The Use of Visible and near Infrared Spectroscopy to Classify the Floral Origin of Honey Samples Produced in Uruguay

Corbella, E. Calvo; Cozzolino, Daniel

doi:10.1255/jnirs.458

Cited by 33 publications

(20 citation statements)

References 27 publications

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“…Fig 2 (B) shows average absorption spectra of the honey samples (50 pixels of each sample). The spectra illustrate typical spectral features and absorption bands similar to those reported for honey [51]. A clear absorption peak in the visible range at 400-425 nm is observed, which can be attributed to color variation of honey samples.…”

Section: Hyperspectral Image Classificationsupporting

confidence: 66%

“…Some methods require sample preparation and are destructive, for example instrumental methods such as: atomic absorption spectroscopy (AAS) [10], high-performance liquid chromatography [11], gas chromatography coupled with mass spectrometry (GC-MS) [12][13], electro-spray mass spectrometry (ES-MS) [14], inductively-coupled plasma optical emission spectrometry (ICP-OES) [7], thin-layer chromatography (TLC) [15], and high-performance anion-exchange chromatography with pulsed amperometric detection (HPAED-PAD) [16]. Different spectroscopy techniques such as, nuclear magnetic resonance (NMR) [17][18], Fourier transform-Raman (FT-Raman) [19], Fourier-transform infrared (FTIR) [20], mid-infrared spectroscopy [21], and near-infrared (NIR) spectroscopy [21][22][23][24] have been utilized by various researchers. NIR spectroscopy is considered as a fast and non-destructive method for determination of honey floral origin among different spectroscopic techniques [24].…”

Section: Introductionmentioning

confidence: 99%

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VIS/NIR imaging application for honey floral origin determination

Minaei

Shafiee

Polder

et al. 2017

Infrared Physics & Technology

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Nondestructive methods are of utmost importance for honey characterization. This study investigates the potential application of VIS-NIR hyperspectral imaging for detection of honey flower origin detection using machine learning techniques. Hyperspectral images of 52 honey samples were taken in transmittance mode in the visible/near infrared (VIS-NIR) range (400-1000 nm). Three different machine learning algorithms were implemented to predict honey floral origin using honey spectral images. These methods, included radial basis function (RBF) network, support vector machine (SVM), and random forest (RF). Principal component analysis (PCA) was also exploited for dimensionality reduction. According to the obtained results, the best classifier (RBF) achieved a precision of 94% in a five-fold cross validation experiment using only the first two PCs. Mapping of the classifier results to the test set images showed 90% accuracy for honey images. Three types of honey including buckwheat, rapeseed and heather were classified with 100% accuracy. The proposed approach has great potential for honey floral origin detection. As some other honey properties can also be predicted using image features, in addition to floral origin detection, this method may be applied to predict other honey characteristics.

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Section: Hyperspectral Image Classificationsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

VIS/NIR imaging application for honey floral origin determination

Minaei

Shafiee

Polder

et al. 2017

Infrared Physics & Technology

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“…Furthermore, the EC value changes when the amount of plant pollen decreases [43]. Overall, the Malaysian honey samples had similar EC values to those reported for honey samples from Uruguay [44]; Andalusia, Spain [42]; India [2]; and Morocco [41]. …”

Section: Resultsmentioning

confidence: 73%

Physicochemical and antioxidant properties of Malaysian honeys produced by Apis cerana, Apis dorsata and Apis mellifera

Moniruzzaman

Khalil

Sulaiman

et al. 2013

BMC Complement Altern Med

182

141

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BackgroundThe aim of the present study was to evaluate the physicochemical and antioxidant properties of Malaysian monofloral honey samples—acacia, pineapple and borneo honey—and compare them with tualang honey. Acacia and pineapple honey are produced by Apis mellifera bees while borneo and tualang honey are produced by Apis cerana and Apis dorsata bees, respectively.MethodsThe physical parameters of honey, such as pH, moisture content, electrical conductivity (EC), total dissolved solids (TDS), color intensity, total sugar and apparent sucrose content, were measured. Hydroxymethylfurfural (HMF) was measured using high performance liquid chromatography, and a number of biochemical and antioxidant tests were performed to determine the antioxidant properties of the honey samples.ResultsAcacia honey was the most acidic (pH 3.53), whereas pineapple honey had the lowest moisture content (14.86%), indicating that both types of honey can resist microbial spoilage more effectively when compared to tualang honey (pH 3.80 and 17.53% moisture content). Acacia honey contained the highest EC (0.76 mS/cm), whereas borneo honey had the highest (377 ppm) TDS. The mean HMF content in Malaysian honey was 35.98 mg/kg. Tualang honey, which is amber color, had the highest color intensity (544.33 mAU). Acacia honey is the sweetest, and contained the highest concentration of total sugar, reducing sugar and apparent sucrose. Tualang honey had the highest concentration of phenolic compounds (352.73 ± 0.81 mg galic acid/kg), flavonoids (65.65 ± 0.74 mg catechin/kg), DPPH (59.89%), FRAP values (576.91 ± 0.64 μM Fe (II)/100 g) and protein content (4.83 ± 0.02 g/kg) as well as the lowest AEAC values (244.10 ± 5.24 mg/kg), indicating its strong antioxidant properties. Proline, an important amino acid that is present in honey was also measured in the present study and it was found at the highest concentration in pineapple honey. Several strong correlations were found among the biochemical and antioxidant parameters of all the Malaysian honeys.ConclusionAlthough Malaysian honeys are of good quality, tualang honey contains the strongest antioxidant properties by far.

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“…Unlike honey, that gets much of its character from the floral species in surrounding fields (Corbella and Cozzolino, 2005), maple syrup quality is influenced by the timing of sap harvest. Early season syrups tend to be light-colored, while late season batches are much darker, with a more robust taste (Morselli and Whalen, 1991).…”

Section: Introductionmentioning

confidence: 99%