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.
Fluorescent carbon dots (CDs) have become a potent and adaptable nanomaterial in recent years for the sensitive and specific detection of heavy metal ions. Ferric ion (Fe3+) is one of the most damaging metal ions that can infiltrate the human body and the environment. In this study, blue-emitting carbon dots (CDs) were successfully synthesized from lemon juice using a hydrothermal process. The sensitivity of CDs to Fe3+ ions was examined using various concentrations of Fe3+ (0-400 µM) under different pH conditions (3, 5, 7, 9, 11, and 13) by measuring UV-Vis absorbance at 200-700 nm. The findings showed that the interaction between CDs and Fe3+ is significantly influenced by pH, resulting in varying absorbance enhancement responses. To get more profound insights into this pH-dependent performance, multivariate analysis techniques, including principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares regression (PLSR) were utilized. By combining these techniques with experimental data, significant correlations between pH levels, π - π ∗ and n - π ∗ electronic transitions of CDs, and Fe3⁺ sensing performance were identified. According to the PLSR model, pH 7 is ideal for real-world uses since it offers the optimum balance between Fe3⁺ solubility and CDs sensing capabilities, closely matching environmental and physiological conditions. This work contributes to the knowledge of CDs-based sensing mechanisms and emphasizes the value of multivariate analysis in boosting material performance for real-world applications in the biochemical and environmental domains.