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 Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

2019 ◽  
Vol 9 (5) ◽  
pp. 1027 ◽  
Author(s):  
Insuck Baek ◽  
Moon Kim ◽  
Byoung-Kwan Cho ◽  
Changyeun Mo ◽  
Jinyoung Barnaby ◽  
...  
Keyword(s):  
High Throughput Screening ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Wavelength Region ◽  
Support Vector ◽  
Rice Grain ◽  
Rice Seed ◽  
Forward Selection ◽  
Burkholderia Glumae ◽  
Rice Seeds

The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) technique to find optimal wavelengths and develop a model for detecting discolored, diseased rice seed infected by bacterial panicle blight (Burkholderia glumae), a seedborne pathogen. For this purpose, the HSI data spanning the visible/near-infrared wavelength region between 400 and 1000 nm were collected for 500 sound and discolored rice seeds. For selecting optimal wavelengths to use for detecting diseased seed, a sequential forward selection (SFS) method combined with various spectral pretreatments was employed. To evaluate performance based on optimal wavelengths, support vector machine (SVM) and linear and quadratic discriminant analysis (LDA and QDA) models were developed for detection of discolored seeds. As a result, the violet and red regions of the visible spectrum were selected as key wavelengths reflecting the characteristics of the discolored rice seeds. When using only two or only three selected wavelengths, all of the classification methods achieved high classification accuracies over 90% for both the calibration and validation sample sets. The results of the study showed that only two to three wavelengths are needed to differentiate between discolored, diseased and sound rice, instead of using the entire HSI wavelength regions. This demonstrates the feasibility of developing a low cost multispectral imaging technology based on these selected wavelengths for non-destructive and high-throughput screening of diseased rice seed.

A comparison of point and imaging visible-near infrared spectroscopy for determining soil organic carbon

2018 ◽  
Vol 26 (2) ◽  
pp. 133-146 ◽  
Author(s):  
Mohammad Sadegh Askari ◽  
Sharon M O'Rourke ◽  
Nicholas M Holden
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Sample ◽  
Hyperspectral Imaging ◽  
Wavelength Range ◽  
Spectral Resolution ◽  
Near Infrared ◽  
Predictive Ability ◽  
Imaging Systems ◽  
Sample Representation

This study evaluated whether the accuracy of soil organic carbon measurement by laboratory hyperspectral imaging can match that of standard point spectroscopy operating in the visible–near infrared. Hyperspectral imaging allows a greater amount of spectral information to be collected from the soil sample compared to standard spectroscopy, accounting for greater sample representation. A total of 375 representative Irish soils were scanned by two-point spectrometers (a Foss NIR Systems 6500 labelled S-1 and a Varian FT-IR 3100 labelled S-2) and two laboratory hyperspectral imaging systems (two push broom line-scanning hyperspectral imaging systems manufactured by DV optics and Spectral Imaging Ltd, respectively, labelled S-3 and S-4). The objectives were (a) to compare the predictive ability of spectral datasets for soil organic carbon prediction for each instrument evaluated and (b) to assess the impact of imposing a common wavelength range and spectral resolution on soil organic carbon model accuracy. These objectives examined the predictive ability of spectral datasets for soil organic carbon prediction based on optimal settings of each instrument in (a) and introduced a constraint in wavelength range and spectral resolution to achieve common settings for instruments in (b). Based on optimal settings for each instrument, the deviation (root-mean square error of prediction) from the best fit line between laboratory measured and predicted soil organic carbon, ranked the instruments as S-1 (26.3 g kg−1) < S-2 (29.4 g kg−1) < S-3 (34.3 g kg−1) < S-4 (41.1 g kg−1). The S-1 model outperformed in all partial least squares regression performance indicators, and across all spectral ranges, and produced the most favourable outcomes in means testing, variance testing and identification of significant variables. It is assumed that a larger wavelength range produced more accurate soil organic carbon predictions for S-1 and S-2. Under common instrument settings, the prediction accuracy for S-3 that was almost equal to S-1. It is concluded that under standard operating procedures, greater soil sample representation captured by hyperspectral imaging can equal the quality of the spectra from point spectroscopy. This result is important for the development of laboratory hyperspectral imaging for soil image analysis.

scholarly journals Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging

Foods ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 356 ◽  
Author(s):  
Zhu ◽  
Feng ◽  
Zhang ◽  
Bao ◽  
He
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Principal Component ◽  
Imaging Systems ◽  
Support Vector ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Different Temperatures ◽  
Learning Machine ◽  
Spinach Leaves

Spinach is prone to spoilage in the course of preservation. Spinach leaves stored at different temperatures for different durations will have varying degrees of freshness. In order to monitor the freshness of spinach leaves during storage, a rapid and non-destructive method—hyperspectral imaging technology—was applied in this study. Visible near-infrared reflectance (Vis-NIR) (380–1030 nm) and near-infrared reflectance (NIR) (874–1734 nm) hyperspectral imaging systems were used. Spinach leaves preserved at different temperatures with different durations (0, 3, 6, 9 days at 4 °C and 0, 1, 2 days at 20 °C) were studied. Principal component analysis (PCA) was adopted as a qualitative analysis method. The second-order derivative spectra were utilized to select effective wavelengths. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and extreme learning machine (ELM) were used to build models based on full spectra and effective wavelengths. All three models achieved good results, with accuracies above 92% for both Vis-NIR spectra and NIR spectra. ELM obtained the best results, with all accuracies reaching 100%. The overall results indicate the possibility of the freshness identification of spinach preserved at different temperatures for different durations using two kinds of hyperspectral imaging systems.

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.

