Utilization of visible/near-infrared spectroscopic and wavelength selection methods in sugar prediction and potatoes classification

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.

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

International Journal of Analytical Chemistry ◽

10.1155/2017/6018769 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 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.

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Closed-Loop Moving Windows Wavelength Selection Method With Application To Near-Infrared Spectroscopic Analysis

2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) ◽

10.1109/whispers.2019.8921132 ◽

2019 ◽

Author(s):

Liwen Pang ◽

Jiemei Chen ◽

Tao Pan

Keyword(s):

Closed Loop ◽

Near Infrared ◽

Spectroscopic Analysis ◽

Selection Method ◽

Wavelength Selection ◽

Infrared Spectroscopic Analysis ◽

Infrared Spectroscopic ◽

Moving Windows

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Wavelength Selection Method Based on Absorbance Value Optimization to Near-Infrared Spectroscopic Analysis

Frontiers in Physics ◽

10.3389/fphy.2021.663573 ◽

2021 ◽

Vol 9 ◽

Author(s):

Lijun Yao ◽

Xiaowen Shi ◽

Tao Pan ◽

Jiemei Chen

Keyword(s):

Information Content ◽

Near Infrared ◽

Spectroscopic Analysis ◽

Light Transmittance ◽

Upper And Lower Bounds ◽

Optimization Approach ◽

Wavelength Selection ◽

Successive Projections Algorithm ◽

Infrared Spectroscopic Analysis ◽

Infrared Spectroscopic

Regarding absorption spectrum, high absorption corresponds to low light transmittance and relatively loud noise, whereas low absorption corresponds to low information content, which interferes with the modeling of spectral analysis. Appropriate absorbance level is necessary to improve spectral information content and reduces noise level. In this study, based on the selection of the upper and lower bounds of absorbance, the absorbance value optimization partial least squares (AVO-PLS) method was proposed for appropriate wavelength model selection. Near-infrared spectroscopic analysis of hyperlipidemia indicators, namely, total cholesterol (TC), and triglyceride (TG), was conducted to validate the predicted performance of AVO-PLS. Well-performed wavelength selection methods, namely, moving-window PLS (MW-PLS) of continuous type-and successive projections algorithm (SPA) of discrete type, were also conducted for comparison. The spectra were first corrected using Savitzky–Golay smoothing. Modeling was performed based on the multiple partitioning of calibration and prediction sets to avoid data over-fitting and achieve parameter stability. The selected absorbance ranged from 0.45 to 0.86 for TC and from 0.45 to 0.92 for TG, and the corresponding waveband combinations were 1,376–1,388 and 1,560–1840 nm for TC and 1,376–1,390 and 1,552–1,846 nm for TG. Among them, the waveband combination of TG covers TC’s one, and can be used for the high-precision cooperativity analysis of the two indicators. Using the independent validation samples, the RMSEP and RP of 0.164 mmol l−1 and 0.990 for TC and 0.096 mmol l−1 and 0.997 for TG were obtained by the cooperativity model. And the sensitivity and specificity for hyperlipidemia were 98.0 and 100%, respectively. These values were better than those of MW-PLS and SPA. Importantly, the proposed AVO-PLS is a novel multi-band optimization approach for improving prediction performance and applicability. This method is expected to obtain more applications.

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