Visualizing distribution of moisture content in tea leaves using optimization algorithms and NIR hyperspectral imaging

Sun, Jun; Zhou, Xin; Hu, Yongguang; Wu, Xiaohong; Zhang, Xiaodong; Wang, Pei

doi:10.1016/j.compag.2019.03.004

Cited by 99 publications

(45 citation statements)

References 20 publications

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“…SPA is proposed as a flexible variable selection strategy for multivariate calibration (Fan et al, 2019). The technique is an advanced selective method designed to reduce the number of variables used for modeling, minimize collinearity from each wavelength number position (Liu & He, 2009), and improve the conditioning of multiple linear regression by minimizing collinearity effects in the calibration data set (Sun et al, 2019). The above optimization algorithms could remove the spectral regions with large noise and irrelative information and enhanced the predictive ability of modeling (Basati, Jamshidi, Rasekh, & Abbaspour‐Gilandeh, 2018; Li & Su, 2018).…”

Section: Methodsmentioning

confidence: 99%

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

Huang

Ren

Sun

et al. 2020

Food Science & Nutrition

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The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least‐squares discriminant analysis (PLS‐DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near‐infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA‐SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades.

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Section: Methodsmentioning

confidence: 99%

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

Huang

Ren

Sun

et al. 2020

Food Science & Nutrition

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“…Recent research activity at this direction demonstrated the potential of NIR-HSI to perform spatially-resolved quantitative analysis and visualization of moisture content distribution in tea leaves ( Sun J. et al., 2019 ). A sophisticated suit of data-analytical methods was employed for this purpose, successive projections algorithm (SPA) coupled with stepwise regression (SPA-SR), and competitive adaptive reweighted sampling (CARS) coupled with stepwise regression (CARS-SR).…”

Section: Applications Of Spectroscopic Imaging In Plant-oriented Studmentioning

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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“…The two‐dimensional image was captured and stored by a high sensitive camera at the spectral signal intensity of the filter band, and the gray value was normalized as the fluorescence value of the corresponding fluorescence parameters (Zhou et al, ). In order to observe the heterogeneity of images, pseudo‐color palettes with different color codes and values were usually used to represent the heterogeneity of images pixel by pixel (Pan, Cui, Wu, Yuan, & Liu, ; Shcherbo et al, ; Sun, Zhou, Hu, et al, ) as shown in Figure .…”

Section: Fluorescence Spectrum Information Extractionmentioning

confidence: 99%

Fluorescence hyperspectral image technique coupled with HSI method to predict solanine content of potatoes

Sun

Yang

et al. 2019

J Food Process Preserv

Self Cite

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In order to ensure the edibility of potatoes, fluorescence hyperspectral images of potato samples were obtained to predict the solanine content in potatoes. For the best ROI (region of interest), the S‐component of saturation was extracted by the HSI colorimetric technology to characterize the bud eye of potatoes in three‐dimensional geometric space. The effective bud eye was located as the geometric center of ROI and the average spectral information was obtained. After pretreatment and selection of feature wavelengths, the predicting mode of SVR was established and was optimized by adjusting the penalty coefficient c and the core coefficient g of radial basis function (RBF). Finally, the determinant coefficient of the model was 0.9143 and the root mean square error was 0.0296, which could basically meet the application requirements. It was concluded that the method based on hyperspectral fluorescence image and HSI colorimetry could predict the solanine content in potatoes accurately through the optimized SVR model. Practical applications The solanine content in potatoes is an important indicator for judging the edibility of potatoes. The traditional manual observation method has a high misidentification rate and is prone to waste. The chemical detection processes are also tedious and time‐consuming. Compared with traditional methods and chemical methods, hyperspectral imaging technology can detect the solanine content in potatoes rapidly, accurately, and nondestructively. In this study, we generated the best prediction model by SVR, which was established a base on characteristic wavelengths through best ROI selection. After comparing, it was found that the bud eye region is the best ROI and the spectral data obtained here has the best predictive ability. All these results provided a theoretical basis for the portable detection of solanine content in potatoes.

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Visualizing distribution of moisture content in tea leaves using optimization algorithms and NIR hyperspectral imaging

Cited by 99 publications

References 20 publications

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

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

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

Fluorescence hyperspectral image technique coupled with HSI method to predict solanine content of potatoes

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