scholarly journals Quantification of leghaemoglobin content in pea nodules based on near infrared hyperspectral imaging spectroscopy and chemometrics

2018 ◽  
Author(s):  
Damien Eylenbosch ◽  
Benjamin Dumont ◽  
Vincent Baeten ◽  
Bernard Bodson ◽  
Pierre Delaplace ◽  
...  
Keyword(s):  
Regression Model ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging Spectroscopy ◽  
Reference Method ◽  
Multiple Linear Regression Model ◽  
Validation Dataset ◽  
Full Spectrum ◽  
Imaging Device

Leghaemoglobin content in nodules is closely related to the amount of nitrogen fixed by the legume–rhizobium symbiosis. It is, therefore, commonly measured in order to assess the effect of growth-promoting parameters such as fertilisation on the symbiotic nitrogen fixation efficiency of legumes. The cyanmethaemoglobin method is a reference method in leghaemoglobin content quantification, but this method is time-consuming, requires accurate and careful technical operations and uses cyanide, a toxic reagent. As a quicker, simpler and non-destructive alternative, a method based on near infrared (NIR) hyperspectral imaging was tested to quantify leghaemoglobin in dried nodules. Two approaches were evaluated: (i) the partial least squares (PLS) approach was applied to the full spectrum acquired with the hyperspectral device and (ii) the potential of multispectral imaging was also tested through the preselection of the most relevant wavelengths and the building of a multiple linear regression model. The PLS approach was tested on mean spectra acquired from samples containing several nodules and acquired separately from individual nodules. Peas (Pisum sativum L.) were cultivated in a greenhouse. The nodules were harvested on four different dates in order to obtain variations in leghaemoglobin content. The leghaemoglobin content measured with the cyanmethaemoglobin method in fresh nodules ranged between 1.4 and 4.2 mg leghaemoglobin g–1 fresh nodule. A PLS regression model was calibrated on leghaemoglobin content measured with the reference method and mean NIR spectra of dried nodules acquired with a hyperspectral imaging device. On a validation dataset, the PLS model predicted the leghaemoglobin content in nodule samples well (R2 = 0.90, root mean square error of prediction = 0.26). The multispectral approach showed similar performance. Applied to individual nodules, the PLS model highlighted a wide variability of leghaemoglobin content in nodules harvested from the same plant. These results show that NIR hyperspectral imaging could be used as a rapid and safe method to quantify leghaemoglobin in pea nodules.

scholarly journals Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection

Foods ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Nader Ekramirad ◽  
Alfadhl Y. Khaled ◽  
Lauren E. Doyle ◽  
Julia R. Loeb ◽  
Kevin D. Donohue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Cydia Pomonella ◽  
Region Of Interest ◽  
Codling Moth ◽  
Ensemble Classifier ◽  
Validation Dataset

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars.

scholarly journals Comparison of spectral selection methods in the development of classification models from visible near infrared hyperspectral imaging data

2019 ◽  
Author(s):  
Aoife Gowen ◽  
Jun-Li Xu ◽  
Ana Herrero-Langreo
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Imaging Spectroscopy ◽  
Model Performance ◽  
Data Sampling ◽  
Classification Models ◽  
Imaging Data ◽  
Selection Of

Applications of hyperspectral imaging (HSI) to the quantitative and qualitative measurement of samples have grown widely in recent years, due mainly to the improved performance and lower cost of imaging spectroscopy instrumentation. Data sampling is a crucial yet often overlooked step in hyperspectral image analysis, which impacts the subsequent results and their interpretation. In the selection of pixel spectra for the calibration of classification models, the spatial information in HSI data can be exploited. In this paper, a variety of sampling strategies for selection of pixel spectra are presented, exemplified through five case studies. The strategies are compared in terms of the proportion of global variability captured, practicality and predictive model performance. The use of variographic analysis as a guide to the spatial segmentation prior to sampling leads to the selection of representative subsets while reducing the variation in model performance parameters over repeated random selection.

3D Plant Modelling Using Spectral Data from Visible to Near Infrared Range

2015 ◽  
pp. 273-294
Author(s):  
Ali Zia ◽  
Jie Liang
Keyword(s):  
Hyperspectral Imaging ◽  
Material Properties ◽  
Near Infrared ◽  
Infrared Range ◽  
Plant Surface ◽  
Imaging Device ◽  
Plant Phenomics ◽  
Physical Traits ◽  
Plant Modelling ◽  
Near Infrared Range

Plant phenomics research requires different types of sensors employed to measure the physical traits of plant surface and to estimate the biomass. Of particular interests is the hyperspectral imaging device which captures wavelength indexed band images that characterize material properties of objects under study. This chapter introduces a proof of concept research that builds 3D plant model directly from hyperspectral images captured in a controlled lab environment. The method presented in this chapter allows fine structural-spectral information of an object be captured and integrated into the 3D model, which can be used to support further research and applications. The hyperspectral imaging has shown clear advantages in segmenting plant from its background and is very promising in generating comprehensive 3D plant models.

scholarly journals Near-infrared Spectroscopy and Hyperspectral Imaging for Sugar Content Evaluation in Potatoes over Multiple Growing Seasons

2020 ◽  
Author(s):  
Ahmed M Rady ◽  
Daniel E Guyer ◽  
Nicholas J Watson
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Sucrose Concentration ◽  
Sugar Content ◽  
Imaging Systems ◽  
Potato Tubers ◽  
Forward Selection ◽  
Concentration Data ◽  
Growing Seasons

