Non-Destructive Assessment of Edible Seed Chemical Content by Near-Infrared Hyperspectral Imaging for the Improvement of Nutritional Traits in Bottle Gourd (Lagenaria Siceraria)

2021 ◽  
Vol 7 (5) ◽  
pp. 75-81
Author(s):  
Nadège Aurelie N’dri-Aya ◽  
◽  
Irié Vroh-Bi ◽  
◽  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Principal Component ◽  
Bottle Gourd ◽  
Lagenaria Siceraria ◽  
Absorption Bands ◽  
Tropical Regions ◽  
Nutrition Security ◽  
Chemical Content ◽  
Non Destructive

The edible seeds of bottle gourd [Lagenaria siceraria (Molina) Standl.] are rich in oils, proteins and minerals of high nutritional quality. They are highly prized in pan tropical regions where they constitute valuable resources for food and nutrition security. In this study, near-infrared hyperspectral imaging (NIR-HSI) was combined with chemometrics to assess the variability of seed chemical content of African cultivars for the selection of nutritional traits. Six hundred seeds of four accessions belonging to two cultivars were collected from the Ivory Coast (West Africa) and analysed. The NIR-HSI spectra collected on whole seeds in the 1100-2400 nm range revealed that the main absorption bands of the seed chemical content were associated with water, lipids and proteins. The absorbance values between seeds of the same accession in these spectral regions varied up to 1.8 folds. Among the two chemometric tools used, principal component analysis (PCA) did not separate the accessions while Partial Least Squares Discriminant Analysis (PLS-DA) discriminated the accessions with 87.33 % to 94.67 %, and the cultivars with 90 % to 92 % correct classification. Seed oils from bottle gourd are for instance rich in linoleic acid which is an essential fatty acid for human health. The non-destructive and qualitative determination of the content of single seeds was demonstrated in the study and provides the opportunity to select superior seeds for the improvement of key nutritional traits in bottle gourd. Lagenaria siceraria, near-infrared hyperspectral imaging, seed chemical content, PCA, PLS-DA, nutrition security

scholarly journals Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

2011 ◽  
Vol 29 (No. 6) ◽  
pp. 595-602 ◽  
Author(s):  
Q. Lü ◽  
M.-j. Tang ◽  
J.-r. Cai ◽  
J.-w. Zhao ◽  
S. Vittayapadung
Keyword(s):  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Detection System ◽  
Image Data ◽  
Principal Component ◽  
Quality Analysis ◽  
Economic Losses ◽  
Hyperspectral Image Data ◽  
Multispectral Detection ◽  
Non Destructive

It is necessary to develop a non-destructive technique for kiwifruit quality analysis because the machine injury could lower the quality of fruit and incur economic losses. Bruises are not visible externally owing to the special physical properties of kiwifruit peel.We proposed the hyperspectral imaging technique to inspect the hidden bruises on kiwifruit. The Vis/NIR (408–1117 nm) hyperspectral image data was collected. Multiple optimal wavelength (682, 723, 744, 810, and 852 nm) images were obtained using principal component analysis on the high dimension spectral image data (wavelength range from 600 nm to 900 nm). The bruise regions were extracted from the component images of the five waveband images using RBF-SVM classification. The experimental results showed that the error of hidden bruises detection on fruits by means of hyperspectral imaging was 12.5%. It was concluded that the multiple optimal waveband images could be used to constructs a multispectral detection system for hidden bruises on kiwifruits.

Non-destructive evaluation of bacteria-infected watermelon seeds using visible/near-infrared hyperspectral imaging

2016 ◽  
Vol 97 (4) ◽  
pp. 1084-1092 ◽  
Author(s):  
Hoonsoo Lee ◽  
Moon S. Kim ◽  
Yu-Rim Song ◽  
Chang-Sik Oh ◽  
Hyoun-Sub Lim ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Non Destructive Evaluation ◽  
Non Destructive

scholarly journals Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

2020 ◽  
Vol 10 (3) ◽  
pp. 1173 ◽  
Author(s):  
Zhiqi Hong ◽  
Yong He
Keyword(s):  
Hyperspectral Imaging ◽  
Nutritional Value ◽  
Geographical Origin ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Imaging Systems ◽  
Support Vector ◽  
Spectral Ranges ◽  
Tea Leaf ◽  
Non Destructive

Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification.

