Rapid identification of the geographic origin of Taiping Houkui green tea using near‐infrared spectroscopy combined with a variable selection method

Jin, Ge; Xu, Yifan; Cui, Chuanjian; Zhu, Yongfa; Zong, Jianfa; Cai, Hao; Ning, Jingming; Wei, Chaoling; Hou, Ruyan

doi:10.1002/jsfa.11964

Cited by 16 publications

(8 citation statements)

References 35 publications

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“…Results showed that the GA-BP model achieved the highest accuracy of 96.6% in discriminating coal mine dust wettability, followed by PSO-ELM, ELM, and BP models. Ge et al [30] collected 114 samples of Taiping Monkey Kui green tea from four production areas, establishing SNV-ELM and SNV-GA-ELM models based on the combination of near-infrared spectroscopy and chemometrics to accurately identify green teas from specific geographic origins. The ELM model combined with SNV preprocessing achieved an accuracy of 93.07%, while the ELM model after SNV preprocessing combined with GA feature variables achieved an accuracy of 95.35% for the test set.…”

Section: Extraction Of Characteristicsmentioning

confidence: 99%

Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information

Han,

Sha,

Dai

et al. 2024

Foods

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The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky–Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.

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Section: Extraction Of Characteristicsmentioning

confidence: 99%

Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information

Han,

Sha,

Dai

et al. 2024

Foods

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“…The spectral curves of these samples show the typical plant characteristics of green tea, which are consistent with our early work and those spectra measured by commercial instruments in other studies. [40][41][42][43][44][45][46] The reduction of spectral sampling points would result in the decrease of identification accuracy, which also reflects in the smoothness of the curves. Fortunately, we found that a spectral resolution of 10-20 nm is sufficient for the qualitative analysis of sugar-adulterating green tea in our previous work, and the characteristic bands mainly fall into 1300-1700 nm.…”

Section: Spectral Acquisition Of Adulterated Teamentioning

confidence: 99%

Near‐Infrared InGaAs Intelligent Spectral Sensor by 3D Heterogeneous Hybrid Integration

Wang

Dong

Huang

et al. 2023

Advanced Photonics Research

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The applications of near‐infrared spectroscopy (NIRS) are limited due to the bulky size, low integration, and poor intelligence in edge computing of traditional spectrometers. In this work, the authors develop an on‐chip InGaAs intelligent spectral sensor, which consists of a linear variable filter for wavelength selection, a linear focal plane array as detector and a chip processor for neural‐network inferring, based on an advanced 3D heterogeneous hybrid‐integration. More than 200 spectral channels with a spectral resolution of 1.25% central wavelength are acquired in a wide waveband from 900 to 1700 nm. Immediate results are provided onsite for users, especially applicable to nonspecialists, owing to the embedded algorithmic model in‐sensor. As a proof of applications, the authors experimentally detect adulterating green tea and achieve a real‐time identification with accuracy better than 90%. This spectral sensor with edge‐artificial‐intelligence breaks the dependence on external algorithms and paves the way for NIRS into miniaturization, integration, and intelligence, which brings more possibilities in incorporating with the consumer devices and the Internet of Things.

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“…The tea industry is important for achieving rural revitalization and consolidating poverty alleviation results in many regions of China (Hu et al., 2022). Taiping Houkui tea, produced in the Huangshan region of Anhui Province, China, is characterized by two leaves embracing a bud, flat and straight shape, and hidden white fuzz (Jin et al., 2022; Liu et al., 2023; Zhou et al., 2022). The processing technology of Taiping Houkui is unique.…”

Section: Introductionmentioning

confidence: 99%

Continuous production machine for separating and shaping Taiping Houkui tea

Wu,

Zhong,

Wang

et al. 2024

Journal of Food Science

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In response to the challenges of low automation and a lack of a continuous processing system for Taiping Houkui tea, this study proposed a design scheme for a continuous processing line and built a continuous processing prototype for testing by combining the production requirements of Taiping Houkui tea, the characteristics of withered leaves, and the existing relevant production equipment. First, the physical properties of Taiping Houkui tea were determined. A simulation was performed using the Hertz–Mindlin model, and the motion states of the tea leaves were obtained under different conditions to define the parameter design range of the experimental platform and verify its structural rationality. Then, the response surface methodology was used to optimize the working parameter ranges and obtain the best working parameters for the feeding and kneading mechanisms. Finally, a continuous production prototype was constructed for further production verification. The experimental results show that the success rate of continuous production on this platform was 70.68%, with an average output of approximately 0.4 kg/h for Taiping Houkui dry tea on a single slide track, and the produced tea was similar to manually made tea. This demonstrates that the continuous production technique has high feasibility and provides a reference for continuous production of Taiping Houkui tea.

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Rapid identification of the geographic origin of Taiping Houkui green tea using near‐infrared spectroscopy combined with a variable selection method

Cited by 16 publications

References 35 publications

Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information

Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information

Near‐Infrared InGaAs Intelligent Spectral Sensor by 3D Heterogeneous Hybrid Integration

Continuous production machine for separating and shaping Taiping Houkui tea

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