{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T20:00:07Z","timestamp":1776283207444,"version":"3.50.1"},"reference-count":186,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100015322","name":"Instituto Polit\u00e9cnico de Bragan\u00e7a","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100015322","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Curr Food Sci Tech Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose of Review<\/jats:title>\n                <jats:p>Sensory evaluation holds vital significance in the food sector. Typically, humans conduct sensory analysis. Humans, being the ultimate consumers, assess food traits effectively. However, human judgment is influenced by various factors. Hence, countering subjectivity is crucial for objective evaluation while retaining hedonic insights.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Recent Findings<\/jats:title>\n                <jats:p>Food\u2019s sensory assessment primarily employs humans. Various techniques differentiate, depict, or rank food. Modern sensory tools, aiming to enhance objectivity and reliability, are emerging to supplement or supplant human assessment. This advance can bolster quality, consistency, and safety by mimicking human senses such as smell, taste, and vision, mitigating risks tied to human assessors.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Summary<\/jats:title>\n                <jats:p>This paper provides a review about sensory analysis of food using technological methodologies. A review of different technological tools to analyze sensory characteristics of food, as well as a discussion of how those technological tools can relate to humans\u2019 perception of food is presented.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s43555-024-00019-7","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T07:02:33Z","timestamp":1706511753000},"page":"77-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Emerging Methods for the Evaluation of Sensory Quality of Food: Technology at Service"],"prefix":"10.1007","volume":"2","author":[{"given":"Sandra S. Q.","family":"Rodrigues","sequence":"first","affiliation":[]},{"given":"Lu\u00eds G.","family":"Dias","sequence":"additional","affiliation":[]},{"given":"Alfredo","family":"Teixeira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,29]]},"reference":[{"key":"19_CR1","volume-title":"Sensory evaluation techniques","author":"M Meilgaard","year":"2007","unstructured":"Meilgaard M, Civille GV, Carr BT. Sensory evaluation techniques. 4th ed. Boca Raton, Florida: CRC Press Inc.; 2007.","edition":"4"},{"issue":"2","key":"19_CR2","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1016\/j.foodres.2012.06.037","volume":"48","author":"P Varela","year":"2012","unstructured":"Varela P, Ares G. Sensory profiling, the blurred line between sensory and consumer science. A review of novel methods for product characterization. Food Res Int. 2012;48(2):893\u2013908. https:\/\/doi.org\/10.1016\/j.foodres.2012.06.037.","journal-title":"Food Res Int"},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1111\/j.1745-459X.2011.00352.x","volume":"26","author":"G Ares","year":"2011","unstructured":"Ares G, Bruzzone F, Gim\u00e9nez A. Is a consumer panel able to reliably evaluate the texture of dairy desserts using unstructured intensity scales? Evaluation of global and individual performance. J Sens Stud. 2011;26:363\u201370.","journal-title":"J Sens Stud"},{"issue":"2","key":"19_CR4","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.foodqual.2013.06.010","volume":"30","author":"M Meyners","year":"2013","unstructured":"Meyners M, Castura JC, Carr BT. Existing and new approaches for the analysis of CATA data. Food Qual Prefer. 2013;30(2):309\u201319. https:\/\/doi.org\/10.1016\/j.foodqual.2013.06.010.","journal-title":"Food Qual Prefer"},{"issue":"Part A","key":"19_CR5","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.foodqual.2016.09.013","volume":"56","author":"L Ant\u00fanez","year":"2017","unstructured":"Ant\u00fanez L, Vidal L, de Saldamando L, Gim\u00e9nez A, Ares G. Comparison of consumer-based methodologies for sensory characterization: case study with four sample sets of powdered drinks. Food Qual Prefer. 2017;56(Part A):149\u201363. https:\/\/doi.org\/10.1016\/j.foodqual.2016.09.013.","journal-title":"Food Qual Prefer"},{"key":"19_CR6","doi-asserted-by":"publisher","first-page":"103986","DOI":"10.1016\/j.foodqual.2020.103986","volume":"86","author":"SR Jaeger","year":"2020","unstructured":"Jaeger SR, Chheang SL, Jin D, Roigard CM, Ares G. Check-all-that-apply (CATA) questions: sensory term citation frequency reflects rated term intensity and applicability. Food Qual Prefer. 2020;86:103986. https:\/\/doi.org\/10.1016\/j.foodqual.2020.103986.","journal-title":"Food Qual Prefer"},{"key":"19_CR7","doi-asserted-by":"publisher","first-page":"141776","DOI":"10.1016\/j.scitotenv.2020.141776","volume":"753","author":"RCV Carneiro","year":"2021","unstructured":"Carneiro RCV, Wang C, Yu J, O\u2019Keefe SF, Duncan SE, Gallagher CD, Burlingame GA, Dietrich AM. Check-if-apply approach for consumers and utilities to communicate about drinking water aesthetics quality. Sci Total Environ. 2021;753:141776.","journal-title":"Sci Total Environ"},{"key":"19_CR8","doi-asserted-by":"publisher","first-page":"104858","DOI":"10.1016\/j.foodqual.2023.104858","volume":"108","author":"S Wang","year":"2023","unstructured":"Wang S, Ng KH, Yee KH, Tang Y, Meng R, He W. Comparison of Pivot profile CATA, and Pivot-CATA for the sensory profiling of instant black coffee. Food Qual Prefer. 2023;108:104858. https:\/\/doi.org\/10.1016\/j.foodqual.2023.104858.","journal-title":"Food Qual Prefer"},{"key":"19_CR9","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1016\/j.foodres.2018.01.062","volume":"106","author":"J Liu","year":"2018","unstructured":"Liu J, Bredie WLP, Sherman E, Harbertson JF, Heymann H. Comparison of rapid descriptive sensory methodologies: free-choice profiling, flash profile, and modified flash profile. Food Res Int. 2018;106:892\u2013900.","journal-title":"Food Res Int"},{"key":"19_CR10","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.foodqual.2017.08.003","volume":"63","author":"J Bi","year":"2018","unstructured":"Bi J, Kuesten C, Lee HS, O\u2019Mahony M. Paired versions of various sensory discrimination forced-choice methods and the same-different area theorem. Food Qual Prefer. 2018;63:97\u2013106.","journal-title":"Food Qual Prefer"},{"key":"19_CR11","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.foodqual.2018.11.008","volume":"73","author":"J Bi","year":"2019","unstructured":"Bi J, Kuesten C. The four-interval, two-alternative forced-choice (4I2AFC): a powerful sensory discrimination method to detect small, directional changes particularly suitable for visual or manual evaluations. Food Qual Prefer. 2019;73:202\u20139.","journal-title":"Food Qual Prefer"},{"key":"19_CR12","doi-asserted-by":"publisher","first-page":"103889","DOI":"10.1016\/j.foodqual.2020.103889","volume":"83","author":"LM Hamilton","year":"2020","unstructured":"Hamilton LM, Lahne J. Assessment of instructions on panelist cognitive framework and free sorting task results: a case study of cold brew coffee. Food Qual Prefer. 2020;83:103889.","journal-title":"Food Qual Prefer"},{"key":"19_CR13","doi-asserted-by":"publisher","first-page":"104026","DOI":"10.1016\/j.foodqual.2020.104026","volume":"86","author":"L Hayward","year":"2020","unstructured":"Hayward L, Jantzi H, Smith A, McSweeney MB. How do consumers describe cool climate wines using projective mapping and ultra-flash profile? Food Qual Prefer. 2020;86:104026.","journal-title":"Food Qual Prefer"},{"key":"19_CR14","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1016\/j.foodres.2018.12.032","volume":"121","author":"C Wilson","year":"2019","unstructured":"Wilson C, Brand J, Toit W, Buica A. Matrix effects influencing the perception of 3-mercaptohexan-1-Ol (3MH) and 3-mercaptohexyl acetate (3MHA) in different chenin blanc wines by projective mapping (PM) with ultra flash profiling (UFP) intensity ratings. Food Res Int. 2019;121:633\u201340.","journal-title":"Food Res Int"},{"key":"19_CR15","doi-asserted-by":"publisher","first-page":"103900","DOI":"10.1016\/j.foodqual.2020.103900","volume":"83","author":"A Barton","year":"2020","unstructured":"Barton A, Hayward L, Richardson CD, McSweeney MB. Use of different panellists (experienced, trained, consumers and experts) and the projective mapping task to evaluate white wine. Food Qual Prefer. 2020;83:103900.","journal-title":"Food Qual Prefer"},{"key":"19_CR16","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.3390\/foods12081579","volume":"12","author":"J Heo","year":"2023","unstructured":"Heo J, Kim SS, Kim M-R, Kwak HS. Comparison of sensory profiling by descriptive analysis, free-choice profiling, and polarized sensory positioning on bottled water. Foods. 2023;12:1579. https:\/\/doi.org\/10.3390\/foods12081579.","journal-title":"Foods"},{"key":"19_CR17","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.foodqual.2017.05.003","volume":"61","author":"S Spinelli","year":"2017","unstructured":"Spinelli S, Dinnella C, Masi C, Paolo G, Prescott J. Investigating preferred coffee consumption contexts using open-ended questions. Food Qual Prefer. 