Wu Qianyi, Mousa Magdi A A, Al-Qurashi Adel D, Ibrahim Omer H M, Abo-Elyousr Kamal A M, Rausch Kent, Abdel Aal Ahmed M K, Kamruzzaman Mohammed
Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
Department of Arid Land Agriculture, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Curr Res Food Sci. 2023 Mar 17;6:100483. doi: 10.1016/j.crfs.2023.100483. eCollection 2023.
Quinoa is one of the highest nutritious grains, and global consumption of quinoa flour has increased as people pay more attention to health. Due to its high value, quinoa flour is susceptible to adulteration. Cross-contamination between quinoa flour and other flour can be easily neglected due to their highly similar appearance. Therefore, detecting adulteration in quinoa flour is important to consumers, industries, and regulatory agencies. In this study, portable hyperspectral imaging in the visible near-infrared (VNIR) spectral range (400-1000 nm) was applied as a rapid tool to detect adulteration in quinoa flour. Quinoa flour was adulterated with wheat, rice, soybean, and corn in the range of 0-98% with 2% increments. Partial least squares regression (PLSR) models were developed, and the best model for detecting the % authentic flour (quinoa) was obtained by the raw spectral data with Rp of 0.99, RMSEP of 3.08%, RPD of 8.77, and RER of 25.32. The model was improved, by selecting only 13 wavelengths using bootstrapping soft shrinkage (BOSS), to Rp of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method.
藜麦是营养最丰富的谷物之一，随着人们对健康的关注度提高，全球藜麦粉的消费量有所增加。由于其价值高，藜麦粉容易被掺假。藜麦粉与其他面粉之间的交叉污染很容易被忽视，因为它们外观极为相似。因此，检测藜麦粉中的掺假情况对消费者、行业和监管机构而言都很重要。在本研究中，将可见近红外（VNIR）光谱范围（400 - 1000 nm）的便携式高光谱成像技术作为一种快速工具用于检测藜麦粉中的掺假情况。用小麦、大米、大豆和玉米以2%的增量在0 - 98%的范围内对藜麦粉进行掺假。建立了偏最小二乘回归（PLSR）模型，通过原始光谱数据获得了检测真实面粉（藜麦）百分比的最佳模型，其Rp为0.99，RMSEP为3.08%，RPD为8.77，RER为25.32。通过使用自举重采样软收缩（BOSS）仅选择13个波长对模型进行了改进，Rp达到0.99，RMSEP为2.93%，RPD为9.18，RER为26.60。还生成了一张可视化图来预测掺假样品中藜麦的含量水平。本研究结果证明了VNIR高光谱成像技术作为复杂传统方法的替代方法用于检测藜麦粉掺假情况的能力。
