scholarly journals Determination of Total Polysaccharides and Total Flavonoids in Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging and Multivariate Analysis

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2395 ◽  
Author(s):  
Juan He ◽  
Lidan Chen ◽  
Bingquan Chu ◽  
Chu Zhang
Keyword(s):  
Least Squares ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Prediction Models ◽  
Accurate Determination ◽  
Chrysanthemum Morifolium ◽  
Support Vector ◽  
Total Flavonoids ◽  
Total Flavonoids Content

The rapid and nondestructive determination of active compositions in Chrysanthemum morifolium (Hangbaiju) is of great value for producers and consumers. Hyperspectral imaging as a rapid and nondestructive technique was used to determine total polysaccharides and total flavonoids content in Chrysanthemum morifolium. Hyperspectral images of different sizes of Chrysanthemum morifolium flowers were acquired. Pixel-wise spectra within all samples were preprocessed by wavelet transform (WT) followed by standard normal variate (SNV). Partial least squares (PLS) and least squares-support vector machine (LS-SVM) were used to build prediction models using sample average spectra calculated by preprocessed pixel-wise spectra. The LS-SVM model performed better than the PLS models, with the determination of the coefficient of calibration (R2c) and prediction (R2p) being over 0.90 and the residual predictive deviation (RPD) being over 3 for total polysaccharides and total flavonoids content prediction. Prediction maps of total polysaccharides and total flavonoids content in Chrysanthemum morifolium flowers were successfully obtained by LS-SVM models, which exhibited the best performances. The overall results showed that hyperspectral imaging was a promising technique for the rapid and accurate determination of active ingredients in Chrysanthemum morifolium, indicating the great potential to develop an online system for the quality determination of Chrysanthemum morifolium.

scholarly journals Nondestructive Determination and Visualization of Quality Attributes in Fresh and Dry Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging

2019 ◽  
Vol 9 (9) ◽  
pp. 1959 ◽  
Author(s):  
Juan He ◽  
Susu Zhu ◽  
Bingquan Chu ◽  
Xiulin Bai ◽  
Qinlin Xiao ◽  
...  
Keyword(s):  
Chlorogenic Acid ◽  
Least Squares ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Quality Attributes ◽  
Chrysanthemum Morifolium ◽  
Support Vector ◽  
Nondestructive Determination ◽  
Dicaffeoylquinic Acid ◽  
Calibration Models

Rapid and nondestructive determination of quality attributes in fresh and dry Chrysanthemum morifolium is of great importance for quality sorting and monitoring during harvest and trade. Near-infrared hyperspectral imaging covering the spectral range of 874–1734 nm was used to detect chlorogenic acid, luteolin-7-O-glucoside, and 3,5-O-dicaffeoylquinic acid content in Chrysanthemum morifolium. Fresh and dry Chrysanthemum morifolium flowers were studied for harvest and trade. Pixelwise spectra were preprocessed by wavelet transform (WT) and area normalization, and calculated as average spectrum. Successive projections algorithm (SPA) was used to select optimal wavelengths. Partial least squares (PLS), extreme learning machine (ELM), and least-squares support vector machine (LS-SVM) were used to build calibration models based on full spectra and optimal wavelengths. Calibration models of fresh and dry flowers obtained good results. Calibration models for chlorogenic acid in fresh flowers obtained best performances, with coefficient of determination (R2) over 0.85 and residual predictive deviation (RPD) over 2.50. Visualization maps of chlorogenic acid, luteolin-7-O-glucoside, and 3,5-O-dicaffeoylquinic acid in single fresh and dry flowers were obtained. The overall results showed that hyperspectral imaging was feasible to determine chlorogenic acid, luteolin-7-O-glucoside, and 3,5-O-dicaffeoylquinic acid. Much more work should be done in the future to improve the prediction performance.

scholarly journals Rapid and accurate determination of tissue optical properties using least-squares support vector machines

2011 ◽  
Vol 2 (3) ◽  
pp. 592 ◽  
Author(s):  
Ishan Barman ◽  
Narahara Chari Dingari ◽  
Narasimhan Rajaram ◽  
James W. Tunnell ◽  
Ramachandra R. Dasari ◽  
...  
Keyword(s):  
Optical Properties ◽  
Support Vector Machines ◽  
Least Squares ◽  
Accurate Determination ◽  
Support Vector ◽  
Tissue Optical Properties ◽  
Vector Machines

scholarly journals Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Zhengyan Xia ◽  
Chu Zhang ◽  
Haiyong Weng ◽  
Pengcheng Nie ◽  
Yong He
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Support Vector ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Selection Methods ◽  
Wavelength Selection ◽  
Ophiopogon Japonicus

Hyperspectral imaging (HSI) technology has increasingly been applied as an analytical tool in fields of agricultural, food, and Traditional Chinese Medicine over the past few years. The HSI spectrum of a sample is typically achieved by a spectroradiometer at hundreds of wavelengths. In recent years, considerable effort has been made towards identifying wavelengths (variables) that contribute useful information. Wavelengths selection is a critical step in data analysis for Raman, NIRS, or HSI spectroscopy. In this study, the performances of 10 different wavelength selection methods for the discrimination of Ophiopogon japonicus of different origin were compared. The wavelength selection algorithms tested include successive projections algorithm (SPA), loading weights (LW), regression coefficients (RC), uninformative variable elimination (UVE), UVE-SPA, competitive adaptive reweighted sampling (CARS), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), and genetic algorithms (GA-PLS). One linear technique (partial least squares-discriminant analysis) was established for the evaluation of identification. And a nonlinear calibration model, support vector machine (SVM), was also provided for comparison. The results indicate that wavelengths selection methods are tools to identify more concise and effective spectral data and play important roles in the multivariate analysis, which can be used for subsequent modeling analysis.

