scholarly journals Measurement of Early Disease Blueberries Based on Vis/NIR Hyperspectral Imaging System

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5783
Author(s):  
Yuping Huang ◽  
Dezhen Wang ◽  
Ying Liu ◽  
Haiyan Zhou ◽  
Ye Sun
Keyword(s):  
Hyperspectral Imaging ◽  
Spectral Range ◽  
Imaging System ◽  
Early Stage ◽  
Partial Least Square ◽  
Least Square ◽  
Early Disease ◽  
Spectral Correlation ◽  
Fungal Damage ◽  
Optimal Classification

Blueberries, which are rich in nutrition, are susceptible to fungal infection during postharvest or storage. However, early detection of diseases in blueberry is challenging because of their opaque appearance and the inconspicuousness of spots in the early stage of disease. The goal of this study was to investigate the potential of hyperspectral imaging over the spectral range of 400–1000 nm to discriminate early disease in blueberries. Scanning electron microscope observation verified that fungal damage to the cellular structure takes place during the early stages. A total of 400 hyperspectral images, 200 samples each of healthy and early disease groups, were collected to obtain mean spectra of each blueberry samples. Spectral correlation analysis was performed to select an effective spectral range. Partial least square discrimination analysis (PLSDA) models were developed using two types of spectral range (i.e., full wavelength range of 400–1000 nm and effective spectral range of 685–1000 nm). The results showed that the effective spectral range made it possible to provide better classification results due to the elimination of the influence of irrelevant variables. Moreover, the effective spectral range combined with an autoscale preprocessing method was able to obtain optimal classification accuracies, with recognition rates of 100% and 99% for healthy and early disease blueberries. This study demonstrated that it is feasible to use hyperspectral imaging to measure early disease blueberries.

Hyperspectral Imaging for Predicting Soluble Solid Content of Starfruit

2015 ◽  
Vol 73 (1) ◽  
Author(s):  
Feri Candra ◽  
Syed Abd. Rahman Abu Bakar
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Solid Content ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Soluble Solid Content ◽  
Calibration Model ◽  
Soluble Solid

Hyperspectral imaging technology is a powerful tool for non-destructive quality assessment of fruits. The objective of this research was to develop novel calibration model based on hyperspectral imaging to estimate soluble solid content (SSC) of starfruits. A hyperspectral imaging system, which consists of a near infrared  camera, a spectrograph V10, a halogen lighting and a conveyor belt system, was used in this study to acquire hyperspectral  images of the samples in visible and near infrared (500-1000 nm) regions. Partial least square (PLS) was used to build the model and to find the optimal wavelength. Two different masks were applied for obtaining the spectral data. The optimal wavelengths were evaluated using multi linear regression (MLR). The coefficient of determination (R2) for validation using the model with first mask (M1) and second mask (M2) were 0.82 and 0.80, respectively.

scholarly journals Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat

2021 ◽  
Vol 13 (6) ◽  
pp. 1128
Author(s):  
Iman Tahmasbian ◽  
Natalie K Morgan ◽  
Shahla Hosseini Bai ◽  
Mark W Dunlop ◽  
Amy F Moss
Keyword(s):  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Benefits ◽  
Partial Least Square ◽  
Least Square ◽  
Statistical Population ◽  
Processing Plants ◽  
C And N

Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400–2500 nm), and two HSI cameras operated in 400–1000 nm (VNIR) and 1000–2500 nm (SWIR) spectral regions. Partial least square regression (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their spectral reflectance, with 6 additional samples used for testing the models. The accuracy of the HSI predictions (full spectra) were similar or slightly higher than those of NIRS (NIRS Rc2 for C = 0.90 and N = 0.96 vs. HSI Rc2 for C (VNIR) = 0.97 and N (SWIR) = 0.97). The most important spectral region for C prediction identified using HSI reflectance was 400–550 nm with R2 of 0.93 and RMSE of 0.17% in the calibration set and R2 of 0.86, RMSE of 0.21% and ratio of performance to deviation (RPD) of 2.03 in the test set. The most important spectral regions for predicting N concentrations in the feed samples included 1451–1600 nm, 1901–2050 nm and 2051–2200 nm, providing prediction with R2 ranging from 0.91 to 0.93, RMSE ranging from 0.06% to 0.07% in the calibration sets, R2 from 0.96 to 0.99, RMSE of 0.06% and RPD from 3.47 to 3.92 in the test sets. The prediction accuracy of HSI and NIRS were comparable possibly due to the larger statistical population (larger number of pixels) that HSI provided, despite the fact that HSI had smaller spectral range compared with that of NIRS. In addition, HSI enabled visualising the variability of C and N in the samples. Therefore, HSI is advantageous compared to NIRS as it is a multifunctional tool that poses many potential applications in data collection and quality assurance within feed mills and poultry processing plants. The ability to more accurately measure and visualise the properties of feed ingredients has potential economic benefits and therefore additional investigation and development of HSI in this application is warranted.

Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy

2020 ◽  
Vol 28 (2) ◽  
pp. 70-80 ◽  
Author(s):  
Perez Mukasa ◽  
Collins Wakholi ◽  
Akbar Faqeerzada Mohammad ◽  
Eunsoo Park ◽  
Jayoung Lee ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Imaging Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Successive Projections Algorithm ◽  
Hinoki Cypress ◽  
Sorting System ◽  
Shortwave Infrared ◽  
Successive Projections

The combination of hyperspectral imaging with multivariate data analysis methods has recently been applied to develop a nondestructive technique, required to determine the seed viability of artificially aged vegetable and cereal seeds. In this study, the potential of shortwave infrared hyperspectral imaging to determine the viability of naturally aged seeds was investigated and thereafter a model for online seed sorting system was developed. The hyperspectral images of 400 Hinoki cypress tree seeds were acquired, and germination tests were conducted for viability confirmation, which indicated 31.5% of the viable seeds. Partial least square discriminant analysis models with 179 variables in the wavelength region of 1000–1800 nm were developed with a maximum model accuracy of 98.4% and 93.8% in both the calibration and validation sets, respectively. The partial least square discriminant analysis beta coefficient revealed the key wavelengths to differentiate viable from nonviable seeds, determined based on the differences in the chemical compositions of the seeds, including their lipid and fatty acid contents, which may control the germination ability of the seeds. The most effective wavelengths were selected using two model-based variable selection methods (i.e., the variable importance of projection (15 variables) and the successive projections algorithm (8 variables)) to develop the model. The successive projections algorithm wavelength selection method was considered to develop a viability model, and its application to the raw data resulted in a prediction accuracy of 94.7% in the calibration set and 92.2% in the validation set. These results demonstrate the potential of shortwave infrared hyperspectral imaging spectroscopy as a powerful nondestructive method to determine the viability of Hinoki cypress seeds. This method could be applied to develop an online seed sorting system for seed companies and nurseries.

scholarly journals Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

2019 ◽  
Vol 9 (21) ◽  
pp. 4587 ◽  
Author(s):  
Giuseppe Bonifazi ◽  
Giuseppe Capobianco ◽  
Silvia Serranti
Keyword(s):  
Hyperspectral Imaging ◽  
Spectral Response ◽  
Principal Component ◽  
Short Wave ◽  
Partial Least Square ◽  
Least Square ◽  
Construction And Demolition Waste ◽  
Infrared Range ◽  
Demolition Waste ◽  
Control Approach

Asbestos-Containing Materials (ACMs) are hazardous and prohibited to be sold or used as recycled materials. In the past, asbestos was widely used, together with cement, to produce “asbestos cement-based” products. During the recycling process of Construction and Demolition waste (C&DW), ACM must be collected and deposited separately from other wastes. One of the main aims of the recycling strategies applied to C&DW was thus to identify and separate ACM from C&DW (e.g., concrete and brick). However, to obtain a correct recovery of C&DW materials, control methodologies are necessary to evaluate the quality and the presence of harmful materials, such as ACM. HyperSpectral Imaging (HSI)-based sensing devices allow performing the full detection of materials constituting demolition waste. ACMs are, in fact, characterized by a spectral response that nakes them is different from the “simple” matrix of the material/s not embedding asbestos. The described HSI quality control approach is based on the utilization of a platform working in the short-wave infrared range (1000–2500 nm). The acquired hyperspectral images were analyzed by applying different chemometric methods: Principal Component Analysis for data exploration and hierarchical Partial Least-Square-Discriminant Analysis (PLS-DA) to build classification models. Following this approach, it was possible to set up a repeatable, reliable and efficient technique able to detect ACM presence inside a C&DW flow stream. Results showed that it is possible to discriminate and identify ACM inside C&DW. The recognition is potentially automatic, non-destructive and does not need any contact with the investigated products.

scholarly journals Development of a Novel Embedded Relay Lens Microscopic Hyperspectral Imaging System for Cancer Diagnosis: Use of the Mice with Oral Cancer to Be the Example

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Yao-Fang Hsieh ◽  
Mang Ou-Yang ◽  
Jeng-Ren Duann ◽  
Jin-Chern Chiou ◽  
Nai-Wen Chang ◽  
...  
Keyword(s):  
Oral Cancer ◽  
Hyperspectral Imaging ◽  
Cancer Diagnosis ◽  
Spectral Resolution ◽  
Imaging System ◽  
Early Stage ◽  
High Spectral Resolution ◽  
Normal Cells ◽  
M 10

This paper develops a novel embedded relay lens microscopic hyperspectral imaging system (ERL-MHSI) with high spectral resolution (nominal spectral resolution of 2.8 nm) and spatial resolution (30 μm × 10 μm) for cancer diagnosis. The ERL-MHSI system has transmittance and fluorescence mode. The transmittance can provide the morphological information for pathological diagnosis, and the fluorescence of cells or tissue can provide the characteristic signature for identification of normal and abnormal. In this work, the development of the ERL-MHSI system is discussed and the capability of the system is demonstrated by diagnosing early stage oral cancer of twenty mice in vitro. The best sensitivity for identifying normal cells and squamous cell carcinoma (SCC) was 100%. The best specificity for identifying normal cells and SCC was 99%. The best sensitivity for identifying normal cells and dysplasia was 99%. The best specificity for identifying normal cells and dysplasia was 97%. This work also utilizes fractal dimension to analyze the morphological information and find the significant different values between normal and SCC.

