Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics

Huang, Jing; Ren, Guangxin; Sun, Yuxuan; Jin, Shanshan; Li, Luqing; Wang, Yujie; Ning, Jingming; Zhang, Zhengzhu

doi:10.1002/fsn3.1489

Cited by 20 publications

(9 citation statements)

References 47 publications

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“…For example, the content of cellulose and hemicellulose can determine the age and tenderness of the tea leaves, and the content and proportion of amino acids, tea polyphenols, and caffeine can determine the nutrition, and also the taste of the tea. However, many other factors affect the tea levels [ 1 , 2 ]. Because of the complexity and subtlety of tea level standards, the boundaries of tea levels are not clear, which gives profiteers the opportunity to cheat customers with low-level teas at high prices.…”

Section: Introductionmentioning

confidence: 99%

Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM

Ding

Yan

et al. 2022

Foods

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In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm−1 using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor c and the kernel function parameter g in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%.

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Section: Introductionmentioning

confidence: 99%

Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM

Ding

Yan

et al. 2022

Foods

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“…To differentiate milk samples according to species' origin, various chemical analytical techniques have been used from all the wellknown categories in food authentication (Medina, Perestrelo, Silva, Pereira, & Câmara, 2019), such as chromatographic, spectroscopic, mass spectrometry techniques, and electronic sensors. Moreover, chemometrics and spectroscopic techniques are widely used in food science nowadays (De Luca et al, 2019;Huang et al, 2020;Li, Li, Li, Liu, & Wang, 2019;Mabood et al, 2019;Sánchez-Rodríguez, Sánchez-López, Marinas, Urbano, & Caridad, 2019;Velázquez Ríos et al, 2019). A huge number of publications exists for discrimination of milk or cheese samples and they combine the spectroscopic technique FTIR (Fourier transformed infrared spectroscopy) and chemometrics regarding species' origin (Cirak et al, 2018;Nicolaou et al, 2010;Pappas et al, 2008;Terouzi, Omari, Boutoial, & Oussama, 2016).…”

Section: Introductionmentioning

confidence: 99%

Chemometric analysis combined with FTIR spectroscopy of milk and Halloumi cheese samples according to species’ origin

Tarapoulouzi

Kokkinofta

Theocharis

2020

Food Science & Nutrition

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Food adulteration is an issue of major concern, as numerous foodstuffs and beverages do not follow their labeling. Our research interest is in the field of authenticity of dairy products and particularly cheese. Adulteration of dairy products is a well‐known phenomenon, and there are numerous published studies specifically on the authenticity of cheese. In fact, substitution of a portion of fat and/or proteins, adulteration with milk of other species’ origin, and mislabeling of ingredients are some of the main issues that the science of dairy products’ authenticity is regularly facing. Discrimination of dairy products can be determined through several chemical or microbiological methods as presented in the literature. In addition, chemometric analysis is an important tool for interpretation of a huge load of measurements. The aim of this study is to discriminate between various milk samples, which is the primary ingredient of dairy products. Milk samples with different trademarks were analyzed. That data was combined with Halloumi cheese samples for chemometric discrimination of species’ origin. The innovative point of this study is the fact that it is the first time that a research study related to dairy products includes Halloumi cheese which is a traditional Cypriot cheese, not well‐studied until now. The first step of the methodology was the freeze‐drying via lyophilization of the samples. Fourier transformed infrared spectroscopy (FTIR) was chosen for their chemical characterization. Moreover, interpretation of the measurements was carried out by chemometric analysis using SIMCA software. For this study, FTIR data combined with chemometrics have given a very good discrimination of the samples according to their species’ origin. Chemometric methods such as PCA and OPLS‐DA have been used with great success. In the future, this model will be studied regarding geographical origin of the samples.

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“…In the Figure S1, we drew Figure 2a,b, and c, which are the spectra of the combination of “stem + equator” and “stem + bottom” after normalization pretreatment and the spectrum of “stem + bottom” combination after pretreatment by MA method, respectively. Compared with the original spectrogram, we can see that the spectrogram after pretreatment is more “normalized,” which is the result of eliminating the environmental noise, laying a foundation for improving the accuracy of the model (Huang et al, 2020; Xu et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

Nondestructive detection of tomato quality based on multiregion combination model

Limin

Zhao

et al. 2022

J Food Process Engineering

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To quickly and accurately identify the quality of tomatoes, a method was proposed to predict the total soluble solid content (SSC), total titratable acidity (TA), and vitamin C (VC) content of tomatoes based on a multiregion combined model of the visible–near‐infrared spectrum. The results show that the competitive adaptive re‐weighted sampling algorithm combined with the partial least squares regression (CARS‐PLSR) model has the best prediction effect on SSC, TA, and VC content in “stem + equator”, “stem + bottom” and “stem + bottom” combinations. The prediction accuracy is 97.2%, 96.7%, and 97.7%, respectively, and the relative percent deviation (RPD) value is 5.870, 5.401, and 5.942, respectively. Practical Application This indicates that the CARS‐PLSR model based on the multiregion combination of visible–near‐infrared spectroscopy is reliable for predicting tomatoes' SSC, TA, and VC content. The results provide a theoretical basis for developing a portable fruit quality detector.

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Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics

Cited by 20 publications

References 47 publications

Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM

Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM

Chemometric analysis combined with FTIR spectroscopy of milk and Halloumi cheese samples according to species’ origin

Nondestructive detection of tomato quality based on multiregion combination model

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