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 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 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.

Feasibility Study of Discriminating and Quantifying Low Levels of Melamine Contamination in Fishmeal by Fourier Transform near Infrared Spectroscopy

2012 ◽  
Vol 239-240 ◽  
pp. 181-192
Author(s):  
Xiao Li Liu ◽  
Gang Jia ◽  
Mei Qin Wang ◽  
Cai Mei Wu ◽  
Xiu Qun Wu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Least Squares ◽  
Near Infrared Spectroscopy ◽  
Euclidean Distance ◽  
Near Infrared ◽  
Support Vector ◽  
Processing Methods ◽  
Low Concentrations ◽  
Calibration Models

This study was conducted to demonstrate the feasibility of Fourier near-infrared spectroscopy (FT-NIRS) to detect and quantifying low concentrations (3.00–1056.80mg kg-1) of melamine contamination in fishmeal and to choose a better chemo metric method by comparing the results of the models based on different chemo metric methods. The qualitative calibration models were established based on PCA-Euclidean Distance and least squares-support vector machine (LS-SVM) respectively and the quantitative calibration models were established based on partial least squares (PLS) regression algorithm and least squares-support vector machine (LS-SVM) respectively. Savitzky-Golay second derivative with smoothing over five points and vector normalization were the best pre-processing methods. A qualitative model, established based on this pre-processing method, was capable of identifying the testing set samples with melamine concentrations higher than 136mg kg-1, with a 100% correct classification rate. Further, the qualitative models based on PCA-Euclidean distance, S-G first derivative with smoothing over nine points and vector normalization pre-processing methods and the frequency ranges of 9099-8246 cm-1 and 7398-6545cm-1 were the best parameters selected by the optimizing process. Quantitative models based on these parameters accurately predicted the samples with melamine concentration of higher than 208mg kg-1, with the mean relative forecasting deviation less than 5%. The model based on LS-SVM was obviously not better than that based on PLS. The results show that FT-NIR can be used to detect and quantify low concentrations of melamine contamination in fishmeal.

scholarly journals Application of Hyperspectral Imaging and Acoustic Emission Techniques for Apple Quality Prediction

2017 ◽  
Vol 60 (4) ◽  
pp. 1391-1401 ◽  
Author(s):  
Nader Ekramirad ◽  
Ahmed Rady ◽  
Akinbode A. Adedeji ◽  
Reza Alimardani
Keyword(s):  
Acoustic Emission ◽  
Least Squares ◽  
Hyperspectral Imaging ◽  
Storage Period ◽  
Quality Attributes ◽  
Support Vector ◽  
Fruit Firmness ◽  
Color Parameters ◽  
Mean Square Errors ◽  
Non Destructive

Abstract. There is a growing demand for developing effective non-destructive quality assessment methods with quick response, high accuracy, and low cost for fresh fruits. In this study, hyperspectral reflectance imaging (400 to 1000 nm) and acoustic emission (AE) tests were applied to ‘GoldRush’ apples (total number, n = 180) to predict fruit firmness, total soluble solids (TSS), and surface color parameters (L*, a*, b*) during an eight-week storage period. Partial least squares (PLS) regression, least squares support vector machine (LS-SVM), and multivariate linear regression (MLR) methods were used to establish models to predict the quality attributes of the apples. The results showed that hyperspectral imaging (HSI) could accurately predict all the attributes except TSS, while the AE method was capable of predicting fruit firmness, b* color index, and TSS. Overall, HSI regression using PLS had better comprehensive ability for predicting firmness, TSS, and color parameters (L*, a*, b*) than AE, with correlation coefficients of prediction (rp) of 0.92, 0.41, 0.83, 0.87, and 0.94 and root mean square errors of prediction (RMSEP) of 4.32 (N), 1.78 (°Brix), 3.41, 2.28, and 4.29, respectively, while AE regression using LS-SVM gave rp values of 0.88, 0.74, 0.34, 0.37, and 0.81 and RMSEP values of 4.26 (N), 0.64 (°Brix), 4.69, 1.8, and 5.17 for firmness, TSS, and color parameters (L*, a*, b*), respectively. The results show the potential of these two non-destructive methods for predicting some of the quality attributes of apples. Keywords: Apple, Acoustic emission, Fruit quality, Hyperspectral imaging, Regression model.

scholarly journals Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires

2020 ◽  
Author(s):  
L. Granlund ◽  
M. Keinänen ◽  
T. Tahvanainen
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Support Vector ◽  
High Resolution Imaging ◽  
Factors Affecting ◽  
Vector Machines ◽  
Resolution Imaging ◽  
Shortwave Infrared ◽  
Type Classification ◽  
Wide Potential

Abstract Aims Hyperspectral imaging (HSI) has high potential for analysing peat cores, but methodologies are deficient. We aimed for robust peat type classification and humification estimation. We also explored other factors affecting peat spectral properties. Methods We used two laboratory setups: VNIR (visible to near-infrared) and SWIR (shortwave infrared) for high resolution imaging of intact peat profiles with fen-bog transitions. Peat types were classified with support vector machines, indices were developed for von Post estimation, and K-means clustering was used to analyse stratigraphic patterns in peat quality. With separate experiments, we studied spectral effects of drying and oxidation. Results Despite major effects, oxidation and water content did not impede robust HSI classification. The accuracy between Carex peat and Sphagnum peat in validation was 80% with VNIR and 81% with SWIR data. The spectral humification indices had accuracies of 82% with VNIR and 56%. Stratigraphic HSI patterns revealed that 36% of peat layer shifts were inclined by over 20 degrees. Spectral indices were used to extrapolate visualisations of element concentrations. Conclusions HSI provided reliable information of basic peat quality and was useful in visual mapping, that can guide sampling for other analyses. HSI can manage large amounts of samples to widen the scope of detailed analysis beyond single profiles and it has wide potential in peat research beyond the exploratory scope of this paper. We were able to confirm the capacity of HSI to reveal shifts of peat quality, connected to ecosystem-scale change.

scholarly journals A New Plant Indicator (Artemisia lavandulaefolia DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Lu Xu ◽  
Qiong Shi ◽  
Bang-Cheng Tang ◽  
Shunping Xie
Keyword(s):  
Support Vector Machine ◽  
Fourier Transform ◽  
Least Squares ◽  
Near Infrared ◽  
Mercury Level ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Support Vector ◽  
Reference Analysis

A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.

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