2017;61:63\u201373.","journal-title":"Food Qual Prefer"},{"key":"19_CR18","doi-asserted-by":"publisher","first-page":"255","DOI":"10.3390\/foods11030255","volume":"11","author":"C Marques","year":"2022","unstructured":"Marques C, Correia E, Dinis L-T, Vilela A. An overview of sensory characterization techniques: from classical descriptive analysis to the emergence of novel profiling methods. Foods. 2022;11:255. https:\/\/doi.org\/10.3390\/foods11030255.","journal-title":"Foods"},{"key":"19_CR19","doi-asserted-by":"publisher","first-page":"1514","DOI":"10.1080\/00036846.2018.1527460","volume":"51","author":"S Huseynov","year":"2019","unstructured":"Huseynov S, Kassas B, Segovia MS, Palma MA. Incorporating biometric data in models of consumer choice. Appl Econ. 2019;51:1514\u201331.","journal-title":"Appl Econ"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"\u2022 de Wijk RA, Ushiama S, Ummels M, Zimmerman P, Kaneko D, Vingerhoeds MH. Reading food experiences from the face: effects of familiarity and branding of soy sauce on facial expressions and video-based RPPG heart rate. Foods. 2021;10:1345. A reference about the application of biometric measurements on the sensory evaluation of food.","DOI":"10.3390\/foods10061345"},{"key":"19_CR21","doi-asserted-by":"publisher","first-page":"686","DOI":"10.3389\/fnins.2020.00686","volume":"14","author":"D Li","year":"2020","unstructured":"Li D, Jia J, Wang X. Unpleasant food odors modulate the processing of facial expressions: an event-related potential study. Front Neurosci. 2020;14:686\u201397.","journal-title":"Front Neurosci"},{"key":"19_CR22","doi-asserted-by":"publisher","first-page":"330","DOI":"10.3390\/foods10020330","volume":"10","author":"A Mehta","year":"2021","unstructured":"Mehta A, Sharma C, Kanala M, Thakur M, Harrison R, Torrico DD. Self-reported emotions and facial expressions on consumer acceptability: a study using energy drinks. Foods. 2021;10:330.","journal-title":"Foods"},{"key":"19_CR23","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.foodqual.2019.02.012","volume":"75","author":"J Delarue","year":"2019","unstructured":"Delarue J, Brasset A, Jarrot F, Abiven F. Taking control of product testing context thanks to a multi-sensory immersive room. A case study on alcohol-free beer. Food Qual Prefer. 2019;75:78\u201386.","journal-title":"Food Qual Prefer"},{"key":"19_CR24","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.foodqual.2019.04.007","volume":"76","author":"M Hannum","year":"2019","unstructured":"Hannum M, Forzley S, Popper R, Simons CT. Does environment matter? Assessments of wine in traditional booths compared to an immersive and actual wine bar. Food Qual Prefer. 2019;76:100\u20138.","journal-title":"Food Qual Prefer"},{"key":"19_CR25","doi-asserted-by":"publisher","unstructured":"\u2022 Munekata PES, Finardi S, de Souza CK, Meinert C, Pateiro M, Hoffmann TG, Dom\u00ednguez R, Bertoli SL, Kumar M, Lorenzo JM. Applications of electronic nose, electronic eye and electronic tongue in quality, safety and shelf life of meat and meat products: a review. Sensors. 2023;23:672. https:\/\/doi.org\/10.3390\/s23020672. A detailed review on the use of electronic devices mimicking human senses in the evaluation of food sensory quality.","DOI":"10.3390\/s23020672"},{"key":"19_CR26","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.3390\/foods12051059","volume":"12","author":"B Zhu","year":"2023","unstructured":"Zhu B, Gao H, Yang F, Li Y, Yang Q, Liao Y, Guo H, Xu K, Tang Z, Gao N, Zhang Y, He J. Comparative characterization of volatile compounds of Ningxiang pig, Duroc and their crosses (Duroc \u00d7 Ningxiang) by using SPME-GC-MS. Foods. 2023;12:1059. https:\/\/doi.org\/10.3390\/foods12051059.","journal-title":"Foods"},{"key":"19_CR27","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.cofs.2021.03.014","volume":"41","author":"S Fuentes","year":"2021","unstructured":"Fuentes S, Tongson E, Viejo CG. Novel digital technologies implemented in sensory science and consumer perception. Curr Opin Food Sci. 2021;41:99\u2013106. https:\/\/doi.org\/10.1016\/j.cofs.2021.03.014.","journal-title":"Curr Opin Food Sci"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"\u2022\u2022 Torrico DD, Mehta A, Borssato AB. New methods to assess sensory responses: a brief review of innovative techniques in sensory evaluation. Current opinion in food science 2023;49:100978. This is a brief review about the main objective methods to evaluate food sensory quality. It is an important base paper when investigating this subject.","DOI":"10.1016\/j.cofs.2022.100978"},{"key":"19_CR29","doi-asserted-by":"publisher","unstructured":"Aznan A, Viejo CG, Pang A, Fuentes S. Review of technology advances to assess rice quality traits and consumer perception. Food Res Int. 2023;172:113105. https:\/\/doi.org\/10.1016\/j.foodres.2023.113105.","DOI":"10.1016\/j.foodres.2023.113105"},{"issue":"2","key":"19_CR30","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1016\/j.foodres.2013.09.015","volume":"54","author":"B Farneti","year":"2013","unstructured":"Farneti B, Alarc\u00f3n AA, Cristescu SM, Costa G, Harren FJM, Holthuysen NTE, Woltering EJ. Aroma volatile release kinetics of tomato genotypes measured by PTR-MS following artificial chewing. Food Res Int. 2013;54(2):1579\u201388. https:\/\/doi.org\/10.1016\/j.foodres.2013.09.015.","journal-title":"Food Res Int"},{"issue":"8","key":"19_CR31","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.3390\/foods9081074","volume":"9","author":"S Silva","year":"2020","unstructured":"Silva S, Guedes C, Rodrigues S, Teixeira A. Non-destructive imaging and spectroscopic techniques for assessment of carcass and meat quality in sheep and goats: a review. Foods. 2020;9(8):1074.","journal-title":"Foods"},{"key":"19_CR32","first-page":"e00202","volume":"3","author":"S Barbieri","year":"2017","unstructured":"Barbieri S, Soglia F, Palagano R, Tesini F, Bendini A, Petracci M, Cavani C, Toschi TG. Sensory and rapid instrumental methods as a combined tool for quality control of cooked ham. Heliyon. 2017;3:e00202.","journal-title":"Heliyon"},{"key":"19_CR33","doi-asserted-by":"publisher","first-page":"112494","DOI":"10.1016\/j.foodres.2023.112494","volume":"165","author":"SC Hutchings","year":"2023","unstructured":"Hutchings SC, Dixit Y, Al-Sarayreh M, Torrico DD, Realini CE, Jaeger SR, Reis MM. A critical review of social media research in sensory-consumer science. Food Res Int. 2023;165:112494. https:\/\/doi.org\/10.1016\/j.foodres.2023.112494.","journal-title":"Food Res Int"},{"key":"19_CR34","doi-asserted-by":"publisher","unstructured":"- Mishra M. CHAPTER 1: Spectroscopic techniques for the analysis of food quality, chemistry, and function. In Advanced spectroscopic techniques for food quality, ed. A. K. Shukla, The Royal Society of Chemistry, 2022, pp 1\u201322. https:\/\/doi.org\/10.1039\/9781839165849-00001.","DOI":"10.1039\/9781839165849-00001"},{"issue":"6","key":"19_CR35","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1051\/lait:2000149","volume":"80","author":"S Herbert","year":"2000","unstructured":"Herbert S, Riou NM, Devaux MF, Riaublanc A, Bouchet B, Gallant DJ, Dufour \u00c9. Monitoring the identity and the structure of soft cheeses by fluorescence spectroscopy. Le Lait. 2000;80(6):621\u201334.","journal-title":"Le Lait"},{"issue":"2","key":"19_CR36","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.chemolab.2004.07.007","volume":"75","author":"J Christensen","year":"2005","unstructured":"Christensen J, Becker EM, Frederiksen CS. Fluorescence spectroscopy and PARAFAC in the analysis of yogurt. Chemom Intell Lab Syst. 2005;75(2):201\u20138.","journal-title":"Chemom Intell Lab Syst"},{"key":"19_CR37","doi-asserted-by":"publisher","first-page":"95","DOI":"10.9734\/AIR\/2014\/7184","volume":"2","author":"K Nikolova","year":"2014","unstructured":"Nikolova K, Eftimov T, Aladjadjiyan A. Fluorescence spectroscopy as method for quality control of honey. Adv Res. 2014;2:95\u2013108.","journal-title":"Adv Res"},{"issue":"2","key":"19_CR38","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/S0309-1740(01)00121-8","volume":"60","author":"B Egelandsdal","year":"2002","unstructured":"Egelandsdal B, Wold JP, Sponnich A, Neeg\u00e5rd S, Hildrum KI. On attempts to measure the tenderness of longissimus dorsi muscles using fluorescence emission spectra. Meat Sci. 2002;60(2):187\u2013202.","journal-title":"Meat Sci"},{"issue":"03n04","key":"19_CR39","doi-asserted-by":"publisher","first-page":"1840025","DOI":"10.1142\/S0129156418400256","volume":"27","author":"B Wu","year":"2018","unstructured":"Wu B, Dahlberg K, Gao X, Smith J, Bailin J. A rapid method based on fluorescence spectroscopy for meat spoilage detection. Int J High Speed Electron Syst. 2018;27(03n04):1840025.","journal-title":"Int J High Speed Electron Syst"},{"issue":"5","key":"19_CR40","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/S0963-9969(02)00174-6","volume":"36","author":"\u00c9 Dufour","year":"2003","unstructured":"Dufour \u00c9, Frencia JP, Kane E. Development of a rapid method based on front-face fluorescence spectroscopy for the monitoring of fish freshness. Food Res Int. 2003;36(5):415\u201323.","journal-title":"Food Res Int"},{"key":"19_CR41","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.lwt.2019.01.021","volume":"103","author":"A Hassoun","year":"2019","unstructured":"Hassoun A, Sahar A, Lakhal L, A\u00eft-Kaddour A. Fluorescence spectroscopy as a rapid and non-destructive method for monitoring quality and authenticity of fish and meat products: impact of different preservation conditions. LWT. 2019;103:279\u201392.","journal-title":"LWT"},{"issue":"6","key":"19_CR42","doi-asserted-by":"publisher","first-page":"536","DOI":"10.4315\/0362-028X-57.6.536","volume":"57","author":"LG Rice","year":"1994","unstructured":"Rice LG, Ross PF. Methods for detection and quantitation of fumonisins in corn, cereal products and animal excreta. J Food Protect. 