An attempt to classify the botanical origin of honey using visible spectroscopy.

BACKGROUND: The production of honey, and especially the unifloral varieties, is limited by factors such as weather conditions or the availability of nectar flow and honeydew. This results in a deficit in supply leading to the adulteration of honey. If they are not properly labeled, customers cannot distinguish artificial / synthetic products from real honey. Currently, the basic, commonly used method for determining the varieties of honey (botanical origin) is palynological analysis. However, this procedure is quite difficult owing to the dearth of experienced staff in the field of melissopalynology. RESULTS: A method for identifying and classifying natural honey accurately based on its botanical origin has therefore been proposed. This analysis would rely on the visible light spectra transmitted through a relatively thin layer of the substance of interest, regardless of deviations in thickness. We present algorithms for analyzing the transmittance spectra-parametrization based on polynomial approximation (PMA) and applying a method for spectra selection and reduction (SSR) and a classical classification model (decision tree). A comparison is presented of the classification of four varieties of honey, confirmed by pollen analysis, obtained from the analysis of optically measured transmittance spectra of the samples. The algorithms that are compared contain a decision tree that uses raw data, data reduced by principal component analysis (PCA), and data after calculations based on the proposed algorithms alone (PMA and SSR) and together with the PCA method. CONCLUSION: This novel method produced outstanding results in comparison with the standard PCA method and is helpful in identifying the botanical origin of honey effectively. © 2021 Society of Chemical Industry.

Spectral technique for detection of changes in eggshells caused by Mycoplasma synoviae.

Mycoplasma synoviae (MS) is a major pathogen in chicken and turkeys, causing subclinical infection. MS infections are highly prevalent and may potentate and be involved in sinovitis, respiratory syndromes, as well as lead to eggshell apex abnormality (EAA). A deformed, inhomogeneous eggshell is susceptible to cracks and breaks through which microbes get in and additionally entails higher water loss in the egg during the entire incubation process. Not all eggs with eggshell apex abnormality possess characteristic deformation and that is why some eggs may be incorrectly classified during a visual inspection. To minimize the above risk, the spectral VIS technique and the analysis based on the classification tree method-CTM is proposed. The method makes use of specially defined parameters extracted from the shape of transmittance spectra of eggshells. Directional coefficients of the lines adjusted to the specific ranges of the transmittance spectrum are used in the process of classifying samples as those from MS-carrying hens and from healthy hens. Three CTM-based classifiers were created for a group of white, brown, and mixed shells. After comparing, it can be concluded that the best results were obtained for the group of brown shells (accuracy 88%, specificity 88%, and false negative rate 13%). The authors present a non-invasive spectral method that utilizes eggshells, i.e., the natural waste from chicken farms. The method enables entering data into the classifiers described in the article. The process provides an opportunity to correctly assign, the examined shell to the group of shells with increased risk-with approx. 86% accuracy. This means that, if a few of such results are registered, the herd is eligible more specific studies targeting MS bacteria. Regular spectral testing can support the detection of egg lesions in MS positive flocks.