[en] Forty-one apple samples from 7 geographical regions and 3 botanical origins in China were investigated. A total of 29 volatile compounds have been identified by flash GC E-nose. They are 17 esters, 5 alcohols, 3 aldehydes, 1 ketone, and 3 others. A principal component analysis was employed to study the relationship between varieties and volatiles. A partial least squares discriminant analysis (PLS-DA), stepwise linear discriminant analysis (SLDA), and decision tree (DT) are used to discriminate apples from 4 geographical regions (34 apple samples) and 3 botanical origins (36 apple samples). The most influential markers identified by PLS-DA are 2-hexadecanone, methyl decanoate, tetradecanal, 1,8-cineole, hexyl 2-butenoate, (Z)-2-octenal, methyl 2-methylbutanoate, ethyl butyrate, dimethyl trisulfide, methyl formate, ethanol, S(-)2-methyl-1-butanol, ethyl acetate, pentyl acetate, butyl butanoate, butyl acetate, and ethyl octanoate. From the present work, SLDA reveals the best discrimination results in geographical regions and botanical origins, which are 88.2% and 88.9%, respectively. Although machine learning DT is attempted to classify apple samples, the results are not satisfactory.
Disciplines :
Chemistry Agriculture & agronomy
Author, co-author :
Wu, Xinye ; Université de Liège - ULiège > TERRA Research Centre
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Bibliography
Meike, R.; Dean, D.L.; Baird, T. Understanding Apple Attribute Preferences of US Consumers. Foods 2022, 11, 166. [CrossRef] [PubMed]
Zardo, D.M.; Zielinski, A.A.F.; Alberti, A.; Nogueira, A. Phenolic compounds and antioxidant capacity of brazilian apples. Food Nutr. Sci. 2015, 6, 727. [CrossRef]
Zhang, Q. The Evolution and Advantage Evaluation of Apple Production Layout in China. Ph.D. Thesis, Northwest A & F University, Xianyang, China, 2021. [CrossRef]
Lu, X.; Gao, Y.; Wang, K.; Sun, S.M.; Li, L.W.; Li, H.F.; Li, Q.S.; Feng, J.R.; Wang, D.J. Analysis of Aroma Characteristics in Different Cultivated Apple Strains. Sci. Agric. Sin. 2022, 55, 543–557. [CrossRef]
Wu, X.; Bi, J.; Fauconnier, M.L. Characteristic Volatiles and Cultivar Classification in 35 Apple Varieties: A Case Study of Two Harvest Years. Foods 2022, 11, 690. [CrossRef]
Shi, H.; Zhang, M.; Adhikari, B. Advances of electronic nose and its application in fresh foods: A review. Crit. Rev. Food Sci. Nutr. 2018, 58, 2700–2710. [CrossRef]
Xiao, Z.; Yu, D.; Niu, Y.; Chen, F.; Song, S.; Zhu, J.; Zhu, G. Characterization of aroma compounds of Chinese famous liquors by gas chromatography-mass spectrometry and flash GC electronic-nose. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2014, 945, 92–100. [CrossRef]
Goodner, K.; Rouseff, R. Practical Analysis of Flavor and Fragrance Materials. In Practical Analysis of Flavor and Fragrance Materials; Blackwell: West Sussex, UK, 2011. [CrossRef]
Song, J.; Chen, Q.; Bi, J.; Meng, X.; Wu, X.; Qiao, Y.; Lyu, Y. GC/MS coupled with MOS e-nose and flash GC e-nose for volatile characterization of Chinese jujubes as affected by different drying methods. Food Chem. 2020, 331, 127201. [CrossRef]
Jońca, J.; Pawnuk, M.; Arsen, A.; Sówka, I. Electronic Noses and Their Applications for Sensory and Analytical Measurements in the Waste Management Plants—A Review. Sensors 2022, 22, 1510. [CrossRef]
Gliszczyńskaświgło, A.; Chmielewski, J. Electronic nose as a tool for monitoring the authenticity of food. a review. Food Anal. Methods 2017, 10, 1800–1816. [CrossRef]
Baietto, M.; Wilson, A.D. Electronic-nose applications for fruit identification, ripeness and quality grading. Sensors 2015, 15, 899–931. [CrossRef]
Pruteanu, E.M.; Duta, D.E.; Hincu, F.A.; Calu, M. Electronic nose for discrimination of Romanian apples. Lucr. Stiintifice. In Proceedings of the 2nd International Symposium, New Research in Biotechnology” USAMV, Bucharest, Romania, 2009; pp. 398–404. Available online: https://www.researchgate.net/publication/295909056_Electronic_nose_for_discrimination_of_ Romanian_apples (accessed on 16 May 2022).