A Hyperspectral Imaging System for Identification of Faecal and Ingesta Contamination on Poultry Carcasses

2003 ◽  
Vol 11 (4) ◽  
pp. 269-281 ◽  
Author(s):  
Kurt C. Lawrence ◽  
William R. Windham ◽  
Bosoon Park ◽  
R. Jeff Buhr
Keyword(s):  
Real Time ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Ccd Camera ◽  
Sample Population ◽  
Multivariate System ◽  
Ratio Image ◽  
On Line

A method and system for detecting faecal and ingesta contaminants on poultry carcasses were demonstrated. A visible/near infrared monochromator, which measured reflectance and principal component analysis were first used to identify key wavelengths from faecal and uncontaminated skin samples. Measurements at 434, 517, 565 and 628 nm were identified and used for evaluation with a hyperspectral imaging system. The hyperspectral imaging system, which was a line-scan (pushbroom) imaging system, consisted of a hyperspectral camera, fibre-optic line lights, a computer and frame grabber. The hyperspectral imaging camera consisted of a high-resolution charge coupled device (CCD) camera, a prism-grating-prism spectrograph, focusing lens, associated optical hardware and a motorised controller. The imaging system operated from about 400 to 900 nm. The hyperspectral imaging system was calibrated for wavelength, distance and percent reflectance and analysis of calibrated images at the key wavelengths indicated that single-wavelength images were inadequate for detecting contaminants. However, a ratio of images at two of the key wavelengths was able to identify faecal and ingesta contaminants. Specifically, the ratio of the 565-nm image divided by the 517-nm image produced good results. The ratio image was then further processed by masking the background and either enhancing the image contrast with a non-linear histogram stretch, or applying a faecal threshold. The results indicated that, for the limited sample population, more than 96% of the contaminants were detected. Thus, the hyperspectral imaging system was able to detect contaminants and showed feasibility, but was too slow for real-time on-line processing. Therefore, a multivariate system operating at 565 and 517 nm, which should be capable of operating at real-time on-line processing speed, should be used. Further research with such a system needs to be conducted.

scholarly journals Evaluation of Visible-Near Infrared Reflectance Spectra of Avocado Leaves as a Non-destructive Sensing Tool for Detection of Laurel Wilt

Plant Disease ◽  
2012 ◽  
Vol 96 (11) ◽  
pp. 1683-1689 ◽  
Author(s):  
Sindhuja Sankaran ◽  
Reza Ehsani ◽  
Sharon A. Inch ◽  
Randy C. Ploetz
Keyword(s):  
Discriminant Analysis ◽  
Vegetation Index ◽  
Near Infrared ◽  
Principal Component ◽  
Persea Americana ◽  
Near Infrared Reflectance ◽  
Laurel Wilt ◽  
Linear Discriminant ◽  
Raffaelea Lauricola ◽  
Non Destructive

Laurel wilt, caused by the fungus Raffaelea lauricola, affects the growth, development, and productivity of avocado, Persea americana. This study evaluated the potential of visible-near infrared spectroscopy for non-destructive sensing of this disease. The symptoms of laurel wilt are visually similar to those caused by freeze damage (leaf necrosis). In this work, we performed classification studies with visible-near infrared spectra of asymptomatic and symptomatic leaves from infected plants, as well as leaves from freeze-damaged and healthy plants, both of which were non-infected. The principal component scores computed from principal component analysis were used as input features in four classifiers: linear discriminant analysis, quadratic discriminant analysis (QDA), Naïve-Bayes classifier, and bagged decision trees (BDT). Among the classifiers, QDA and BDT resulted in classification accuracies of higher than 94% when classifying asymptomatic leaves from infected plants. All of the classifiers were able to discriminate symptomatic-infected leaves from freeze-damaged leaves. However, the false negatives mainly resulted from asymptomatic-infected leaves being classified as healthy. Analyses of average vegetation indices of freeze-damaged, healthy (non-infected), asymptomatic-infected, and symptomatic-infected leaves indicated that the normalized difference vegetation index and the simple ratio index were statistically different.

Classification of individual cotton seeds with respect to variety using near-infrared hyperspectral imaging

2016 ◽  
Vol 8 (48) ◽  
pp. 8498-8505 ◽  
Author(s):  
Sófacles Figueredo Carreiro Soares ◽  
Everaldo Paulo Medeiros ◽  
Celio Pasquini ◽  
Camilo de Lelis Morello ◽  
Roberto Kawakami Harrop Galvão ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
New Strategy ◽  
Cotton Seeds ◽  
Non Destructive

This paper proposes the use of Near Infrared Hyperspectral Imaging (NIR-HSI) as a new strategy for fast and non-destructive classification of cotton seeds with respect to variety.

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