LED-induced fluorescence system for tea classification and quality assessment

Dong, Yongjiang; Liu, Xuan; Mei, Liang; Feng, Chao; Yan, Chunsheng; He, Sailing

doi:10.1016/j.jfoodeng.2014.03.027

Cited by 31 publications

(15 citation statements)

References 32 publications

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“…Differences in chlorophyll content between different samples can result in differences in fluorescent intensity. The fluorescence distribution in the blue‐green spectral region (450 to 600 nm) is due to the synthetic reflection of catechins, theaflavins, and anthocyanins (Dong et al., ; Li‐Xia, Ming, Wen‐Juan, Qin‐Zeng, & Xiao‐Qin, ). In summary, the fluorescence distribution of tea in the visible‐near‐infrared region indicates the feasibility of using FHSI technology to detect tea quality.…”

Section: Introductionmentioning

confidence: 99%

“…By combining appropriate analytical methods, it cannot only detect the main components of tea (Zhao, Wang, Ouyang, & Chen, 2011), but also effectively classify tea varieties (Puneet et al, 2018;Sun et al, 2018). Fluorescence spectroscopy, which is a mature, simple, fast, accurate and nondestructive technique, has been found to be highly effective in tea classification and quality assessment due to its high sensitivity (Dong et al, 2014;Seetohul, Scott, O'Hare, Ali, & Islam, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms

Sun

et al. 2019

Journal of Food Science

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In order to rapidly and nondestructively identify tea grades, fluorescence hyperspectral imaging (FHSI) technology was proposed in this paper. A total of 309 Tieguanyin tea samples with three different grades were collected and the fluorescence hyperspectral data was acquired by hyperspectrometer (400 to 1000 nm). The characteristic wavelengths were respectively selected by Bootstrapping Soft Shrinkage (BOSS), Variable Iterative Space Shrinkage Approach (VISSA) and Model Adaptive Space Shrinkage (MASS) algorithms. Then, Support Vector Machine (SVM) was applied to establishing the relationship between the characteristic peaks, the full spectra, three characteristic spectra and the labels of tea grades. The results showed that VISSA‐SVM model had the best classification performance, but the model precision can still be improved. Thus, Artificial Bee Colony (ABC) algorithm was introduced to optimize the parameters of SVM model. The accuracy and Kappa coefficient of test set of VISSA‐ABC‐SVM model were improved to 97.436% and 0.962, respectively. Therefore, the combination of fluorescence hyperspectra with VISSA‐ABC‐SVM model can accurately identify the grade of Tieguanyin tea. Practical Application The rapid and accurate nondestructive tea grade identification method contributes to the construction of the tea online grade detection system. FHSI technology can solve the shortcomings of the reported methods and improved the identification accuracy of tea grades. It can be applied to the rapid detection of tea quality by tea companies, tea market, tea farmers and other demanders.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms

Sun

et al. 2019

Journal of Food Science

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“…Despite this successful application, the follow-up approach with the same samples and device differentiating organic and nonorganic apples was not successful. Recording a similar wavelength range but with monochromatic light inducing fluorescence, Dong et al, 2014 [ 99 ], were able to classify teas according to the type of cultivar and type of processing (green or black tea).…”

Section: Current Applications Of Handheld Devices For Food Authenticationmentioning

confidence: 99%

Handheld Devices for Food Authentication and Their Applications: A Review

Müller-Maatsch

Ruth

2021

Foods

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This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.

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“…The influence of CDOM was tested in a similar fashion to the turbidity gradient experiment. Commercial black tea (Lipton Yellow Label, UK) solutions were used as analogues (Seetohul et al ; Dong et al ) to natural freshwater CDOM (Fellman et al ; Nelson and Siegel ). A working solution was first prepared by putting one tea bag in 450 mL of 75°C distilled water.…”

Section: Materials and Proceduresmentioning

confidence: 99%

Interpretation of total phytoplankton and cyanobacteria fluorescence from cross‐calibrated fluorometers, including sensitivity to turbidity and colored dissolved organic matter

Cremella

Huot

Bonilla

2018

Limnology & Ocean Methods

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In vivo pigment fluorescence methods allow simple real-time detection and quantification of freshwater algae and cyanobacteria. Available models are still limited to high-cost fluorometers, validated for single instruments or individual water bodies, preventing data comparison between multiple instruments, and thus, restricting their use in large-scale monitoring programs. Moreover, few models include corrections for optical interference (water turbidity and colored dissolved organic matter, CDOM). In this study, we developed simple models to predict phytoplankton and cyanobacterial chlorophyll a (Chl a) concentrations based on Chl a and C-phycocyanin in vivo fluorescence, using multiple low-cost handheld fluorometers. We aimed to: (1) fit models to mixed cyanobacterial and microalgal cultures; (2) cross-calibrate nine fluorometers of the same brand and series; (3) correct the CDOM and turbidity effects; and (4) test the algorithms' performance with natural samples. We achieved comparable results between nine instruments after the cross-calibration, allowing their simultaneous use. We obtained algorithms for total and cyanobacterial Chl a estimation. We developed parametric corrections to remove CDOM and turbidity interferences in the algorithms. Five sampling sites (from a lake, a stream, and an estuary) were used to test the algorithms using eight cross-calibrated fluorometers. The models showed their best performance after CDOM and turbidity corrections (total Chl a: R 2 = 0.99, RMSE = 7.8 μg Chl a L −1 ; cyanobacterial Chl a: R 2 = 0.98, RMSE = 9.8 μg Chl a L −1 ). In summary, our models can quantify total phytoplankton and cyanobacterial Chl a in real time with multiple low-cost fluorometers, allowing its implementation in large-scale monitoring programs.

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LED-induced fluorescence system for tea classification and quality assessment

Cited by 31 publications

References 32 publications

Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms

Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms

Handheld Devices for Food Authentication and Their Applications: A Review

Interpretation of total phytoplankton and cyanobacteria fluorescence from cross‐calibrated fluorometers, including sensitivity to turbidity and colored dissolved organic matter

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