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  IAEA have just published a book on AI accelerating nuclear applications, science and technology.  Chapter 5 deals with AI applications to food and agriculture, and in particular to food authenticity methods, food fraud detection and traceability. The advantages and limitations for AI, and ML (machine learning) applications are discussed in sample preparation and calibration involved with authenticity methodology, the advantages of data sharing, but with the proviso that data-driven decision-making is only as good as the data used. 

Read the abstract here and the full pdf version of the book and Chapter 5 in the above link

Raman spectroscopy represents an increasingingly useful technique for food authentication being a fast, reliable non-targeted method, requiring a minimum sample preparation step. However, in the case of honey, there are limitations to its application caused by sugar crystallisation effects and fluorescence in dark coloured honeys. Romanian researchers have developed a simple sample preparation of honey by a 1:1 w/v dilution in distilled water, which overcomes the limitations and gives reliable and reproductible spectra. 

Read the abstract here