用于商业奶粉认证的非靶向近红外光谱法和软独立建模类比法分类：一项使用十一种潜在掺假物的研究。

Non-targeted NIR spectroscopy and SIMCA classification for commercial milk powder authentication: A study using eleven potential adulterants.

作者信息

Karunathilaka Sanjeewa R, Yakes Betsy Jean, He Keqin, Chung Jin Kyu, Mossoba Magdi

机构信息

U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Regulatory Science, 5001 Campus Drive, College Park, MD 20740, USA.

University of Maryland, Joint Institute for Food Safety and Applied Nutrition, 2134 Patapsco Building, College Park, MD 20742, USA.

出版信息

Heliyon. 2018 Sep 21;4(9):e00806. doi: 10.1016/j.heliyon.2018.e00806. eCollection 2018 Sep.

A non-targeted detection method using near-infrared (NIR) spectroscopy combined with chemometric modeling was developed for the rapid screening of commercial milk powder (MP) products as authentic or potentially mixed with known and unknown adulterants. Two benchtop FT-NIR spectrometers and a handheld NIR device were evaluated for model development. The performance of SIMCA classification models was then validated using an independent test set of genuine MP samples and a set of gravimetrically prepared mixtures consisting of MPs spiked with each of eleven potential adulterants. Classification models yielded 100% sensitivities for the benchtop spectrometers. Better specificity, which was influenced by the nature of the adulterant, was obtained for the benchtop FT-NIR instruments than for the handheld NIR device, which suffered from lower spectral resolution and a narrower spectral range. FT-NIR spectroscopy and SIMCA classification models show promise for the rapid screening of commercial MPs for the detection of potential adulteration.

开发了一种使用近红外（NIR）光谱结合化学计量学建模的非靶向检测方法，用于快速筛选市售奶粉（MP）产品是正品还是可能与已知和未知掺假物混合。评估了两台台式傅里叶变换近红外（FT-NIR）光谱仪和一台手持式近红外设备用于模型开发。然后使用一组独立的正品MP样品测试集和一组通过重量法制备的混合物（由添加了十一种潜在掺假物中的每一种的MP组成）对软独立建模类比（SIMCA）分类模型的性能进行验证。台式光谱仪的分类模型灵敏度为100%。与手持式近红外设备相比，台式FT-NIR仪器获得了更好的特异性（受掺假物性质影响），手持式近红外设备的光谱分辨率较低且光谱范围较窄。FT-NIR光谱和SIMCA分类模型有望用于快速筛选市售MP以检测潜在掺假。