An optimized green preparation method for the successful application of Raman spectroscopy in honey studies

Botanical honey recognition and quantitative mixture detection based on Raman spectroscopy and machine learning

The development of new approaches for honey recognition, based on spectroscopic techniques, presents a huge market potential especially because of the fast development of portable equipment. As an emerging approach, the association between Raman spectroscopy and Artificial Intelligence (i.e. Machine Learning algorithms) for food and beverages recognition starts to prove its efficiency, becoming an important candidate for the development of a practical application. Through this study, new recognition models for the rapid and efficient botanical differentiation of investigated honey varieties were developed, allowing the correct prediction of each type in a percentage better than 81%. The performances of the constructed models were expressed in terms of precision, sensitivity, and specificity. Moreover, through this approach, the detection of honey mixtures was possible to be made and an estimative percentage of the mixture components was obtained. Thus, the applicative potential of this new approach for honey recognition as well as a qualitative and quantitative estimation of the honey mixture was demonstrated.

Determination of sugar content in honey using LC-Raman and programmable pump-Raman methods

We combined (i) liquid chromatography and Raman spectrometry (LC-Raman) and (ii) programmable pump and Raman spectrometry (PP-Raman) to separate and identify compounds in a mixture. These techniques were applied to conduct a quantitative analysis of the sugars in honey. The spectral and temporal axes of the LC-Raman data were analyzed using the MCR-ALS analysis procedure, which enabled the separation and identification of four sugars (glucose, fructose, sucrose, and trehalose). The PP-Raman method was employed to examine the sugar concentration dependence of the intensity pattern of the Raman spectrum, and the linear concentration dependence of the intensity was obtained. The sugar contents were quantitatively determined from the integrated area of the elution peaks. The result was consistent with those derived from mass spectrometry and previous studies. The origin of the errors in the derived sugar contents is discussed. Our study presents a novel quantitative LC-Raman spectrometric method that does not rely on resonance or surface enhancement effects.

Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics

Honey is a natural product that is considered globally one of the most widely important foods. Various studies on authenticity detection of honey have been fulfilled using visible and near-infrared (Vis-NIR) spectroscopy techniques. However, there are limited studies on stingless bee honey (SBH) despite the increase of market demand for this food product. The objective of this work was to present the potential of Vis-NIR absorbance spectroscopy for profiling, classifying, and quantifying the adulterated SBH. The SBH sample was mixed with various percentages (10−90%) of adulterants, including distilled water, apple cider vinegar, and high fructose syrup. The results showed that the region at 400−1100 nm that is related to the color and water properties of the samples was effective to discriminate and quantify the adulterated SBH. By applying the principal component analysis (PCA) on adulterants and honey samples, the PCA score plot revealed the classification of the adulterants and adulterated SBHs. A partial least squares regression (PLSR) model was developed to quantify the contamination level in the SBH samples. The general PLSR model with the highest coefficient of determination and lowest root means square error of cross-validation (RCV2=0.96 and RMSECV=5.88 %) was acquired. The aquaphotomics analysis of adulteration in SBH with the three adulterants utilizing the short-wavelength NIR region (800−1100 nm) was presented. The structural changes of SBH due to adulteration were described in terms of the changes in the water molecular matrix, and the aquagrams were used to visualize the results. It was revealed that the integration of NIR spectroscopy with aquaphotomics could be used to detect the water molecular structures in the adulterated SBH.