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To explore a fast, simple, and accurate method to identify adulteration in flaxseed oil, the Raman spectral data of 130 samples containing flaxseed, canola, cottonseed, and adulterated oils were obtained using a portable fiber optic Raman spectrometer. The Raman spectral results showed that the Raman spectra of the flaxseed and canola oils had noticeable peak shifts, whereas the peak positions of the flaxseed and cottonseed oils were relatively similar. Clear peak intensity differences were observed in the flaxseed, cottonseed, and canola oils, mainly at 868 cm–1, 1022 cm–1, 1265 cm–1, and 1655 cm–1, with Raman shift intensities in the following order: Iflaxseed oil > Icottonseed oil > Icanola oil. Similarly, the peak intensity of the flaxseed and adulterated oils also exhibited certain differences (at 868 cm–1, 1022 cm–1, 1265 cm–1, and 1655 cm–1), and the Raman shift intensity tended to decrease gradually with the increasing content of canola and cottonseed oils in the flaxseed oil. Additionally, the results of Raman spectroscopy combined with the “oil microscopy” method exhibited large variations in the radar patterns of the flaxseed, canola, and cottonseed oils, whereas the radar patterns of the flaxseed and adulterated oils closely resembled each other. The results indicated that Raman spectroscopy in combination with oil microscopy more effectively revealed the subtle differences in the Raman shift intensity, serving as a more visual and comprehensive approach for differentiating the quality variations between pure flaxseed oil and other oil species and adulterated oil. Practical Application This study analyzed the Raman spectra of flaxseed, canola, cottonseed, and adulterated oils using fiber optic Raman spectroscopy. Combined with the oil microscopy method for comprehensive evaluation and analysis, it is feasible to effectively identify the quality differences among flaxseed, canola, cottonseed, and adulterated oils.

This work proposes a fast and accurate method based on near-infrared (NIR) spectroscopy with partial least-squares discriminant analysis (PLS-DA) and aquaphotomics to identify toxic honeys. PLS-DA was used to construct an optimal model for distinguishing toxic honey from non-toxic honey. The models based on preprocessed NIR spectra have an accuracy of 92.73% and were more accurate than the model based on raw NIR spectra. Based on the aquaphotomics analysis of the first overtone of water (1300–1600 nm), we found that the 1398 nm, 1440 nm, and 1472 nm bands can be used as markers to distinguish toxic honeys. Compared to non-toxic honeys, G. elegans-containing toxic honeys have a significantly smaller number of water molecules with multiple hydrogen bonds, due to the hydrogen bonding of the C-O-C, C = O, and NH2 groups of gelsemine and koumine. These groups replace hydrogen bonds between glucose/polysaccharide molecules and water.

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