Maple Syrup Adulteration: Fluorescence Fingerprints as a Source of Information for Enhanced Detection

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.

This study evaluates the capabilities and limitations of EEMs to scout for adulteration markers and discriminate between pure and adulterated maple syrup samples.

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 (λex = 250-500 nm, and λem = 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.

EEMs of the samples allowed identifying valuable discriminatory information. The efficacy of the ratio of the emission intensities at λem = 350 and 425 nm (I425/I350) when λex = 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.

This study aids in providing a quick, non-destructive, and green monitoring tool for maple syrup adulteration based on its intrinsic fluorophores.

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.