Abstract Sugar content is one of the most important properties of potato tubers as it directly affects their processing and the final product quality, especially for fried products. In this study, data obtained from spectroscopic (interactance and reflectance) and hyperspectral imaging systems were used individually or fused to develop non-cultivar nor growing season-specific regression and classification models for potato tubers based on glucose and sucrose concentration. Data was acquired over three growing seasons for two potato cultivars. The most influential wavelengths were selected from the imaging systems using interval partial least squares for regression and sequential forward selection for classification. Hyperspectral imaging showed the highest regression performance for glucose with a correlation coefficient (ratio of performance to deviation) or r(RPD) of 91.8(2.41) which increased to 94%(2.91) when the data was fused with the interactance data. The sucrose regression results had the highest accuracy using data obtained from the interactance system with r(RPD) values of 74.5%(1.40) that increased to 84.4%(1.82) when the data was fused with the reflectance data. Classification was performed to identify tubers with either high or low sugar content. Classification performance showed accuracy values as high as 95% for glucose and 80.1% for sucrose using hyperspectral imaging, with no noticeable improvement when data was fused from the other spectroscopic systems. When testing the robustness of the developed models over different seasons, it was found that the regression models had r(RPD) values of 55(1.19)–90.3%(2.34) for glucose and 35.8(1.07)–82.2%(1.29) for sucrose. Results obtained in this study demonstrate the feasibility of developing a rapid monitoring system using multispectral imaging and data fusion methods for online evaluation of potato sugar content.

Prediction of water content in biscuits using near infrared hyperspectral imaging spectroscopy and chemometrics

2020 ◽  
Vol 28 (3) ◽  
pp. 140-147
Author(s):  
Eloïse Lancelot ◽  
Philippe Courcoux ◽  
Sylvie Chevallier ◽  
Alain Le-Bail ◽  
Benoît Jaillais
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Content ◽  
Hyperspectral Imaging ◽  
Analysis Of Variance ◽  
Near Infrared ◽  
Imaging Spectroscopy ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Water Contents

The possibility of using near infrared hyperspectral imaging spectroscopy to quantify the water content in commercial biscuits was investigated. Principal component analysis was successfully applied to hyperspectral images of commercial biscuits to monitor their water contents. Variables were selected and water contents quantified using analysis of variance, followed by multiple linear regression, and the results were compared with those obtained with variable importance in projection partial least squares. Initially equal to 212, the number of variables after application of analysis of variance was equal to 10. Analysis of variance–multiple linear regression gave better results: the coefficient of determination (R2) was higher than 0.92 and the root mean square error of validation was less than 0.015. The “prediction images” obtained were very relevant and can be used to study biscuit defects. The methodology developed could be implemented at the industrial level for biscuit quality control and for online monitoring of the uniform distribution of water in the superficial layer of biscuits.

scholarly journals Assessing Laser Cleaning of a Limestone Monument by Fiber Optics Reflectance Spectroscopy (FORS) and Visible and Near-Infrared (VNIR) Hyperspectral Imaging (HSI)

Minerals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1052
Author(s):  
Costanza Cucci ◽  
Olga De Pascale ◽  
Giorgio S. Senesi
Keyword(s):  
Fiber Optics ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Conservation Status ◽  
Spectral Characteristics ◽  
Imaging Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Laser Cleaning ◽  
Cleaning Process ◽  
Invasive Approach

Fiber optics reflectance spectroscopy (FORS) and visible and near-infrared (VNIR) hyperspectral imaging (HSI) were applied to assess and control the laser cleaning process of a deeply darkened limestone surface collected from the historic entrance gate of Castello Svevo, Bari, Italy. Both techniques enabled us to verify the different degree of removal of a thick deposit of black crust from the surface of the walls. Results obtained were in good agreement with those of previous studies of the elemental composition achieved by application of laser-induced breakdown spectroscopy (LIBS). Coupling FORS and VNIR-HSI provided important information on the optimal conditions to evaluate the conservation status and determine the more appropriate level of cleaning restoration, thus avoiding over- and/or under-cleaning. Imaging spectroscopy was used to obtain maps of areas featuring the same or different spectral characteristics, so to achieve a sufficient removal of unwanted layers, without modifying the surface underneath, and to increase the efficiency of traditional cleaning techniques. The performance of the combined non-invasive approach used in this work shows promise for further applications to other types of rocks and highlights the potential for in situ assessment of the laser cleaning process based on reflectance spectroscopy.

scholarly journals Over 1000 nm Near-Infrared Multispectral Imaging System for Laparoscopic In Vivo Imaging

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2649
Author(s):  
Toshihiro Takamatsu ◽  
Yuichi Kitagawa ◽  
Kohei Akimoto ◽  
Ren Iwanami ◽  
Yuto Endo ◽  
...  
Keyword(s):  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Data Sets ◽  
Imaging Device ◽  
Tumor Implantation ◽  
Before And After ◽  
Validation Procedure ◽  
Leave One Out

In this study, a laparoscopic imaging device and a light source able to select wavelengths by bandpass filters were developed to perform multispectral imaging (MSI) using over 1000 nm near-infrared (OTN-NIR) on regions under a laparoscope. Subsequently, MSI (wavelengths: 1000–1400 nm) was performed using the built device on nine live mice before and after tumor implantation. The normal and tumor pixels captured within the mice were used as teaching data sets, and the tumor-implanted mice data were classified using a neural network applied following a leave-one-out cross-validation procedure. The system provided a specificity of 89.5%, a sensitivity of 53.5%, and an accuracy of 87.8% for subcutaneous tumor discrimination. Aggregated true-positive (TP) pixels were confirmed in all tumor-implanted mice, which indicated that the laparoscopic OTN-NIR MSI could potentially be applied in vivo for classifying target lesions such as cancer in deep tissues.

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.

Sign in / Sign up

Export Citation Format

Share Document