Classification and Prediction of Bee Honey Indirect Adulteration Using Physiochemical Properties Coupled with K-Means Clustering and Simulated Annealing-Artificial Neural Networks (SA-ANNs)

Abstract

The higher demand and limited availability of honey led to different forms of honey adulteration. Honey adulteration is either direct by addition of various syrups to natural honey or indirect by feeding honey bees with sugar syrups. Therefore, a need has emerged for reliable and cost-effective quality control methods to detect honey adulteration in order to ensure both safety and quality of honey. In this study, honey is adulterated by feeding honey bees with various proportions of sucrose syrup (0 to 100%). Various physiochemical properties of the adulterated honey are studied including sugar profile, pH, acidity, moisture, and color. The results showed that increasing sucrose syrup in the feed resulted in a decrease in glucose and fructose contents significantly, from 33.4 to 29.1% and 45.2 to 35.9%, respectively. Sucrose content, however, increased significantly from 0.19 to 1.8%. The pH value increased significantly from 3.04 to 4.63 with increase in sucrose feed. Acidity decreased slightly but nonsignificantly with increase in sucrose feed and varied between 7.0 and 4.00 meq/kg for 0% and 100% sucrose, respectively. Honey’s lightness (L value) also increased significantly from 59.3 to 68.84 as sucrose feed increased. Other color parameters were not significantly changed by sucrose feed. K-means clustering is used to classify the level of honey adulteration by using the above physiological properties. The classification results showed that both glucose content and total sugar content provided 100% accurate classification while pH values provided the worst results with 52% classification accuracy. To further predict the percent honey adulteration, simulated annealing coupled with artificial neural networks (SA-ANNs) was used with sugar profile as an input. RBF-ANN was found to provide the best prediction results with SSE = 0.073, RE = 0.021, and overall R2 = 0.992. It is concluded that honey sugar profile can provide an accurate and reliable tool for detecting indirect honey adulteration by sucrose solution.

Determination of the Carbohydrate Profile and Invertase Activity of Adulterated Honeys after Bee Feeding

“…Likely, Cordella et al [3] found that the adulteration with glucose-fructose-maltose syrups resulted in modifications of the honey samples' sugar composition. Furthermore, Al-Mahasneh et al [26] noted that increasing sucrose syrup in the feeding resulted in a significant decrease in glucose and fructose contents. On the other hand, according to Paradkar and Irudayaraj [27], feeding colonies with small amounts of inverted syrups had a significant effect on the indicated honey's fructose and glucose levels.…”

Adulteración de la miel de abeja

“…Las condiciones ambientales y el manejo técnico del apiario son los principales factores que influyen en el rendimiento, calidad de la miel y otros productos apícolas (Medina-Cuéllar et al, 2014); más aún, prácticas de alimentación inapropiadas en la colmena podrían afectar la fortaleza de las colmenas y derivar en la adulteración indirecta del contenido de azúcares en la miel (Cordella et al, 2005;Kandolf et al, 2015Kandolf et al, , 2017, la cual puede detectarse eficazmente utilizando un algoritmo de agrupamiento de K medias basado en el contenido de glucosa o el contenido total de azúcar en la miel, un método de medición fácil y económico (Al-Mahasneh et al, 2021). En consideración, (Atencio et al 2023), presentaron los beneficios para la salud al consumir miel de abeja pura, entre los que figuran propiedades antibacterianas, antioxidantes y antiinflamatorias (útiles para el tratamiento de heridas, quemaduras, padecimientos gástricos, respiratorios, oftálmicos y dérmicos, por citar algunos), estímulo del crecimiento de tejidos y fortalecimiento de las defensas del cuerpo y ayuda a recuperarse de la fatiga.…”

Enhancing Honey Adulteration Detection With Optimal Subspace Wavelength Reduction in Vis-NIR Reflection Spectroscopy

“…The challenge is exacerbated by the resemblance between fake and pure honey, hampering detection [12]. Conventional quality assessment methods, such as physicochemical analyses, exhibit limitations in accurately identifying adulteration [13], [14], [15], [16]. Consequently, the need for innovative methodologies in honey quality control is evident.…”