Honey Authentication: A review of the issues and challenges associated with honey adulteration

Bose, Debalina; Padmavati, Manchikanti

doi:10.1016/j.fbio.2024.105004

Food Bioscience

2024

DOI: 10.1016/j.fbio.2024.105004

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Honey Authentication: A review of the issues and challenges associated with honey adulteration

Debalina Bose,

Manchikanti Padmavati

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Cited by 3 publications

References 67 publications

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Machine vision combined with deep learning–based approaches for food authentication: An integrative review and new insights

Shen,

Wang,

Nawazish

et al. 2024

Comp Rev Food Sci Food Safe

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Food fraud undermines consumer trust, creates economic risk, and jeopardizes human health. Therefore, it is essential to develop efficient technologies for rapid and reliable analysis of food quality and safety for food authentication. Machine vision–based methods have emerged as promising solutions for the rapid and nondestructive analysis of food authenticity and quality. The Industry 4.0 revolution has introduced new trends in this field, including the use of deep learning (DL), a subset of artificial intelligence, which demonstrates robust performance and generalization capabilities, effectively extracting features, and processing extensive data. This paper reviews recent advances in machine vision and various DL‐based algorithms for food authentication, including DL and lightweight DL, used for food authenticity analysis such as adulteration identification, variety identification, freshness detection, and food quality identification by combining them with a machine vision system or with smartphones and portable devices. This review explores the limitations of machine vision and the challenges of DL, which include overfitting, interpretability, accessibility, data privacy, algorithmic bias, and design and deployment of lightweight DLs, and miniaturization of sensing devices. Finally, future developments and trends in this field are discussed, including the development of real‐time detection systems that incorporate a combination of machine vision and DL methods and the expansion of databases. Overall, the combination of vision‐based techniques and DL is expected to enable faster, more affordable, and more accurate food authentication methods.

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Machine vision combined with deep learning–based approaches for food authentication: An integrative review and new insights

Shen,

Wang,

Nawazish

et al. 2024

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

show abstract

Preliminary Studies on the Use of an Electrical Method to Assess the Quality of Honey and Distinguish Its Botanical Origin

Wilczyńska,

Banach,

Żak

et al. 2024

Applied Sciences

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This study aimed to determine the possibility of deploying an innovative electrical method and to establish the usefulness of conductivity and dielectric parameters for assessing the quality of Polish honeys, as well as for distinguishing their botanical origin. An attempt was also made to determine which standard physicochemical parameter could be replaced by conductivity and dielectric parameters. The experimental material consisted of seven varieties of honey (linden, rapeseed, buckwheat, goldenrod, phacelia, multifloral, acacia), obtained from beekeepers from northern Poland. Their quality was assessed based on their physicochemical parameters, biological activity, and color. Electrical parameters were measured using a measuring system consisting of an LCR meter, and own-construction sensor. Conductivity (Z, G) and dielectric (Cs, Cp) parameters were measured. Statistical analysis of the results of measurements of electrical parameters of the seven types of honey tested allowed classifying them in terms of their conductivity properties into two groups of single-flower honeys and one group of multi-flower honeys. This proves the feasibility of identifying their botanical origin using the electrical method, which is characterized by non-invasiveness, measurement speed, and high sensitivity. The usefulness of parameters Z and G in replacing quality parameters was confirmed mainly for single-flower honeys: buckwheat, linden, rapeseed, and phacelia.

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Rapid Authentication of Intact Stingless Bee Honey (SBH) by Portable LED-Based Fluorescence Spectroscopy and Chemometrics

Suhandy,

Al Riza,

Yulia

et al. 2024

Foods

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Indonesian stingless bee honey (SBH) of Geniotrigona thoracica is popular and traded at an expensive price. Brown rice syrup (RS) is frequently used as a cheap adulterant for an economically motivated adulteration (EMA) in SBH. In this study, authentic Indonesian Geniotrigona thoracica SBH of Acacia mangium (n = 100), adulterated SBH (n = 120), fake SBH (n = 100), and RS (n = 200) were prepared. In short, 2 mL of each sample was dropped directly into an innovative sample holder without any sample preparation including no dilution. Fluorescence intensity was acquired using a fluorescence spectrometer. This portable instrument is equipped with a 365 nm LED lamp as the fixed excitation source. Principal component analysis (PCA) was calculated for the smoothed spectral data. The results showed that the authentic SBH and non-SBH (adulterated SBH, fake SBH, and RS) samples could be well separated using the smoothed spectral data. The cumulative percentage variance of the first two PCs, 98.4749% and 98.4425%, was obtained for calibration and validation, respectively. The highest prediction accuracy was 99.5% and was obtained using principal component analysis–linear discriminant analysis (PCA-LDA). The best partial least square (PLS) calibration was obtained using the combined interval with R2cal = 0.898 and R2val = 0.874 for calibration and validation, respectively. In the prediction, the developed model could predict the adulteration level in the adulterated honey samples with an acceptable ratio of prediction to deviation (RPD) = 2.282, and range error ratio (RER) = 6.612.

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Honey Authentication: A review of the issues and challenges associated with honey adulteration

Cited by 3 publications

References 67 publications

Machine vision combined with deep learning–based approaches for food authentication: An integrative review and new insights

Machine vision combined with deep learning–based approaches for food authentication: An integrative review and new insights

Preliminary Studies on the Use of an Electrical Method to Assess the Quality of Honey and Distinguish Its Botanical Origin

Rapid Authentication of Intact Stingless Bee Honey (SBH) by Portable LED-Based Fluorescence Spectroscopy and Chemometrics

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