LAUSR

Abstract To classify apple juices based on their volatile composition and aroma properties, and to determine the correlation between the volatile compounds and odor attributes, the aroma profiles of 31 apple juice samples from four varieties and four PDO regions (China) were determined. Thirty-nine volatile compounds were identified by headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Twenty-three odor attributes belonging to five categories (fruity, vegetable, spices, floral and other) were detected via descriptive sensory analysis. Principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were separately used on the volatile concentrations and geometric means of the aroma descriptors to establish classification models. PCA and SLDA allowed the discrimination of the juice samples from different apple varieties according to the GC and sensory datasets. For the samples from different geographical origins, SLDA provided a 93.5% prediction accuracy based on both datasets. Both the volatile composition and aroma characteristics could serve as effective indices to determine the variety and geographic origin. Moreover, partial least squares regression (PLSR) yielded satisfactory models to predict seven aroma descriptors in apple juices (melon, pineapple, banana, apple, strawberry, cut green and hay) and clarified the effects of volatile components on the formation of these aroma sensations.