PDF(Pubmed)
Abstract：
This study examines the use of hyperspectral imaging for the identification of stale food items by analyzing minute changes in their spectral signatures. An algorithm is proposed for the detection of subtle alterations in spectral signatures and is validated through intra-class classification comparisons among various stages of adulterating food samples acquired using a spectroradiometer. The analysis reveals that the spectral angle mapper proves effective for inter-class classification of consumable food items but faces challenges in classifying slight changes in spectral signatures within the same category. In contrast, DNA encoding demonstrates reliability, despite the generated code-words being independent of the actual intensity of received reflectance at each band. DNA encoding can provide insights into the nature of absorbance or reflectance at each band, making it a valuable tool for intra-class classification. Additionally, a novel concept called spectral velocity is introduced for subclass pattern matching. This method of single-pixel analysis relies on artificially constructed nD-vectors derived from spectral signatures. The findings suggest that the combination of hyperspectral imaging and DNA encoding offers a valuable tool for the quality assurance of consumable food items and demonstrates its potential for ensuring food safety and quality, ultimately contributing to human health.
摘要：
这项研究通过分析光谱特征的微小变化来研究高光谱成像在识别陈旧食品中的用途。提出了一种算法，用于检测光谱特征的细微变化，并通过使用光谱辐射计获取的掺假食品样品的各个阶段之间的类内分类比较来验证。分析揭示,光谱角度映射器证明对于可消费食品的类别间分类是有效的,但在对同一类别内的光谱特征的轻微变化进行分类时面临挑战。相比之下,DNA编码证明了可靠性，尽管生成的码字与每个波段接收到的反射率的实际强度无关。DNA编码可以深入了解每个波段的吸光度或反射率的性质，使其成为类内分类的有价值的工具。此外,一个新的概念称为频谱速度引入子类模式匹配。这种单像素分析方法依赖于从光谱特征导出的人工构建的nD向量。研究结果表明，高光谱成像和DNA编码的结合为消耗性食品的质量保证提供了有价值的工具，并证明了其确保食品安全和质量的潜力，最终为人类健康做出贡献。
