Honey Traceability and Authenticity. Review of Current Methods Most Used to Face this Problem

Reference	Type of honey	Number of samples	From	Technique	Statistic appr.	Aim of the work	Results
Schellenberg et al. (2010)	Honeydew, unfloral	516	IT, PT, DE, ES, FR, GR, IS, IE, PL, GB, AT, DK.	IRMS		GO	70% of correct classified samples
Zheng et al. (2016)	Unfloral, multifloral	75	CN	1H NMR	PCA, OPLS-DA	GO and BO	Discrimination of sample variations by analysing saccharides and all types of amino acids and organic carboxylic acids
Minaei et al. (2017)	8 Robinia pseudoacacia L., 10 Fagopyrum esculentum Moench, 9 Calluna vulgaris L., 15 Tilia spp., 10 Brassica napus L.	52	PL	VIS-NIR (range 400–1000 nm)	PCA	BO	Three machine learning algorithms (radial basis function, support vector machine and random forest of which the best achieved 94% accuracy
Lastra-Mejías et al. (2018)	Unfloral, multifloral	10	ES, MX.	Fluorescence spectroscopy	PLS-2	BO	Simple approach, 95% accuracy
Chen et al. (2019)	Longan, non-longan	163	TH, TW	IRMS	LSD	Anti-fraud	7% adulterated samples, Differences between Taiwan and Thailand longan honeys
Suhandy & Yulia (2021)	Durian, multifloral	400	ID	UV-VIS spectrometer	PCA	BO	100% correct discrimination
Stefas et al. (2021)	Unfloral, multifloral	45		LIBS	LDA	Anti-fraud	2 predictive models with classification accuracy greater than 90%

Reference	Type of honey	Provenance	Sequencing	Number of samples	Advantages	Problems
Bruni et al. (2015)	Multifloral	IT	RbcL and trnH-psbA plastid	4	high performances, possibility of replacing mellissopalinological analysis in the future	Need improving plant database
Soares et al. (2015)	Unifloral/multifloral	PT	-	4	Solidity, sensitivity, specificity and less margin of human subjectivity in the analysis of the results	-
Laha et al. (2017)	Multifloral	IN	NGS	20		Need improving plant database
Prosser & Hebert (2017)	Mixed	CA/MX	Ion Torrent	7	Successful in 5/7 of the samples	Risk of being acted upon by small fraudulent additions of pollen
Utzeri et al. (2018)	Unifloral/multifloral/honeydrew	MIXED	TrnL-UAA plastid	9	Low cost in prospective and more efficacy	-
Saravanan et al. (2019)	-	IN	3730XL automated DNA Sequencer	29	Quicker, easier than mellissopalinological analysis	Need improving plant barcode database

Reference	Type of honey	Number of samples	From	Analytical technique	Aim	Elements
Conti et al. (2007)	Acacia, Multifloral, Honeydew	69	IT	F-AAS, GF-AAS	BO	Na, K, Ca, Mg, Fe, Cu, Mn
Vanhanen et al. (2011)	Leptosermum, Weinmannia, Honeydrew, Thymus, Metrosideros, Echium, Ixerba, Knighita, Carduus, Trifolium		NZ	ICP-OES	GO	Al, As, B, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, Pb, S, Zn
Oroian et al. (2015)	Acacia, Tilia, Sunflower, Multifloral	52	RO	ICP-MS	GO and BO	As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Zn
Ahmed et al. (2016)	Jujube, Multifloral, Acacia	16	PK, DE, FR	AAS	GO	Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, 0Ni, Pb, Zn
Louppis et al. (2017)	Citrus, Pinus, Abies, Thymus	207	GR	ICP-AES	BO	Ag, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, Mg, Mn, Mo, Ni, Pb, Sb, Se, Si, Ti, Tl, V and Zn
Bontempo et al. (2017)	Acacia, Multifloral, Honeydew, Rhododendron, Chestnut, Eucalyptus	265	IT	ICP-OES	GO and BO	Al, B, Ba, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Rb, Sr, Zn
Bilandžić et al. (2017)	Multifloral, Erica, Arbutus unedo, Canulla, Honeydew, Citrus	26	HR	ICP-MS	BO	Al, CA, Cu, Fe, K, Mg, Mn, Na, Zn, Ag, As, Ba, Be, Cd, Co, Cr, Mo, Ni, Se, Sb, V, U, Th
Di Rosa et al. (2019)	Eucalyptus, Castanea, Hedysarum, Citrus	28	IT	ICP-MS	BO	Na, K, Ca, Mg, Fe, Mn, Zn
Lanjwani & Channa (2019)	Multifloral	15	PK	F-AAS	GO	Fe, Zn, Co, Cu, Mn, Cr, Ni, Pb, Cd, Na, K, Ca, Mg
Squadrone et al. (2020)	Multifloral	75	BALKANS, KZ, SOUTH AMERICA, TZ	ICP-MS	GO and BO	Ag, Al, As, Be, Bi, Cd, Co, Cr, Cu, Fe, Ga, In, Mn, Mo, Ni, Pb, Rb, Sb, Se, Sn, Tl, U, V, Zn