The invention provides an HS-GC-MS and
machine learning-based
component analysis and quality evaluation method and application of katsumadai seeds and processed products thereof, and belongs to the technical field of quality evaluation. According to the method, HS-GC-MS and electronic
nose technologies are adopted,
multivariate statistical methods such as
principal component analysis (PCA) and partial
least squares discriminant analysis (PLS-DA) are combined, the accuracy and efficiency of processed product classification and sensory evaluation are improved, and
odor characteristics and volatile component compositions of
alpinia katsumadai from different producing areas and processed products of the
alpinia katsumadai are deeply analyzed. The result shows that main volatile components in the katsumadai seed and the processed product thereof are similar in composition and mainly comprise 19 olefins, 8 alcohols, 3 aldehydes, 2 ketones, 2 esters, 1
ether and 1
nitrogen-containing compound, the olefins and the ethers account for 95% or above of the total amount of a sample, and the total amount of the
nitrogen-containing compound accounts for 20% or above of the total amount of the sample. Alpha-
caryophyllene, p-cymene,
phellandrene and other olefin components and
ether component
eucalyptol are taken as main components.