A comprehensive review of the current trends and recent advancements on the authenticity of honey

Zhang, Xiao-Hua; Gu, Hui-Wen; Liu, Ren-Jun; Qing, Xiang-Dong; Nie, Jin-Fang

doi:10.1016/j.fochx.2023.100850

Cited by 19 publications

(12 citation statements)

References 97 publications

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“…Thin-layer chromatography has limited application due to the requirement for hydrolysis of oligosaccharides and polysaccharides, and it is prone to false positives due to the presence of small oligosaccharides in pure honey. Although it is time-saving, Raman spectroscopy requires specialized personnel to analyze the samples, limiting its application ( Du et al, 2015 , Zhang et al, 2023 ). Due to the growing problem of honey adulteration, a rapid and accurate detection technique is required.…”

Section: Introductionmentioning

confidence: 99%

Establishment of rapid extraction and sensitive detection system of trace corn syrup DNA in honey

Ye,

Chen,

Huang

et al. 2024

Food Chemistry: Molecular Sciences

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Section: Introductionmentioning

confidence: 99%

Establishment of rapid extraction and sensitive detection system of trace corn syrup DNA in honey

Ye,

Chen,

Huang

et al. 2024

Food Chemistry: Molecular Sciences

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“…On the other hand, there are no standard methods for determining geographical origin. Current methods involve advanced analytical tools coupled with chemometrics [13]. Physicochemical [14][15][16][17], elemental [18], isotopic [19], chromatographic, and hyphenated mass spectrometry methods [20,21], NMR [22,23], DNA-based [24,25], electronic sensing [26,27], and spectroscopic [28][29][30][31][32] techniques are nowadays the most used for predicting botanical and geographical origin, or detecting adulterants [33].…”

Section: Introductionmentioning

confidence: 99%

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

Mara,

Migliorini,

Ciulu

et al. 2024

Foods

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Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors.

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“…Honey adulteration occurs through direct methods like adding sweeteners or blending different honeys while misrepresenting their origin or organic status, and indirectly through beekeepers’ practices such as using artificial feed or improper honey care and storage . Recent studies have proposed various methods for detecting fraudulent practices in the honey industry.…”

Section: Introductionmentioning

confidence: 99%

Detection and Identification of Honey Pollens by YOLOv7: A Novel Framework toward Honey Authenticity

Jubayer,

Ruhad,

Kayshar

et al. 2024

ACS Agric. Sci. Technol.

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Honey, a valuable and globally consumed food product, has significant market potential linked to its origin. However, authenticating honey is challenging due to sophisticated adulteration techniques. This current research introduces an innovative approach employing YOLOv7, a cutting-edge object detection model, to detect and classify honey pollens, thereby bolstering the authentication of honey. Our methodology involved creating a data set comprising three well-known honey varieties (Sundarban, Litchi, and Mustard), supplemented by three sets of unidentified honey pollen images sourced from Kaggle. Subsequently, we assembled a data set consisting of 3000 images representing the pollen types extracted from the known honey samples. To tackle the challenge of limited sample sizes, we employed data augmentation techniques. The efficacy of our approach was evaluated using established statistical measures including detection accuracy, precision, recall, mAP value, and F1 score, yielding impressive values of 98.3, 99.3, 100, 99.2%, and 0.985, respectively. The YOLOv7 model's reliability was validated using Kaggle's unknown honey pollen data sets, which showed that it correctly detected and identified these new pollens based on previous training. Through rigorous experimentation and validation, our study underscores the potential of the YOLOv7 framework in revolutionizing quality control practices within the honey industry, ensuring consumers access to genuine and top-tier honey products through pollen image analysis.

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A comprehensive review of the current trends and recent advancements on the authenticity of honey

Cited by 19 publications

References 97 publications

Establishment of rapid extraction and sensitive detection system of trace corn syrup DNA in honey

Establishment of rapid extraction and sensitive detection system of trace corn syrup DNA in honey

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

Detection and Identification of Honey Pollens by YOLOv7: A Novel Framework toward Honey Authenticity

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