New Wavelength Selection Methods: Part 2

Biogenic amines are a group of nitrogen substances and widely adopted to assess the food safety, especially for the aquatic products. In China, crayfish ( Prokaryophyllus clarkii) have become one of the most famous aquatic products and form a complete industrial value chain. To ensure the safety of the crayfish industrial chain, a rapid and nondestructive method for determining the biogenic amines of crayfish by nearinfrared spectroscopy coupled with chemometrics was proposed in this study. The quantitative models of histamine, tyramine, cadaverine, and putrescine were built by using the partial least squares (PLS) regression. The spectral preprocessing and the wavelength selection methods were adopted to optimize the models. For histamine, cadaverine, and putrescine in peeled or whole tails, reasonable quantitative results can be obtained by using the optimized models; the coefficient of determination (r2) are 0.88 and 0.90, 0.88 and 0.91, 0.89, and 0.84, respectively. As for tyramine in peeled or whole tails, the results are acceptable and the coefficient of determination (r2) is 0.83 and 0.74, respectively.

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

Journal of Food Measurement & Characterization ◽

10.1007/s11694-014-9207-2 ◽

2014 ◽

Vol 9 (1) ◽

pp. 20-34 ◽

Cited By ~ 9

Author(s):

Ahmed Rady ◽

Daniel Guyer

Keyword(s):

Near Infrared ◽

Selection Methods ◽

Wavelength Selection ◽

Infrared Spectroscopic

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Evaluation of wavelength selection methods for 2D fluorescence spectra applied to bioprocesses characterization

Brazilian Journal of Chemical Engineering ◽

10.1590/s0104-66322013000200007 ◽

2013 ◽

Vol 30 (2) ◽

pp. 289-298 ◽

Cited By ~ 4

Author(s):

S. S. Masiero ◽

J. O. Trierweiler ◽

M. Farenzena ◽

M. Escobar ◽

L. F. Trierweiler ◽

...

Keyword(s):

Fluorescence Spectra ◽

Selection Methods ◽

Wavelength Selection

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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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Determination of Cholesterol Using a Novel Magnetohydrodynamic Acoustic-Resonance Near-IR (MARNIR) Spectrometer

Applied Spectroscopy ◽

10.1366/0003702934415237 ◽

1993 ◽

Vol 47 (7) ◽

pp. 887-890 ◽

Cited By ~ 5

Author(s):

Robert G. Buice ◽

Robert A. Lodder

Keyword(s):

Prediction Error ◽

Principal Component Regression ◽

Principal Component ◽

Acoustic Resonance ◽

Selection Methods ◽

Wavelength Selection ◽

Spectral Techniques ◽

Near Ir ◽

Physical Environments

Near-IR spectrometric determination of minor constituents of biological systems is complicated by the fact that near-IR spectra of these materials vary in different chemical and physical environments. In such cases, wavelength selection methods and full-spectral techniques such as partial least-squares and principal component regression (which weight each wavelength in calibration) produce excess error because they must attempt to model both variations in major constituents and variations in the analyte. A magnetohydrodynamic acoustic-resonance near-IR (MARNIR) spectrometer can determine major constituents of biological materials noninvasively and nondestructively, leaving the near-IR spectrum of the analyte to be used quantitatively with less prediction error.

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Comparison of wavelength selection methods for in-vitro estimation of lactate: a new unconstrained, genetic algorithm-based wavelength selection

Scientific Reports ◽

10.1038/s41598-020-73406-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Mohammad Mamouei ◽

Karthik Budidha ◽

Nystha Baishya ◽

Meha Qassem ◽

Panayiotis Kyriacou

Keyword(s):

Genetic Algorithm ◽

Optical Sensors ◽

The Body ◽

Blood Sampling ◽

Critical Conditions ◽

Diabetic Patients ◽

Selection Methods ◽

Wavelength Selection ◽

Lung Injuries

Abstract Biochemical and medical literature establish lactate as a fundamental biomarker that can shed light on the energy consumption dynamics of the body at cellular and physiological levels. It is therefore, not surprising that it has been linked to many critical conditions ranging from the morbidity and mortality of critically ill patients to the diagnosis and prognosis of acute ischemic stroke, septic shock, lung injuries, insulin resistance in diabetic patients, and cancer. Currently, the gold standard for the measurement of lactate requires blood sampling. The invasive and costly nature of this procedure severely limits its application outside intensive care units. Optical sensors can provide a non-invasive, inexpensive, easy-to-use, continuous alternative to blood sampling. Previous efforts to achieve this have shown significant potential, but have been inconclusive. A measure that has been previously overlooked in this context, is the use of variable selection methods to identify regions of the optical spectrum that are most sensitive to and representative of the concentration of lactate. In this study, several wavelength selection methods are investigated and a new genetic algorithm-based wavelength selection method is proposed. This study shows that the development of more accurate and parsimonious models for optical estimation of lactate is possible. Unlike many existing methods, the proposed method does not impose additional locality constraints on the spectral features and therefore helps provide a much more granular interpretation of wavelength importance.

