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Fusion of near-infrared and mid-infrared spectroscopy for the analysis of typical microplastic contaminants in powdered camel milk
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Researchers tested combining two light-based scanning methods to detect tiny plastic particles mixed into powdered camel milk at very low levels. Combining the two methods correctly identified the type of plastic 100% of the time and estimated the amount present with reasonably accurate results for four common plastics.
Microplastics (MPs) are emerging contaminants that pose potential health risks through bioaccumulation. Efficient detection of MPs in complex food matrices, such as high-value camel milk powder (CMP), is critical for food safety. This study investigated the feasibility of using Near-Infrared (NIRS) and Mid-Infrared Spectroscopy (MIRS) combined with data fusion strategy for the rapid qualitative and quantitative analysis of MPs in CMP. Samples with MP concentrations (PP, PE, PET, PS) ranging from 0.01% to 1.00% (w/w) were prepared. The PCA results demonstrated that NIRS and MIRS provide complementary information. NIRS primarily captures overtones and combination bands of fundamental molecular vibrations, whereas MIRS detects fundamental vibrational modes that offer specific structural fingerprints. For qualitative analysis, the PLS-DA model based on low-level fusion achieved the best performance with 100% classification accuracy. For quantitative analysis, mid-level fusion yielded the most robust PLS models, with R 2 CV for PE, PET, PP, and PS reaching 0.745, 0.855, 0.911, and 0.906, respectively. This study establishes a novel, non-destructive, and efficient approach for monitoring microplastic contamination in CMP.
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Evaluation of near infrared spectroscopy for direct detection of common microplastics contamination in camel Milk powder
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Researchers evaluated near infrared spectroscopy as a rapid, non-destructive method for detecting common microplastic contamination in camel milk powder. The technique successfully distinguished between uncontaminated and contaminated samples spiked with polyethylene, polypropylene, polystyrene, and PET at concentrations as low as 0.01% by weight. The findings suggest NIR spectroscopy could serve as a practical quality control tool for screening microplastic contamination in premium dairy products.
Complementarity of portable and benchtop near-infrared spectrometers for microplastic predictive detection in whole camel milk powder
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Scientists tested whether a light-based scanning technology (near-infrared spectroscopy) could detect tiny plastic particles hiding in camel milk powder, using both expensive lab equipment and a smaller, portable version you could use on-site. The portable device wasn't quite as accurate on its own, but when researchers combined data from both devices and applied a correction technique, they got much better results, suggesting this could become a practical, field-ready tool for catching microplastic contamination in food before it reaches consumers.
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Researchers tested whether infrared and Raman spectroscopy could detect microplastics directly in human breast milk without harsh chemical processing that might damage the samples. They found these methods can serve as a useful screening tool for common plastics like polyethylene and polystyrene in milk, which matters because breast milk is a direct route of microplastic exposure for infants.
Identification method of microplastics based on Raman-infrared spectroscopy fusion
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Researchers developed a method that combines Raman and infrared spectroscopy data to improve the identification of microplastics, compensating for the limitations of each technique when used alone. Machine learning was used to process the combined spectral data, improving accuracy for detecting plastics with pigments or additives that interfere with standard measurements.
Improved Microplastic Identification from Simultaneously Collected Photothermal Infrared and Raman Spectra Using Multiview Conformal Prediction
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Scientists have developed a more reliable way to identify tiny plastic particles (microplastics) by combining two different light-based detection methods and using statistics to double-check the results, rather than relying on just one test with an arbitrary cutoff. This matters because accurately spotting microplastics in air, water, and our environment is the first step to understanding how much of this potentially harmful material we're actually exposed to—and this new method proved better at correctly flagging real plastic particles while avoiding false alarms on non-plastic debris.
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