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An application to analyzing and correcting for the effects of irregular topographies on NIR hyperspectral images to improve identification of moldy peanuts
Journal of Food Engineering ( IF 5.5 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.jfoodeng.2020.109915
Deshuai Yuan , Jinbao Jiang , Xiaojun Qiao , Xiaotong Qi , Wenjia Wang

Abstract Near-infrared hyperspectral imaging (NIR-HSI) can be used for nondestructive, rapid, real-time detection in food safety; however, irregular sample topographies introduce variations in the spectral intensity that impair subsequent classification and inversion processes. In this study, the spectral variations in HSI images of peanut samples with irregular topographies were assessed via the classification gradient and singular spectrum analysis (SSA). An SSA based correction model (CMSSA) is proposed that assumes the spectral intensity of all pixels of peanuts should be equal. The method was validated via classification and the coefficient of variation (CV) and was found to eliminate the spectral variation caused by the irregular kernel topography while retaining chemical differences of interest. We anticipate this method will prove useful in food safety detection applications involving the quantitative inversion of parameters.

中文翻译:

不规则地形对近红外高光谱图像影响的分析和校正在提高发霉花生识别中的应用

摘要 近红外高光谱成像(NIR-HSI)可用于食品安全的无损、快速、实时检测;然而,不规则的样本地形会导致光谱强度的变化,从而影响随后的分类和反演过程。在这项研究中,通过分类梯度和奇异光谱分析(SSA)评估了具有不规则地形的花生样品的 HSI 图像的光谱变化。提出了一种基于 SSA 的校正模型 (CMSSA),假设花生的所有像素的光谱强度应该相等。该方法通过分类和变异系数 (CV) 进行了验证,发现消除了由不规则内核地形引起的光谱变化,同时保留了感兴趣的化学差异。
更新日期:2020-09-01
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