Chemical profiling and multivariate data fusion methods for the identification of the botanical origin of honey

Ballabio, Davide; Robotti, Elisa; Grisoni, Francesca; Quasso, Fabio; Bobba, Marco; Vercelli, Serena; Gosetti, Fabio; Calabrese, Giuseppe; Sangiorgi, Emanuele; Orlandi, Marco; Marengo, Emílio

doi:10.1016/j.foodchem.2018.05.084

Cited by 75 publications

(59 citation statements)

References 39 publications

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“…Sulla and heather honeys are typical of Tuscany and other regions of Italy [42]; their chemical composition and phenolic profiles were the objects of several studies [20,22,[43][44][45], while front-face fluorescence investigations were performed on heather honey samples from other European countries only [35,36]. In the present study, fluorescence properties of these two types of monofloral honey showed similar features (Figure 2).…”

Section: Identification Of Main Fluorophores In Sulla and Heather Honeysupporting

confidence: 68%

“…An original "semi-quantitative" spectral analysis was proposed in order to identify specific bioactive compounds from the fluorescence spectra. Results reached from this spectral analysis are compared with those obtained by means of various analytical and chemical-physical methods on Italian honey samples (i.e., acacia, chestnut, arbutus, sulla, and heather) [20,22,[42][43][44][45] and on some of the Tuscan samples investigated here (i.e., heather, marruca, sulla, acacia, clover, chestnut, and sunflower) [17,46]. The approach presented here opens to future perspectives for a quantitative analysis of fluorescence in terms of main fluorophores' content.…”

Section: Introductionmentioning

confidence: 99%

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Front-Face Fluorescence of Honey of Different Botanic Origin: A Case Study from Tuscany (Italy)

2020

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Honey is a natural pure food produced by honeybees from the nectar of various plants, and its chemical composition includes carbohydrates, water, and some minor compounds, which are very important for honey quality and authentication. Most of honey’s minor components are related to the botanic origin, climate, and geographic diversity. In this work, we report an original case study on monofloral honey samples of twelve different botanic origins produced in Tuscany (Italy) based on the ‘semi-quantitative’ analysis of emission, excitation, and synchronous front-face fluorescence spectra. This is the first front-face fluorescence study of Italian honey samples and, to our knowledge, the first fluorescence investigation of honey from inula (Inula viscosa (L.) Aiton), marruca (Paliurus spina-christi Mill.), lavender (Lavandula L. 1753), sulla (Hedysarum coronarium L.), arbutus (or strawberry tree) (Arbutus unedo L., 1753), and alfalfa (Medicago sativa L.) plants. Results obtained from fluorescence spectroscopy are discussed in terms of characteristic spectral emission profiles typical of honey of different botanic origins. Moreover, the spectral analysis based on the decomposition of the front-face fluorescence (FFF) spectra in terms of single main fluorophores’ components is here proposed to identify several minor compounds, such as amino acids, phenolic acids, vitamins, and other fluorescent bioactive molecules.

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Section: Identification Of Main Fluorophores In Sulla and Heather Honeysupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Front-Face Fluorescence of Honey of Different Botanic Origin: A Case Study from Tuscany (Italy)

2020

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“…Tahir and co-workers observed equivalent performance of FT-MIR and FT-Raman spectroscopy for the prediction of phenolic compounds content and the antioxidant activity in honey [ 40 ]. Ballabio et al recently evaluated five different technologies, including FT-MIR, NIR and FT-Raman spectroscopy, for the botanical origin identification of honeys [ 41 ]. The authors reported better classification provided by NIR, respect to FT-MIR and FT-Raman spectroscopy.…”

Section: Discussionmentioning

confidence: 99%

Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation

Damiani

Alonso-Salces

Aubone

et al. 2020

Foods

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In the present work, the provenance discrimination of Argentinian honeys was used as case study to compare the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected among three main honey-producing regions of Argentina over four harvesting seasons. Each sample was fingerprinted by FT-MIR, NIR and FT-Raman spectroscopy. The spectroscopic platforms were compared on the basis of the classification performance achieved under a supervised chemometric approach. Furthermore, low- mid- and high-level data fusion were attempted in order to enhance the classification results. Finally, the best-performing solution underwent to SIMCA modelling with the purpose of reproducing a food authentication scenario. All the developed classification models underwent to a “year-by-year” validation strategy, enabling a sound assessment of their long-term robustness and excluding any issue of model overfitting. Excellent classification scores were achieved by all the technologies and nearly perfect classification was provided by FT-MIR. All the data fusion strategies provided satisfying outcomes, with the mid- and high-level approaches outperforming the low-level data fusion. However, no significant advantage over the FT-MIR alone was obtained. SIMCA modelling of FT-MIR data produced highly sensitive and specific models and an overall prediction ability improvement was achieved when more harvesting seasons were used for the model calibration (86.7% sensitivity and 91.1% specificity). The results obtained in the present work suggested the major potential of FT-MIR for fingerprinting-based honey authentication and demonstrated that accuracy levels that may be commercially useful can be reached. On the other hand, the combination of multiple vibrational spectroscopic fingerprints represents a choice that should be carefully evaluated from a cost/benefit standpoint within the industrial context.

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“…Italian researchers also analyzed honey samples and built models using NIR-Flex 500 spectrometer and Raman spectroscopy for the botanical origin identification and revealed an accuracy amounting to 79% in the case of the NIR measurement [ 64 ].…”

Section: Near Infrared (Nir) Spectroscopy: Historical Background Amentioning

confidence: 99%

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview

Aouadi

Zaukuu

Vitális

et al. 2020

Sensors

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Amid today’s stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

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Chemical profiling and multivariate data fusion methods for the identification of the botanical origin of honey

Cited by 75 publications

References 39 publications

Front-Face Fluorescence of Honey of Different Botanic Origin: A Case Study from Tuscany (Italy)

Front-Face Fluorescence of Honey of Different Botanic Origin: A Case Study from Tuscany (Italy)

Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview

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