Hyperspectral imaging for predicting the allicin and soluble solid content of garlic with variable selection...

Area of Science:

• Agricultural Science
• Analytical Chemistry
• Spectroscopy

Background:

• Allicin and soluble solid content (SSC) in garlic contribute to its characteristic flavor and aroma.
• Conventional methods for measuring allicin and SSC, such as HPLC and refractometry, are time-consuming, labor-intensive, and destructive.

Purpose of the Study:

• To develop and validate a non-invasive method for predicting allicin and SSC in garlic.
• To explore the utility of hyperspectral imaging combined with chemometrics for quality assessment of garlic.

Main Methods:

• Acquisition of hyperspectral images from 100 garlic cloves across two spectral ranges.
• Application of spectral pre-processing techniques, variable selection algorithms (regression coefficients, VIP, SPA), and calibration models (PLS, LS-SVM).
• Selection of effective wavelengths (EWs) and development of predictive models for allicin and SSC.

Main Results:

• The SPA-LS-SVM model achieved high prediction accuracy for SSC (Rpred2 = 0.90, SEP = 1.01%).
• The VIP-LS-SVM model demonstrated strong prediction for allicin (Rpred2 = 0.83, SEP = 0.19 mg g⁻¹).
• Generated chemical images visualized the spatial distribution of allicin and SSC within garlic cloves.

Conclusions:

• Hyperspectral imaging, coupled with chemometrics, provides a fast and non-invasive approach for predicting allicin and SSC in garlic.
• This method offers a viable alternative to traditional destructive analytical techniques for garlic quality control.