Improved generalization of spectral models associated with Vis-NIR spectroscopy for determining the moisture content of different tea leaves

Huang, Zhifu; Sanaeifar, Alireza; Tian, Ya; Zhang, Dongyi; Wang, Hui; Ye, Dapeng; Liu, Yuanyuan

doi:10.1016/j.jfoodeng.2020.110374

Cited by 31 publications

(17 citation statements)

References 35 publications

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“…Previous studies also proposed that the metabolite composition such as water content is different between species under the same growing environment. 50,51 To further improve the accuracy of the model, spectral preprocessing methods such as SG smoothing, SNV, and first Der were used to preprocess the raw reflectance spectra. After spectra preprocessing, PCA was used to extract spectral features and classification models were then reestablished.…”

Section: Modeling Analysismentioning

confidence: 99%

Leaf-based species classification of hybrid cherry tomato plants by using hyperspectral imaging

Wu²,

Zhao

et al. 2023

Journal of Near Infrared Spectroscopy

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Approaches based on near infrared hyperspectral imaging (NIR-HSI) technology combined with machine learning have been developed to classify the leaves of hybrid cherry tomatoes and then identify the species of hybrid cherry tomato plants. The near infrared (NIR) hyperspectral images of 400 cherry tomato leaves (100 per species) were collected in the wavelength range of 900–1700 nm. Machine learning algorithms such as linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM) were employed to construct leaf classification models with the hyperspectral data preprocessed by Savitzky-Golay (SG) smoothing filter, first derivative (first Der) and standard normal variate (SNV). Principle of Component Analysis (PCA) was also used to reduce the data dimension and extract spectral features. It is revealed that the LDA model reaches the highest classification accuracy among the three machine learning algorithms and SNV can lead to higher improvement in model accuracy than other preprocessing methods of SG smoothing and first Der. Analysis based on PCA spectral feature extraction demonstrates that differences occur in internal material content in the leaves of cherry tomato plants with different species, which renders the models being able to distinguish between the species. Another important work was performed to reveal the different effects of the mesophyll and vein regions (VR) on the accuracy of the leaf classification model. It is demonstrated that the classification accuracy is improved by a value of 0.033 or 0.042 when mesophyll substitutes vein or whole leaf as regions of interest (ROI) to extract reflectance spectra for modeling. As a result, the accuracy of the training and test set respectively reached a high value of 0.998 and 0.973 for the LDA classification model combined with the SNV preprocessing method. The results propose that the use of mesophyll region (MR) as ROI can improve the performance of the leaf classification model, which provides a new strategy for efficient and non-destructive classification of different hybrid cherry tomato plants.

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Section: Modeling Analysismentioning

confidence: 99%

Leaf-based species classification of hybrid cherry tomato plants by using hyperspectral imaging

Wu²,

Zhao

et al. 2023

Journal of Near Infrared Spectroscopy

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“…Multivariate scattering correction (MSC) [34] and standard normal variable (SNV) [35] made it possible to effectively reduce the impact of baseline drift, tilt, and other noises. Savitzky-Golay smoothing (SG) [2] could efficiently reduce noise, correct spectral baseline, reduce background interference, and enhance spectral resolution. In this study, the 7-point SG smoothing was used [36].…”

Section: Spectral Pretreatmentmentioning

confidence: 99%

“…Tea is a favorite health drink and one of the most widely used beverages today [1], which contains a range of amino acids, minerals, and vitamins [2]. Research has shown that tea has certain effects on health care and pharmacological impacts [3].…”

Section: Introductionmentioning

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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“…Near infrared (NIR) spectroscopy has been attracted considerable attention from academia and industry, and has been successfully used for quantitative analysis in agriculture, 1,2 fuel, 3,4 food, 5,6 and other fields 7,8 due to its considerable advantages (e.g., rapid, noninvasive, and reagent-free). 9 The near infrared region of the electromagnetic spectrum ranges from 780 nm to 2500 nm, covering the overtone and combination band region of molecular vibrations.…”

Section: Introductionmentioning

confidence: 99%

Development of a calibration model for near infrared spectroscopy using a convolutional neural network

Pan

Bai

et al. 2022

Journal of Near Infrared Spectroscopy

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Development of qualitative or quantitative models is essential to exploit the full potential of near infrared (NIR) spectroscopy. In tandem with one-dimensional convolutional neural network (1D-CNN), a data-driven model is developed using NIR spectroscopy to estimate organic contents. First, the 1D-CNN model is designed to capture the features of the NIR spectra by means of several convolutional and pooling operations. Then, the suitable hyper-parameters of 1D-CNN are obtained by using the grid search algorithm to achieve the optimal performance. Furthermore, the dropout operation is added into the 1D-CNN to suppress the overfitting problem by means of removing some neurons, and the probability distribution of throwing follows the Bernoulli distribution. The developed framework is validated by the application in the sugar content estimation of Huangshan Maofeng tea. The experimental results demonstrate that the key features of the NIR spectra are successfully extracted by the proposed strategy; thereby, a new effective scheme for analyzing NIR spectra is provided for food analysis.

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Improved generalization of spectral models associated with Vis-NIR spectroscopy for determining the moisture content of different tea leaves

Cited by 31 publications

References 35 publications

Leaf-based species classification of hybrid cherry tomato plants by using hyperspectral imaging

Leaf-based species classification of hybrid cherry tomato plants by using hyperspectral imaging

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

Development of a calibration model for near infrared spectroscopy using a convolutional neural network

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