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Tigabu, Mulualem; Odén, Per Christer

doi:10.1023/a:1025037408922

Cited by 24 publications

(4 citation statements)

References 29 publications

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“…NIR-HSI has proved to be a reliable method to analyze soils, water, food, seeds, and other samples (Tigabu and Odén, 2003, Tigabu and Odén, 2004, McLean et al, 2014, da Conceicao et al, 2021, Piarulli et al, 2021, Zhang et al, 2021, Zhao et al, 2021. Little research has focus on the analysis of highly similar materials, such as wood products taken from relative species.…”

Section: Introductionmentioning

confidence: 99%

Identification of hybrid tuliptree and parental species by near infrared hyperspectral imaging （NIR-HSI） spectroscopy

Chen

Gao

et al. 2022

Preprint

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Hybrid tuliptree (Liriodendron sion-americanum P.C. Yieh ex Shang et Z.R. Wang) has good wood quality and been widely used in furniture and other fields. Compared with parental species, hybrid tuliptree displayed faster growth rate, better wood quality and wider adaptability. But the morphological properties of hybrid tuliptree and parental species are highly similar, its identification has become an urgent problem. This study aims to evaluate the capabilities of near infrared hyperspectral imaging (NIR-HSI) technique to identify hybrid tuliptree, Chinese tuliptree, and tuliptree. For this purpose, the building models were carried out using Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) after different data treatment. The results demonstrated that NIR-HSI spectroscopy combined with successive projections algorithm (SPA) treatment and SVM model can achieve the highest prediction accuracy (100%). This study shows that NIR-HSI technique has great potential in the genus Liriodendron identification and is suitable for classifying highly similar woods.

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Section: Introductionmentioning

confidence: 99%

Identification of hybrid tuliptree and parental species by near infrared hyperspectral imaging （NIR-HSI） spectroscopy

Chen

Gao

et al. 2022

Preprint

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“…[10] Regarding qualitative utilization, transmittance-based NIRS was shown to provide high ability in separating seeds of three species of the Larix tree into insect-infested, filled, and empty categories. [11] The accuracy of the separation has been reported to be as high as 100%.…”

Section: Introductionmentioning

confidence: 99%

Near-infrared hyperspectral imaging for classification of mung bean seeds

Phuangsombut

Inagaki

et al. 2018

International Journal of Food Properties

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Hard mung bean seeds pose a problem in the sprouting process as they develop mold and infect neighboring seeds. Near-infrared hyperspectral imaging combined with partial least squares discriminant analysis was applied to develop a classifying model to separate hard mung beans from normal ones. The orientation of the measured beans was found to affect the classification result. The optimal partial least squares discriminant analysis model based on all orientations resulted in a correlation coefficient (R) of 0.919 with a root mean squared error of prediction of 0.197. The nongerminative parts were mapped and were concentrated at one end of the bean. Finally, a germinability index was proposed according to the proportion of colored areas between the germinative and non-germinative parts from the hyperspectral imaging results. ARTICLE HISTORY

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“…These spectral data can be used for qualitative and quantitative analyses of chemical composition of biological samples. For instance, NIR spectroscopy has been successfully applied to discriminate insect-attacked seeds [14-18], empty seeds [16,19,20], and petrified seeds [21] from viable seeds. It has also demonstrated its usefulness to separate filled-viable and dead-filled seeds of Scots pine after incubation and drying for different time periods [22].…”

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confidence: 99%

Multivariate Discriminant Analysis of Single Seed Near Infrared Spectra for Sorting Dead-Filled and Viable Seeds of Three Pine Species: Does One Model Fit All Species?

Tigabu

Daneshvar

Ren

et al. 2019

Forests

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Seed lots of pine species are composed of viable, dead-filled and empty seeds, and the success of complete sorting of dead-filled seeds using the conventional method (Incubation, Drying and Separation in water) is difficult to achieve; leaving a considerable scope for upgrading the sorting efficiency. The objective of this study was to evaluate the prospect of sorting viable and dead-filled seeds of pine species using Near Infrared (NIR) spectroscopy. To demonstrate this, dead-filled and viable seeds of Mason’s pine, slash pine and loblolly pine were incubated in moist medium for three days, dried for six hours and scanned by XDS Rapid Content Analyzer from 780–2500 nm. Orthogonal Projection to Latent Structure-Discriminant Analysis was used to develop discriminant models for each species separately and for all species combined. The results showed that the sensitivity (the model’s ability to correctly classify members of a given class) and the specificity (the model’s ability to reject non-members of a given class) were 100% for each species model and 98%–99% for combined species model. The overall classification accuracy was 100% and 99% for individual species and combined species models, respectively. The absorption band in the 1870–1950 nm with a major peak at 1930 nm, which is related to water, was responsible for discrimination as dead-filled seeds dried quicker than viable seeds during the drying process. Our study is the first attempt to simultaneously discriminate dead-filled and viable seeds of pines by NIR spectroscopy. The results demonstrates that a global calibration model of seed lots of several pine species can be equally effective as the individual species model to discriminate viable and dead-filled seeds by NIR spectroscopy, thereby ensuring precision sowing (also known as single seed sowing) in nurseries.

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Cited by 24 publications

References 29 publications

Identification of hybrid tuliptree and parental species by near infrared hyperspectral imaging （NIR-HSI） spectroscopy

Identification of hybrid tuliptree and parental species by near infrared hyperspectral imaging （NIR-HSI） spectroscopy

Near-infrared hyperspectral imaging for classification of mung bean seeds

Multivariate Discriminant Analysis of Single Seed Near Infrared Spectra for Sorting Dead-Filled and Viable Seeds of Three Pine Species: Does One Model Fit All Species?

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