Single-step Raman spectroscopy for maple syrup quality control

Maple syrup quality evaluation involves multiple parameters, including color classification, flavor assessment, and the measurement of sugar concentration, which are influenced by the complex chemical composition of the syrup matrix. Conventional quality assessment relies on separate analytical and sensory approaches, creating a bottleneck in the industrial processing of maple syrup. As such, rapid techniques are needed to assess multiple quality control parameters from a single measurement. In this study, Raman spectroscopy combined with machine learning was evaluated to characterize maple syrup quality attributes using 721 samples representing different color grades (golden, amber, dark, and very dark) and flavor categories. After spectral preprocessing, principal component analysis (PCA) was performed to investigate natural spectral variations among samples. PCA revealed differences associated with syrup color and flavor categories; however, the overlap observed among several groups indicated that the relationship between Raman fingerprints and maple syrup properties involves complex spectral variations that are not fully resolved by linear unsupervised analysis. To improve discrimination, convolutional neural network (CNN) models were developed to extract nonlinear spectral features directly from Raman data. For color classification, the CNN achieved prediction accuracies of 91%, 68%, 73%, and 92% for golden, amber, dark, and very dark syrups, respectively. Flavor classification showed strong discrimination for several defect categories, including OK (83%), √ (76%), VR4 (73%), VR5 (78%), VR13 (100%), and CT (83%), while lower performance for VR1 (55%), VR2 (66%), and VR12 (64%) reflected the challenge of differentiating sensory defects with overlapping chemical characteristics. VR13 showed a high classification rate in the evaluated data; however, the small number of independent VR13 samples prevents a robust estimate of general performance for this class. In addition to Raman fingerprints, fluorescence background variations were evaluated as an indicator of optical properties and were found to correlate with maple syrup light transmittance values used for color grading. The quantitative capability of Raman spectroscopy was further assessed by predicting sucrose concentrations using standard sucrose solutions (45-67%, w/w). Concentration-dependent Raman features were extracted and used to develop a random forest regression (RFR) model, achieving coefficients of determination (R ²) of 0.87. Application of the model to real maple syrup samples yielded sucrose predictions with an average bias of 0.35%, indicating that Raman-derived spectral information can be used to evaluate both compositional and quality-related characteristics of maple syrup. Overall, the results reported demonstrate the power of Raman spectroscopy for a multiparametric analysis of maple syrup.