Detection and Quantitation of Adulterated Paprika Samples Using Second-Order HPLC-FLD Fingerprints and Chemometrics

化学计量学 偏最小二乘回归 指纹(计算) 色谱法 高效液相色谱法 数学 化学 模式识别(心理学) 人工智能 统计 计算机科学
作者
Xiao‐Dong Sun,Min Zhang,Pengjiao Wang,Junhua Chen,Sheng-Jun Yang,Peng Luo,Xiu-Li Gao
出处
期刊:Foods [Multidisciplinary Digital Publishing Institute]
卷期号:11 (15): 2376-2376 被引量:5
标识
DOI:10.3390/foods11152376
摘要

Paprika is a widely consumed spice in the world and its authentication has gained interest considering the increase in adulteration cases in recent years. In this study, second-order fingerprints acquired by liquid chromatography with fluorescence detection (HPLC-FLD) were first used to detect and quantify adulteration levels of Chinese paprika samples. Six different adulteration cases, involving paprika production region, cultivar, or both, were investigated by pairs. Two strategies were employed to reduce the data matrices: (1) chromatographic fingerprints collected at specific wavelengths and (2) fusion of the mean data profiles in both spectral and time dimensions. Afterward, the fingerprint data with different data orders were analyzed using partial least squares (PLS) and n-way partial least squares (N-PLS) regression models, respectively. For most adulteration cases, N-PLS based on second-order fingerprints provided the overall best quantitation results with cross-validation and prediction errors lower than 2.27% and 20.28%, respectively, for external validation sets with 15-85% adulteration levels. To conclude, second-order HPLC-FLD fingerprints coupled with chemometrics can be a promising screening technique to assess paprika quality and authenticity in the control and prevention of food frauds.
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