1994;57(6):536\u201340.","journal-title":"J Food Protect"},{"key":"19_CR43","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1007\/s12161-011-9359-1","volume":"5","author":"I Zekovi\u0107","year":"2012","unstructured":"Zekovi\u0107 I, Lenhardt L, Drami\u0107anin T, Drami\u0107anin MD. Classification of intact cereal flours by front-face synchronous fluorescence spectroscopy. Food Anal Method. 2012;5:1205\u201313.","journal-title":"Food Anal Method"},{"key":"19_CR44","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.foodcont.2016.01.029","volume":"66","author":"MH Ahmad","year":"2016","unstructured":"Ahmad MH, Nache M, Waffenschmidt S, Hitzmann B. Characterization of farinographic kneading process for different types of wheat flours using fluorescence spectroscopy and chemometrics. Food Control. 2016;66:44\u201352.","journal-title":"Food Control"},{"issue":"3","key":"19_CR45","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1016\/j.jcs.2013.02.007","volume":"57","author":"AS J\u00e4\u00e4skel\u00e4inen","year":"2013","unstructured":"J\u00e4\u00e4skel\u00e4inen AS, Holopainen-Mantila U, Tamminen T, Vuorinen T. Endosperm and aleurone cell structure in barley and wheat as studied by optical and Raman microscopy. J Cereal Sci. 2013;57(3):543\u201350.","journal-title":"J Cereal Sci"},{"key":"19_CR46","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1016\/j.foodchem.2014.10.041","volume":"173","author":"E Guzm\u00e1n","year":"2015","unstructured":"Guzm\u00e1n E, Baeten V, Pierna JAF, Garc\u00eda-Mesa JA. Evaluation of the overall quality of olive oil using fluorescence spectroscopy. Food Chem. 2015;173:927\u201334.","journal-title":"Food Chem"},{"issue":"6","key":"19_CR47","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1093\/jaoac\/83.6.1435","volume":"83","author":"NB Kyriakidis","year":"2000","unstructured":"Kyriakidis NB, Skarkalis P. Fluorescence spectra measurement of olive oil and other vegetable oils. J AOAC Int. 2000;83(6):1435\u20139.","journal-title":"J AOAC Int"},{"key":"19_CR48","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.aca.2018.02.042","volume":"1015","author":"F Tsopelas","year":"2018","unstructured":"Tsopelas F, Konstantopoulos D, Kakoulidou AT. Voltammetric fingerprinting of oils and its combination with chemometrics for the detection of extra virgin olive oil adulteration. Anal Chim Acta. 2018;1015:8\u201319.","journal-title":"Anal Chim Acta"},{"key":"19_CR49","doi-asserted-by":"publisher","first-page":"130480","DOI":"10.1016\/j.foodchem.2021.130480","volume":"366","author":"MNB Manuel","year":"2022","unstructured":"Manuel MNB, da Silva AC, Lopes GS, Ribeiro LPD. One-class classification of special agroforestry Brazilian coffee using NIR spectrometry and chemometric tools. Food Chem. 2022;366:130480.","journal-title":"Food Chem"},{"key":"19_CR50","doi-asserted-by":"publisher","first-page":"107625","DOI":"10.1016\/j.foodcont.2020.107625","volume":"121","author":"DB Fioresi","year":"2021","unstructured":"Fioresi DB, Pereira LL, da Silva Oliveira EC, Moreira TR, Ramos AC. Mid infrared spectroscopy for comparative analysis of fermented arabica and robusta coffee. Food Control. 2021;121:107625.","journal-title":"Food Control"},{"key":"19_CR51","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1146\/annurev-food-032818-121155","volume":"10","author":"J Ma","year":"2019","unstructured":"Ma J, Sun DW, Pu H, Cheng JH, Wei Q. Advanced techniques for hyperspectral imaging in the food industry: principles and recent applications. Annu Rev Food Sci T. 2019;10:197\u2013220.","journal-title":"Annu Rev Food Sci T"},{"key":"19_CR52","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.jfoodeng.2017.06.012","volume":"214","author":"A Baiano","year":"2017","unstructured":"Baiano A. Applications of hyperspectral imaging for quality assessment of liquid based and semi-liquid food products: a review. J Food Eng. 2017;214:10\u20135.","journal-title":"J Food Eng"},{"key":"19_CR53","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.aca.2004.08.057","volume":"525","author":"I Esteban-D\u00edez","year":"2004","unstructured":"Esteban-D\u00edez I, Gonz\u00e1lez-S\u00e1iz JM, Pizarro C. Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy. Anal Chim Acta. 2004;525:171\u201382.","journal-title":"Anal Chim Acta"},{"issue":"4","key":"19_CR54","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/0958-6946(95)00016-V","volume":"5","author":"SL Duce","year":"1995","unstructured":"Duce SL, Amin MHG, Horsfield MA, Tyszka M, Hall LD. Nuclear magnetic resonance imaging of dairy products in two and three dimensions. Int Dairy J. 1995;5(4):311\u20139.","journal-title":"Int Dairy J"},{"issue":"10","key":"19_CR55","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.idairyj.2004.06.011","volume":"15","author":"T Lucas","year":"2005","unstructured":"Lucas T, Wagener M, Barey P, Mariette F. NMR assessment of mix and ice cream. Effect of formulation on liquid water and ice. Int Dairy J. 2005;15(10):1064\u201373.","journal-title":"Int Dairy J"},{"issue":"4","key":"19_CR56","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1016\/S0308-8146(00)00192-8","volume":"71","author":"CS De Angelis","year":"2000","unstructured":"De Angelis CS, Curini R, Delfini M, Brosio E, D\u2019ascenzo F, Bocca B. Amino acid profile in the ripening of Grana Padano cheese: a NMR study. Food Chem. 2000;71(4):495\u2013502.","journal-title":"Food Chem"},{"issue":"2","key":"19_CR57","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.foodchem.2003.08.021","volume":"86","author":"JP Renou","year":"2004","unstructured":"Renou JP, Bielicki G, Deponge C, Gachon P, Micol D, Ritz P. Characterization of animal products according to geographic origin and feeding diet using nuclear magnetic resonance and isotope ratio mass spectrometry. Part II: beef meat. Food chem. 2004;86(2):251\u20136.","journal-title":"Food chem"},{"issue":"8","key":"19_CR58","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.3168\/jds.S0022-0302(03)73847-8","volume":"86","author":"MI Kuo","year":"2003","unstructured":"Kuo MI, Anderson ME, Gunasekaran S. Determining effects of freezing on pasta filata and non-pasta filata mozzarella cheeses by nuclear magnetic resonance imaging. J Dairy Sci. 2003;86(8):2525\u201336.","journal-title":"J Dairy Sci"},{"issue":"7","key":"19_CR59","doi-asserted-by":"publisher","first-page":"2064","DOI":"10.1021\/jf020919x","volume":"51","author":"HC Bertram","year":"2003","unstructured":"Bertram HC, Jakobsen HJ, Andersen HJ, Karlsson AH, Engelsen SB. Post-mortem changes in porcine M. longissimus studied by solid-state 13C cross-polarization magic-angle spinning nuclear magnetic resonance spectroscopy. J Agr Food Chem. 2003;51(7):2064\u20139.","journal-title":"J Agr Food Chem"},{"key":"19_CR60","doi-asserted-by":"crossref","unstructured":"Bertram HC. NMR spectroscopy and NMR metabolomics in relation to meat quality. In New aspects of meat quality (pp. 355\u2013371). Woodhead Publishing. 2017.","DOI":"10.1016\/B978-0-08-100593-4.00014-X"},{"issue":"12","key":"19_CR61","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1016\/j.tifs.2003.07.001","volume":"14","author":"FJ Hidalgo","year":"2003","unstructured":"Hidalgo FJ, Zamora R. Edible oil analysis by high-resolution nuclear magnetic resonance spectroscopy: recent advances and future perspectives. Trends Food Sci Tech. 2003;14(12):499\u2013506.","journal-title":"Trends Food Sci Tech"},{"key":"19_CR62","doi-asserted-by":"crossref","unstructured":"Kaletunc G, Breslauer KJ. (Eds.). Characterization of cereals and flours: properties, analysis and applications. CRC press. 2003.","DOI":"10.1201\/9780203911785"},{"key":"19_CR63","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.foodchem.2013.10.136","volume":"150","author":"BO Petersen","year":"2014","unstructured":"Petersen BO, Nilsson M, B\u00f8jstrup M, Hindsgaul O, Meier S. 1H NMR spectroscopy for profiling complex carbohydrate mixtures in non-fractionated beer. Food chem. 2014;150:65\u201372.","journal-title":"Food chem"},{"issue":"2","key":"19_CR64","doi-asserted-by":"publisher","first-page":"103","DOI":"10.14429\/dlsj.2.11379","volume":"2","author":"S Lakshmi","year":"2017","unstructured":"Lakshmi S, Pandey AK, Ravi N, Chauhan OP, Gopalan N, Sharma RK. Non-destructive quality monitoring of fresh fruits and vegetables. Def Life Sci J. 2017;2(2):103\u201310.","journal-title":"Def Life Sci J"},{"key":"19_CR65","doi-asserted-by":"crossref","unstructured":"Charles M, Romano A, Yener S, Barnab\u00e0 M, Navarini L, M\u00e4rk TD, ... Gasperi F. Understanding flavour perception of espresso coffee by the combination of a dynamic sensory method and in-vivo nosespace analysis. Food Res Int. 2015;69:9\u201320.","DOI":"10.1016\/j.foodres.2014.11.036"},{"issue":"6\u20139","key":"19_CR66","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1016\/j.idairyj.2004.06.013","volume":"15","author":"G Downey","year":"2005","unstructured":"Downey G, Sheehan E, Delahunty C, O\u2019Callaghan D, Guinee T, Howard V. Prediction of maturity and sensory attributes of cheddar cheese using near-infrared spectroscopy. Int Dairy J. 2005;15(6\u20139):701\u20139.","journal-title":"Int Dairy J"},{"key":"19_CR67","doi-asserted-by":"crossref","unstructured":"- Blazquez C, Downey G, O\u2019Callaghan D, Howard V, Delahunty C, Sheehan E, ... O\u2019Donnell CP. Modelling of sensory and instrumental texture parameters in processed cheese by near infrared reflectance spectroscopy. J Dairy Res. 2006;73(1):58\u201369.","DOI":"10.1017\/S0022029905001536"},{"key":"19_CR68","doi-asserted-by":"publisher","first-page":"125480","DOI":"10.1016\/j.foodchem.2019.125480","volume":"305","author":"M Bergamaschi","year":"2020","unstructured":"Bergamaschi M, Cipolat-Gotet C, Cecchinato A, Schiavon S, Bittante G. Chemometric authentication of farming systems of origin of food (milk and ripened cheese) using infrared spectra, fatty acid profiles, flavor fingerprints, and sensory descriptions. Food chem. 2020;305:125480.","journal-title":"Food chem"},{"key":"19_CR69","doi-asserted-by":"crossref","unstructured":"- Jin G, Wang YJ, Li M, Li T, Huang WJ, Li L, ... Ning J. Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system. Food Chem. 2021;358:129815.","DOI":"10.1016\/j.foodchem.2021.129815"},{"key":"19_CR70","doi-asserted-by":"publisher","first-page":"119522","DOI":"10.1016\/j.saa.2021.119522","volume":"252","author":"Y Song","year":"2021","unstructured":"Song Y, Wang X, Xie H, Li L, Ning J, Zhang Z. Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors. Spectrochim Acta Part A Mol Biomol Spectrosc. 2021;252:119522.","journal-title":"Spectrochim Acta Part A Mol Biomol Spectrosc"},{"key":"19_CR71","doi-asserted-by":"crossref","unstructured":"- Manthou E, Lago SL, Dagres E, Lianou A, Tsakanikas P, Panagou EZ, ... Nychas GJE. Application of spectroscopic and multispectral imaging technologies on the assessment of ready-to-eat pineapple quality: a performance evaluation study of machine learning models generated from two commercial data analytics tools. Comput Electron Agr. 2020;175:105529.","DOI":"10.1016\/j.compag.2020.105529"},{"issue":"3","key":"19_CR72","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1366\/0003702864509114","volume":"40","author":"M Martens","year":"1986","unstructured":"Martens M, Martens H. Near-infrared reflectance determination of sensory quality of peas. Appl Spectrosc. 1986;40(3):303\u201310.","journal-title":"Appl Spectrosc"},{"issue":"12","key":"19_CR73","doi-asserted-by":"publisher","first-page":"5790","DOI":"10.1021\/jf010509t","volume":"49","author":"PN Jensen","year":"2001","unstructured":"Jensen PN, S\u00f8rensen G, Engelsen SB, Bertelsen G. Evaluation of quality changes in walnut kernels (Juglans regia L.) by Vis\/NIR spectroscopy. J Agr Food Chem. 2001;49(12):5790\u20136.","journal-title":"J Agr Food Chem"},{"issue":"1","key":"19_CR74","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/S0309-1740(99)00085-6","volume":"54","author":"J Br\u00f8ndum","year":"2000","unstructured":"Br\u00f8ndum J, Byrne DV, Bak LS, Bertelsen G, Engelsen SB. Warmed-over flavour in porcine meat\u2014a combined spectroscopic, sensory and chemometric study. Meat sci. 2000;54(1):83\u201395.","journal-title":"Meat sci"},{"issue":"1","key":"19_CR75","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/0309-1740(94)90096-5","volume":"38","author":"KI Hildrum","year":"1994","unstructured":"Hildrum KI, Nilsen BN, Mielnik M, Naes T. Prediction of sensory characteristics of beef by near-infrared spectroscopy. Meat sci. 1994;38(1):67\u201380.","journal-title":"Meat sci"},{"issue":"4","key":"19_CR76","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1016\/j.meatsci.2003.08.012","volume":"66","author":"RJ Beattie","year":"2004","unstructured":"Beattie RJ, Bell SJ, Farmer LJ, Moss BW, Patterson D. Preliminary investigation of the application of Raman spectroscopy to the prediction of the sensory quality of beef silverside. Meat Sci. 2004;66(4):903\u201313.","journal-title":"Meat Sci"},{"issue":"3","key":"19_CR77","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1016\/j.meatsci.2007.01.011","volume":"76","author":"S Andr\u00e9s","year":"2007","unstructured":"Andr\u00e9s S, Murray I, Navajas EA, Fisher AV, Lambe NR, B\u00fcnger L. Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy. Meat sci. 2007;76(3):509\u201316.","journal-title":"Meat sci"},{"key":"19_CR78","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.foodres.2018.02.007","volume":"107","author":"M Zhao","year":"2018","unstructured":"Zhao M, Nian Y, Allen P, Downey G, Kerry JP, O\u2019Donnell CP. Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef. Food Res Int. 2018;107:27\u201340.","journal-title":"Food Res Int"},{"issue":"12","key":"19_CR79","first-page":"113","volume":"12","author":"L Sun","year":"2012","unstructured":"Sun L, Chen B, Gao R, Yuan L, Li W. Review on Raman spectroscopy application in food analysis. J Chinese Institute Food Sci Technol. 2012;12(12):113\u20138.","journal-title":"J Chinese Institute Food Sci Technol"},{"key":"19_CR80","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.infrared.2015.11.004","volume":"74","author":"S Lohumi","year":"2016","unstructured":"Lohumi S, Lee S, Lee H, Kim MS, Lee W-H, Cho B-K. Application of hyperspectral imaging for characterization of intramuscular fat distribution in beef. Infrared Phys Technol. 2016;74:1\u201310.","journal-title":"Infrared Phys Technol"},{"issue":"5","key":"19_CR81","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1111\/1541-4337.12149","volume":"14","author":"W Cheng","year":"2015","unstructured":"Cheng W, Cheng JH, Sun DW, Pu H. Marbling analysis for evaluating meat quality: methods and techniques. Compr Rev Food Sci Food Saf. 2015;14(5):523\u201335.","journal-title":"Compr Rev Food Sci Food Saf"},{"key":"19_CR82","doi-asserted-by":"publisher","first-page":"109204","DOI":"10.1016\/j.meatsci.2023.109204","volume":"202","author":"J Zuo","year":"2023","unstructured":"Zuo J, Peng Y, Li Y, Zou W, Chen Y, Huo D, Chao K. Nondestructive detection of nutritional parameters of pork based on NIR hyperspectral imaging technique. Meat Sci. 2023;202:109204.","journal-title":"Meat Sci"},{"issue":"1","key":"19_CR83","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1079\/ASC20055","volume":"82","author":"N Barlocco","year":"2006","unstructured":"Barlocco N, Vadell A, Ballesteros F, Galietta G, Cozzolino D. Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy. Anim Sci. 2006;82(1):111\u20136.","journal-title":"Anim Sci"},{"key":"19_CR84","doi-asserted-by":"crossref","unstructured":"Teixeira A, Oliveira A, Paulos K, Leite A, Marcia A, Amorim A, ... Rodrigues S. An approach to predict chemical composition of goat longissimus thoracis et lumborum muscle by Near Infrared Reflectance spectroscopy. Small Ruminant Res. 2015;126:40\u201343.","DOI":"10.1016\/j.smallrumres.2015.03.006"},{"key":"19_CR85","doi-asserted-by":"crossref","unstructured":"Su H, Sha K, Zhang L, Zhang Q, Xu Y, Zhang R, ... Sun B. Development of near infrared reflectance spectroscopy to predict chemical composition with a wide range of variability in beef. Meat Sci. 2014;98(2):110\u2013114.","DOI":"10.1016\/j.meatsci.2013.12.019"},{"key":"19_CR86","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.meatsci.2017.06.002","volume":"133","author":"L Vel\u00e1squez","year":"2017","unstructured":"Vel\u00e1squez L, Cruz-Tirado J, Siche R, Quevedo R. An application based on the decision tree to classify the marbling of beef by hyperspectral imaging. Meat Sci. 2017;133:43\u201350.","journal-title":"Meat Sci"},{"key":"19_CR87","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.meatsci.2017.04.010","volume":"132","author":"C Craigie","year":"2017","unstructured":"Craigie C, Johnson P, Shorten P, Charteris A, Maclennan G, et al. Application of hyperspectral imaging to predict the pH, intramuscular fatty acid content and composition of lamb M. longissimus lumborum at 24 h post mortem. Meat Sci. 2017;132:19\u201328.","journal-title":"Meat Sci"},{"key":"19_CR88","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.1007\/s11947-013-1228-z","volume":"7","author":"F Zhu","year":"2014","unstructured":"Zhu F, Zhang H, Shao Y, He Y, Ngadi M. Mapping of fat and moisture distribution in Atlantic salmon using near-infrared hyperspectral imaging. Food Bioprocess Technol. 2014;7:1208\u201314.","journal-title":"Food Bioprocess Technol"},{"key":"19_CR89","doi-asserted-by":"crossref","unstructured":"Tao F, Mba O, Liu L, Ngadi M. Rapid and non-destructive assessment of polyunsaturated fatty acids contents in salmon using near-infrared hyperspectral imaging. In Hyperspectral imaging sensors: innovative applications and sensor standards, ed. DP Bannon. Bellingham, WA: SPIE. 2017.","DOI":"10.1117\/12.2262862"},{"key":"19_CR90","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.ifset.2013.10.013","volume":"21","author":"J-H Cheng","year":"2014","unstructured":"Cheng J-H, Sun D-W, Zeng X-A, Pu H-B. Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging. Innov Food Sci Emerg Technol. 2014;21:179\u201387.","journal-title":"Innov Food Sci Emerg Technol"},{"key":"19_CR91","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.foodchem.2018.07.013","volume":"270","author":"J-H Cheng","year":"2019","unstructured":"Cheng J-H, Sun D-W, Liu G, Chen YN. Developing a multispectral model for detection of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) changes in fish fillet using physarum network and genetic algorithm (PN-GA) method. Food Chem. 2019;270:181\u20138.","journal-title":"Food Chem"},{"key":"19_CR92","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.postharvbio.2015.12.007","volume":"114","author":"Q Zhu","year":"2016","unstructured":"Zhu Q, Guan J, Huang M, Lu R, Mendoza F. Predicting bruise susceptibility of \u2018Golden Delicious\u2019 apples using hyperspectral scattering technique. Postharvest Biol Technol. 