Cider as referred to in this study is the product of fermented apple juice that may contain 0.5% to 8.5% alcohol by volume (ABV) (Alcohol and Tobacco Tax and Trade Bureau, 2015). Cider is the fastest growing segment of the U.S. alcohol beverage market; production increased 22-fold over the last 10 years, from 775,031 gal of cider in 2007 to 17,503,535 gal in 2016 (Alcohol and Tobacco Tax and Trade Bureau, 2017). A major challenge to a sustained growth of the U.S. cider industry is the increasing labor shortages faced by apple growers across the nation (Thilmany, 2001). Cider apples are harvested by hand in the United States, and when labor is unavailable, profits are unrealized from unharvested fruit and their incurred production costs. Since 2011, the Washington State University (WSU) cider program has been evaluating the mechanization of cider apple harvest as a long-term solution to growers’ vulnerable dependency on hand labor. Alexander et al. (2016) and Miles and King (2014) have demonstrated the efficacy of mechanically harvesting ‘Brown Snout’ specialty cider apples with an over-the-row shake-and-catch small fruit harvester in terms of fruit yield and juice quality characteristics (i.e., the raw materials). Mechanical harvest imparted greater bruising to ‘Brown Snout’ fruit than hand harvest, and this physical damage resulted in significant yield loss postharvest when fruit were not immediately processed or cold-stored. The juice quality characteristics of ‘Brown Snout’ fruit did not differ because of harvest method when fully mature fruit were stored for 0, 2, or 4 weeks at 32 or 56 °F.
The present study advanced the assessment of mechanical harvest of ‘Brown Snout’ by evaluating cider quality (i.e., the finished product). Sensory evaluation of cider quality was performed using a trained panel and an e-tongue. Sensory panels, involving screened individuals trained to describe their sensory experiences using specific terminology and metrics, are often used to guide product development in contrast to consumer tasting panels that are used to test market acceptability (Meilgaard et al., 2006). Sensory panelists’ perceptions can provide an indication of ordinary consumer response and can detect minute differences in product characteristics. Sensory panelists can also become fatigued and subject to biases, and the process incurs a large time commitment and accordingly high cost. The e-tongue is a powerful tool that provides for an analytical measurement of taste profiles. E-tongues can be calibrated to consistently provide objective data, contrary to the human brain whose reaction can vary daily in response to biological, emotional, and environmental variables. Furthermore, e-tongues can evaluate samples that are potentially harmful to humans. However, e-tongues lack the streamlined integration of the human sensory system that can combine data from multiple senses to form classifications or judgments.
It was expected that machine harvest of ‘Brown Snout’ fruit would ultimately provide for a darker colored and less astringent and bitter cider than hand harvest. The physical damage imparted by mechanical harvest results in fruit that is vulnerable to enzymatic oxidation, a process that results in browning and chemical alteration of phenolics, as well as physiochemical interactions that can include binding of phenolics with insoluble solids (e.g., polysaccharides) (Guyot et al., 2008; Lea, 1990; Nicolas et al., 1994; Renard et al., 2001). The size, structure, and stereochemistry of phenolics, predominantly procyanidins in cider, are important as they influence the perception of cider’s beverage characteristic astringency and bitterness (Lea and Arnold, 1978). Also, mechanization of harvest could provide for a metallic taste as this response has been associated with food products that are oxidized and have had prolonged contact with metals (deMan, 1999). Experiments have shown machine-harvested grapes (Vitis vinifera) to contain up to six times the content of iron than hand-harvested grapes, levels that can result in a perceived metallic taste (Loubère, 1990). It was also expected that duration of ambient storage of fruit would augment the effect of harvest method as time would allow for a greater extent of oxidation and polymerization of phenolics, especially in fruit damaged during harvest. Variation in sensory perception due to year of harvest was expected as the previous mechanization studies assessing fruit yield and juice quality characteristics demonstrated a significant year effect (Alexander et al., 2016; Miles and King, 2014). Finally, the e-tongue has been shown to complement human evaluations of similar products such as wine, but the need for further optimization would not be surprising as this study is the first to involve cider (Baldwin et al., 2011). Legin et al. (2003) demonstrated the ability of an e-tongue to distinguish red and white wines from different geographical areas as well as vintages, and the ability to predict the scoring of trained panelists with 8% to 13% error.
