Pattern Recognition Approach for the Screening of Potential Adulteration of Traditional and Bourbon Barrel-Aged Maple Syrups by Spectral Fingerprinting and Classical Methods

At a Glance

Metadata	Details
Publication Date	2022-07-25
Journal	Foods
Authors	K. J. Zhu, Didem Peren Aykas, Luis Rodriguez‐Saona
Institutions	Adnan Menderes University, The Ohio State University
Citations	6
Analysis	Full AI Review Included

Executive Summary

This research successfully developed and validated rapid, non-destructive methods for authenticating traditional and premium Bourbon Barrel-Aged (BBL) maple syrups using portable mid-infrared (FT-IR) and benchtop Raman spectroscopy combined with chemometrics.

Adulteration Detection: 15% (6 out of 40) of tested commercial maple syrup samples, labeled as “pure,” exhibited abnormal sugar profiles (high invert sugar) and/or unusual volatile compositions, indicating potential adulteration.
Authentication Performance: Soft Independent Modeling of Class Analogy (SIMCA) models, built on spectral fingerprints, achieved 100% sensitivity and specificity in discriminating authentic traditional and BBL syrups from suspicious/adulterated samples.
BBL Fingerprinting: The spectral variance used for BBL authentication was primarily driven by compounds containing alcoholic groups (e.g., ethanol, isoamyl alcohol, isobutanol), confirming the absorption of bourbon residuals during the aging process.
Quantitative Accuracy: Partial Least Squares Regression (PLSR) models provided robust quantitative predictions for total soluble solids (Brix) and sucrose content, with external validation correlation coefficients (Rval) consistently greater than 0.95 for both FT-IR and Raman systems.
Methodology Value: This study demonstrates that miniaturized vibrational spectroscopy offers a cost-effective, fast, and field-deployable solution for quality control and fraud prevention in the high-value maple syrup industry, overcoming the limitations of traditional, time-consuming chromatographic methods.

Technical Specifications

Parameter	Value	Unit	Context
FT-IR Spectral Range	4000 to 700	cm^-1	Mid-Infrared Spectroscopy
FT-IR Resolution	4	cm^-1	Spectral Collection
FT-IR Co-added Scans	64	N/A	Signal-to-Noise Ratio Improvement
FT-IR Crystal Type	Triple-reflection Diamond	ATR	Sampling Interface
Raman Laser Wavelength	1064	nm	Excitation Source
Raman Spectral Range	350 to 1500	cm^-1	Spectral Collection
Raman Resolution	4	cm^-1	Spectral Collection
Raman Integration Time	3	s	Per 3 co-added scans
HPLC Column Temperature	80	°C	Sugar separation
GC-MS SPME Equilibration	40	°C	Volatile trapping temperature
GC-MS Desorption Temperature	250	°C	Fiber cleaning/compound release
SIMCA ICD (FT-IR, BBL vs Traditional)	4.8	N/A	Interclass Distance (High discrimination)
PLSR Rval (FT-IR Brix)	0.98	N/A	External Validation Correlation Coefficient
PLSR SEP (FT-IR Sucrose)	1.66	%	Standard Error of Prediction

Key Methodologies

The study utilized a multi-modal analytical approach combining classical reference methods with advanced vibrational spectroscopy and chemometrics.

Sample Preparation and Reference Analysis:
- Samples (Traditional, BBL, and Table Syrups) were stored at 4 °C and equilibrated to room temperature prior to analysis.
- Brix Measurement: Total soluble solids measured using a heat-controlled refractometer at 22 °C.
- Sugar Profiling (HPLC): Sucrose, fructose, and glucose quantified using High-Performance Liquid Chromatography (HPLC) with a refractive index detector and a Rezex RCM-Monosaccharide Ca+ column (80 °C).
- Volatile Profiling (GC-MS): Volatile compounds were extracted using Solid Phase Microextraction (SPME) at 40 °C and analyzed via Gas Chromatography-Mass Spectrometry (GC-MS). Key compounds identified included ethanol, isoamyl alcohol, and 1,1-diethoxy-2-methylpropane (unique to authentic BBL samples).
Vibrational Spectroscopy Data Acquisition:
- Portable FT-IR: Used a portable system with a diamond ATR crystal. Spectra were collected from 4000 to 700 cm^-1 (4 cm^-1 resolution) with 64 co-added scans.
- Benchtop Raman: Used a compact system with a 1064 nm laser. Spectra were collected from 350 to 1500 cm^-1 (4 cm^-1 resolution) with 3 co-added scans.
Chemometric Modeling and Validation:
- Preprocessing: All spectral data were preprocessed using Mean-Centering and the Savitzky-Golay (SG) algorithm (35-point filter) to resolve overlapping signals and reduce noise.
- Classification (SIMCA): Supervised classification models were built to establish distinct classes for Traditional and BBL syrups. The models successfully identified all suspicious samples as non-pure, achieving 100% accuracy on the external validation set.
- Quantification (PLSR): Partial Least Squares Regression models were developed for predicting Brix and sucrose content. Models utilized 4 to 5 factors and were validated using cross-validation (leave-one-out) and an independent external validation set.

Commercial Applications

The developed methodology provides a robust, rapid, and cost-effective solution for quality assurance and fraud mitigation across several industries:

Food Quality Control & Authentication: Rapid screening of high-value liquid foods (e.g., honey, syrups, spirits) to ensure compliance with standards of identity and grade specifications.
Supply Chain Integrity: Deployment of portable FT-IR devices at receiving docks or distribution centers for real-time, non-destructive verification of product authenticity before processing or sale.
Specialty Product Verification: Authentication of premium, aged products (like BBL maple syrup) by fingerprinting unique volatile markers absorbed during the aging process, protecting brand value.
Sweetener Industry: Fast quantification of total soluble solids (Brix) and specific sugars (sucrose) in liquid matrices, replacing slower laboratory chromatography methods.
Beverage Analysis: The ability of the FT-IR and Raman models to differentiate based on alcoholic compounds suggests applicability in screening distilled spirits or wines for adulteration or verifying aging claims.

View Original Abstract

This study aims to generate predictive models based on mid-infrared and Raman spectral fingerprints to characterize unique compositional traits of traditional and bourbon barrel (BBL)-aged maple syrups, allowing for fast product authentication and detection of potential ingredient tampering. Traditional (n = 23) and BBL-aged (n = 17) maple syrup samples were provided by a local maple syrup farm, purchased from local grocery stores in Columbus, Ohio, and an online vendor. A portable FT-IR spectrometer with a triple-reflection diamond ATR and a compact benchtop Raman system (1064 nm laser) were used for spectra collection. Samples were characterized by chromatography (HPLC and GC-MS), refractometry, and Folin-Ciocalteu methods. We found the incidence of adulteration in 15% (6 out of 40) of samples that exhibited unusual sugar and/or volatile profiles. The unique spectral patterns combined with soft independent modeling of class analogy (SIMCA) identified all adulterated samples, providing a non-destructive and fast authentication of BBL and regular maple syrups and discriminated potential maple syrup adulterants. Both systems, combined with partial least squares regression (PLSR), showed good predictions for the total °Brix and sucrose contents of all samples.

Tech Support

Original Source

DOI: https://doi.org/10.3390/foods11152211

References

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1980 - Detection of Adulterated Maple Products by Stable Carbon Isotope Ratio [Crossref]
1995 - Maple Syrup Authenticity Analysis by Anion-Exchange Liquid Chromatography with Pulsed Amperometric Detection [Crossref]