core in apples. To address this issue, various diameter correction methods including a novel method we proposed were employed in this study to improve the accuracy of near-infrared detection of moldy core in apples, and these methods were also compared. The results indicate that the moldy core classification model is significantly influenced by apple diameter, with the uncorrected model achieving only 83.64 % accuracy in the prediction set. After adopting the diameter information fusion correction method, the performance of model has been slightly improved, with the accuracy of prediction set increasing by 0.91 %. Further improvement is achieved when using spectral normalization based on correlation and spectral correction based on diameter transformation, which has raised the accuracy of prediction set to 86.36 %. And the spectral correction based on polynomial transformation method proposed in this study has significantly improved the model performance, with the calibration and prediction sets achieving sensitivity, specificity, and accuracy of 85.22 %, 95.24 %, 90.00 %, and 85.45 %, 92.7 %, 89.09 %, respectively. Compared to the uncorrected model, the accuracy of the model in prediction set has been improved by 5.45 %. The model also demonstrates a 4.54 % enhancement over the one corrected using the diameter information fusion method. Additionally, when evaluated against the models using spectral normalization based on correlation and spectral correction based on diameter transformation, the accuracy has increased by 2.73 %. Therefore, the method of spectral correction based on polynomial transformation that proposed in this study effectively reduces the impact of apple diameter on transmission spectra, improving the detection accuracy of moldy core in apples and supporting rapid, non-destructive, high-precision detection.

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使用不同的直径校正方法提高近红外检测苹果霉核的准确性

苹果霉核是一种常见病，早期症状在果实表面并不明显。当患病苹果与健康苹果混杂在一起时，整体果实质量会下降，导致健康苹果腐烂。因此，迫切需要一种快速、非破坏性的检测方法。然而，苹果直径的变化会严重影响近红外透射光谱的强度，从而影响检测苹果霉核的准确性。为解决这一问题，本研究采用了多种直径校正方法（包括我们提出的一种新方法）来提高苹果霉核的近红外检测精度，并对这些方法进行了比较。结果表明，霉核分类模型受苹果直径的影响很大，未经校正的模型在预测集中的准确率仅为 83.64%。采用直径信息融合校正方法后，模型的性能略有提高，预测集的准确率提高了 0.91%。采用基于相关性的光谱归一化和基于直径变换的光谱校正方法后，预测集的准确率进一步提高到 86.36%。本研究提出的基于多项式变换的光谱校正方法显著提高了模型性能，校正集和预测集的灵敏度、特异度和准确度分别达到了 85.22 %、95.24 %、90.00 % 和 85.45 %、92.7 %、89.09 %。与未经校正的模型相比，该模型在预测组中的准确率提高了 5.45%。与使用直径信息融合方法校正的模型相比，该模型也提高了 4.54%。此外，与使用基于相关性的光谱归一化和基于直径变换的光谱校正的模型相比，精度提高了 2.73%。因此，本研究提出的基于多项式变换的光谱校正方法可有效降低苹果直径对透射光谱的影响，提高苹果霉核的检测精度，支持快速、无损、高精度检测。

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来源期刊

Postharvest Biology and Technology 农林科学-农艺学

CiteScore

12.00

自引率

11.40%

发文量

309

审稿时长

38 days

期刊介绍： The journal is devoted exclusively to the publication of original papers, review articles and frontiers articles on biological and technological postharvest research. This includes the areas of postharvest storage, treatments and underpinning mechanisms, quality evaluation, packaging, handling and distribution of fresh horticultural crops including fruit, vegetables, flowers and nuts, but excluding grains, seeds and forages. Papers reporting novel insights from fundamental and interdisciplinary research will be particularly encouraged. These disciplines include systems biology, bioinformatics, entomology, plant physiology, plant pathology, (bio)chemistry, engineering, modelling, and technologies for nondestructive testing. Manuscripts on fresh food crops that will be further processed after postharvest storage, or on food processes beyond refrigeration, packaging and minimal processing will not be considered.