Non-Targeted Detection and Quantification of Food Adulteration of High-Quality Stingless Bee Honey (SBH) via a Portable LED-Based Fluorescence Spectroscopy

Abstract

Stingless bee honey (SBH) is rich in phenolic compounds and available in limited quantities. Authentication of SBH is important to protect SBH from adulteration and retain the reputation and sustainability of SBH production. In this research, we use portable LED-based fluorescence spectroscopy to generate and measure the fluorescence intensity of pure SBH and adulterated samples. The spectrometer is equipped with four UV-LED lamps (peaking at 365 nm) as an excitation source. Heterotrigona itama, a popular SBH, was used as a sample. 100 samples of pure SBH and 240 samples of adulterated SBH (levels of adulteration ranging from 10 to 60%) were prepared. Fluorescence spectral acquisition was measured for both the pure and adulterated SBH samples. Principal component analysis (PCA) demonstrated that a clear separation between the pure and adulterated SBH samples could be established from the first two principal components (PCs). A supervised classification based on soft independent modeling of class analogy (SIMCA) achieved an excellent classification result with 100% accuracy, sensitivity, specificity, and precision. Principal component regression (PCR) was superior to partial least squares regression (PLSR) and multiple linear regression (MLR) methods, with a coefficient of determination in prediction (R2p) = 0.9627, root mean squared error of prediction (RMSEP) =4.1579%, ratio prediction to deviation (RPD) = 5.36, and range error ratio (RER) = 14.81. The LOD and LOQ obtained were higher compared to several previous studies. However, most predicted samples were very close to the regression line, which indicates that the developed PLSR, PCR, and MLR models could be used to detect HFCS adulteration of pure SBH samples. These results showed the proposed portable LED-based fluorescence spectroscopy has a high potential to detect and quantify food adulteration in SBH, with the additional advantages of being an accurate, affordable, and fast measurement with minimum sample preparation.

Adulteración de la miel de abeja

“…Así mismo, se puede utilizar un método óptico laser para predecir la posible sustancia utilizada en la adulteración como agua, almidón, solución azucarada, entre otros (El-Raie et al, 2015); además, de que se puede emplear infrarrojo cercano (NIR) (Tan et al, 2021;Raypah et al, 2022a, b), espectroscopía infrarroja por transformada de Fourier (FTIR) (Damto et al, 2023), metabolómica de 1H-NMR no dirigida con quimiometría (Yong et al, 2022), espectroscopía de fluorescencia portátil basada en LED (Suhandy et al, 2023) e inclusive un refractómetro digital de bolsillo (Lyasota et al, 2023), para detectar miel adulterada.…”

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

“…Current methods involve advanced analytical tools coupled with chemometrics [13]. Physicochemical [14][15][16][17], elemental [18], isotopic [19], chromatographic, and hyphenated mass spectrometry methods [20,21], NMR [22,23], DNA-based [24,25], electronic sensing [26,27], and spectroscopic [28][29][30][31][32] techniques are nowadays the most used for predicting botanical and geographical origin, or detecting adulterants [33]. Usually, botanical issues are investigated using chromatographic techniques and physicochemical analyses [12,14,34].…”

The Authentication of Gayo Arabica Green Coffee Beans with Different Cherry Processing Methods Using Portable LED-Based Fluorescence Spectroscopy and Chemometrics Analysis

“…LED-based fluorescence spectroscopy with multiple LED excitation lamps will have a low spectral resolution compared with conventional spectroscopy systems [26][27][28]. On the other hand, Suhandy et al [29] reported an authentication method based on portable and single LED-based fluorescence spectroscopy to assess adulteration in stingless bee honey (SBH). Preliminary laboratory work on the application of LEDbased fluorescence spectroscopy on instant coffee geographic discrimination has been reported [30].…”