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

Abstract

BackgroundSaffron 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 also serious. It is urgent for us to find a simple and rapid identification and quantitative estimation of adulteration in saffron. Therefore, the excitation‐emission matrix 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 algorithm (ATLD). The 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 for rapid detection of adulteration in saffron. 2DPCA‐kNN and ATLD‐DD‐SIMCA methods were adopted for the classification of chemical EEM data firstly 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. N‐PLS algorithm shows potential in prediction of adulteration levels. These methods are expected to solve more complex problems in food authenticity.This article is protected by copyright. All rights reserved.

Data-Driven Version of Multiway Soft Independent Modeling of Class Analogy (N-Way DD-SIMCA): Theory and Application

“…19 A related publication adapted the original DD-SIMCA model for use in the case of alternating trilinear decomposition (ATLD) of multiway fluorescence data. 27 We note that PARAFAC can only be applied as a processing tool if the data are trilinear; otherwise, Tucker models should be selected.…”