Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis

Abstract

Aroma assessment remains difficult and uncertain in the present sensory assessment system. It is highly desirable to develop a new assessment method to discriminate the quality of various teas in the tea market. In the present work, based on linear discriminant analysis and principal component analysis, the aroma of dry and wet samples of different Xi-hu Longjing and Pu-erh teas were tested and differentiated by electronic noses (e-nose). The results confirm that e-nose can discriminate different priced Xi-hu Longjing tea samples in the range of 80-800 RMB/500 g and varying storage years of Pu-erh tea samples. Furthermore, for the detection of both dry and wet samples of Longjing and Pu-erh teas, the results reveal that all samples have specific aroma characteristics that e-nose can recognize. More importantly, contribution analysis in sensors indicates that nitrogen oxides, methane and alcohols are the characteristic components that contribute to the fragrances of different priced Xi-hu Longjing teas, while nitrogen oxides, aromatic benzene and amines make the fragrances of Pu-erh teas with different storage years disparate.Practical Application: This work demonstrates that e-nose can rapidly distinguish tea products with different price levels and varying storage years. With the advantages of ease of use, high portability and flexibility, e-nose will be widely expanded and applied in refined processing and the development of flavored foods.

Identification and characterization of volatile compounds in Lentinula edodes during vacuum freeze‐drying

“…However, SM1 samples were separated from them along PC2, which proved that odors in L. edodes were gradually changed after primary drying. Moreover, the loading plot obtained from PCA can be used to further discuss the contribution of the sensor to distinguishing samples, as it intuitively shows the influence of each sensor on the two principal components (Yuan et al., 2019). As presented in Figure 6b, the LY2/LG sensor (sensitive to oxidative molecules) could identify SM1 samples more effectively, which meant it was probably the content of oxidative molecules that discriminated SM1 samples from fresh L edodes .…”

“…However, SM1 samples were separated from them along PC2, which proved that odors in L. edodes were gradually changed after primary drying. Moreover, the loading plot obtained from PCA can be used to further discuss the contribution of the sensor to distinguishing samples, as it intuitively shows the influence of each sensor on the two principal components (Yuan et al, 2019).…”

Rapid characterization of the volatile profiles in Pu‐erh tea by gas phase electronic nose and microchamber/thermal extractor combined with TD‐GC‐MS

“…As an objective sensory apparatus, electronic nose (e-nose) can be used to analyze, identify and detect complex odors in a short time (Wu et al, 2018;X. Yang, Chen, et al, 2020;Yuan et al, 2019). It gives the overall information about volatile components, known as the "fingerprint" data.…”

“…At present, the quality evaluation of Pu‐erh tea aroma is heavily relied on the experience of sensory evaluation, and theoretical research is largely scarce. As an objective sensory apparatus, electronic nose (e‐nose) can be used to analyze, identify and detect complex odors in a short time (Wu et al., 2018; X. Yang, Chen, et al., 2020; Yuan et al., 2019). It gives the overall information about volatile components, known as the “fingerprint” data.…”

An Online Tea Fixation State Monitoring Algorithm Based on Image Energy Attention Mechanism and Supervised Clustering (IEAMSC)

“…Currently, the major procedures used in the tea research includes: spectral analysis [9,10], electronic nose aroma analysis [11][12][13], chemical composition analysis [14,15], neural network analysis [16,17], and image-based texture analysis [18,19], etc.…”