2016;114:86\u201394.","journal-title":"Postharvest Biol Technol"},{"issue":"2","key":"19_CR93","doi-asserted-by":"publisher","first-page":"189","DOI":"10.3390\/app7020189","volume":"7","author":"Y Lu","year":"2017","unstructured":"Lu Y, Huang Y, Lu R. Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: a review. Appl Sci. 2017;7(2):189.","journal-title":"Appl Sci"},{"issue":"12","key":"19_CR94","doi-asserted-by":"publisher","first-page":"3251","DOI":"10.2527\/jas.2006-187","volume":"84","author":"XJ Yang","year":"2006","unstructured":"Yang XJ, Albrecht E, Ender K, Zhao RQ, Wegner J. Computer image analysis of intramuscular adipocytes and marbling in the longissimus muscle of cattle. J Anim Sci. 2006;84(12):3251\u20138.","journal-title":"J Anim Sci"},{"issue":"4","key":"19_CR95","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1016\/j.meatsci.2008.05.036","volume":"80","author":"CJ Du","year":"2008","unstructured":"Du CJ, Sun DW, Jackman P, Allen P. Development of a hybrid image processing algorithm for automatic evaluation of intramuscular fat content in beef M. longissimus dorsi. Meat Sci. 2008;80(4):1231\u20137.","journal-title":"Meat Sci"},{"issue":"2","key":"19_CR96","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.meatsci.2009.03.010","volume":"83","author":"P Jackman","year":"2009","unstructured":"Jackman P, Sun DW, Allen P. Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. Meat Sci. 2009;83(2):187\u201394.","journal-title":"Meat Sci"},{"issue":"3","key":"19_CR97","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1016\/j.meatsci.2004.09.010","volume":"69","author":"L Faucitano","year":"2005","unstructured":"Faucitano L, Huff P, Teuscher F, Gariepy C, Wegner J. Application of computer image analysis to measure pork marbling characteristics. Meat Sci. 2005;69(3):537\u201343.","journal-title":"Meat Sci"},{"key":"19_CR98","doi-asserted-by":"publisher","first-page":"143","DOI":"10.3390\/foods10010143","volume":"10","author":"A Teixeira","year":"2021","unstructured":"Teixeira A, Silva SR, Hasse M, Almeida JMH, Dias L. Intramuscular fat prediction using color and image analysis of B\u00edsaro pork breed. Foods. 2021;10:143.","journal-title":"Foods"},{"key":"19_CR99","doi-asserted-by":"publisher","first-page":"1368","DOI":"10.3390\/ani11051368","volume":"11","author":"AC Batista","year":"2021","unstructured":"Batista AC, Santos V, Afonso J, Guedes C, Azevedo J, Teixeira A, Silva S. Evaluation of an image analysis approach to predicting primal cuts and lean in light lamb carcasses. Animals-Basel. 2021;11:1368.","journal-title":"Animals-Basel"},{"issue":"2","key":"19_CR100","doi-asserted-by":"publisher","first-page":"192","DOI":"10.24263\/2304-974X-2018-7-2-4","volume":"7","author":"J Lukinac","year":"2018","unstructured":"Lukinac J, Juki\u0107 M, Mastanjevi\u0107 K, Lu\u010dan M. Application of computer vision and image analysis method in cheese-quality evaluation: a review. Ukrainian Food J. 2018;7(2):192\u2013214.","journal-title":"Ukrainian Food J"},{"key":"19_CR101","doi-asserted-by":"crossref","unstructured":"- V\u00e9lez-Rivera N, Blasco J, Chanona-P\u00e9rez J, Calder\u00f3n-Dom\u00ednguez G, de Jes\u00fas Perea-Flores M, Arzate-V\u00e1zquez I, ... Farrera-Rebollo R. Computer vision system applied to classification of \u201cManila\u201d mangoes during ripening process. Food Bioprocess Tech 2014;7:1183\u20131194.","DOI":"10.1007\/s11947-013-1142-4"},{"issue":"1","key":"19_CR102","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.compag.2009.08.006","volume":"70","author":"K Chen","year":"2010","unstructured":"Chen K, Sun X, Qin C, Tang X. Color grading of beef fat by using computer vision and support vector machine. Comput Electron Agr. 2010;70(1):27\u201332.","journal-title":"Comput Electron Agr"},{"key":"19_CR103","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/s11947-008-0106-6","volume":"3","author":"RA Quevedo","year":"2010","unstructured":"Quevedo RA, Aguilera JM, Pedreschi F. Color of salmon fillets by computer vision and sensory panel. Food Bioprocess Tech. 2010;3:637\u201343.","journal-title":"Food Bioprocess Tech"},{"key":"19_CR104","doi-asserted-by":"publisher","first-page":"340114","DOI":"10.1016\/j.aca.2022.340114","volume":"1221","author":"P Vahdatiyekta","year":"2022","unstructured":"Vahdatiyekta P, Zniber M, Bobacka J, Huynh TP. A review on conjugated polymer-based electronic tongues. Anal Chim Acta. 2022;1221:340114.","journal-title":"Anal Chim Acta"},{"issue":"8","key":"19_CR105","doi-asserted-by":"publisher","first-page":"3001","DOI":"10.1109\/JSEN.2013.2263125","volume":"13","author":"Y Tahara","year":"2013","unstructured":"Tahara Y, Toko K. Electronic tongues\u2013a review. IEEE Sens J. 2013;13(8):3001\u201311.","journal-title":"IEEE Sens J"},{"issue":"3","key":"19_CR106","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1111\/ijfs.13977","volume":"54","author":"X Tian","year":"2019","unstructured":"Tian X, Wang J, Shen R, Ma Z, Li M. Discrimination of pork\/chicken adulteration in minced mutton by electronic taste system. Int J Food Sci Technol. 2019;54(3):670\u20138.","journal-title":"Int J Food Sci Technol"},{"key":"19_CR107","doi-asserted-by":"crossref","unstructured":"Chen R, Liu XC, Xiang J, Sun W, Tomasevic I. Prospects and challenges for the application of salty and saltiness-enhancing peptides in low-sodium meat products. Meat Sci 2023;204:109261.","DOI":"10.1016\/j.meatsci.2023.109261"},{"key":"19_CR108","doi-asserted-by":"crossref","unstructured":"Xiang C, Li S, Zhang D, Huang C, Zhao Y, Zheng X, ... Chen L. Characterization and discrimination of the flavor profiles of Chinese indigenous sheep breeds via electronic sensory, smart instruments and chemometrics. J Food Compos Anal 2023;122:105458.","DOI":"10.1016\/j.jfca.2023.105458"},{"key":"19_CR109","doi-asserted-by":"crossref","unstructured":"Xu X, Sun C, Liu B, Zhou Q, Xu P, Liu M, ... Jiang Q. Flesh flavor of red swamp crayfish (Procambarus clarkii Girard, 1852) processing by GS-IMS and electronic tongue is changed by dietary animal and plant protein. Food Chem. 2022;373:131453.","DOI":"10.1016\/j.foodchem.2021.131453"},{"key":"19_CR110","doi-asserted-by":"crossref","unstructured":"Tian X, Li ZJ, Chao YZ, Wu ZQ, Zhou MX, Xiao ST, ... Zhe J. Evaluation by electronic tongue and headspace-GC-IMS analyses of the flavor compounds in dry-cured pork with different salt content. Food Res Int. 2020;137;109456.","DOI":"10.1016\/j.foodres.2020.109456"},{"key":"19_CR111","doi-asserted-by":"crossref","unstructured":"Gil L, Barat JM, Baigts D, Mart\u00ednez-M\u00e1\u00f1ez R, Soto J, Garcia-Breijo E, ... Llobet E. Monitoring of physical\u2013chemical and microbiological changes in fresh pork meat under cold storage by means of a potentiometric electronic tongue. Food Chem. 2011;126(3):1261\u20131268.","DOI":"10.1016\/j.foodchem.2010.11.054"},{"issue":"7","key":"19_CR112","doi-asserted-by":"publisher","first-page":"5099","DOI":"10.3390\/s90705099","volume":"9","author":"AD Wilson","year":"2009","unstructured":"Wilson AD, Baietto M. Applications and advances in electronic-nose technologies. Sensors. 2009;9(7):5099\u2013148.","journal-title":"Sensors"},{"key":"19_CR113","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1016\/j.meatsci.2011.02.022","volume":"88","author":"M Nurjuliana","year":"2011","unstructured":"Nurjuliana M, Che Man YB, Mat Hashim D, Mohamed AKS. Rapid identification of pork for halal authentication using the electronic nose and gas chromatography mass spectrometer with headspace analyzer. Meat Sci. 2011;88:638\u201344.","journal-title":"Meat Sci"},{"key":"19_CR114","doi-asserted-by":"publisher","first-page":"602","DOI":"10.3390\/foods11040602","volume":"11","author":"C Huang","year":"2022","unstructured":"Huang C, Gu Y. A machine learning method for the quantitative detection of adulterated meat using a MOS-based E-nose. Foods. 2022;11:602.","journal-title":"Foods"},{"key":"19_CR115","doi-asserted-by":"publisher","first-page":"221700","DOI":"10.1109\/ACCESS.2020.3043394","volume":"8","author":"R Sarno","year":"2020","unstructured":"Sarno R, Triyana K, Sabilla SI, Wijaya DR, Sunaryono D, Fatichah C. Detecting pork adulteration in beef for halal authentication using an optimized electronic nose system. IEEE Access. 2020;8:221700\u201311.","journal-title":"IEEE Access"},{"key":"19_CR116","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.foodcont.2018.11.038","volume":"98","author":"Q Wang","year":"2019","unstructured":"Wang Q, Li L, Ding W, Zhang D, Wang J, Reed K, Zhang B. Adulterant identification in mutton by electronic nose and gas chromatography-mass spectrometer. Food Control. 2019;98:431\u20138.","journal-title":"Food Control"},{"key":"19_CR117","doi-asserted-by":"publisher","first-page":"128931","DOI":"10.1016\/j.snb.2020.128931","volume":"326","author":"DR Wijaya","year":"2021","unstructured":"Wijaya DR, Sarno R, Zulaika E. DWTLSTM for electronic nose signal processing in beef quality monitoring. Sens Actuators, B Chem. 2021;326:128931.","journal-title":"Sens Actuators, B Chem"},{"key":"19_CR118","doi-asserted-by":"publisher","first-page":"228e236","DOI":"10.1016\/j.foodchem.2013.06.073","volume":"145","author":"L Huang","year":"2014","unstructured":"Huang L, Zhao JW, Chen QS, Zhang YH. Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chem. 2014;145:228e236.","journal-title":"Food Chem"},{"issue":"9","key":"19_CR119","doi-asserted-by":"publisher","first-page":"2146","DOI":"10.3390\/s19092146","volume":"19","author":"P Li","year":"2019","unstructured":"Li P, Ren Z, Shao K, Tan H, Niu Z. Research on distinguishing fish meal quality using different characteristic parameters based on electronic nose technology. Sensors. 2019;19(9):2146.","journal-title":"Sensors"},{"key":"19_CR120","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1007\/s12161-019-01682-6","volume":"13","author":"E Mirzaee-Ghaleh","year":"2020","unstructured":"Mirzaee-Ghaleh E, Taheri-Garavand A, Ayari F, Lozano J. Identification of fresh-chilled and frozen-thawed chicken meat and estimation of their shelf life using an E-nose machine coupled fuzzy KNN. Food Anal Methods. 2020;13:678\u201389.","journal-title":"Food Anal Methods"},{"key":"19_CR121","doi-asserted-by":"crossref","unstructured":"Shi Y, Li Z, Shi J, Zhang F, Zhou X, Li Y, ... Zou X. Titanium dioxide-polyaniline\/silk fibroin microfiber sensor for pork freshness evaluation. Sens Actuators B: Chemical. 2018;260:465\u2013474.","DOI":"10.1016\/j.snb.2018.01.078"},{"issue":"14","key":"19_CR122","doi-asserted-by":"publisher","first-page":"3225","DOI":"10.3390\/s19143225","volume":"19","author":"S Grassi","year":"2019","unstructured":"Grassi S, Benedetti S, Opizzio M, di Nardo E, Buratti S. Meat and fish freshness assessment by a portable and simplified electronic nose system (mastersense). Sensors. 2019;19(14):3225.","journal-title":"Sensors"},{"key":"19_CR123","doi-asserted-by":"publisher","first-page":"108994","DOI":"10.1016\/j.foodcont.2022.108994","volume":"138","author":"S Grassi","year":"2022","unstructured":"Grassi S, Benedetti S, Magnani L, Pianezzola A, Buratti S. Seafood freshness: e-nose data for classification purposes. Food Control. 2022;138:108994.","journal-title":"Food Control"},{"key":"19_CR124","doi-asserted-by":"publisher","first-page":"104780","DOI":"10.1016\/j.chemolab.2023.104780","volume":"235","author":"C Hawko","year":"2023","unstructured":"Hawko C, Hucher N, Crunaire S, Leger C, Locoge N, Verriele M, Savary G. How the experimental design associated with objectivized sensory analysis can be used to predict odor quality of gaseous mixtures? Chemom Intell Lab Syst. 2023;235:104780. https:\/\/doi.org\/10.1016\/j.chemolab.2023.104780.","journal-title":"Chemom Intell Lab Syst"},{"key":"19_CR125","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1007\/s00217-019-03304-1","volume":"245","author":"C-M Nie","year":"2019","unstructured":"Nie C-M, Zhong X-X, He L, Gao Y, Zhang X, Wang C-M, Du X. Comparison of different aroma-active compounds of Sichuan dark brick tea (Camellia sinensis) and Sichuan Fuzhuan brick tea using gas chromatography\u2013mass spectrometry (GC\u2013MS) and aroma descriptive profile tests. Eur Food Res Technol. 2019;245:1963\u201379. https:\/\/doi.org\/10.1007\/s00217-019-03304-1.","journal-title":"Eur Food Res Technol"},{"key":"19_CR126","doi-asserted-by":"publisher","first-page":"3338","DOI":"10.1002\/fsn3.2923","volume":"10","author":"C-M Wang","year":"2022","unstructured":"Wang C-M, Du X, Nie C-N, Zhang X, Tan X-Q, Li Q. Evaluation of sensory and safety quality characteristics of \u201chigh mountain tea.\u201d Food Sci Nutr. 2022;10:3338\u201354. https:\/\/doi.org\/10.1002\/fsn3.2923.","journal-title":"Food Sci Nutr"},{"key":"19_CR127","doi-asserted-by":"publisher","unstructured":"- Xu C. Electronic eye for food sensory evaluation. Evaluation technologies for food quality. Elsevier Inc. 2019;37\u201359p. https:\/\/doi.org\/10.1016\/B978-0-12-814217-2.00004-475.","DOI":"10.1016\/B978-0-12-814217-2.00004-475"},{"key":"19_CR128","doi-asserted-by":"publisher","first-page":"17006","DOI":"10.1038\/s41598-019-53210-5","volume":"9","author":"YX Cui","year":"2019","unstructured":"Cui YX, Liu RX, Lin ZZ, Chen PJ, Wang LL, Wang YL, Chen SQ. Quality evaluation based on color grading: quality discrimination of the Chinese medicine Corni Fructus by an E-eye. Sci Rep. 2019;9:17006.","journal-title":"Sci Rep"},{"issue":"3","key":"19_CR129","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.17533\/udea.vitae.v27n3a01","volume":"27","author":"R Ordo\u00f1ez-Araque","year":"2022","unstructured":"Ordo\u00f1ez-Araque R, Rodr\u00edguez-Villacres J, Urresto-Villegas J. Electronic nose, tongue and eye: their usefulness for the food industry. Vitae. 2022;27(3):e1. https:\/\/doi.org\/10.17533\/udea.vitae.v27n3a01.","journal-title":"Vitae"},{"issue":"10","key":"19_CR130","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.talanta.2018.11.04681","volume":"30","author":"G Orlandi","year":"2019","unstructured":"Orlandi G, Calvini R, Foca G. Data fusion of electronic eye and electronic tongue signals to monitor grape ripening. Talanta. 2019;30(10):181\u20139. https:\/\/doi.org\/10.1016\/j.talanta.2018.11.04681.","journal-title":"Talanta"},{"issue":"6","key":"19_CR131","doi-asserted-by":"publisher","first-page":"217","DOI":"10.3390\/foods806021782","volume":"8","author":"D Ismael","year":"2019","unstructured":"Ismael D, Ploeger A. Development of a sensory method to detect food-elicited emotions using emotion-color association and eye-tracking. Foods. 2019;8(6):217. https:\/\/doi.org\/10.3390\/foods806021782.","journal-title":"Foods"},{"issue":"17","key":"19_CR132","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.foodqual.2017.09.01483","volume":"64","author":"K Motoki","year":"2018","unstructured":"Motoki K, Saito T, Nouchi R. Tastiness but not healthfulness captures automatic visual attention. Preliminary evidence from an eye-tracking study. Food Qual Prefer. 2018;64(17):148\u201353. https:\/\/doi.org\/10.1016\/j.foodqual.2017.09.01483.","journal-title":"Food Qual Prefer."},{"issue":"2","key":"19_CR133","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.jpor.2018.11.01184","volume":"63","author":"Y Yasui","year":"2019","unstructured":"Yasui Y, Tanaka J, Kakudo M, Tanaka M. Relationship between preference and gaze in modified food using eye tracker. J Prosthodont Res. 2019;63(2):210\u20135. https:\/\/doi.org\/10.1016\/j.jpor.2018.11.01184.","journal-title":"J Prosthodont Res."},{"issue":"1","key":"19_CR134","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13593-016-0352-3","volume":"36","author":"A Luvisi","year":"2016","unstructured":"Luvisi A. Electronic identification technology for agriculture, plant, and food. A review. Agron Sustain Dev. 2016;36(1):1\u201314. https:\/\/doi.org\/10.1007\/s13593-016-0352-3.","journal-title":"Agron Sustain Dev"},{"issue":"October","key":"19_CR135","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/srep37034","volume":"6","author":"F Foroni","year":"2016","unstructured":"Foroni F, Pergola G, Rumiati RI. Food color is in the eye of the beholder: the role of human trichromatic vision in food evaluation. Sci Rep. 2016;6(October):6\u201311. https:\/\/doi.org\/10.1038\/srep37034.","journal-title":"Sci Rep."},{"issue":"171","key":"19_CR136","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.procs.2020.04.069","volume":"2020","author":"M Sreeraj","year":"2019","unstructured":"Sreeraj M, Joy J, Kuriakose A. CLadron\u2217: AI assisted device for identifying artificially ripened climacteric fruits. Procedia Comput Sci. 2019;2020(171):635\u201343. https:\/\/doi.org\/10.1016\/j.procs.2020.04.069.","journal-title":"Procedia Comput Sci"},{"issue":"9","key":"19_CR137","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.foodres.2018.04.025","volume":"109","author":"Y Yang","year":"2018","unstructured":"Yang Y, Zhao C, Tian G. Characterization of physical properties and electronic sensory analyses of citrus oil-based nanoemulsions. Food Res Int. 2018;109(9):149\u201358. https:\/\/doi.org\/10.1016\/j.foodres.2018.04.025.","journal-title":"Food Res Int"},{"issue":"2","key":"19_CR138","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.foodcont.2007.02.012","volume":"19","author":"AJ Sanchez","year":"2008","unstructured":"Sanchez AJ, Albarracin W, Grau R, Ricolfe C, Barat JM. Control of ham salting by using image segmentation. Food Control. 2008;19(2):135\u201342. https:\/\/doi.org\/10.1016\/j.foodcont.2007.02.012.","journal-title":"Food Control"},{"key":"19_CR139","doi-asserted-by":"publisher","unstructured":"Fongaro l, Alamprese C, Casiraghi E. Ripening of salami: assessment of colour and aspect evolution using image analysis and multivariate image analysis. Meat Sci. 2015;101:73-77https:\/\/doi.org\/10.1016\/j.meatsci.2014.11.005.","DOI":"10.1016\/j.meatsci.2014.11.005"},{"issue":"1","key":"19_CR140","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0260-8774(03)00183-3","volume":"61","author":"T Brosnan","year":"2004","unstructured":"Brosnan T, Sun D-W. Improving quality inspection of food products by computer vision\u2013\u2013a review. J Food Eng. 2004;61(1):3\u201316. https:\/\/doi.org\/10.1016\/S0260-8774(03)00183-3.","journal-title":"J Food Eng"},{"key":"19_CR141","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.meatsci.2015.11.009","volume":"113","author":"X Sun","year":"2016","unstructured":"Sun X, Young J, Liu JH, Bachmeier L, Somers RM, Chen KJ, Newman D. Prediction of pork color attributes using computer vision system. Meat Sci. 2016;113:62\u20134.","journal-title":"Meat Sci"},{"key":"19_CR142","doi-asserted-by":"publisher","first-page":"296","DOI":"10.22175\/mmb2018.06.0015","volume":"2","author":"X Sun","year":"2018","unstructured":"Sun X, Young J, Liu JH, Chen Q, Newman D. Predicting pork color scores using computer vision and support vector machine technology. Meat Muscle Biol. 2018;2:296\u2013302.","journal-title":"Meat Muscle Biol"},{"key":"19_CR143","doi-asserted-by":"publisher","first-page":"101447","DOI":"10.1016\/j.psj.2021.101447","volume":"100","author":"B Wang","year":"2021","unstructured":"Wang B, Yang H, Lu F, Yu F, Wang X, Zou Y, Liu D, Zhang J, Xia W. Establish intelligent detection system to evaluate the sugar smoking of chicken thighs. Poult Sci. 2021;100:101447.","journal-title":"Poult Sci"},{"key":"19_CR144","doi-asserted-by":"publisher","first-page":"106693","DOI":"10.1016\/j.foodcont.2019.06.019","volume":"106","author":"I Mu\u00f1oz","year":"2019","unstructured":"Mu\u00f1oz I, Gou P, Fulladosa E. Computer image analysis for intramuscular fat segmentation in dry-cured ham slices using convolutional neural networks. Food Control. 