scholarly journals Estimation of strawberry firmness using hyperspectral imaging: a comparison of regression models

2021 ◽  
Author(s):  
Binu Devassy ◽  
Sony George
Keyword(s):  
Hyperspectral Imaging ◽  
Regression Models ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Regression Techniques ◽  
Important Quality ◽  
Non Destructive ◽  
Better Than

Firmness is one of the most important quality measures of strawberries, and is related to other aspects of the fruit, such as flavour, ripeness and internal characteristics. The most popular method for measuring firmness is puncturing with a penetrometer, which is destructive and time-consuming. In the present study, we make an attempt to predict the firmness of strawberries in a fast, non-destructive and non-contact way using hyperspectral imaging (HSI) and data analysis with various regression techniques. The primary goal of this research is to investigate and compare the firmness prediction capability of seven prominent regression techniques. We have performed HSI data acquisition of 150 strawberries and optimised seven regression models using the spectral information to predict strawberry firmness. These models are linear, ridge, lasso, k-neighbours, random forest, support vector and partial least square regression. The res ults show that HSI data with regression models has the potential to predict firmness in a rapid, non-destructive manner. Out of these seven regression models, the k-neighbours regression model outperformed all other methods with a standard error of prediction of 0.14, which is better than that of the state-of-the-art results.

scholarly journals Characterisation of oxide layers on technical copper based on visible hyperspectral imaging

2019 ◽  
Author(s):  
Jan Stiedl ◽  
Georgette Azemtsop M. ◽  
Barbara Boldrini ◽  
Simon Green ◽  
Thomas Chassé ◽  
...  
Keyword(s):  
Real Time ◽  
Hyperspectral Imaging ◽  
Automotive Industry ◽  
Metallic Copper ◽  
Reference Method ◽  
Partial Least Square ◽  
Least Square ◽  
Good Prediction ◽  
Inspection Method ◽  
Oxide Layers

The detection and characterisation of oxide layers on metallic copper samples plays an important role for power electronic modules in the automotive industry. However, since precise identification of oxide layers by visual inspection is difficult and time consuming due to inhomogeneous colour distribution, a reliable and efficient method for estimating their thickness is needed. In this study, hyperspectral imaging in the visible wavelength range (425–725 nm) is proposed as an in-line inspection method for analysing oxide layers in real-time during processing of copper components such as printed circuit boards in the automotive industry. For implementation in the production line a partial least square regression (PLSR) model was developed with a calibration set of n = 12 with about 13,000 spectra per sample to determine the oxide layer thickness on top of the technical copper surfaces. The model shows a good prediction performance in the range of 0–30 nm compared to Auger electron spectroscopy depth profiles as a reference method. The root mean square error (RMSE) is 1.75 nm for calibration and 2.70 nm for full cross-validation. Applied to an external dataset of four new samples with about 13,000 spectra per sample the model provides an RMSE of 1.84 nm for prediction and demonstrates the robustness of the model during real-time processing. The results of this study prove the ability and usefulness of the proposed method to estimate the thickness of oxide layers on technical copper. Hence, the application of hyperspectral imaging for the industrial process control of electronic devices is very promising.

Algorithms for mapping kidney tissue oxygenation during normothermic machine perfusion using hyperspectral imaging

2018 ◽  
Vol 63 (5) ◽  
pp. 557-566 ◽  
Author(s):  
Wenke Markgraf ◽  
Philipp Feistel ◽  
Christine Thiele ◽  
Hagen Malberg
Keyword(s):  
Hyperspectral Imaging ◽  
Kidney Tissue ◽  
Partial Least Square ◽  
Least Square ◽  
Tissue Oxygen Saturation ◽  
Calibration Data ◽  
Machine Perfusion ◽  
Severe Problem ◽  
Promising Alternative ◽  
Normothermic Machine Perfusion

Abstract The lack of donor grafts is a severe problem in transplantation medicine. Hence, the improved preservation of existing and the usage of organs that were deemed untransplantable is as urgent as ever. The development of novel preservation techniques has come into focus. A promising alternative to traditional cold storage is normothermic machine perfusion (NMP), which provides the benefit of improving the organs’ viability and of assessing the organs’ status under physiological conditions. For this purpose, methods for evaluating organ parameters have yet to be developed. In a previous study, we determined the tissue oxygen saturation (StO2) of kidneys during NMP with hyperspectral imaging (HSI) based on a discrete wavelength (DW) algorithm. The aim of the current study was to identify a more accurate algorithm for StO2 calculation. A literature search revealed three candidates to test: a DW algorithm and two full spectral algorithms – area under a curve and partial least square regression (PLSR). After obtaining suitable calibration data to train each algorithm, they were evaluated during NMP. The wavelength range from 590 to 800 nm was found to be appropriate for analyzing StO2 of kidneys during NMP. The PLSR method shows good results in analyzing the tissues’ oxygen status in perfusion experiments.

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