This tutorial introduces how NIR spectroscopy works for honey analysis, explores practical workflows, discusses real-world applications, and outlines best practices for implementing this technique in food labs.
• NIR spectroscopy enables rapid, non-destructive analysis of honey quality.
• NIR effectively detects adulteration and verifies honey’s botanical and geographical origin.
• Chemometric models are essential for interpreting NIR spectral data accurately.
• NIR can be easily integrated into routine quality control workflows in food labs.
Introduction and Relevance
Honey, a globally cherished natural product, faces persistent threats from adulteration, mislabeling, and fraud—issues that compromise quality, safety, and consumer trust. Traditional analytical methods such as high performance liquid chromatography (HPLC) and gas chromatography–mass spectrometry (GC–MS) are accurate but often destructive, time-consuming, and costly. To address the growing need for rapid, robust, and non-destructive alternatives, Near-Infrared (NIR) spectroscopy has emerged as a transformative tool in the food industry (1–4).
The relevance of NIR spectroscopy in honey analysis stems from its ability to simultaneously quantify multiple compositional parameters while also enabling authentication based on botanical and geographical origin. Coupled with multivariate calibration and classification models, for example, principal components analysis (PCA), partial least squares (PLS), and linear discriminant analysis (LDA), NIR systems can detect adulterants like glucose syrup and invert sugar and determine key quality attributes such as 5-hydroxymethylfurfural (5-HMF), moisture, and proline content. This tutorial article offers a practical roadmap for food analysts and spectroscopists to apply NIR spectroscopy in routine honey testing, with a focus on accuracy, speed, and regulatory compliance (1–4).
Near-Infrared Spectroscopy for Honey Authentication: A Practical Mini-Tutorial for Food Quality Labs © Alyn Baby-chronicles-stock.adobe.com
Principles and Definitions
Near-Infrared Spectroscopy (NIR):
NIR spectroscopy operates in the spectral region of ~780–2500 nm and measures molecular overtone and combination vibrations, primarily involving C-H, O-H, and N-H bonds. These vibrations reflect the chemical composition of honey, including sugars, water, proteins, and organic acids (1-6).
Chemometric Modeling:
Due to the overlapping and broad absorbance bands in NIR spectra, statistical tools such as PCA and partial least squares regression (PLSR) are required to extract meaningful data. These techniques allow for both qualitative classification and quantitative prediction (1–6).
Key Honey Quality Parameters:
How It Works in Practice
Sample Preparation:
Minimal preparation is needed. Honey samples can be scanned directly using transmission or transflectance cells. In-line and at-line measurements are also feasible using fiber optic probes. Temperature equilibration (~25 °C) is advised for reproducibility (1–4).
Spectral Acquisition:
Data Preprocessing:
Model Building (1):
Validation:
Application Examples
1. Quantification of Sugar and Moisture Content:
Biswas and Chaudhari’s review demonstrated that NIR spectroscopy, coupled with PLSR, could predict glucose, fructose, and moisture content with high accuracy (R² > 0.95), matching results from classical HPLC and refractometry (1).
2. Detection of Adulteration:
Sophisticated NIR-based classification models can distinguish pure honey from adulterated samples containing corn syrup or rice syrup, even at 5–10% inclusion levels. PCA combined with LDA enables robust detection with over 90% classification accuracy (1).
3. Botanical and Geographical Origin Classification:
Spectral patterns, when analyzed via PCA or SIMCA, can differentiate honeys from different floral sources (for example, acacia vs. clover) or countries of origin (2). This application supports regulatory enforcement and supply chain verification.
Tips and Common Pitfalls
Potential Figures and Diagrams (Text Summary)
Although this mini-tutorial does not include visual figures, the following descriptions summarize what would typically be presented graphically in a research publication setting:
These text-based summaries reinforce that NIR spectroscopy, supported by chemometric models, provides both qualitative discrimination and quantitative estimation in honey analysis, all without requiring destructive sampling or extensive sample preparation.
Conclusion and Practical Takeaways
NIR spectroscopy offers a rapid, eco-friendly, and effective solution for ensuring honey authenticity and quality control. By enabling simultaneous analysis of key parameters and the detection of fraud, NIR systems reduce dependency on laborious chemical assays. Their integration into industrial quality assurance workflows helps enforce labeling compliance, enhance consumer safety, and streamline production (1–4).
For best results, analysts should pay close attention to sample handling, model development, and validation. When combined with chemometric tools, NIR spectroscopy becomes a powerful ally in the fight against food fraud, not only in honey but across the broader food industry (1–4).
References
(1) Biswas, A.; Chaudhari, S. R. Exploring the Role of NIR Spectroscopy in Quantifying and Verifying Honey Authenticity: A Review. Food Chem. 2024, 445, 138712. DOI: 10.1016/j.foodchem.2024.138712
(2) Ruoff, K.; Luginbühl, W.; Bogdanov, S.; Bosset, J. O.; Estermann, B.; Ziolko, T.; Amadò, R. Authentication of the Botanical Origin of Honey by Near-Infrared Spectroscopy. J. Agric. Food Chem. 2006, 54 (18), 6867–6872. DOI: 10.1021/jf060770f
(3) Calle, J. L. P.; Punta-Sánchez, I.; González-de-Peredo, A. V.; Ruiz-Rodríguez, A.; Ferreiro-González, M.; Palma, M. Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning. Foods 2023, 12 (13), 2491. DOI: 10.3390/foods12132491
(4) Woodcock, T.; Downey, G.; Kelly, J. D.; O’Donnell, C. Geographical Classification of Honey Samples by Near-Infrared Spectroscopy: A Feasibility Study. J. Agric. Food Chem. 2007, 55 (22), 9128–9134. DOI: 10.1021/jf072010q
(5) Workman, J.; Weyer, L. Practical Guide and Spectral Atlas for Interpretive Near-Infrared Spectroscopy, 2nd ed.; CRC Press: Boca Raton, FL, 2012.
(6) Osborne, B. G.; Fearn, T.; Hindle, P. H. Practical NIR Spectroscopy with Applications in Food and Beverage Analysis, 2nd ed.; Longman Scientific & Technical: Essex, U.K., 1993.
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