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A Review of Recent Studies Employing Hyperspectral Imaging for the Determination of Food Adulteration

Photochem ◽

10.3390/photochem1020008 ◽

2021 ◽

Vol 1 (2) ◽

pp. 125-146

Author(s):

Havva Tümay Temiz ◽

Berdan Ulaş

Keyword(s):

Hyperspectral Imaging ◽

Meat Products ◽

Selection Methods ◽

Future Perspectives ◽

Wavelength Selection ◽

Food Adulteration ◽

Cereal Products ◽

Alternative Approaches ◽

Fish And Fishery Products

Applications of hyperspectral imaging (HSI) methods in food adulteration detection have been surveyed in this study. Subsequent to the research on existing literature, studies were evaluated based on different food categories. Tea, coffee, and cocoa; nuts and seeds; herbs and spices; honey and oil; milk and milk products; meat and meat products; cereal and cereal products; and fish and fishery products are the eight different categories investigated within the context of the present study. A summary of studies on these topics was made, and articles reported in 2019 and 2020 were explained in detail. Research objectives, data acquisition systems, and algorithms for data analysis have been introduced briefly with a particular focus on feature wavelength selection methods. In light of the information extracted from the related literature, methods and alternative approaches to increasing the success of HSI based methods are presented. Furthermore, challenges and future perspectives are discussed.

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Hyperspectral Wavelength Selection and Integration for Bruise Detection of Korla Pears

Journal of Spectroscopy ◽

10.1155/2019/6715247 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

Yiming Fang ◽

Fan Yang ◽

Zhu Zhou ◽

Lujun Lin ◽

Xiaoqin Li

Keyword(s):

State Of The Art ◽

Hyperspectral Data ◽

Projection Algorithm ◽

Selection Methods ◽

Wavelength Selection ◽

Lower Detection ◽

Wide Variability ◽

Successive Projection ◽

Optimal Feature ◽

Successive Projection Algorithm

Wavelength selection is a challenging job for the detection of the bruises on pears using hyperspectral imaging. Most modern research used the feature wavelength set selected by a single selection method which is generally unable to handle the wide variability of the hyperspectral data. A novel framework was proposed in this work to increase the performance of the bruise detection, through combining three state-of-the-art variable selection methods and the concept of feature-level integration. Successive projection algorithm, competitive adaptive reweighted sampling, and RELIEF were first applied to the spectra of the Korla pear, respectively. Then, the corresponding feature wavelength subsets were integrated and an optimal feature wavelength set was constructed. An ELM-based classifier was employed for the pear bruise identification finally. Experimental results demonstrated that the feature wavelength integration resulted in lower detection errors. The proposed method is simple and promising for bruise detection of Korla pears, and it can be utilized for other types of defects on fruits.

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Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards

Remote Sensing ◽

10.3390/rs12203426 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3426 ◽

Cited By ~ 2

Author(s):

Antonio Santos-Rufo ◽

Francisco-Javier Mesas-Carrascosa ◽

Alfonso García-Ferrer ◽

Jose Emilio Meroño-Larriva

Keyword(s):

Genetic Algorithm ◽

Water Resource Management ◽

Partial Least Square ◽

Least Square ◽

Identification Accuracy ◽

High Spectral Resolution ◽

Selection Methods ◽

Wavelength Selection ◽

Linear Discriminant ◽

Variable Elimination

Identifying and mapping irrigated areas is essential for a variety of applications such as agricultural planning and water resource management. Irrigated plots are mainly identified using supervised classification of multispectral images from satellite or manned aerial platforms. Recently, hyperspectral sensors on-board Unmanned Aerial Vehicles (UAV) have proven to be useful analytical tools in agriculture due to their high spectral resolution. However, few efforts have been made to identify which wavelengths could be applied to provide relevant information in specific scenarios. In this study, hyperspectral reflectance data from UAV were used to compare the performance of several wavelength selection methods based on Partial Least Square (PLS) regression with the purpose of discriminating two systems of irrigation commonly used in olive orchards. The tested PLS methods include filter methods (Loading Weights, Regression Coefficient and Variable Importance in Projection); Wrapper methods (Genetic Algorithm-PLS, Uninformative Variable Elimination-PLS, Backward Variable Elimination-PLS, Sub-window Permutation Analysis-PLS, Iterative Predictive Weighting-PLS, Regularized Elimination Procedure-PLS, Backward Interval-PLS, Forward Interval-PLS and Competitive Adaptive Reweighted Sampling-PLS); and an Embedded method (Sparse-PLS). In addition, two non-PLS based methods, Lasso and Boruta, were also used. Linear Discriminant Analysis and nonlinear K-Nearest Neighbors techniques were established for identification and assessment. The results indicate that wavelength selection methods, commonly used in other disciplines, provide utility in remote sensing for agronomical purposes, the identification of irrigation techniques being one such example. In addition to the aforementioned, these PLS and non-PLS based methods can play an important role in multivariate analysis, which can be used for subsequent model analysis. Of all the methods evaluated, Genetic Algorithm-PLS and Boruta eliminated nearly 90% of the original spectral wavelengths acquired from a hyperspectral sensor onboard a UAV while increasing the identification accuracy of the classification.

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