Geometrical and spectral data stitching for combining hyperspectral imaging systems

2017 ◽  
Vol 65 (6) ◽  
Author(s):  
Fabian Stark ◽  
Maik Rosenberger ◽  
Paul-Gerald Dittrich ◽  
Rafael Celestre ◽  
Michael Hänsel ◽  
...  
Keyword(s):  
Image Registration ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Imaging Systems ◽  
Data Sets ◽  
Correction Algorithm ◽  
Optical Aberrations ◽  
Optical Aberration ◽  
Spectral Ranges

AbstractOne way to increase the amount of information acquired via hyperspectral imaging and therefore to increase the possibility of data analysis is combining the spatial and spectral information of hyperspectral data sets. The aforementioned data sets are obtained by cameras covering different spectral ranges. The purpose of this article is to develop an algorithm which is able to combine two data sets acquired by two hyperspectral pushbroom imagers, covering the visible (VIS) and the near infrared (NIR) wavelength range. Initially, the effect of optical aberrations, as well as errors via the image registration were examined. Subsequently a correction algorithm for both the optical aberration and the image registration is elaborated.

scholarly journals Non-destructive and rapid discrimination of hard-to-cook beans using hyperspectral imaging

2018 ◽  
pp. 7.1-7.8
Author(s):  
Lina Diaz-Contreras ◽  
Chyngyz Erkinbaev ◽  
Jitendra Paliwal
Keyword(s):  
Hyperspectral Imaging ◽  
Nutritional Value ◽  
Near Infrared ◽  
Storage Conditions ◽  
Imaging Systems ◽  
Electromagnetic Spectrum ◽  
Rapid Discrimination ◽  
Spectral Ranges ◽  
And Control ◽  
Non Destructive

Dry beans stored under sub-optimal conditions tend to develop hard-to-cook (HTC) defect, which extends the cooking time making them less palatable while reducing their nutritional value. The current methods of identifying HTC beans are time-consuming, destructive, and unreliable. A rapid non-destructive inspection technique for pre-screening beans could help identify and discard HTC beans prior to processing. To this end, the potential of hyperspectral imaging technique covering the entire visible to near infrared (NIR) spectral range (400‒2500 nm) was evaluated for rapid and non-destructive identification of HTC beans. The HTC phenomenon was artificially induced in healthy white beans using two different combinations of suboptimal storage conditions of temperature and relative humidity (35℃, 75% RH for 45 days and 60℃, 75% RH for 10 days). Subsequently, the beans were cooked for specified durations and their hardness measured using a texture analyzer. The HTC and control (i.e. easy-to-cook (ETC)) beans were scanned with push-broom hyperspectral imaging systems. Results indicate that both sets of storage conditions rendered the beans HTC but the phenomenon induced by the two different methods was detected in different spectral ranges using hyperspectral imaging. Wavelengths across the entire visible and NIR ranges of electromagnetic spectrum were found useful in detecting HTC as beans stored at 35℃ and 75% RH for 45 days were identified mainly in the 1000‒2500 nm range and those stored at 60℃ and 75% RH for 10 days were identified in the 400‒1000 nm region. The degree of HTC defect could not be ascertained using this technique and requires further investigation.

Application of hyperspectral imaging of peat profiles to the case of fen-bog transition in aapa mires

2020 ◽  
Author(s):  
Lars Granlund ◽  
Teemu Tahvanainen ◽  
Markku Keinänen
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Method Development ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
High Sensitivity ◽  
Aerial Image ◽  
Support Vector ◽  
Infrared Range ◽  
Ecosystem Structure

&lt;p&gt;Hyperspectral imaging (HSI) is a promising precision tool for analysing chronological peat strata from vegetation transitions in peatlands. We explored the potential of HSI in identifying transitions in peat-forming vegetation and degree of peat humification. The changes in aapa mire complexes during recent decades have been assessed by various remote sensing methods (aerial image time series, satellite data and high-resolution UAV multispectral imaging) and HSI methods have been developed to support the data from other sources. Rapid growth of Sphagnum mosses over string-patterned aapa mires in the north-boreal zone has immense significance, since it can alter ecosystem structure and functions such as carbon sequestration. HSI is well suited for analysis of recent ecosystem changes, since it can be applied for large sample sets with extremely fine spatial detail. Additionally, peat layers have complex 3D structures that can be overlooked by other sampling methods.&lt;/p&gt;&lt;p&gt;The HSI data was collected in laboratory conditions with two spectral imaging cameras, covering the visible to near-infrared range (VNIR 400-1000 nm), short-wave infrared range (SWIR, 1000-2500 nm). We used various methods such as PCA, k-means clustering and support vector machines for both quantitative and qualitative analysis of peat. &amp;#160;Our analyses revealed detailed spectral changes that matched with transitions in peat quality and composition. Methodological issues unique to peat samples, such as the effect of oxidation and water content, were assessed for method development. We also used HSI to estimate quality changes that would easily be overlooked or only found by most laborious conventional techniques, like high-frequency microscopic counting of plant remains. Here, the spectral results can be used to guide sampling for microscopic routines, for example.&lt;/p&gt;&lt;p&gt;Results with Carex and Sphagnum peat proved that efficient image-based classification methods for identifying peat transitions can be developed. Our SVM models in the VNIR and SWIR regions were able to distinguish Sphagnum and Carex peat with overall accuracy of validation 80 % and 81 %, respectively. We also developed simple NDI indices for the estimation of von Post humification index that worked with accuracy of 86 % and 59 % for VNIR and SWIR, respectively. In combination with data collected from other sources (remote sensing, ground-truthing, conventional laboratory analysis), peat spectral analyses give strong inference of changes. In our study system, results indicate high sensitivity of northern aapa mires to ecosystem-scale changes.&lt;/p&gt;

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