Cultivar classification of cloudy apple juices from substandard fruits in China based on aroma profile analyzed by HS-SPME/GC-MS. (March 2019)

• Record Type: Journal Article
• Authors: Liu, Xuan; Deng, Jiankang; Bi, Jinfeng; Wu, Xinye; Zhang, Biao
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 102(2019)
• Page Start: 304
• Page End: 309
• Publication Date: 2019-03
• Subjects: Substandard apple -- Cloudy juice -- Volatile compounds -- Classification -- Multivariate data analysis; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2018.12.043
• Languages: English
• ISSNs: 0023-6438

Abstract

Abstract: Aroma profiles of cloudy apple juices from substandard apple with different cultivars were investigated by headspace solid phase micro-extraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS). 38 volatile compounds (including esters, alcohols, acids, aldehydes, terpenes, etc.) were identified. 30 Cloudy apple juices samples of 5 cultivars in China were characterized and classified by different chemometric analysis based on volatile compounds. Discrimination models were established based on volatile profiles of cloudy apple juices by chemometric techniques including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and stepwise linear discriminant analysis (SLDA). Satisfactory discriminations were achieved by SLDA based on volatile compounds according to cultivar with prediction accuracy of 93.3%. The results suggested that SLDA could be used to discriminate substandard cloudy apple juices based on the volatile compounds. The obtained results could also provide data basis for selecting substandard apple fruits of different cultivars for juice production with better aroma profile. Highlights: Aroma profiles of cloudy juices from 5 substandard apple cultivars were studied. SLDA used to classify cloudy apple juices based on cultivars. VIP scores of SLDA models identified volatiles relevant in apple juice discrimination.