Application of Near-Infrared Hyperspectral Imaging with Variable Selection Methods to Determine and Visualize Caffeine Content of Coffee Beans

2016 ◽  
Vol 10 (1) ◽  
pp. 213-221 ◽  
Author(s):  
Chu Zhang ◽  
Hao Jiang ◽  
Fei Liu ◽  
Yong He
Keyword(s):  
Variable Selection ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Selection Methods ◽  
Caffeine Content ◽  
Coffee Beans

scholarly journals Detection of Insect Damage in Green Coffee Beans Using VIS-NIR Hyperspectral Imaging

2020 ◽  
Vol 12 (15) ◽  
pp. 2348
Author(s):  
Shih-Yu Chen ◽  
Chuan-Yu Chang ◽  
Cheng-Syue Ou ◽  
Chou-Tien Lien
Keyword(s):  
Energy Minimization ◽  
Near Infrared ◽  
Principal Component ◽  
Detection Algorithm ◽  
Band Selection ◽  
Spectral Signature ◽  
Support Vector ◽  
Insect Damage ◽  
Selection Methods ◽  
Coffee Beans

The defective beans of coffee are categorized into black beans, fermented beans, moldy beans, insect damaged beans, parchment beans, and broken beans, and insect damaged beans are the most frequently seen type. In the past, coffee beans were manually screened and eye strain would induce misrecognition. This paper used a push-broom visible-near infrared (VIS-NIR) hyperspectral sensor to obtain the images of coffee beans, and further developed a hyperspectral insect damage detection algorithm (HIDDA), which can automatically detect insect damaged beans using only a few bands and one spectral signature. First, by taking advantage of the constrained energy minimization (CEM) developed band selection methods, constrained energy minimization-constrained band dependence minimization (CEM-BDM), minimum variance band prioritization (MinV-BP), maximal variance-based bp (MaxV-BP), sequential forward CTBS (SF-CTBS), sequential backward CTBS (SB-CTBS), and principal component analysis (PCA) were used to select the bands, and then two classifier methods were further proposed. One combined CEM with support vector machine (SVM) for classification, while the other used convolutional neural networks (CNN) and deep learning for classification where six band selection methods were then analyzed. The experiments collected 1139 beans and 20 images, and the results demonstrated that only three bands are really need to achieve 95% of accuracy and 90% of kappa coefficient. These findings show that 850–950 nm is an important wavelength range for accurately identifying insect damaged beans, and HIDDA can indeed detect insect damaged beans with only one spectral signature, which will provide an advantage in the process of practical application and commercialization in the future.

scholarly journals Optimal Regression Method for Near-Infrared Spectroscopic Evaluation of Articular Cartilage

2017 ◽  
Vol 71 (10) ◽  
pp. 2253-2262 ◽  
Author(s):  
Mithilesh Prakash ◽  
Jaakko K. Sarin ◽  
Lassi Rieppo ◽  
Isaac O. Afara ◽  
Juha Töyräs
Keyword(s):  
Articular Cartilage ◽  
Variable Selection ◽  
Least Squares ◽  
Multivariate Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Regression Technique ◽  
Selection Methods ◽  
Tissue Properties ◽  
Infrared Spectroscopic

Near-infrared (NIR) spectroscopy has been successful in nondestructive assessment of biological tissue properties, such as stiffness of articular cartilage, and is proposed to be used in clinical arthroscopies. Near-infrared spectroscopic data include absorbance values from a broad wavelength region resulting in a large number of contributing factors. This broad spectrum includes information from potentially noisy variables, which may contribute to errors during regression analysis. We hypothesized that partial least squares regression (PLSR) is an optimal multivariate regression technique and requires application of variable selection methods to further improve the performance of NIR spectroscopy-based prediction of cartilage tissue properties, including instantaneous, equilibrium, and dynamic moduli and cartilage thickness. To test this hypothesis, we conducted for the first time a comparative analysis of multivariate regression techniques, which included principal component regression (PCR), PLSR, ridge regression, least absolute shrinkage and selection operator (Lasso), and least squares version of support vector machines (LS-SVM) on NIR spectral data of equine articular cartilage. Additionally, we evaluated the effect of variable selection methods, including Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), backward interval PLS (BiPLS), genetic algorithm (GA), and jackknife, on the performance of the optimal regression technique. The PLSR technique was found as an optimal regression tool (R2Tissue thickness = 75.6%, R2Dynamic modulus = 64.9%) for cartilage NIR data; variable selection methods simplified the prediction models enabling the use of lesser number of regression components. However, the improvements in model performance with variable selection methods were found to be statistically insignificant. Thus, the PLSR technique is recommended as the regression tool for multivariate analysis for prediction of articular cartilage properties from its NIR spectra.

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.

Sign in / Sign up

Export Citation Format

Share Document