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 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.

NIR hyperspectral imaging with machine learning to detect and classify codling moth infestation in apples

2021 ◽  
Author(s):  
Nader Ekramirad ◽  
Alfadhl Y. Khaled ◽  
Lauren E. Doyle ◽  
Chadwick A. Parrish ◽  
Raul T. Villanueva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hyperspectral Imaging ◽  
Codling Moth

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.

scholarly journals Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Daiki Sato ◽  
Toshihiro Takamatsu ◽  
Masakazu Umezawa ◽  
Yuichi Kitagawa ◽  
Kosuke Maeda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastrointestinal Stromal Tumor ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Submucosal Tumor ◽  
Stromal Tumor ◽  
Training Data ◽  
Support Vector ◽  
Gastric Gist ◽  
Mucosal Layer

AbstractThe diagnosis of gastrointestinal stromal tumor (GIST) using conventional endoscopy is difficult because submucosal tumor (SMT) lesions like GIST are covered by a mucosal layer. Near-infrared hyperspectral imaging (NIR-HSI) can obtain optical information from deep inside tissues. However, far less progress has been made in the development of techniques for distinguishing deep lesions like GIST. This study aimed to investigate whether NIR-HSI is suitable for distinguishing deep SMT lesions. In this study, 12 gastric GIST lesions were surgically resected and imaged with an NIR hyperspectral camera from the aspect of the mucosal surface. Thus, the images were obtained ex-vivo. The site of the GIST was defined by a pathologist using the NIR image to prepare training data for normal and GIST regions. A machine learning algorithm, support vector machine, was then used to predict normal and GIST regions. Results were displayed using color-coded regions. Although 7 specimens had a mucosal layer (thickness 0.4–2.5 mm) covering the GIST lesion, NIR-HSI analysis by machine learning showed normal and GIST regions as color-coded areas. The specificity, sensitivity, and accuracy of the results were 73.0%, 91.3%, and 86.1%, respectively. The study suggests that NIR-HSI analysis may potentially help distinguish deep lesions.

scholarly journals Identification of early liver toxicity gene biomarkers using comparative supervised machine learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Brandi Patrice Smith ◽  
Loretta Sue Auvil ◽  
Michael Welge ◽  
Colleen Bannon Bushell ◽  
Rohit Bhargava ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Liver Toxicity ◽  
Toxicity Testing ◽  
High Specificity ◽  
Supervised Machine Learning ◽  
Validation Dataset ◽  
Learning Approaches ◽  
Liver Necrosis ◽  
Microarray Quality Control

Abstract Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory approval and is an expensive and time-consuming process. The identification and utilization of early exposure gene signatures and robust predictive models in regulatory toxicity testing has the potential to reduce time and costs substantially. In this study, comparative supervised machine learning approaches were applied to the rat liver TG-GATEs dataset to develop feature selection and predictive testing. We identified ten gene biomarkers using three different feature selection methods that predicted liver necrosis with high specificity and selectivity in an independent validation dataset from the Microarray Quality Control (MAQC)-II study. Nine of the ten genes that were selected with the supervised methods are involved in metabolism and detoxification (Car3, Crat, Cyp39a1, Dcd, Lbp, Scly, Slc23a1, and Tkfc) and transcriptional regulation (Ablim3). Several of these genes are also implicated in liver carcinogenesis, including Crat, Car3 and Slc23a1. Our biomarker gene signature provides high statistical accuracy and a manageable number of genes to study as indicators to potentially accelerate toxicity testing based on their ability to induce liver necrosis and, eventually, liver cancer.

scholarly journals Application of Near-Infrared Hyperspectral Imaging with Machine Learning Methods to Identify Geographical Origins of Dry Narrow-Leaved Oleaster (Elaeagnus angustifolia) Fruits

Foods ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 620 ◽  
Author(s):  
Pan Gao ◽  
Wei Xu ◽  
Tianying Yan ◽  
Chu Zhang ◽  
Xin Lv ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Support Vector ◽  
Elaeagnus Angustifolia ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Discriminant Models ◽  
Second Derivative Spectra ◽  
Origin Identification