In this 2-year study, fermented juice produced from machine-harvested and hand-harvested ‘Brown Snout’ that was ambient stored (56 °F) for 0, 2, and 4 weeks postharvest was evaluated using a trained panel and e-tongue. The sensory profiles of the ciders were compared by the harvest method, fruit ambient storage time, and year of harvest, and the complementation of human evaluations by the e-tongue in profiling cider was assessed.
Alcohol and Tobacco Tax and Trade Bureau 2015 Code of federal regulations, 27 CFR Part 24. 17 Mar. 2016. <https://www.ttb.gov/other/regulations.shtml>
Alcohol and Tobacco Tax and Trade Bureau 2017 Cider statistics CY 2007–2016. U.S. Dept. Treasury, Washington, DC
Alexander, T.R., King, J., Scheenstra, E. & Miles, C.A. 2016 Yield, fruit damage, yield loss and juice quality characteristics of machine and hand-harvested ‘Brown Snout’ specialty cider apple stored at ambient conditions in northwest Washington HortTechnology 26 614 619
Baker, A.K., Vixie, B., Rasco, B.A., Ovissipour, M. & Ross, C.F. 2014 Development of a lexicon for caviar and its usefulness for determining consumer preference J. Food Sci. 79 S2533 S2541
Baldwin, E.A., Bai, J., Plotto, A. & Dea, S. 2011 Electronic noses and tongues: Applications for the food and pharmaceutical industries Sensors (Basel) 11 4744 4766
Ballester, J., Mihnea, M., Peyron, D. & Valentin, D. 2013 Exploring minerality of Burgundy Chardonnay wines: A sensory approach with wine experts and trained panelists Austral. J. Grape Wine Res. 19 140 152
Blanpied, G.D. & Silsby, K.J. 1992 Predicting harvest date windows for apples. Cornell Coop. Ext. Publ. Info. Bul. 221
Bleibaum, R.N., Stone, H., Tan, T., Labreche, S., Saint-Martin, E. & Isz, S. 2002 Comparison of sensory and consumer results with electronic nose and tongue sensors for apple juices Food Qual. Prefer. 13 409 422
Chu, C.L.G. & Wilson, K.R. 2000 Evaluating maturity of ‘McIntosh’ and ‘Red Delicious’ apples. Ontario Ministry Agr. Food Rural Affairs Publ. Order No. 00-025. 3 May 2016. <http://www.omafra.gov.on.ca/english/crops/facts/00-025.htm>
Coetzee, C. & du Toit, W.J. 2012 A comprehensive review on Sauvignon Blanc aroma with a focus on certain positive volatile thiols Food Res. Intl. 45 287 298
Cortell, J.M., Sivertsen, H.K., Kennedy, J.A. & Heymann, H. 2008 Influence of vine vigor on Pinot Noir fruit composition, wine chemical analysis, and wine sensory attributes Amer. J. Enol. Viticult. 59 1 10
deMan, J.M. 1999 Principles of food chemistry. 3rd ed. Springer-Verlag, Berlin, Germany
Diako, C., McMahon, K., Mattinson, S., Evans, M. & Ross, C.F. 2016 Alcohol, tannins, and mannoprotein and their interactions influence the sensory properties of selected commercial Merlot wines: A preliminary study J. Food Sci. 81 S2039 S2048
Fontoin, H., Saucier, C., Teissedre, P-L. & Glories, Y. 2008 Effect of pH, ethanol and acidity on astringency and bitterness of grape seed tannin oligomers in model wine solution Food Qual. Prefer. 19 286 291
Guyot, S., Bernillon, S., Poupard, P. & Renard, C.M. 2008 Multiplicity of phenolic oxydation products in apple juices and ciders, from synthetic medium to commercial products, p. 278–292. In: F. Daayf and V. Lattanzio (eds.). Recent advances in polyphenol research. Wiley-Blackwell, Oxford, UK
Harbertson, J.F. 2009 A guide to the fining of wine. Washington State Univ. Ext. Publ. EM016
Hudelson, J. 2011 Wine faults: Causes, effects, cures. Board and Bench Publ., San Francisco, CA
Lea, A. 1990 Bitterness and astringency: The procyanidins of fermented apple ciders, p. 123–143. In: R.L. Rousseff (ed.). Bitterness in foods and beverages. Elsevier, Oxford, UK
