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This study assessed the potential application of fluorescence spectroscopy in detecting adulteration of milk fat with vegetable oil and characterizing the samples according to the source of the fat. Pure butterfat was adulterated with different vegetable oils at various concentrations (0, 5, 10, 15, 20, 30, and 40%). Nonfat and reduced-fat milk were also adulterated with vegetable oils to simulate full-fat milk (3.2%). The 2- and 3-dimensional front-face fluorescence spectroscopy and gas chromatography were used to obtain the fluorescence spectra and fatty acid profile, respectively. Principal component analysis and 3-way partial least squares regression analysis were applied to analyze the data. The pure and adulterated samples were discriminated based on the total concentration of saturated fatty acids and unsaturated fatty acids, and also on the 3 major fluorophores: tryptophan, tocopherols, and riboflavin. Fluorescence spectroscopy was able to detect up to 5% of adulteration of vegetable oil into the butterfat. The saturated fatty acids showed higher predictability than the unsaturated fatty acids (R(2) = 0.73-0.92 vs. 0.20-0.65, respectively). The study demonstrated the high potential of fluorescence spectroscopy to rapidly detect adulteration of milk fat with vegetable oil, and discriminate commercial butter and milk according to the source of the fat.

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Thirteen milk brands comprising 76 pasteurized and UHT milk samples of various compositions (whole, reduced fat, skimmed, low lactose, and high protein) were obtained from local supermarkets, and milk samples manufactured in various countries were discriminated using front-face fluorescence spectroscopy (FFFS) coupled with chemometric tools. The emission spectra of Maillard reaction products and riboflavin (MRP/RF; 400 to 600 nm) and tryptophan (300 to 400 nm) were recorded using FFFS, and the excitation wavelengths were set at 360 nm for MRP/RF and 290 nm for tryptophan. Principal component analysis (PCA) was applied to analyze the normalized spectra. The PCA of spectral information from MRP/RF discriminated the milk samples originating in different countries, and PCA of spectral information from tryptophan discriminated the samples according to composition. The fluorescence spectral data were compared with liquid chromatography-mass spectrometry results for the glycation extent of the milk samples, and a positive association (R(2)=0.84) was found between the degree of glycation of α-lactalbumin and the MRP/RF spectral data. This study demonstrates the ability and sensitivity of FFFS to rapidly discriminate and classify commercial milk with various compositions and processing conditions.

In this work, an efficient and green approach has been presented to prepare carbon quantum dots (CQDs) from watermelon juice through a microfluidic steam-based method, with a view to enabling continuous production at scale, i.e., to save time, costs, or energy as compared to conventional production using an autoclave. The evolution of the product formation through multifarious intermediates generated in different stages of the reaction process was characterized. Computational fluid dynamics simulations reveal the pressure and velocity profiles in the microchannel to exert process control. These determine the quality of the obtained CQDs by influencing the particle size transformations and manifold chemicals along the microchannel axis. The optimal reaction conditions and reaction mechanism for the synthesis of CQDs were investigated. Additionally, the synthesized CQDs demonstrated good fluorescence properties as well as a specific response to NO 2 − {\text{NO}}_{2}^{-} in both fluorescence and spectrophotometric modes, providing great potential for their application in environmental monitoring.