2019;106:106693.","journal-title":"Food Control"},{"key":"19_CR145","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1016\/j.compag.2017.11.017","volume":"142","author":"APAC Barbon","year":"2017","unstructured":"Barbon APAC, Barbon S, Campos GFC, Seixas JL, Peres LM, Mastelini SM, Andreo N, Ulrici A, Bridi AM. Development of a flexible computer vision system for marbling classification. Comput Electron Agric. 2017;142:536\u201344.","journal-title":"Comput Electron Agric"},{"key":"19_CR146","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.meatsci.2018.03.020","volume":"143","author":"JH Liu","year":"2018","unstructured":"Liu JH, Sun X, Young JM, Bachmeier LA, Newman DJ. Predicting pork loin intramuscular fat using computer vision system. Meat Sci. 2018;143:18\u201323.","journal-title":"Meat Sci"},{"key":"19_CR147","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.meatsci.2018.03.005","volume":"140","author":"X Sun","year":"2018","unstructured":"Sun X, Young J, Liu JH, Newman D. Prediction of pork loin quality using online computer vision system and artificial intelligence model. Meat Sci. 2018;140:72\u20137.","journal-title":"Meat Sci"},{"key":"19_CR148","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1111\/1541-4337.12431","volume":"18","author":"M Petracci","year":"2019","unstructured":"Petracci M, Soglia F, Madruga M, Carvalho L, Ida E, Est\u00e9vez M. Wooden-breast, white striping, and spaghetti meat: causes, consequences and consumer perception of emerging broiler meat abnormalities. Compr Rev Food Sci Food Saf. 2019;18:565\u201383.","journal-title":"Compr Rev Food Sci Food Saf"},{"key":"19_CR149","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1016\/j.lwt.2015.08.021","volume":"65","author":"M Chmiel","year":"2016","unstructured":"Chmiel M, S\u0142owi\u0144ski M, Dasiewicz K, Florowski T. Use of computer vision system (CVS) for detection of PSE pork meat obtained from m. semimembranosus. LWT-Food Sci Technol. 2016;65:532\u20136.","journal-title":"LWT-Food Sci Technol"},{"key":"19_CR150","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.lwt.2016.06.054","volume":"73","author":"M Chmiel","year":"2016","unstructured":"Chmiel M, S\u0142owi\u0144ski M. The use of computer vision system to detect pork defects. LWT-Food Sci Technol. 2016;73:473\u201380.","journal-title":"LWT-Food Sci Technol"},{"key":"19_CR151","doi-asserted-by":"crossref","unstructured":"Geronimo BC, Mastelini SM, Carvalho RH, Barbon SJr, Barbin DF, Shimokomaki M, Ida EI. Computer vision system and near-infrared spectroscopy for identification and classification of chicken with wooden breast, and physicochemical and technological characterization. Infrared Phys Technol. 2019;96:303\u2013310.","DOI":"10.1016\/j.infrared.2018.11.036"},{"key":"19_CR152","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.lwt.2015.11.042","volume":"67","author":"P Zapotoczny","year":"2016","unstructured":"Zapotoczny P, Szczypi\u0144ski PM, Daszkiewicz T. Evaluation of the quality of cold meats by computer-assisted image analysis. LWT-Food Sci Technol. 2016;67:37\u201349.","journal-title":"LWT-Food Sci Technol"},{"key":"19_CR153","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.biosystemseng.2017.12.009","volume":"167","author":"N Teimouri","year":"2018","unstructured":"Teimouri N, Omid M, Mollazade K, Mousazadeh H, Alimardani R, Karstoft H. On-line separation and sorting of chicken portions using a robust vision-based intelligent modelling approach. Biosyst Eng. 2018;167:8\u201320.","journal-title":"Biosyst Eng"},{"key":"19_CR154","doi-asserted-by":"publisher","first-page":"117842","DOI":"10.1016\/j.saa.2019.117842","volume":"228","author":"D Zhang","year":"2020","unstructured":"Zhang D, Feng X, Xu C, Xia D, Liu S, Gao S, Zheng F, Liu Y. Rapid discrimination of Chinese dry-cured hams based on Tri-step infrared spectroscopy and computer vision technology. Spectrochim Acta-Part A Mol Biomol Spectrosc. 2020;228:117842.","journal-title":"Spectrochim Acta-Part A Mol Biomol Spectrosc"},{"key":"19_CR155","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.cofs.2021.02.012","volume":"41","author":"I Tomasevic","year":"2021","unstructured":"Tomasevic I, Djekic I, Font-i-Furnols M, Terjung N, Lorenzo JM. Recent advances in meat color research. Curr Opin Food Sci. 2021;41:81\u20137.","journal-title":"Curr Opin Food Sci"},{"key":"19_CR156","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.biosystemseng.2016.01.015","volume":"144","author":"DF Barbin","year":"2016","unstructured":"Barbin DF, Mastelini SM, Barbon S, Campos GFC, Barbon APAC, Shimokomaki M. Digital image analyses as an alternative tool for chicken quality assessment. Biosyst Eng. 2016;144:85\u201393.","journal-title":"Biosyst Eng"},{"key":"19_CR157","doi-asserted-by":"publisher","unstructured":"- Hassoun A, A\u00eft-Kaddour A, Abu-Mahfouz AM, Rathod NB, Bader F, Barba FJ, Biancolillo A, Cropotova J, Galanakis CM, Jambrak AR. et al. The fourth industrial revolution in the food industry\u2014part I: industry 4.0 technologies. Crit Rev Food Sci Nutr. 2022;Feb 3:1\u201317. https:\/\/doi.org\/10.1080\/10408398.2022.2034735.","DOI":"10.1080\/10408398.2022.2034735"},{"key":"19_CR158","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.foodqual.2019.05.011","volume":"77","author":"B Mahieu","year":"2019","unstructured":"Mahieu B, Visalli M, Schlich P, Thomas A. Eating chocolate, smelling perfume or watching video advertisement: does it make any difference on emotional states measured at home using facial expressions? Food Qual Prefer. 2019;77:102\u20138.","journal-title":"Food Qual Prefer"},{"issue":"4","key":"19_CR159","doi-asserted-by":"publisher","first-page":"e93823","DOI":"10.1371\/journal.pone.0093823","volume":"9","author":"RA de Wijk","year":"2014","unstructured":"de Wijk RA, He W, Mensink MGJ, Verhoeven RHG, de Graaf C. ANS responses and facial expressions differentiate between the taste of commercial breakfast drinks. PLoS ONE. 2014;9(4):e93823. https:\/\/doi.org\/10.1371\/journal.pone.0093823.","journal-title":"PLoS ONE"},{"key":"19_CR160","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.foodqual.2018.04.004","volume":"68","author":"C Crist","year":"2018","unstructured":"Crist C, Duncan S, Arnade E, Leitch K, O\u2019Keefe S, Gallagher D. Automated facial expression analysis for emotional responsivity using an aqueous bitter model. Food Qual Prefer. 2018;68:349\u201359. https:\/\/doi.org\/10.1016\/j.foodqual.2018.04.004.","journal-title":"Food Qual Prefer"},{"key":"19_CR161","doi-asserted-by":"publisher","first-page":"329","DOI":"10.3389\/fpsyg.2020.00329","volume":"11","author":"L Kulke","year":"2020","unstructured":"Kulke L, Feyerabend D, Schacht A. A comparison of the Affectiva iMotions facial expression analysis software with EMG for identifying facial expressions of emotion. Front Psychol. 2020;11:329\u201337.","journal-title":"Front Psychol"},{"key":"19_CR162","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.foodqual.2017.11.010","volume":"65","author":"DD Torrico","year":"2018","unstructured":"Torrico DD, Fuentes S, Viejo CG, Ashman H, Gunaratne NM, Gunaratne TM, Dunshea FR. Images and chocolate stimuli affect physiological and affective responses of consumers: a cross-cultural study. Food Qual Prefer. 2018;65:60\u201371.","journal-title":"Food Qual Prefer"},{"key":"19_CR163","doi-asserted-by":"crossref","unstructured":"- Wakihira T, Morimoto M, Higuchi S, Nagatomi Y. Can facial expressions predict beer choices after tasting? A proof of concept study on implicit measurements for a better understanding of choice behavior among beer consumers. Food Qual Prefer. 2022;104580.","DOI":"10.1016\/j.foodqual.2022.104580"},{"key":"19_CR164","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.physbeh.2018.02.051","volume":"200","author":"CG Viejo","year":"2019","unstructured":"Viejo CG, Fuentes S, Howell K, Torrico DD, Dunshea FR. Integration of non-invasive biometrics with sensory analysis techniques to assess acceptability of beer by consumers. Physiol Behav. 