Owing to the inherent characteristics of ground beef, adulteration presents a substantial risk for suppliers and consumers alike. This study developed a robust and novel method for identifying replacement fraud in ground beef with beef liver, beef heart, and pork using Near Infrared-Hyperspectral Imaging (NIR-HSI) coupled with chemometric and other statistical methods. More specifically, NIR-HSI provided an efficient and accurate means of identifying each type of adulteration using the classification model Genetic Algorithm (GA) - Backpropagation Artificial Neural Network (BPANN), showing perfect sensitivity and specificity (a value of 1.00) for the calibration and the validation sets for all types of adulteration. As an alternative to chemometric analysis, Hyperspectral Imaging-Root Mean Square (HSI-RMS) value, based on the RMS calculation, was determined to discriminate types of adulterations without the need of resource-intensive modelling. This HSI-RMS approach provides a simple-to-use method that avoids the complexity of HSI data processing and aims to directly understand the similarity between different spectra of one sample in the pixel level. Different types of adulteration show noticeable differences reflected in the HSI-RMS value (varying from 55 to 1439), which demonstrate the potential of HSI-RMS concept as a novel and valuable alternative for assessing the HSI data and facilitating the identification of adulterants.

中文翻译：

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使用近红外高光谱成像 (NIR-HSI) 识别碎牛肉中低度掺假的建模和数值方法

由于碎牛肉的固有特性，掺假给供应商和消费者带来了巨大的风险。这项研究开发了一种稳健而新颖的方法，利用近红外高光谱成像 (NIR-HSI) 结合化学计量学和其他统计方法，识别牛肝、牛心和猪肉碎牛肉中的替代欺诈。更具体地说，NIR-HSI 使用分类模型遗传算法 (GA) - 反向传播人工神经网络 (BPANN) 提供了一种高效、准确的方法来识别每种类型的掺假，显示出完美的校准灵敏度和特异性（值为 1.00）以及所有类型掺假的验证集。作为化学计量分析的替代方法，基于 RMS 计算确定高光谱成像均方根 (HSI-RMS) 值，无需资源密集型建模即可区分掺假类型。这种 HSI-RMS 方法提供了一种简单易用的方法，避免了 HSI 数据处理的复杂性，旨在直接了解一个样本的不同光谱之间在像素级别的相似性。不同类型的掺假在 HSI-RMS 值（从 55 到 1439 不等）中体现出明显的差异，这证明了 HSI-RMS 概念作为评估 HSI 数据和促进掺假品识别的新颖且有价值的替代方案的潜力。