Honey fraud detection based on sugar syrup adulterations by HPLC-UV fingerprinting and chemometrics

In recent years, honey-producing sector has faced the increasing presence of adulterated honeys, implying greateconomic losses and questioning the quality of this highly appreciated product by the society. Due to the highsugar content of honey, sugar syrups are among its most common adulterants, being also the most difficult todetect even with isotope ratio techniques depending on the origin of the sugar syrup plant source. In this work, ahoney authentication method based on HPLC-UV fingerprinting was developed, exhibiting a 100% classificationrate of honey samples against a great variety of sugar syrups (agave, corn, fiber, maple, rice, sugar cane andglucose) by partial least squares-discriminant analysis (PLS-DA). In addition, the detection and level quantitationof adulteration using syrups as adulterants (down to 15%) was accomplished by partial least squares (PLS)regression with low prediction errors by both internal and external validation (values below 12.8% and 19.7%,respectively

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machinelearning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictor

Elemental fingerprinting combined with machine learning techniques as a powerful tool for geographical discrimination of honeys from nearby regions

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors

Variación y cambio lingüístico en el aula: nuevas propuestas pedagógicas para acercar sincronía y diacronía

Memoria ID-0070. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2014-2015