NIR hyperspectral imaging coupled with chemometrics for nondestructive assessment of phosphorus and potassium contents in tea leaves

Wang, Yujie; Jin, Ge; Li, Luqing; Liu, Ying; Kalkhajeh, Yusef Kianpoor; Ning, Jingming; Zhang, Zhengzhu

doi:10.1016/j.infrared.2020.103365

Cited by 52 publications

(23 citation statements)

References 38 publications

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“…Successive projections algorithm (SPA) is a forward selection algorithm [34]. It is a useful tool for selecting small variable quantum sets with less collinearity [37]. In general, a wavelength signal is randomly selected as the initial criterion, and then the iteration begins.…”

Section: Spectral Characteristic Wavelength Selectionmentioning

confidence: 99%

The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

Kang

2022

Molecules

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Tieguanyin is one of the top ten most popular teas and the representative of oolong tea in China. In this study, a rapid and non-destructive method is developed to detect adulterated tea and its degree. Benshan is used as the adulterated tea, which is about 0%, 10%, 20%, 30%, 40%, and 50% of the total weight of tea samples, mixed with Tieguanyin. Taking the fluorescence spectra from 475 to 1000 nm, we then established the 2-and 6-class discriminant models. The 2-class discriminant models had the best evaluation index when using SG-CARS-SVM, which can reach a 100.00% overall accuracy, 100.00% specificity, 100% sensitivity, and the least time was 1.2088 s, which can accurately identify pure and adulterated tea; among the 6-class discriminant models (0% (pure Tieguanyin), 10, 20, 30, 40, and 50%), with the increasing difficulty of adulteration, SNV-RF-SVM had the best evaluation index, the highest overall accuracy reached 94.27%, and the least time was 0.00698 s. In general, the results indicated that the two classification methods explored in this study can obtain the best effects. The fluorescence hyperspectral technology has a broad scope and feasibility in the non-destructive detection of adulterated tea and other fields.

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Section: Spectral Characteristic Wavelength Selectionmentioning

confidence: 99%

The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

Kang

2022

Molecules

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“…The fluorescence hyperspectral data of Oolong tea samples were acquired using a GaiaFluo(/Pro)-VN-HR series of fluorescence hyperspectral test system manufactured by Sichuan Dualix Spectral Image Technology Co. Ltd. The hyperspectral camera has the advantages of high sensitivity and high signal-to-noise ratio in the 350 nm to 1100 nm band [30]. The spectral resolution is 2.8 nm with a pixel of 2048 × 946.…”

Section: Fluorescence Hyperspectral Image Acquisitionmentioning

confidence: 99%

“…The region of interest (ROI) [32] is reasonably selected, which is directly related to the quality of the extracted data. In this experiment, ROI were selected through ENVI 5.3 [30]. Using ENVI to set a rectangular box surrounded by 20 samples of each image as ROI and calculating the average value of the spectra of all pixels in the region as the final spectral value of a sample, the extracted data can cover the entire tea sample, avoid the edge of the sample plate and effectively reflect the sample information.…”

Section: Fluorescence Hyperspectral Data Extractionmentioning

confidence: 99%

“…MSC and SNV can effectively reduce the impact of baseline drift, tilt, and other noise [36]. SD and FD processing can effectively reduce the influence of sample background and baseline drift, enhancing the resolution and sensitivity of overlapping peaks [30]. WT has the unique benefits of fine-scaled expression of time, frequency division, multiscale and multiresolution analysis.…”

Section: Spectral Preprocessingmentioning

confidence: 99%

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Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Huang

et al. 2021

Agriculture

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A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea.

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“…Development of quantitative analytical methods based on NIR-HSI for elucidating nutrient distribution and dynamics in plants remains an active field of research. Wang et al (2020) used HSI instrumentation operating in 908-1735 nm wavelength region (11,013-5,763 cm -1 ) to monitor P and K contents in tea plant leaves. The study involved 87 leaf samples from five different cultivars.…”

Section: Analysis In Vivo Of Macro-and Micronutrientsmentioning

confidence: 99%

Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review

et al. 2020

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Detailed knowledge about plant chemical constituents and their distributions from organ level to sub-cellular level is of critical interest to basic and applied sciences. Spectral imaging techniques offer unparalleled advantages in that regard. The core advantage of these technologies is that they acquire spatially distributed semi-quantitative information of high specificity towards chemical constituents of plants. This forms invaluable asset in the studies on plant biochemical and structural features. In certain applications, non-invasive analysis is possible. The information harvested through spectral imaging can be used for exploration of plant biochemistry, physiology, metabolism, classification, and phenotyping among others, with significant gains for basic and applied research. This article aims to present a general perspective about vibrational spectral imaging/micro-spectroscopy in the context of plant research. Within the scope of this review are infrared (IR), near-infrared (NIR) and Raman imaging techniques. To better expose the potential and limitations of these techniques, fluorescence imaging is briefly overviewed as a method relatively less flexible but particularly powerful for the investigation of photosynthesis. Included is a brief introduction to the physical, instrumental, and data-analytical background essential for the applications of imaging techniques. The applications are discussed on the basis of recent literature.

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NIR hyperspectral imaging coupled with chemometrics for nondestructive assessment of phosphorus and potassium contents in tea leaves

Cited by 52 publications

References 38 publications

The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review

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