Abstract：
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 RMScut-off 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.
摘要：
由于碎牛肉的固有特性，掺假给供应商和消费者都带来了巨大的风险。这项研究开发了一种稳健而新颖的方法，用于识别带有牛肝的碎牛肉中的替代欺诈，牛肉心,和猪肉使用近红外高光谱成像（NIR-HSI）以及化学计量学和其他统计方法。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）提供了一种有效且准确的方法来识别每种类型的掺假，显示所有类型掺假的校准和验证集的完美灵敏度和特异性（值为1.00）。作为化学计量分析的替代方法，高光谱成像均方根(HSI-RMS)值,基于RMScut-off计算，决定区分掺假的类型，而不需要资源密集型建模。这种HSI-RMS方法提供了一种简单易用的方法，该方法避免了HSI数据处理的复杂性，旨在直接了解像素级一个样品的不同光谱之间的相似性。不同类型的掺假显示出HSI-RMS值（从55到1439变化）的明显差异，这证明了HSI-RMS概念作为评估HSI数据和促进识别掺假物的新颖和有价值的替代方案的潜力。
