Rapid detection and quantification of adulteration in saffron by excitation–emission matrix fluorescence combined with multi‐way chemometrics

Yue Chen; Hai‐Long Wu; Tong Wang; Juan‐Ni Wu; Bing‐Bing Liu; Yu‐Jie Ding; Ru‐Qin Yu

doi:10.1002/jsfa.13028

journal article Oct 18, 2023

Rapid detection and quantification of adulteration in saffron by excitation–emission matrix fluorescence combined with multi‐way chemometrics

Yue Chen

Hai‐Long Wu

Tong Wang

Juan‐Ni Wu Bing‐Bing Liu Yu‐Jie Ding Ru‐Qin Yu

Journal of the Science of Food and Agriculture Vol. 104 No. 3 pp. 1391-1398 · Wiley

View at Publisher Save 10.1002/jsfa.13028

Abstract

AbstractBACKGROUNDSaffron has gained people's attention and love for its unique flavor and valuable edible value, but the problem of saffron adulteration in the market is serious. It is urgent for us to find a simple and rapid identification and quantitative estimation of adulteration in saffron. Therefore, excitation–emission matrix (EEM) fluorescence combined with multi‐way chemometrics was proposed for the detection and quantification of adulteration in saffron.RESULTSThe fluorescence composition analysis of saffron and saffron adulterants (safflower, marigold and madder) were accomplished by alternating trilinear decomposition (ATLD) algorithm. ATLD and two‐dimensional principal component analysis combined with k‐nearest neighbor (ATLD‐kNN and 2DPCA‐kNN) and ATLD combined with data‐driven soft independent modeling of class analogies (ATLD‐DD‐SIMCA) were applied to rapid detection of adulteration in saffron. 2DPCA‐kNN and ATLD‐DD‐SIMCA methods were adopted for the classification of chemical EEM data, first with 100% correct classification rate. The content of adulteration of adulterated saffron was predicted by the N‐way partial least squares regression (N‐PLS) algorithm. In addition, new samples were correctly classified and the adulteration level in adulterated saffron was estimated semi‐quantitatively, which verifies the reliability of these models.CONCLUSIONATLD‐DD‐SIMCA and 2DPCA‐kNN are recommended methods for the classification of pure saffron and adulterated saffron. The N‐PLS algorithm shows potential in prediction of adulteration levels. These methods are expected to solve more complex problems in food authenticity. © 2023 Society of Chemical Industry.

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Details

Published: Oct 18, 2023
Vol/Issue: 104(3)
Pages: 1391-1398
License: View

Authors

Y

Yue Chen