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Milk is a valuable contributor to a healthy diet as it contains nutritional components such as fats, proteins, carbohydrates, calcium, phosphorous and vitamins. This research aimed to differentiate milk from animal, plant and human sources based on light propagation and random-laser properties. Experimental, statistical and theoretical analyses were used. Light propagation in different types of milk such as almond milk, oat milk, soy milk, fresh milk, goat milk and human breast milk was measured using the spectrometry method. Near-IR and visible light transmission through the diluted milk samples were compared. Soy milk and fresh milk have the highest absorbance and fluorescence of light, respectively, due to a high content of fat, protein and carbohydrates. Principal component analysis was used to determine the accuracy of the experimental results. The research method is comprehensive as it covers light propagation from 350 nm to 1650 nm of wavelength range and non-intrusive as it does not affect the sample. Meanwhile, analysis of milk was also conducted based on random-laser properties such as multiple emission peaks and lasing threshold. Higher fat content in milk produces a lower random lasing threshold. Thus, we found that milk from animals, plants and humans can be analyzed using light absorption, fluorescence and random lasers. The research method might be useful for future study of milk contaminants that change the properties of milk.

This study focuses on the discrimination of extracted animal fats in liquid form using laser induced breakdown spectroscopy (LIBS) technique assisted with principal component analysis (PCA). The interaction of laser and liquid sample produces liquid splashing due to strong shock wave effect and subsequently generates lower intensity of LIBS signals. LIBS difficulties in liquid are resolved using paper substrate to enhance LIBS emission intensity. Laser pulse from Q-switched Nd:YAG laser with energy of 220 mJ and frequency of 1 Hz was used to ablate extracted animal fats. The obtained LIBS spectra of extracted animal fats were further evaluated using PCA. LIBS spectra are compressed and visualised as data points in the score plot of PCA. PCA results demonstrated that data points from different extracted animal fats were clustered separately in the score plot with variance greater than 90%. The findings show LIBS system assisted with PCA was capable to differentiate various extracted animal fats.

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