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A simple approach for rapid detection and quantification of adulterants in stingless bees (Heterotrigona itama) honey

Se, Kuan Wei and Ghoshal, Sib Krishna and Abdul Wahab, Roswanira and Raja Ibrahim, Raja Kamarulzaman and Lani, Mohd. Nizam (2018) A simple approach for rapid detection and quantification of adulterants in stingless bees (Heterotrigona itama) honey. Food Research International, 105 (NA). pp. 453-460. ISSN 0963-9969

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Official URL: http://dx.doi.org/10.1016/j.foodres.2017.11.012


In this study, we propose an easy approach by combining the Fourier transform infrared and attenuated total reflectance (FTIR-ATR) spectroscopy together with chemometrics analysis for rapid detection and accurate quantification of five adulterants such as fructose, glucose, sucrose, corn syrup and cane sugar in stingless bees (Heterotrigona itama) honey harvested in Malaysia. Adulterants were classified using principal component analysis and soft independent modeling class analogy, where the first derivative of the spectra in the wavenumber range of 1180–750 cm− 1 was utilized. The protocol could satisfactorily discriminate the stingless bees honey samples that were adulterated with the concentrations of corn syrup above 8% (w/w) and cane sugar over 2% (w/w). Feasibility of integrating FTIR-ATR with chemometrics for precise quantification of the five adulterants was affirmed using partial least square regression (PLSR) analysis. The study found that optimal PLSR analysis achieved standard error of calibrations and standard error of predictions within an acceptable range of 0.686–1.087% and 0.581–1.489%, respectively, indicating good predictive capability. Hence, the method developed here for detecting and quantifying adulteration in H. itama honey samples is accurate and rapid, requiring only 7–8 min to complete as compared to 3 h for the standard method, AOAC method 998.12.

Item Type:Article
Uncontrolled Keywords:stingless bee (Heterotrigona itama), adulteration, FTIR, principle component analysis, soft independent modeling class analogy, partial least square regression
Subjects:Q Science > QC Physics
ID Code:97084
Deposited By: Yanti Mohd Shah
Deposited On:12 Sep 2022 15:59
Last Modified:12 Sep 2022 15:59

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