Narrow-leaved oleaster (Elaeagnus angustifolia) fruit is a kind of natural product used as food and traditional medicine. Narrow-leaved oleaster fruits from different geographical origins vary in chemical and physical properties and differ in their nutritional and commercial values. In this study, near-infrared hyperspectral imaging covering the spectral range of 874–1734 nm was used to identify the geographical origins of dry narrow-leaved oleaster fruits with machine learning methods. Average spectra of each single narrow-leaved oleaster fruit were extracted. Second derivative spectra were used to identify effective wavelengths. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to build discriminant models for geographical origin identification using full spectra and effective wavelengths. In addition, deep convolutional neural network (CNN) models were built using full spectra and effective wavelengths. Good classification performances were obtained by these three models using full spectra and effective wavelengths, with classification accuracy of the calibration, validation, and prediction set all over 90%. Models using effective wavelengths obtained close results to models using full spectra. The performances of the PLS-DA, SVM, and CNN models were close. The overall results illustrated that near-infrared hyperspectral imaging coupled with machine learning could be used to trace geographical origins of dry narrow-leaved oleaster fruits.

Near-infrared hyperspectral image at a glance: Some personal thoughts

NIR news ◽  
2020 ◽  
Vol 31 (5-6) ◽  
pp. 8-14
Author(s):  
José Manuel Amigo
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Constructive Criticism ◽  
Constructive Discussion

First of all, I want to transmit my most humble thanks to all people who believe that I deserve the “2019 Thomas Hirschfeld” award (kindly supported by FOSS) for my work on near-infrared spectroscopy and, especially, applied on hyperspectral images. I must confess that this award caught me by surprise and that I felt a bit overwhelmed when I received it. It is an honour full of respect and responsibility. I have been given the opportunity of writing this article, and I will profit it to express different personal thoughts about general but relevant aspects of near infrared applied to hyperspectral imaging. Also, since I am more a practitioner in chemometrics (or machine learning or data mining, or …) than a developer, I will also include some insights about the beautiful combination of near-infrared hyperspectral image with chemometrics. This article is just a glimpse of constructive criticism with personal thoughts that comes from my little experience in this field. Therefore, and of course, all opinions here are open for constructive discussion with the only purpose of learning (like the machines do nowadays).

scholarly journals Data Fusion of Two Hyperspectral Imaging Systems with Complementary Spectral Sensing Ranges for Blueberry Bruising Detection

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4463 ◽  
Author(s):  
Shuxiang Fan ◽  
Changying Li ◽  
Wenqian Huang ◽  
Liping Chen
Keyword(s):  
Feature Selection ◽  
Data Fusion ◽  
Hyperspectral Imaging ◽  
Multispectral Imaging ◽  
Feature Selection Method ◽  
Tunable Filter ◽  
Support Vector ◽  
Spectroscopic Techniques ◽  
Decision Level ◽  
Spectral Ranges

Currently, the detection of blueberry internal bruising focuses mostly on single hyperspectral imaging (HSI) systems. Attempts to fuse different HSI systems with complementary spectral ranges are still lacking. A push broom based HSI system and a liquid crystal tunable filter (LCTF) based HSI system with different sensing ranges and detectors were investigated to jointly detect blueberry internal bruising in the lab. The mean reflectance spectrum of each berry sample was extracted from the data obtained by two HSI systems respectively. The spectral data from the two spectroscopic techniques were analyzed separately using feature selection method, partial least squares-discriminant analysis (PLS-DA), and support vector machine (SVM), and then fused with three data fusion strategies at the data level, feature level, and decision level. The three data fusion strategies achieved better classification results than using each HSI system alone. The decision level fusion integrating classification results from the two instruments with selected relevant features achieved more promising results, suggesting that the two HSI systems with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve blueberry internal bruising detection. This study was the first step in demonstrating the feasibility of the fusion of two HSI systems with complementary spectral ranges for detecting blueberry bruising, which could lead to a multispectral imaging system with a few selected wavelengths and an appropriate detector for bruising detection on the packing line.

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