Application of Visible and Near-Infrared Hyperspectral Imaging to Determine Soluble Protein Content in Oilseed  Rape Leaves

Zhang, Chu; Liu, Fei; Kong, Wenwen; He, Yong

doi:10.3390/s150716576

Cited by 71 publications

(48 citation statements)

References 28 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An RPD value greater than 2.5 means that the model performs excellently; a RPD between 2.0 and 2.5 indicates a very good model; and an RPD from 1.8 to 2.0 represents a good model for quantitative analysis. 36 The smallest cross-validation errors were obtained in the models with two (chlorophyll), nine (carotenoid) and four (anthocyanin) latent variables. These small numbers of latent variables contributed to efficient and reliable predictions, since models based wileyonlinelibrary.com/jsfa on many latent variables are very dependent on the dataset, giving poor and overfitted prediction results.…”

Section: Resultsmentioning

confidence: 94%

“…RPD values varied from 8.014 to 11.732, indicating that PLSR models were robust in predicting lettuce chlorophyll, carotenoid and anthocyanin content. An RPD value greater than 2.5 means that the model performs excellently; a RPD between 2.0 and 2.5 indicates a very good model; and an RPD from 1.8 to 2.0 represents a good model for quantitative analysis …”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The predictive capacities of the models were evaluated with the statistics root mean square error (RMSE), mean bias error (BIAS), coefficient of determination (R 2 ), standard deviation to root mean square error ratio (RPD), standard error of laboratory (SEL), standard error of prediction (SEP) and variable importance for the projection (VIP). 30,36,37 The optimal number of latent variables was determined as that which minimized the RMSE of the cross-validation process. 6,10 The RPD was used for evaluating the model calibration performance.…”

Section: Partial Least Squares Analysismentioning

confidence: 99%

See 3 more Smart Citations

Non‐destructive prediction of pigment content in lettuce based on visible–NIR spectroscopy

et al. 2016

View full text Add to dashboard Cite

The proposed models were adequate for estimating lettuce pigments in a quick and non-destructive way, representing an alternative to conventional measurement methods. Prediction accuracies were improved by using the detrending, smoothing and first derivative pretreatments to the original spectral signatures prior to estimating lettuce chlorophyll, carotenoid and anthocyanin content, respectively. © 2016 Society of Chemical Industry.

show abstract

Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Partial Least Squares Analysismentioning

confidence: 99%

See 2 more Smart Citations

Non‐destructive prediction of pigment content in lettuce based on visible–NIR spectroscopy

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Many approaches are available for selecting sensitive wavelengths; and identifying prominent peaks and/or valleys with Bw, 2 nd derivative and PCA-loading are among the most commonly used (Barbin et al, 2012;Rodríguez-Pulido et al, 2013;Zhang et al, 2015). In the present study, important wavelengths were selected from the Bw plot in the PLS regression model (Zhang et al, 2015). The 2 nd derivative by Savitzky-Golay method was used to identify key wavelengths related to variations in classification (Barbin et al, 2012).…”

Section: Important Wavelength Selectionmentioning

confidence: 99%

Screening of transgenic maize using near infrared spectroscopy and chemometric techniques

Feng

Yin²,

Zhang

et al. 2018

Span. j. agric. res.

Self Cite

View full text Add to dashboard Cite

The applicability of near infrared (NIR) spectroscopy combined with chemometrics was examined to develop fast, low-cost and non-destructive spectroscopic methods for classification of transgenic maize plants. The transgenic maize plants containing both cry1Ab/cry2Aj-G10evo proteins and their non-transgenic parent were measured in the NIR diffuse reflectance mode with the spectral range of 700-1900 nm. Three variable selection algorithms, including weighted regression coefficients, principal component analysis -loadings and second derivatives were used to extract sensitive wavelengths that contributed the most discrimination information for these genotypes. Five classification methods, including K-nearest neighbor, Soft Independent Modeling of Class Analogy, Naive Bayes Classifier, Extreme Learning Machine (ELM) and Radial Basis Function Neural Network were used to build discrimination models based on the preprocessed full spectra and sensitive wavelengths. The results demonstrated that ELM had the best performance of all methods, even though the model's recognition ability decreased as the variables in the training of neural networks were reduced by using only the sensitive wavelengths. The ELM model calculated on the calibration set showed classification rates of 100% based on the full spectrum and 90.83% based on sensitive wavelengths. The NIR spectroscopy combined with chemometrics offers a powerful tool for evaluating large number of samples from maize hybrid performance trials and breeding programs.

show abstract

Prediction of winter wheat leaf chlorophyll content based on VIS/NIR spectroscopy using ANN and PLSR

Sharabiani

Soltani-Nazarloo

Taghinezhad

et al. 2022

Food Science & Nutrition

View full text Add to dashboard Cite

Visible–near‐infrared spectroscopy is known for its rapid and nondestructive characteristics designed to predict leaf chlorophyll content (LCC) of winter wheat. It is believed that the nonlinear technique is preferable to the linear method. The canopy reflectance was applied to generate the LCC prediction model. To accomplish such an objective, artificial neural networks (ANN), along with partial least squares regression (PLSR), nonlinear, and linear evaluation methods have been employed and evaluated to predict wheat LCC. The wheat leaves reflectance spectra were initially preprocessed using Savitzky–Golay smoothing, differentiation (first derivative), SNV (Standard Normal Variate), MSC (Multiplicative Scatter Correction), and their combinations. Afterward, a model for LCC using the reflectance spectra was developed by means of the PLS and ANN. The vis/NIR spectroscopy samples at the 350–1400 nm wavelength were preprocessed using S. Golay smoothing, D 1 , SNV, and MSC. The preprocessing with SNV‐S.G, followed by PLS and ANN modeling, was able to achieve the most accurate prediction, with the correlation coefficient of 0.92 and 0.97, along with the root mean square error of 0.9131 and 0.7305 receptivity. The experimental findings also revealed that the suggested method utilizing the PLS and ANN model with SNV‐S. G preprocessing was practically feasible to estimate the chlorophyll content of a particular winter wheat leaf area according to the visible and near‐infrared spectroscopy sensors, achieving improved precision and accuracy. The nonlinear technique was proposed as a more refined technique for LCC estimating.

show abstract

Application of Visible and Near-Infrared Hyperspectral Imaging to Determine Soluble Protein Content in Oilseed Rape Leaves

Cited by 71 publications

References 28 publications

Non‐destructive prediction of pigment content in lettuce based on visible–NIR spectroscopy

Non‐destructive prediction of pigment content in lettuce based on visible–NIR spectroscopy

Screening of transgenic maize using near infrared spectroscopy and chemometric techniques

Prediction of winter wheat leaf chlorophyll content based on VIS/NIR spectroscopy using ANN and PLSR

Contact Info

Product

Resources

About