文献类型： 外文期刊

作者： Singh, Maleeka ¹ ; Zhang, Maia ¹ ; Espinal-Ruiz, Mauricio ¹ ; Rathnayake, Sujani ² ; Xue, Jun ³ ; Shi, John ³ ; Liu, Xiaoli ⁴ ; Hanner, Robert ² ; Corradini, Maria G. ¹ ;

作者机构： 1.Univ Guelph, Dept Food Sci, Guelph, ON N1G 2W1, Canada

2.Univ Guelph, Dept Integrat Biol, Guelph, ON N1G 2W1, Canada

3.Agr & Agri Food Canada AAFC, Guelph, ON N1G 5C9, Canada

4.Jiangsu Acad Agr Sci, Inst Agroprod Proc, Nanjing 210014, Jiangsu, Peoples R China

5.Arrell Food Inst, Guelph, ON N1G 2W1, Canada

6.Missouri Univ Sci & Technol, Ctr Res Energy & Environm, Rolla, MO 65409 USA

期刊名称：JOURNAL OF AOAC INTERNATIONAL （ 影响因子：1.7； 五年影响因子：1.8 ）

ISSN： 1060-3271

年卷期： 2025 年

页码：

收录情况： SCI

摘要： Background Maple syrup is often adulterated by dilution or substitution with other syrups due to its high demand and price. Fingerprinting techniques, e.g., DNA barcoding, detect adulteration in other foods. However, extensive processing during the transformation of sap into syrup degrades the genetic material, lowering the efficacy of this approach. In contrast, fluorescence fingerprints, obtained from excitation-emission matrixes (EEMs), rely on a sample's intrinsic fluorophores to provide valuable information for detecting adulteration. Objective This study evaluates the capabilities and limitations of EEMs to scout for adulteration markers and discriminate between pure and adulterated maple syrup samples. Methods EEMs of pure amber and dark maple syrups and admixtures with common adulterants (beet, corn, and rice syrups at 1-50%) were obtained using a spectrophotometer (lambda ex = 250-500 nm, and lambda em = 280-650 nm). The major components of the EEMs were identified using parallel factor analysis (PARAFAC) and confirmed by LC-tandem MS (LC-MS/MS). The ratio of intensities of the two most prevalent EEM features was calculated. An artificial neural network (ANN) and a convolutional neural network (CNN) were developed to analyze the EEMs based on emissions at two selected excitation wavelengths and the full EEM image, respectively, to discriminate presence and level of adulteration. Results EEMs of the samples allowed identifying valuable discriminatory information. The efficacy of the ratio of the emission intensities at lambda em = 350 and 425 nm (I425/I350) when lambda ex = 290 nm to identify potential fraud (70-86% correct identifications) depended on the adulterant. This ratio was particularly effective for beet syrup adulteration, even at concentrations <2%. Applying machine learning algorithms improved detection for all adulterants. ANN correctly identified adulteration type and level (90 and 82%). The CNN approach accurately classified 75-99% of adulterated syrups but required additional computational power and denser data sets. Conclusion This study aids in providing a quick, non-destructive, and green monitoring tool for maple syrup adulteration based on its intrinsic fluorophores. Highlights Maple syrup is often adulterated with other syrups due to high demand and price. DNA barcoding is ineffective in detecting maple syrup adulteration due to DNA degradation. Fluorescence fingerprints or EEMs allow scouting for discriminatory markers in maple syrup. Machine learning algorithms (ANN and CNN) applied to EEM data can aid detection.