Cheng, H.; Qin, Z.H.; Guo, X.F.; Hu, X.S.; Wu, J.H. Geographical origin identification of propolis using GC-MS and electronic nose combined with principal component analysis. Food Res. Int. 2013, 51, 813–822. [CrossRef]
Melucci, D.; Bendini, A.; Tesini, F.; Barbieri, S.; Zappi, A.; Vichi, S.; Conte, L.; Gallina Toschi, T. Rapid direct analysis to discriminate geographic origin of extra virgin olive oils by flash gas chromatography electronic nose and chemometrics. Food Chem. 2016, 204, 263–273. [CrossRef] [PubMed]
Rottiers, H.; Tzompa Sosa, D.A.; van de Vyver, L.; Hinneh, M.; Everaert, H.; de Wever, J.; Messens, K.; Dewettinck, K. Discrimination of Cocoa Liquors Based on Their Odor Fingerprint: A Fast GC Electronic Nose Suitability Study. Food Anal. Methods 2019, 12, 475–488. [CrossRef]
Zhou, C.L.; Mi, L.; Hu, X.Y.; Zhu, B.H. Evaluation of three pumpkin species: Correlation with physicochemical, antioxidant properties and classification using SPME-GC–MS and E-nose methods. J. Food Sci. Technol. 2017, 54, 3118–3131. [CrossRef] [PubMed]
Różańska, A.; Dymerski, T.; Namieśnik, J. Novel analytical method for detection of orange juice adulteration based on ultra-fast gas chromatography. Mon. Fur Chem. 2018, 149, 1615–1621. [CrossRef] [PubMed]
Zou, X.; Zhao, J. Comparative analyses of apple aroma by a tin-oxide gas sensor array device and GC/MS. Food Chem. 2008, 107, 120–128. [CrossRef]
Giannetti, V.; Boccacci Mariani, M.; Mannino, P.; Marini, F. Volatile fraction analysis by HS-SPME/GC-MS and chemometric modeling for traceability of apples cultivated in the Northeast Italy. Food Control 2017, 78, 215–221. [CrossRef]
Aprea, E.; Corollaro, M.L.; Betta, E.; Endrizzi, I.; Demattè, M.L.; Biasioli, F.; Gasperi, F. Sensory and instrumental profiling of 18 apple cultivars to investigate the relation between perceived quality and odour and flavour. Food Res. Int. 2012, 49, 677–686. [CrossRef]
Liu, X.; Deng, J.; Bi, J.; Wu, X.; Zhang, B. Cultivar classification of cloudy apple juices from substandard fruits in China based on aroma profile analyzed by HS-SPME/GC-MS. LWT 2019, 102, 304–309. [CrossRef]
Qiu, S.; Gao, L.; Wang, J. Classification and regression of ELM, LVQ and SVM for E-nose data of strawberry juice. J. Food Eng. 2015, 144, 77–85. [CrossRef]
Qiu, S.; Wang, J.; Gao, L. Discrimination and characterization of strawberry juice based on electronic nose and tongue: Comparison of different juice processing approaches by LDA, PLSR, RF, and SVM. J. Agric. Food Chem. 2014, 62, 6426–6434. [CrossRef] [PubMed]
Qiu, S.; Wang, J. Application of Sensory Evaluation, HS-SPME GC-MS, E-Nose, and E-Tongue for Quality Detection in Citrus Fruits. J. Food Sci. 2015, 80, S2296–S2304. [CrossRef] [PubMed]
Gan, Z.; Yang, Y.; Li, J.; Wen, X.; Zhu, M.; Jiang, Y.; Ni, Y. Using sensor and spectral analysis to classify botanical origin and determine adulteration of raw honey. J. Food Eng. 2016, 178, 151–158. [CrossRef]
Buratti, S.; Benedetti, S.; Scampicchio, M.; Pangerod, E.C. Characterization and classification of Italian Barbera wines by using an electronic nose and an amperometric electronic tongue. Anal. Chim. Acta 2004, 525, 133–139. [CrossRef]
Ordukaya, E.; Karlik, B. Quality Control of Olive Oils Using Machine Learning and Electronic Nose. J. Food Qual. 2017, 2017, 9272404. [CrossRef]
Blanpied, G.D.; Silsby, K.J. Predicting Harvest Date Window for Apples. In Information Bulletin 221; Cornell Cooperative Extension; Available online: http://www.uvm.edu/~{}fruit/treefruit/tf_horticulture/AppleHortBasics/Readings/Predicting+Harvest+ Date+Window+for+Apples.pdf (accessed on 3 May 2022).
