主成分分析
偏最小二乘回归
线性判别分析
气味
多元统计
数学
欧几里德距离
统计
化学计量学
多维标度
模式识别(心理学)
多元分析
层次聚类
统计分析
食品科学
食品
化学
指纹(计算)
食品质量
生物
作者
Jiaqi Liu,Hao Duan,Li Wang,Rui Qin,Jiao Liu,Hong Shi Liu,Shuyuan Bao,Wenjie Yan
出处
期刊:Foods
[Multidisciplinary Digital Publishing Institute]
日期:2025-09-29
卷期号:14 (19): 3381-3381
被引量:3
标识
DOI:10.3390/foods14193381
摘要
L.) from eight major production regions, providing a scientific basis for quality evaluation and geographical traceability. VOC profiling was conducted using gas chromatography-ion mobility spectrometry (GC-IMS), and regional differences were assessed through multivariate statistical analyses, including Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), Euclidean distance, and hierarchical clustering. Key differential compounds were identified by variable importance in projection (VIP) and relative odor activity value (ROAV) analyses, with aldehydes and esters emerging as the primary contributors to the discrimination of samples across regions. VOC fingerprints of safflower were further established, and a combined VIP-ROAV strategy was proposed for the screening of characteristic compounds. These findings provide a reliable reference for safflower quality control and offer practical guidance for its geographical authentication in the food industry.
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