2019;200:139\u201347.","journal-title":"Physiol Behav"},{"key":"19_CR165","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.meatsci.2018.06.006","volume":"144","author":"DD Torrico","year":"2018","unstructured":"Torrico DD, Hutchings SC, Ha M, Bittner EP, Fuentes S, Warner RD, Dunshea FR. Novel techniques to understand consumer responses towards food products: a review with a focus on meat. Meat Sci. 2018;144:30\u201342.","journal-title":"Meat Sci"},{"key":"19_CR166","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.3390\/foods10061237","volume":"10","author":"M Gupta","year":"2021","unstructured":"Gupta M, Torrico DD, Hepworth G, Gras SL, Ong L, Cottrell JJ, Dunshea FR. Differences in hedonic responses, facial expressions and self-reported emotions of consumers using commercial yogurts: a cross-cultural study. Foods. 2021;10:1237.","journal-title":"Foods"},{"key":"19_CR167","doi-asserted-by":"publisher","unstructured":"Wagner J, Wilkin JD, Szymkowiak A, Grigor J. Sensory and affective response to chocolate differing in cocoa content: a TDS and facial electromyography approach. Physiology & behavior 2023: in press. https:\/\/doi.org\/10.1016\/j.physbeh.2023.114308.","DOI":"10.1016\/j.physbeh.2023.114308"},{"key":"19_CR168","doi-asserted-by":"publisher","first-page":"109124","DOI":"10.1016\/j.meatsci.2023.109124","volume":"199","author":"B Mena","year":"2023","unstructured":"Mena B, Torrico DD, Hutchings S, Ha M, Ashman H, Warner RD. Understanding consumer liking of beef patties with different firmness among younger and older adults using FaceReader\u2122 and biometrics. Meat Sci. 2023;199:109124. https:\/\/doi.org\/10.1016\/j.meatsci.2023.109124.","journal-title":"Meat Sci"},{"key":"19_CR169","doi-asserted-by":"publisher","unstructured":"da Quinta N, Baranda A, R\u00edos Y, Llorente R, Naranjo AB, de Mara\u00f1on IM. Methodology design to apply automatic facial coding and SCR measure with context information during the observation, olfaction, manipulation, and consumption of liquid foods. Sci Talks. 2023;5:100155. https:\/\/doi.org\/10.1016\/j.sctalk.2023.100155.","DOI":"10.1016\/j.sctalk.2023.100155"},{"key":"19_CR170","doi-asserted-by":"publisher","first-page":"104125","DOI":"10.1016\/j.foodqual.2020.104125","volume":"92","author":"RA de Wijk","year":"2021","unstructured":"de Wijk RA, Noldus LP. Using implicit rather than explicit measures of emotions. Food Qual Prefer. 2021;92:104125.","journal-title":"Food Qual Prefer"},{"key":"19_CR171","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.foodqual.2017.12.012","volume":"65","author":"L Verastegui-Tena","year":"2018","unstructured":"Verastegui-Tena L, van Trijp H, Piqueras-Fiszman B. Heart rate and skin conductance responses to taste, taste novelty, and the (dis) confirmation of expectations. Food Qual Prefer. 2018;65:1\u20139.","journal-title":"Food Qual Prefer"},{"key":"19_CR172","doi-asserted-by":"publisher","unstructured":"Adhikari K (2023) Application of selected neuroscientific methods in consumer sensory analysis: a review. Journal of Food Science 2023:88:A53\u2013 A64. In: Volume 88, issue S1, special issue: advances in sensory science: from perceptions to consumer acceptance, Pages: A1-A226. https:\/\/doi.org\/10.1111\/1750-3841.16526.","DOI":"10.1111\/1750-3841.16526"},{"key":"19_CR173","unstructured":"Anbarasan R, Mahendran R. Non-invasive sensory techniques: a new approach to food sensory analysis and market survey. Read more at: https:\/\/www.foodinfotech.com\/nist-a-new-approach-to-food-sensory-analysis\/. Consulted July, 19; 2023."},{"key":"19_CR174","doi-asserted-by":"publisher","unstructured":"Woodman GF. A brief introduction to the use of event-related potentials (ERPs) in studies of perception and attention. Atten Percept Psychophys. 2010;72(8). https:\/\/doi.org\/10.3758\/APP.72.8.2031.","DOI":"10.3758\/APP.72.8.2031"},{"issue":"4","key":"19_CR175","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1002\/fsat.3504_13.x","volume":"35","author":"SE Kemp","year":"2021","unstructured":"Kemp SE, Nyambayo I, Rogers L, Sanderson T, Villarino CB. Trends in food sensory science. Food Sci Technol. 2021;35(4):46\u201350. https:\/\/doi.org\/10.1002\/fsat.3504_13.x.","journal-title":"Food Sci Technol"},{"key":"19_CR176","doi-asserted-by":"publisher","first-page":"105864","DOI":"10.1016\/j.bandc.2022.105864","volume":"159","author":"I Zsoldos","year":"2022","unstructured":"Zsoldos I, Sinding C, Chambaron S. Using event-related potentials to study food-related cognition: an overview of methods and perspectives for future research. Brain Cogn. 2022;159:105864. https:\/\/doi.org\/10.1016\/j.bandc.2022.105864.","journal-title":"Brain Cogn"},{"issue":"6","key":"19_CR177","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1038\/nrn3745","volume":"15","author":"GJ Morton","year":"2014","unstructured":"Morton GJ, Meek TH, Schwartz MW. Neurobiology of food intake in health and disease. Nat Rev Neurosci. 2014;15(6):367\u201378. https:\/\/doi.org\/10.1038\/nrn3745.","journal-title":"Nat Rev Neurosci"},{"issue":"4","key":"19_CR178","doi-asserted-by":"publisher","first-page":"1032","DOI":"10.1007\/s11682-017-9766-z","volume":"12","author":"TD Masterson","year":"2018","unstructured":"Masterson TD, Kirwan CB, Davidson LE, Larson MJ, Keller KL, Fearnbach SN, Evans A, LeCheminant JD. Brain reactivity to visual food stimuli after moderate-intensity exercise in children. Brain Imaging Behav. 2018;12(4):1032\u201341. https:\/\/doi.org\/10.1007\/s11682-017-9766-z.","journal-title":"Brain Imaging Behav"},{"key":"19_CR179","doi-asserted-by":"publisher","first-page":"478","DOI":"10.3390\/bios13040478","volume":"13","author":"L Billeci","year":"2023","unstructured":"Billeci L, Sanmartin C, Tonacci A, Taglieri I, Bachi L, Ferroni G, Braceschi GP, Odello L, Venturi F. Wearable sensors to evaluate autonomic response to olfactory stimulation: the influence of short, intensive sensory training. Biosensors. 2023;13:478. https:\/\/doi.org\/10.3390\/bios13040478.","journal-title":"Biosensors"},{"key":"19_CR180","doi-asserted-by":"publisher","first-page":"110873","DOI":"10.1016\/j.foodres.2021.110873","volume":"152","author":"I Hinojosa-Aguayo","year":"2022","unstructured":"Hinojosa-Aguayo I, Garcia-Burgos D, Catena A, Gonz\u00e1lez F. Implicit and explicit measures of the sensory and hedonic analysis of beer: the role of tasting expertise. Food Res Int. 2022;152:110873. https:\/\/doi.org\/10.1016\/j.foodres.2021.110873.","journal-title":"Food Res Int"},{"key":"19_CR181","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.1111\/1750-3841.14275","volume":"83","author":"A Stelick","year":"2018","unstructured":"Stelick A, Penano AG, Riak AC, Dando R. Dynamic context sensory testing\u2013a proof of concept study bringing virtual reality to the sensory booth. J Food Sci. 2018;83:2047\u201351.","journal-title":"J Food Sci"},{"key":"19_CR182","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.3390\/foods10071623","volume":"10","author":"ML Montero","year":"2021","unstructured":"Montero ML, Garrido D, Gallardo RK, Tang J, Ross CF. Consumer acceptance of a ready-to-eat meal during storage as evaluated with a home-use test. Foods. 2021;10:1623.","journal-title":"Foods"},{"key":"19_CR183","doi-asserted-by":"publisher","first-page":"191","DOI":"10.3390\/foods9020191","volume":"9","author":"DD Torrico","year":"2020","unstructured":"Torrico DD, Han Y, Sharma C, Fuentes S, Gonzalez Viejo C, Dunshea FR. Effects of context and virtual reality environments on the wine tasting experience, acceptability, and emotional responses of consumers. Foods. 2020;9:191\u2013207.","journal-title":"Foods"},{"key":"19_CR184","doi-asserted-by":"publisher","first-page":"515","DOI":"10.3390\/foods9040515","volume":"9","author":"Y Kong","year":"2020","unstructured":"Kong Y, Sharma C, Kanala M, Thakur M, Li L, Xu D, Harrison R, Torrico DD. Virtual reality and immersive environments on sensory perception of chocolate products: a preliminary study. Foods. 2020;9:515.","journal-title":"Foods"},{"key":"19_CR185","doi-asserted-by":"publisher","first-page":"147","DOI":"10.3390\/fermentation7030147","volume":"7","author":"Y Dong","year":"2021","unstructured":"Dong Y, Sharma C, Mehta A, Torrico DD. Application of augmented reality in the sensory evaluation of yogurts. Fermentation. 2021;7:147.","journal-title":"Fermentation"},{"key":"19_CR186","doi-asserted-by":"publisher","unstructured":"De Wijk A, Kaneko D, Dijksterhuis GB, van Bergen G, Vingerhoeds MH, Visalli M, Zandstra EH. A preliminary investigation on the effect of immersive consumption contexts on food-evoked emotions using facial expressions and subjective ratings. d e. Food Qual Prefer. 2022;99:104572. https:\/\/doi.org\/10.1016\/j.foodqual.2022.104572.","DOI":"10.1016\/j.foodqual.2022.104572"}],"container-title":["Current Food Science and Technology Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43555-024-00019-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43555-024-00019-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43555-024-00019-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T15:03:50Z","timestamp":1708527830000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43555-024-00019-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,29]]},"references-count":186,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["19"],"URL":"https:\/\/doi.org\/10.1007\/s43555-024-00019-7","relation":{},"ISSN":["2662-8473"],"issn-type":[{"value":"2662-8473","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,29]]},"assertion":[{"value":"8 January 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human or animal subjects performed by the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"All authors approved and agreed to submit the manuscript to this journal.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}