Fotakis, C.; Zervou, M. NMR metabolic fingerprinting and chemometrics driven authentication of Greek grape marc spirits. Food Chem. 2016, 196, 760–768. [CrossRef]
Cho, J.H.; Kurup, P.U. Decision tree approach for classification and dimensionality reduction of electronic nose data. Sens. Actuators B Chem. 2011, 160, 542–548. [CrossRef]
Güney, S.; Atasoy, A. Multiclass classification of n-butanol concentrations with k-nearest neighbor algorithm and support vector machine in an electronic nose. Sens. Actuators B Chem. 2012, 166, 721–725. [CrossRef]
Dębska, B.; Guzowska-Świder, B. Decision trees in selection of featured determined food quality. Anal. Chim. Acta 2011, 705, 261–271. [CrossRef]
Kondo, S.; Setha, S.; Rudell, D.R.; Buchanan, D.A.; Mattheis, J.P. Aroma volatile biosynthesis in apples affected by 1-MCP and methyl jasmonate. Postharvest Biol. Technol. 2005, 36, 61–68. [CrossRef]
Fellman, J.K.; Rudell, D.R.; Mattinson, D.S.; Mattheis, J.P. Relationship of harvest maturity to flavor regeneration after CA storage of ‘Delicious’ apples. Postharvest Biol. Technol. 2003, 27, 39–51. [CrossRef]
Nie, L.; Sun, J.; Chen, H.; Zou, X. Study on Fruit Aroma of Different Apple Cultivars. Sci. Agric. Sin. 2006, 39, 641–646.
Madrera, R.R.; Valles, B.S. Determination of Volatile Compounds in Apple Pomace by Stir Bar Sorptive Extraction and Gas Chromatography-Mass Spectrometry (SBSE-GC-MS). J. Food Sci. 2011, 76, C1326–C1334. [CrossRef] [PubMed]
He, Y.; Zhang, H.; Wen, N.; Hu, R.; Wu, G.; Zeng, Y.; Li, X.; Miao, X. Effects of maltose and lysine treatment on coffee aroma by flash gas chromatography electronic nose and gas chromatography–mass spectrometry. J. Sci. Food Agric. 2018, 98, 154–165. [CrossRef] [PubMed]
Peng, W.; Meng, D.; Yue, T.; Wang, Z.; Gao, Z. Effect of the apple cultivar on cloudy apple juice fermented by a mixture of Lactobacillus acidophilus, Lactobacillus plantarum, and Lactobacillus fermentum. Food Chem. 2021, 340, 127922. [CrossRef]
Zhou, J.; Zhao, D.; Chen, Y.; Kang, G.; Cheng, C. Analysis of apple producing area changes in China. J. Fruit Sci. 2021, 38, 372–384. [CrossRef]
Espino-Díaz, M.; Sepúlveda, D.R.; González-Aguilar, G.; Olivas, G.I. Biochemistry of apple aroma: A review. Food Technol. Biotechnol. 2016, 54, 375–394. [CrossRef]
Li, Y.; Sun, H.; Li, J.; Qin, S.; Niu, Z.; Qiao, X.; Yang, B. Influence of genetic background, growth latitude and bagging treatment on phenolic compounds in fruits of commercial cultivars and wild types of apples (malus sp.). Eur. Food Res. Technol. 2021, 247, 1149–1165. [CrossRef]
Qin, L.; Wei, Q.; Kang, W.; Zhang, Q.; Sun, J.; Liu, S. Comparison of volatile compounds in ‘fuji’ apples in the different regions in china. Food Sci. Technol. Res. 2017, 23, 79–89. [CrossRef]
Wang, A.; Li, T.; Xu, X.; Han, Z. SSR Ana lysis for Apple Cultivars. Acta Horticulturae Sinica. 2005, 32, 875–877.
Gagaoua, M.; Monteils, V.; Couvreur, S.; Picard, B. Beef tenderness prediction by a combination of statistical methods: Chemomet-rics and supervised learning to manage integrative farm-to-meat continuum data. Foods 2019, 8, 274. [CrossRef] [PubMed]
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