Application of artificial neural networks to the classification of soils from São Paulo state using near-infrared spectroscopy

Fidêncio, Paulo Henrique; Ruisánchez, Itziar; Poppi, Ronei J.

doi:10.1039/b107533k

Cited by 44 publications

(21 citation statements)

References 18 publications

(24 reference statements)

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“…Perhaps this was a consequence of the great international impact this sector experimented as a result of the unique non-destructive and non-invasive approach to analysis of agricultural and food products when using NIR technology. Other publications, in the last four years, reveal other applications in the agricultural area [86][87][88][89][90]98,[102][103][104][105] with papers on zootechnology, [86][87][88][89] eucalyptus pulp analysis, 90 and organic matter determination 102,103 and nitrogen content determination 105 in soils. Many of these papers show advancements in data analysis and information extraction, improving the Chemometrics employed for NIR calibration.…”

Section: Nir Spectroscopy In Brazilmentioning

confidence: 99%

Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications

Pasquini¹

2003

J. Braz. Chem. Soc.

715

433

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Este trabalho visa prover uma revisão da teoria da Espectroscopia no Infravermelho Próximo (NIR) e das suas aplicações no campo das Ciências Analíticas. Ele é dirigido ao leitor que não possui um conhecimento profundo sobre espectroscopia vibracional mas que gostaria de ser introduzido às potencialidades analíticas desta técnica fascinante e, ao mesmo tempo, se tornar consciente das suas limitações. A teoria fundamental, uma revisão dos instrumentos modernos existentes e aplicações em inúmeros campos, são apresentados. Este trabalho não pretende suprir uma revisão bibliográfica intensiva mas se refere aos materiais recentes mais significativos e representativos encontrados na literatura técnica. Uma vez que este texto foi produzido em conseqüência do primeiro "workshop" em espectroscopia NIR, que ocorreu em Campinas -Brasil, como uma atividade preliminar ao 11º Encontro Nacional de Química Analítica (ENQA), ele também apresenta o estado da arte na Espectroscopia NIR no Brasil, mostrando seus resultados mais recentes e apontando a necessidade de se elevar esta tecnologia a um nível consistente com aquele de suas atividades econômicas. This paper intends to review the basic theory of Near Infrared (NIR) Spectroscopy and its applications in the field of Analytical Science. It is addressed to the reader who does not have a profound knowledge of vibrational spectroscopy but wants to be introduced to the analytical potentialities of this fascinating technique and, at same time, be conscious of its limitations. Essential theory background, an outline of modern instrument design, practical aspects, and applications in a number of different fields are presented. This work does not intend to supply an intensive bibliography but refers to the most recent, significant and representative material found in the technical literature. Because this paper has been produced as consequence of the First Workshop on Near Infrared Spectroscopy, whose venue was Campinas -Brazil, as a pre-conference activity of the XI National Meeting on Analytical Chemistry (ENQA), it also depicts the state of the art of NIR spectroscopy in Brazil, pointing out the current achievements and the need to take the technology to a level consistent with this country's economical activities.

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Section: Nir Spectroscopy In Brazilmentioning

confidence: 99%

Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications

Pasquini¹

2003

J. Braz. Chem. Soc.

715

433

View full text Add to dashboard Cite

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“…In our case, the inputs were scores from the PCA of different preprocessed spectra data. Each neuron of the hidden layer represents a radial function with equal dimensions to the input data and the number of radial functions depends on the problem to be solved (Fidêncio et al, 2001). The RBFNN discussed in this study has two or three neurons in the output layer, for two or three classes should be determined.…”

Section: Discussionmentioning

confidence: 99%

Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy

Xie

Liu

et al. 2008

J. Zhejiang Univ. Sci. B

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Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.

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“…Fidencio et al, [55] applied CP ANN and RBF ANN in classification of soils using near-infrared spectroscopy. Altendorf et al, [56] developed a set of feed forward ANNs with BP training to predict soil water content at a given depth as a function of soil temperature.…”

Section: Softcomputing Techniques For Hyperspectral Image Classificationmentioning

confidence: 99%

Hyperspectral Image Classification using Softcomputing Techniques: A Review

Rajitha¹,

Bhargavi²,

Jyothi³

2018

IJCA

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Hyperspectral image classification plays a major role in remote image analysis. Hyperspectral images provide both spatial details of airborne imagery and spectral resolution for spectroscopic analysis and narrow band analysis techniques. Available satellite sensors like Hyperion, HyMap and AVIRIS are good sources of hyperspectral data. Applications of hyperspectral images are remote sensing, seed viability study, biotechnology, environmental monitoring, medical diagnose, food, pharmaceuticals and so on. Traditional techniques are difficult to deal with hyperspectral images directly, because hyperspectral images have continuous narrow spectral bands. To overcome this, hyperspectral image classification can be done using different softcomputing techniques. Softcomputing is an emerging field consisting of Fuzzy Logic, Neural Network and Genetic Algorithms. This paper reviews how hyperspectral image classification can be done using different softcomputing techniques.

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Application of artificial neural networks to the classification of soils from São Paulo state using near-infrared spectroscopy

Cited by 44 publications

References 18 publications

Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications

Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications

Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy

Hyperspectral Image Classification using Softcomputing Techniques: A Review

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