Institute for Global Food Security, School of Biological Sciences, Queen's University, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK.
Teagasc Food Research Centre, Food Quality and Sensory Science Department, Dublin, Ireland.
Talanta. 2024 Aug 15;276:126199. doi: 10.1016/j.talanta.2024.126199. Epub 2024 May 3.
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.
由于碎牛肉的固有特性，掺假对供应商和消费者来说都是一个重大风险。本研究采用近红外-高光谱成像（NIR-HSI）结合化学计量学和其他统计方法，开发了一种用于识别碎牛肉中掺用牛肝、牛心和猪肉的新型、强大方法。具体来说，NIR-HSI 为使用分类模型遗传算法（GA）-反向传播人工神经网络（BPANN）识别每种掺假类型提供了一种高效、准确的方法，对于所有类型的掺假，校准集和验证集的灵敏度和特异性（值为 1.00）均达到完美。作为化学计量分析的替代方法，基于均方根（RMS）计算的高光谱成像均方根（HSI-RMS）值被确定为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，并旨在直接在像素级理解一个样本的不同光谱之间的相似性。不同类型的掺假在 HSI-RMS 值中表现出明显的差异（从 55 到 1439），这证明了 HSI-RMS 概念作为评估 HSI 数据和促进掺杂物识别的新型有价值替代方法的潜力。