Legin, A., Rudnitskaya, A., Lvova, L., Vlasov, Y., Di Natale, C. & D’Amico, A.A. 2003 Evaluation of Italian wine by the electronic tongue: Recognition, quantitative analysis and correlation with human sensory perception Anal. Chim. Acta 484 33 44
Loubère, L.A. 1990 The wine revolution in France: The twentieth century. Princeton Univ. Press, Princeton, NJ
Meilgaard, M.C., Carr, B.T. & Civille, G.V. 2006 Sensory evaluation techniques. 4th ed. CRC Press, Boca Raton, FL
Miles, C.A. & King, J. 2014 Yield, labor, and fruit and juice quality characteristics of machine and hand-harvested ‘Brown Snout’ specialty cider apple HortTechnology 24 519 526
Mitchell, P. 2015 Cider & perry production: Principles & practice course notes. Mitchell F&D, Newent, UK
Moulton, G.A. & King, J. 2008 Fruit handbook for western Washington. Washington State Univ. Ext. Publ. EB0937
Munoz, A. 2003 Training time in descriptive analysis, p. 351–356. In: H.R. Moskowitz, A.M. Munoz, and M.C. Gacula (eds.). Viewpoints and controversies in sensory science and consumer product testing. Food Nutrition Press, Trumbull, CT
Nicolas, J.J., Richard-Forget, F.C., Goupy, P.M., Amiot, M.J. & Aubert, S.Y. 1994 Enzymatic browning reactions in apple and apple products Crit. Rev. Food Sci. Nutr. 34 109 157
Noble, A.C., Arnold, R.A., Buechsenstein, J., Leach, E.J., Schmidt, J.O. & Stern, P.M. 1987 Modification of a standardized system of wine aroma terminology Amer. J. Enol. Viticult. 38 143 146
Petignat-Keller, S. 2013 Flavour wheel for apple juice and cider. Agroscope Changins-Wädenswil Res. Sta., Wädenswil, Switzerland
Pickering, G. & Demiglio, P. 2008 The white wine mouthfeel wheel: A lexicon for describing the oral sensations elicited by white wine J. Wine Res. 19 51 67
Renard, C.M., Baron, A., Guyot, S. & Drilleau, J-F. 2001 Interactions between apple cell walls and native apple polyphenols: Quantification and some consequences Intl. J. Biol. Macromol. 29 115 125
Tennant, H. 2017 Costs and considerations for establishing cider apple orchards for mechanical harvest. MS Rpt., Washington State Univ., Pullman, WA
U.S. Department of Agriculture 2013 Web soil survey. 3 May 2017. <http://websoilsurvey.sc.egov.usda.gov.html>
Washington State University 2013 Crop protection guide for tree fruits in Washington. Washington State Univ. Ext. Publ. EB0419
Williams, A.A. 2006 The development of a vocabulary and profile assessment method for evaluating the flavour contribution of cider and perry aroma constituents J. Sci. Food Agr. 26 567 582
Zimmerman, A., King, J., Scheenstra, E. & Miles, C.A. 2016 Evaluation of varietal ciders produced at WSU Mount Vernon NWREC. 26 July 2017. <http://cider.wsu.edu/wp-content/uploads/sites/54/2017/05/CiderEvaluations2016.pdf>
Zimmerman, A., Moulton, G. & Miles, C.A. 2015 Fermentation protocol at WSU Mount Vernon NWREC for production of varietal ciders. 3 May 2017. <http://ext100.wsu.edu/maritimefruit/wp-content/uploads/sites/36/2015/04/CiderFermentationProtocol2015.pdf>
Color attributes (principal and secondary terms), reference standards, and reference intensities [low (L), medium (M), and high (H)] used by the eight trained panelists in evaluating cider apple samples on a 15-cm line scale.
Aroma, flavor, mouthfeel, and taste attributes (principal and secondary terms), reference standards, base solutions of reference standards, and reference intensities used by the eight trained panelists in evaluating apple cider samples on a 15-cm line scale.
Aftertaste attributes used by the eight trained panelists in evaluating apple cider samples on a ordinal and binary scale.