This paper aims to develop a Machin e Learning (ML) - based system for detecting honey adulteration utilizing honey mineral element profiles. The proposed system comprises two phases: preprocessing and classification. The preprocessing phase involves the treatment of missing - value attributes a nd normalization. In the classification phase, we use three supervised ML models: logistic regression, d ecision tree, and random forest, to discriminate between authentic and adulterated honey. To evaluate the performance of the ML models, we use a public dataset comprising measurements of mineral element content of authentic honey, sugar syrups, and adulterated honey. Experimental findings show that mineral element content in honey provides robust discriminative information for detecting honey adulteration . Results also dem onstrate that the random forest - based classifier outperforms other classifiers on this dataset, achieving the highest cross - validation accuracy of 98.37%.

This paper aims to develop a machine learning-based system for automatically detecting honey adulteration with sugar syrup, based on honey hyperspectral imaging data. First, the floral source of a honey sample is classified by a botanical origin identification subsystem. Then, the sugar syrup adulteration is identified, and its concentration is quantified by an adulteration detection subsystem. Both subsystems consist of two steps. The first step involves extracting relevant features from the honey sample using Linear Discriminant Analysis (LDA). In the second step, we utilize the K-Nearest Neighbors (KNN) model to classify the honey botanical origin in the first subsystem and identify the adulteration level in the second subsystem. We assess the proposed system performance on a public honey hyperspectral image dataset. The result indicates that the proposed system can detect adulteration in honey with an overall cross-validation accuracy of 96.39%, making it an appropriate alternative to the current chemical-based detection methods.

This paper proposes a machine learning-based approach for identifying honey floral and geographical sources using mineral element profiles. The proposed method comprises two steps: preprocessing and classification. The preprocessing phase involves missing-value treatment and data normalization. In the classification phase, we employ various supervised classification models for discriminating between six botanical sources and 13 geographical origins of honey. We test the classifiers' performance on a publicly available honey mineral element dataset. The dataset contains mineral element profiles of honeys from various floral and geographical origins. Results show that mineral element content in honey provides discriminative information useful for classifying honey botanical and geographical sources. Results also show that the Random Forests (RF) classifier obtains the best performance on this dataset, achieving a cross-validation accuracy of 99.30% for classifying honey botanical origins and 98.01% for classifying honey geographical origins.

The humble jar of honey might seem sweet and innocent, but experts warn that British shoppers have been getting stung when spending on this staple. Investigations have revealed that unscrupulous honey producers around the world bulk out their products with cheap sugars that are almost impossible to detect. However, scientists have now developed a test which can easily spot the difference between fake and real honey - without even opening the jar. The light-based technique can detect the unique chemical signature of real honey as well as the syrups that try to imitate it. While the test isn't readily available yet, experts told MailOnline that consumers may be able to spot the frauds in their cupboards using nothing more than their phone torch within five to 10 years.

Honey is a very popular and yet it is one of the most mislabeled food item in the world. All over the world, it is becoming highly difficult to identify real honey. Even trusted suppliers tend to mix ingredients like sugar cane, rice syrups, and corn. Some suppliers also go to the extent of adding toxic elements like animal antibiotics, lead, and other heavy metals to it. All these are not safe for human intake.

A team of researchers at Imperial College London and UCL have recently developed a new method to authenticate honey using machine learning and microscopy. Their technique, outlined in a paper pre-published on arXiv, could detect diluted or mislabeled honey at a far lower cost than existing methods. Honey is produced by bees after they collect nectar from flowers, break it down into simple sugars and store it inside honeycombs. Honey is currently the third most counterfeited food product globally. It is often mislabeled, which entails selling one type of honey for another, or is diluted it with other substances, such as sugar syrup.

Honey has been collected and used by humankind as both a food and medicine for thousands of years. However, in the modern economy, honey has become subject to mislabelling and adulteration making it the third most faked food product in the world. The international scale of fraudulent honey has had both economic and environmental ramifications. In this paper, we propose a novel method of identifying fraudulent honey using machine learning augmented microscopy.