A General Strategy for Food Traceability and Authentication Based on Assembly-Tunable Fluorescence Sensor Arrays

Abstract

Food traceability and authentication systems play an important role in ensuring food quality and safety. Current techniques mainly rely on direct measurement by instrumental analysis, which is usually designed for one or a group of specific foods, not available for various food categories. To develop a general strategy for food identification and discrimination, a novel method based on fluorescence sensor arrays is proposed, composed of supramolecular assemblies regulated by non-covalent interactions as an information conversion system. The stimuli-responsiveness and tunability of supramolecular assemblies provided an excellent platform for interacting with various molecules in different foods. In this work, five sensor arrays constructed by supramolecular assemblies composed of pyrene derivatives and perylene derivatives are designed and prepared. Assembly behavior and sensing mechanisms are investigated systematically by spectroscopy techniques. The traceability and authentication effects on several kinds of food from different origins or grades are evaluated and verified by linear discriminant analysis (LDA). It is confirmed that the cross-reactive signals from different sensor units encompassing all molecular interactions can generate a unique fingerprint pattern for each food and can be used for traceability and authentication toward universal food categories with 100% accuracy.

Keywords: fluorescent probe; food traceability and authentication; sensor array; tunable assembly.

Scheme 1

Schematic illustration of supramolecular fluorescence sensor arrays for food traceability and authentication.

Figure 1

Chemical structures of PyBTA, PyBA, PyB, PDI‐BTMA, PDI‐HTMA, PDI‐OTMA, PSS, SDS, and Sug.

Figure 2

Fluorescence and absorption spectra of PyBTA before and after assembly with SDS in HEPES buffer (10 mm, pH = 7), λ _ex (excitation wavelength) = 350 nm.

Figure 3

Fluorescence signal heatmap of sensor array 1 interacting with different molecules in HEPES buffer (10 mm, pH = 7), c(Al³⁺) = c(VB1) = c(L‐Arg) = c(Caf) = 0.5 mm, c(PA) = 0.05 mm, c(Fru) = c(Suc) = 50 mm, c(SA) = 10 µg mL⁻¹, c(WP) = 1.5 µg mL⁻¹, c(CA) = 5 mm, c(CGA) = 0.1 mm. The fluorescence signal is explained in the Experimental Section of Supporting Information.

Figure 4

A) Fluorescence response patterns of sensor array 1 to apple samples from five regions. B) LDA score plots for the discrimination of apple samples from five regions with 95% confidence ellipses.

Figure 5

A) Fluorescence response patterns of sensor array 2 to citrus samples from six regions. B) LDA score plots for the discrimination of citrus samples from six regions with 95% confidence ellipses. HN, Hunan Province; ZJ, Zhejiang Province; GX, Guangxi Province; YN, Yunnan Province.

Figure 6

Fluorescence response patterns of sensor array 3 to A) six types of green tea and B) five types of Pu‐erh tea. LDA score plots for the discrimination of C) six types of green tea and D) five types of Pu‐erh tea with 95% confidence ellipses. Confusion matrix heatmap for classification predictions of E) green tea and F) Pu‐erh tea. DTBLC, Dongting Biluochun; HSMF, Huangshan Maofeng; LAGP, Lu an Guapian; LJ, Longjing Tea; XYMJ, Xinyang Maojian; XZZJ, Xianzhi Zhujian.

Figure 7

LDA score plots for the discrimination of A) seven types of honey from Wang's brand, B) six brands of acacia honey, and C) honey adulterated with different proportions of sirup.

Figure 8

A) Fluorescence response patterns of sensor array 5 to coffee samples from five regions. B) LDA score plots for the discrimination of coffee samples from five regions with 95% confidence ellipses.