Using UV–Vis spectroscopy for simultaneous geographical and varietal classification of tea infusions simulating a home-made tea cup

Diniz, Paulo Henrique Gonçalves Dias; Barbosa, Mayara F.; Milanez, Karla Danielle Tavares de Melo; Pistonesi, Marcelo Fabián; Araújo, Mário César Ugulino de

doi:10.1016/j.foodchem.2015.07.022

Cited by 76 publications

(26 citation statements)

References 19 publications

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“…The maximum absorbance displayed by empty gelatin nanoparticles (GB) was at 220 nm corresponding to π → π* transition of C═O functional groups (Hermanto et al ., ). The spectra of the MO extract shows characteristics absorbance bands at 210 nm and 265 nm (π → π* transition of the aromatic conjugated ring), and at 330 nm (n → π* transition of the non‐bonding electron of hydroxyl groups within the phenolic ring) (Diniz et al ., ). However, due to the encapsulation process, the characteristic bands of MO extract did not appear within GMO1, GMO3 and GMO5.…”

Section: Resultsmentioning

confidence: 97%

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Characterisation of gelatin nanoparticles encapsulated with Moringa oleifera bioactive extract

Hani

Azarian

Torkamani

et al. 2016

Int J of Food Sci Tech

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Summary This study describes the preparation and characterisation of nanoparticles with gelatin as the wall matrix to encapsulate Moringa oleifera (MO) extract using an electrospraying technique. The electrospraying conditions for voltage, flow rate and emitter/collector distance were 20 kV, 0.5 mL h−1 and 10 cm, respectively. Nanoparticles with encapsulated MO extract (1–5%, w/w) were successfully produced and characterised in relation to spectroscopic, morphological and thermal properties. Increasing amounts of MO extract resulted in a significant decrease in the nanocapsule size (ranging from 140 and 179 nm) produced. Spectroscopic analysis indicated no chemical interactions between core and wall materials. The encapsulation efficiency (EE) of MO extract‐loaded nanocapsules obtained was 83.0 ± 4.0%. Surface analysis showed that roughness decreased from 91 nm (empty beads) to 57.5 nm with addition of 3% MO extract. The thermal stability of encapsulated nanoparticles slightly increased and the glass transition temperature (Tg) disappeared due to increase in crystallinity.

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Section: Resultsmentioning

confidence: 97%

“…p* transition of the aromatic conjugated ring), and at 330 nm (n ? p* transition of the non-bonding electron of hydroxyl groups within the phenolic ring) (Diniz et al, 2016). However, due to the encapsulation process, the characteristic bands of MO extract did not appear within GMO1, GMO3 and GMO5.…”

Section: Uv-visible Spectramentioning

confidence: 95%

Characterisation of gelatin nanoparticles encapsulated with Moringa oleifera bioactive extract

Hani

Azarian

Torkamani

et al. 2016

Int J of Food Sci Tech

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“…The optical method has been widely applied in the field of food classification due to its non-destructive and rapid response characteristics. Absorption spectroscopy is an important method for food quality control, classification and authentication; it mainly includes UV-Vis absorption spectroscopy [1,2], near-infrared (NIR) spectroscopy [3] and mid-infrared spectroscopy [4,5] accompanied with chemometrics including partial least square (PLS) and linear discriminant analysis (LDA). Laser-induced breakdown spectroscopy (LIBS) can excite the trace elements contained in samples, and classify the species according to the trace…”

Section: Introductionmentioning

confidence: 99%

Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network

Lin

Lu³

et al. 2019

Sensors

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A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification.

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“…Due to the demand of quality and the swelling in price, food adulteration is still quite common in some food products that include milk [1,2], wine [3,4], table oil [5,6], tea [7], coffee [8], and meats [9][10][11]. For example, it has been testified that 20% of the tested livestock meat were assured of counterfeit in Guangdong Province, China, during 2014-2015 [12].…”

Section: Introductionmentioning

confidence: 99%

“…It can be used to identify and quantify compounds which absorb frequencies that are characteristics of their structure such as molecular potential energy surfaces, the masses of the atoms, and the associated vibronic coupling. Due to the fast and simple operation of the infrared spectroscopy (FT-IR), it has been widely applied in the detection of agricultural products such as wine [3,4], olive oil [31], tea [7], and meat [10,13,32,33].…”

Section: Introductionmentioning

confidence: 99%

Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy

Yang

Liu

et al. 2018

Journal of Spectroscopy

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Consumers concern about food adulteration. Pork meat is the principal adulterated species of beef and mutton. The conventional detection methods have their own limitations; therefore, we sought to develop an efficient and economical identification method using an infrared spectroscopy technique for meat. The Mahalanobis distance method was used to remove outliers in spectrum data. Interferences were eliminated using multiple scatter correction, standard normal variate, Savitzky-Golay smoothing, and normalization. The partial least square discriminant analysis (PLS-DA) and support vector machine (SVM) were used to establish identification models. In the Mahalanobis distance method, the coefficient of test sets was increased from 0.93 to 0.99; the RMSEC and RMSECV were decreased from 0.17 to 0.09 and 0.21 to 0.11 accordingly. The coefficient of determination in-between the calibration and testing sets in PLS-DA reached 0.99 and 0.99, RMSEC was 0.06, and both the RMSECV and RMSEP were 0.08. In contrast, in SVM, methods were 0.97 and 0.96. The RMSEC, RMSECV, and RMSEP were 0.15, 0.17, and 0.24, respectively. In summary, using a combination of infrared spectroscopy technology with PLS-DA was a better identification method than the SVM method that can be used as an effective method to identify pork, beef, and mutton meat samples.

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Using UV–Vis spectroscopy for simultaneous geographical and varietal classification of tea infusions simulating a home-made tea cup

Cited by 76 publications

References 19 publications

Characterisation of gelatin nanoparticles encapsulated with Moringa oleifera bioactive extract

Characterisation of gelatin nanoparticles encapsulated with Moringa oleifera bioactive extract

Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network

Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy

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