scholarly journals Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging

2021 ◽  
Vol 11 (17) ◽  
pp. 8201
Author(s):  
Geonwoo Kim ◽  
Hoonsoo Lee ◽  
Byoung-Kwan Cho ◽  
Insuck Baek ◽  
Moon S. Kim
Keyword(s):  
Least Squares ◽  
Hyperspectral Imaging ◽  
Quantitative Evaluation ◽  
Food Waste ◽  
Near Infrared ◽  
Organic Fertilizer ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
High Sodium ◽  
Distribution Maps

Excessive addition of food waste fertilizer to organic fertilizer (OF) is forbidden in the Republic of Korea because of high sodium chloride and capsaicin concentrations in Korean food. Thus, rapid and nondestructive evaluation techniques are required. The objective of this study is to quantitatively evaluate food-waste components (FWCs) using hyperspectral imaging (HSI) in the visible–near-infrared (Vis/NIR) region. A HSI system for evaluating fertilizer components and prediction algorithms based on partial least squares (PLS) analysis and least squares support vector machines (LS-SVM) are developed. PLS and LS-SVM preprocessing methods are employed and compared to select the optimal of two chemometrics methods. Finally, distribution maps visualized using the LS-SVM model are created to interpret the dynamic changes in the OF FWCs with increasing FWC concentration. The developed model quantitively evaluates the OF FWCs with a coefficient of determination of 0.83 between the predicted and actual values. The developed Vis/NIR HIS system and optimized model exhibit high potential for OF FWC discrimination and quantitative evaluation.

scholarly journals Assessment of pesticide coating on cereal seeds by near infrared hyperspectral imaging

2017 ◽  
Author(s):  
Ph. Vermeulen ◽  
P. Flémal ◽  
O. Pigeon ◽  
P. Dardenne ◽  
J. Fernández Pierna ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Seed Quality ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Ultra Performance Liquid Chromatography

Classical chromatographic methods, such as ultra performance liquid chromatography (UPLC), are used as reference methods to assess seed quality and homogeneous pesticide coating of seeds. These methods have some important drawbacks since they are time consuming, expensive, destructive and require a substantial amount of solvent, among others. Near infrared (NIR) spectroscopy seems to be an interesting alternative technique for the determination of the quality of seed treatment and avoids most of these drawbacks. The objective of this study was to assess the quality of pesticide coating treatment by near infrared hyperspectral imaging (NIR-HSI) by analysing, on a seed-by-seed basis, several seeds simultaneously in comparison to NIR spectroscopy and UPLC as the reference method. To achieve this goal, discrimination—partial least squares discriminant analysis (PLS-DA)—models and regression—partial least squares (PLS)—models were developed. The results obtained by NIR-HSI are compared to the results obtained with NIR spectroscopy and UPLC instruments. This study has shown the potential of NIR hyperspectral imaging to assess the quality/homogeneity of the pesticide coating on seeds.

scholarly journals Nondestructive Determination of Diastase Activity of Honey Based on Visible and Near-Infrared Spectroscopy

Molecules ◽  
2019 ◽  
Vol 24 (7) ◽  
pp. 1244
Author(s):  
Zhenxiong Huang ◽  
Lang Liu ◽  
Guojian Li ◽  
Hong Li ◽  
Dapeng Ye ◽  
...  
Keyword(s):  
Least Squares ◽  
High Temperatures ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Nir Spectroscopy ◽  
Kinetic Mechanism ◽  
National Standard ◽  
Support Vector ◽  
Heating Process

The activities of enzymes are the basis of evaluating the quality of honey. Beekeepers usually use concentrators to process natural honey into concentrated honey by concentrating it under high temperatures. Active enzymes are very sensitive to high temperatures and will lose their activity when they exceed a certain temperature. The objective of this work is to study the kinetic mechanism of the temperature effect on diastase activity and to develop a nondestructive approach for quick determination of the diastase activity of honey through a heating process based on visible and near-infrared (Vis/NIR) spectroscopy. A total of 110 samples, including three species of botanical origin, were used for this study. To explore the kinetic mechanism of diastase activity under high temperatures, the honey of three kinds of botanical origins were processed with thermal treatment to obtain a variety of diastase activity. Diastase activity represented with diastase number (DN) was measured according to the national standard method. The results showed that the diastase activity decreased with the increase of temperature and heating time, and the sensitivity of acacia and longan to temperature was higher than linen. The optimum temperature for production and processing is 60 °C. Unsupervised clustering analysis was adopted to detect spectral characteristics of these honeys, indicating that different botanical origins of honeys can be distinguished in principal component spaces. Partial least squares (PLS) and least squares-support vector machine (LS-SVM) algorithms were applied to develop quantitative relationships between Vis/NIR spectroscopy and diastase activity. The best result was obtained through Gaussian filter smoothing-standard normal variate (GF-SNV) pretreatment and the LS-SVM model, known as GF-SNV-LS-SVM, with a determination coefficient (R2) of prediction of 0.8872, and root mean square error (RMSE) of prediction of 0.2129. The overall results of this paper showed that the diastase activity of honey can be determined quickly and non-destructively with Vis/NIR spectral methods, which can be used to detect DN in the process of honey production and processing, and to maximize the nutrient content of honey.

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