Publication: Analysis Of Lard In Palm Oil Using Long‑wave Near‑infrared (lw‑nir) Spectroscopy And Gas Chromatography‑mass Spectroscopy (gc–ms)
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Date
2022
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Publisher
Springer Nature
Abstract
Adulteration of food products has become a common problem in many countries. Adulteration may take the form of substitution of one species for another, where the food products from one species have been mixed intentionally with either a similar substitute material or a cheaper species. However, the use of pork and lard is a serious matter in Islam because foods containing ingredients from pig sources are haram (unlawful or prohibited) for Muslims to consume. Conventional techniques such as chromatography are time-consuming and require high capital cost. A possible way out is to use the rapid and less expensive near-infrared (NIR) spectroscopy. Therefore, in this study, apart from the conventional gas chromatography analysis, a variant of NIR spectroscopy, i.e., the long-wave NIR (LW-NIR) spectroscopy system at 1350–2450 nm region in combination with chemometrics analysis was used for detecting and quantifying lard adulteration in palm oil (PO). The result has shown that the samples with a minimum level of adulteration as low as 0.5% could still be easily detected with an overall correct classification rate using linear discriminant analysis (LDA) in the Open-source R software. PLS calibration gives good results between predicted and measured data in quantification, showing linear correlation with coefficient of determination (R2) at 0.9987. The result agreed well with the gas chromatography-mass spectroscopy (GC–MS) analysis and suggests the feasibility of LW-NIR spectroscopy as an efficient method for detecting and quantifying lard in palm oil.
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Keywords
Long-wave NIR Spectroscopy , LW-NIR , Lard , Palm Oil , Chemometrics, Adulteration
Citation
Cite this article Hussain, M.N., Basri, K.N., Arshad, S. et al. Analysis of Lard in Palm Oil Using Long-Wave Near-Infrared (LW-NIR) Spectroscopy and Gas Chromatography-Mass Spectroscopy (GC–MS). Food Anal. Methods 16, 349–355 (2023). https://doi.org/10.1007/s12161-022-02423-y