Prediction of dry matter, carbon and ash contents and identification of Calycophyllum spruceanum (Benth) organs by Near-Infrared spectrophotometry

Ciarnoschi, Lucas Dalmolin; Oliveira, Luís Cláudio de; Simeone, M. L. F.; Panero, Francisco dos Santos; Panero, Pedro dos Santos; Rodriguez, Anselmo Ruiz; Filho, Elenilson G. Alves; Pereira, Marcos Gervásio; Silva, Lucielio Manoel da

doi:10.1016/j.microc.2022.107621

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…Infrared spectroscopy can meet all these criteria mentioned above: Green Chemistry, Sustainable Development Goals and White Analytical Chemistry; it has been replacing traditional analysis techniques due to its accuracy and speed in obtaining the results [7].…”

Section: Introductionmentioning

confidence: 99%

“…Several works have been published in the literature using near-infrared spectroscopy and the use of Machine Learning to predict chemical attributes of grains and classify rice, soy, sesame, edible oils, plants and Amazonian oils [2,[7][8][9][10][11][12][13]. Furthermore, it stands out for not requiring any preparation of the samples besides not using reagents, and it does not destroy or modify the sample.…”

Section: Introductionmentioning

confidence: 99%

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Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy

Farias,

Panero,

Riss

et al. 2023

Sensors

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Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy

Farias,

Panero,

Riss

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…To our knowledge, near-infrared spectroscopy (NIR) is an e cient, high-speed, non-destructive technology and have been widely used in analyzing woody materials (Dalmolin Ciarnoschi et al, 2022). Near infrared hyperspectral imaging (NIR-HSI) combines spectroscopy and imaging, enabling the acquisition of the entire spectrum at various locations in the image plane.…”

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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Non-destructive and rapid identification of yeasts by nearinfrared spectroscopy and machine learning

Silva,

Panero,

Correa

et al. 2024

Cad. Pedagógico

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This study aimed to apply near-infrared (NIR) spectroscopy combined with machine learning techniques to identify yeast strains rapidly and practically, comparing the results with traditional molecular identification methods. Yeasts were isolated from the digestive tracts of aquatic mining insects collected in the extreme north of the Western Amazon (Roraima), Brazil, and preserved through cryopreservation and mineral oil methods. Molecular identification involved PCR amplification and sequencing of ribosomal DNA regions. NIR spectroscopy, coupled with multivariate analysis and machine learning algorithms such as principal component analysis (PCA), hierarchical cluster analysis (HCA), k-nearest neighbor (KNN), and soft independent modeling by class analogy (SIMCA), was used to analyze and classify the yeast samples, accurately identified yeast strains at the genus and species levels, achieving 100% accuracy in both the calibration and validation sets. The results indicate that this method provides a rapid, non-destructive, and environmentally friendly alternative to traditional molecular techniques, making it suitable for real-time, in situ analysis with minimal sample manipulation.

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Prediction of dry matter, carbon and ash contents and identification of Calycophyllum spruceanum (Benth) organs by Near-Infrared spectrophotometry

Cited by 3 publications

References 20 publications

Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy

Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy

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

Non-destructive and rapid identification of yeasts by nearinfrared spectroscopy and machine learning

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