Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm

Ong, Pauline; Chen, Suming; Tsai, Chao Yin; Chuang, Yung Kun

doi:10.1016/j.saa.2021.119657

Cited by 21 publications

(5 citation statements)

References 45 publications

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“…The methods for classification modeling are the Random Forest Classifier (RF), the K Nearest Neighbor Classifier (KNN), the Linear Discriminant Classifier (LDC), Support Vector Machines (SVMs), Extreme Learning Machines (ELMs), and the Naive Bayes Classifier (NB) [ 69 , 70 , 71 , 72 , 73 ]. Methods for regression modeling are Partial Least Squares Regression (PLSR), Multiple Linear Regression (MLR), Support Vector Regression (SVR), Extreme Learning Machine Regression (ELMR), Gaussian Process Regression (GPR), Stochastic Gradient Boosting (SGB), Kernel-based Extreme Learning Machines (KELM)s, and Random Forest Regression (RFR) [ 74 , 75 , 76 , 77 , 78 ].…”

Section: Hyperspectral Information Analysis Methods For Tea Fresh Lea...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

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As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

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Section: Hyperspectral Information Analysis Methods For Tea Fresh Lea...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

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“…In PLSR, the high-dimensional data is projected onto a small number of latent variables (LVs) to find the optimal regression coefficients so that a linear combination of the input variables maximizes the covariance between the LVs and the output. When one supposes that the number of LVs is h ( h ≦ p ), the regression coefficients are calculated as follows [ 30 ]:…”

Section: Methodsmentioning

confidence: 99%

Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage

Yao

Sun

Cheng

et al. 2022

Foods

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S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. The hyperspectral images of egg samples at different storage periods were collected in the wavelength range of 401–1002 nm, and the reference S-ovalbumin content was determined by spectrophotometry. The standard normal variate (SNV) was employed to preprocess the raw spectral data. To simplify the calibration models, competitive adaptive reweighted sampling (CARS) was applied to select feature wavelengths from the whole spectral range. Based on the full and feature wavelengths, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models were developed, in which the simplified LSSVM model yielded the best performance with a coefficient of determination for prediction (R2P) of 0.918 and a root mean square error for prediction (RMSEP) of 7.215%. By transferring the quantitative model to the pixels of hyperspectral images, the visualizing distribution maps were generated, providing an intuitive and comprehensive evaluation for the S-ovalbumin content of eggs, which helps to understand the conversion of ovalbumin into S-ovalbumin during storage. The results provided the possibility of implementing a multispectral imaging technique for online monitoring the S-ovalbumin content of eggs.

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“…Subsequently, the hyperspectral characteristic parameters were sorted according to the absolute value of the regression coefficients obtained from the PLSR model. Each time, the HCP with the lowest value was eliminated, and the best combination of independent variables was the independent variable with the largest training determination coefficient during the reverse elimination process [44].…”

Section: Variable Screeningmentioning

confidence: 99%

Estimation Model of Rice Aboveground Dry Biomass Based on the Machine Learning and Hyperspectral Characteristic Parameters of the Canopy

Wang

Yue

et al. 2023

Agronomy

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Accurately estimating aboveground dry biomass (ADB) is crucial. The ADB of rice has primarily been estimated using vegetation indices with several discrete bands; nevertheless, these indices cannot take advantage of continuous bands available with hyperspectral remote sensing. This study analyzed the quantitative relationship between canopy hyperspectral characteristic parameters (HCPs) and the ADB of rice. Twenty HCPs were used, including red edge area (SDr), blue edge area (SDb), and others. The variable-screening methods involved stepwise regression (SR), a regression coefficient (RC), variable importance in projection (vip), and random forest (RF). Stepwise and partial least squares regression methods were employed with traditional linear regression as well as machine learning methods including random forest (RF), a support vector machine (SVM), a BP artificial neural network (BPNN), and an extreme learning machine. Whole- and screening-variable models were constructed to estimate rice ADB at jointing, booting, heading, and maturing stages and across growth stages. Screening-variable models include SVM models based on SR (SVM-sr), RF models based on vip (RF-vip), and others. The results show that the HCPs had a significant correlation with ADB containing elements in the red edge region, namely SDr, SDr/SDb, and (SDr − SDb)/(SDr + SDb) at each growth stage. In addition, the screening performance of vip and SR was better than that of RC and RF, and fewer variables were screened. Moreover, the HCPs of the red edge region were screened using different screening methods at each growth stage. Among them, SDr/SDb and (SDr − SDb)/(SDr + SDb) appeared frequently, indicating they are important. Furthermore, at each growth stage, ADB could be well-estimated using diverse models with the RF modeling method based on vip screening variables found to be the best modeling method for ADB estimation; the independent variables of the RF-vip model involved the (SDr − SDb)/(SDr + SDb) at each growth stage.

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Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm

Cited by 21 publications

References 45 publications

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage

Estimation Model of Rice Aboveground Dry Biomass Based on the Machine Learning and Hyperspectral Characteristic Parameters of the Canopy

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