Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning
José Luis P. Calle,
Irene Punta-Sánchez,
Ana Velasco González-de-Peredo
et al.
Abstract:Honey is one of the most adulterated foods, usually through the addition of sweeteners or low-cost honeys. This study presents a method based on visible near infrared spectroscopy (Vis-NIRs), in combination with machine learning (ML) algorithms, for the correct identification and quantification of adulterants in honey. Honey samples from two botanical origins (orange blossom and sunflower) were evaluated and adulterated with low-cost honey in different percentages (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, a… Show more
“… optical SVM, KNN detection Salmonella spp. In raw meat, egg products, and milk Min et al (2021) doxycycline (DOX), tetracycline, oxytetracydine (OTC), and metacydine (MTC) optical SVM, LDA detection and identification of tetracyclines in river water and milk Xu et al (2020) indigo optical RF determine indigo in cream Zhang et al (2020) honey adulteration optical RF detection of honey adulteration Calle et al (2023) aflatoxin optical RF detection of aflatoxin-polluted corn kernels Cheng and Stasiewicz (2021) α-naphthalene acetic acid (NAA) electrochemical ANN detection of α-naphthalene acetic acid (NAA) residues in food Zhu et al, 2021a , Zhu et al, 2021b aflatoxin B1 and fumonisins electrochemical ANN aflatoxin B1 and fumonisins in maize Leggieri et al (2021) benzoic acid electrochemical ANN benzoic acid in cola-type carbonated beverages Yang et al (2021) pesticide residue optical SVM, RF, ANN determination of pesticide residue in food Khanal et al (2021) xanthine (XT) and hypoxanthine (HX) electrochemical …”
Section: Main Machine Learning Algorithms In Food Safetymentioning
“… optical SVM, KNN detection Salmonella spp. In raw meat, egg products, and milk Min et al (2021) doxycycline (DOX), tetracycline, oxytetracydine (OTC), and metacydine (MTC) optical SVM, LDA detection and identification of tetracyclines in river water and milk Xu et al (2020) indigo optical RF determine indigo in cream Zhang et al (2020) honey adulteration optical RF detection of honey adulteration Calle et al (2023) aflatoxin optical RF detection of aflatoxin-polluted corn kernels Cheng and Stasiewicz (2021) α-naphthalene acetic acid (NAA) electrochemical ANN detection of α-naphthalene acetic acid (NAA) residues in food Zhu et al, 2021a , Zhu et al, 2021b aflatoxin B1 and fumonisins electrochemical ANN aflatoxin B1 and fumonisins in maize Leggieri et al (2021) benzoic acid electrochemical ANN benzoic acid in cola-type carbonated beverages Yang et al (2021) pesticide residue optical SVM, RF, ANN determination of pesticide residue in food Khanal et al (2021) xanthine (XT) and hypoxanthine (HX) electrochemical …”
Section: Main Machine Learning Algorithms In Food Safetymentioning
“…However, these methods are expensive, time-consuming, and potentially destructive [22]. Leveraging Visible-Near Infrared (Vis-NIR) spectroscopy alongside machine learning (ML) algorithms has demonstrated rapid detection of adulteration in honey from single botanical sources [7], [23], [24], [25], [26], and two botanical sources [27]. However, the application of this technology to detect adulteration across several honey types remains unexplored.…”
Section: Introductionmentioning
confidence: 99%
“…Numerous studies have explored the utility of ML algorithms in detecting honey adulteration. However, the majority of these investigations have predominantly relied on absorbance/transmittance Vis-NIR spectroscopy [7], [23], [24], [25], [26], [27]. A comparatively limited body of work has delved into reflectance Vis-NIR spectroscopy, with only a few notable studies contributing to this branch of research [28], [29].…”
The accurate detection of honey adulteration is paramount for maintaining the quality and authenticity of honey products. In this study, we introduce a novel feature selection method, termed Optimal Subspace Wavelength Reduction (OSWR), and integrate it with reflectance Visible-Near Infrared (Vis-NIR) spectroscopy to enhance the discrimination between pure and adulterated honey and predict adulteration levels. OSWR efficiently addresses the dimensionality challenge of large spectral datasets, reducing 2151 wavelengths to a compact and informative set of 39 wavelengths. We comprehensively evaluate machine learning (ML) models, focusing on OSWR as a pivotal component of our methodology. Our results reveal remarkable success in discriminating among pure honey, adulterated honey, and sugar syrup, with an impressive classification accuracy of 96.67% achieved using OSWR, coupled with Standard Normal Variate (SNV) preprocessing, Linear Discriminant Analysis (LDA) feature extraction, and K-Nearest Neighbors (KNN) classification. Furthermore, this study demonstrates the effectiveness of OSWR for predicting adulteration levels, where it achieves an accuracy of as high as 92.67% when coupled with SNV, LDA, and KNN. This work highlights the potential of OSWR as a feature selection method in the context of honey adulteration detection. Through the integration of Vis-NIR spectroscopy and OSWR, our approach offers a tool for enhancing honey products' quality and authenticity assessment, potentially simplifying spectral data analysis.
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