Pruning neural network for architecture optimization applied to near-infrared reflectance spectroscopic measurements. Determination of the nitrogen content in wheat leaves

Mello, César; Poppi, Ronei J.; Andrade, João Carlos de; Cantarella, Heitor

doi:10.1039/a905570c

Cited by 15 publications

(10 citation statements)

References 25 publications

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“…Many of these papers show advancements in data analysis and information extraction, improving the Chemometrics employed for NIR calibration. 102,104,105 In fact, Brazil has shown the expertise necessary for suitable treatment of data generated by NIR spectroscopy with many groups in activity all over Brazil, employing and/or developing Chemometric methodologies.…”

Section: Nir Spectroscopy In Brazilmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications

Pasquini¹

2003

J. Braz. Chem. Soc.

719

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%

Section: Nir Spectroscopy In Brazilmentioning

confidence: 99%

Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications

Pasquini¹

2003

J. Braz. Chem. Soc.

719

433

View full text Add to dashboard Cite

show abstract

“…Existem basicamente dois métodos para a apodização de redes neurais: Optimal Brain Damage 15 (OBD) e Optimal Brain Surgeon [16][17][18] (OBS). Em ambos os métodos as conexões (ou pesos) são cortadas e a correspondente variação no erro E, chamada de saliência, é avaliada.…”

Section: Redes Neurais Com Apodizaçãounclassified

Redes neurais e suas aplicações em calibração multivariada

Cerqueira¹,

Andrade²,

Poppi³

et al. 2001

Quím. Nova

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Recebido em 28/8/00; aceito em 13/3/01 NEURAL NETWORKS AND ITS APPLICATIONS IN MULTIVARIATE CALIBRATION. Neural Networks are a set of mathematical methods and computer programs designed to simulate the information process and the knowledge acquisition of the human brain. In last years its application in chemistry is increasing significantly, due the special characteristics for model complex systems. The basic principles of two types of neural networks, the multi-layer perceptrons and radial basis functions, are introduced, as well as, a pruning approach to architecture optimization. Two analytical applications based on near infrared spectroscopy are presented, the first one for determination of nitrogen content in wheat leaves using multi-layer perceptrons networks and second one for determination of BRIX in sugar cane juices using radial basis functions networks.Keywords: neural networks; backpropagation; radial basis functions; multivariate calibration. Quim. Nova, Vol. 24, No. 6, 864-873, 2001. Divulgação INTRODUÇÃOAtualmente, tem-se observado um grande aumento na disponibilidade de informação em nosso cotidiano. A informação circula entre nós a uma velocidade que seria inconcebível até alguns anos atrás e tende aumentar ainda mais. Hoje em dia o volume de informação dobra a cada quatro anos e num futuro próximo passará a dobrar a cada dois anos. Assim, realmente vivemos no que se pode chamar de sociedade da informação. Grande parte deste aumento no volume de informações se deve ao aparecimento e uso disseminados de microcomputadores equipados com softwares, como Windows TM , que tornam o seu uso extremamente simples, até mesmo para crianças em fase de alfabetização.Uma das atividades humanas que mais se beneficiou com o uso de computadores foi a própria ciência, em particular a química analítica. Até algumas décadas atrás o principal problema enfrentado no desenvolvimento de um método analítico era como se obter os dados. Gastava-se muito tempo, dinheiro e esforço para se obter um pequeno conjunto de dados.Graças a computadorização dos instrumentos o principal problema não é mais como obter os dados, mas como tratá-los de modo eficiente. Tipicamente tais instrumentos fornecem um grande conjunto de dados e para que tais dados sejam convertidos em informação útil deve-se usar métodos matemáticos e estatísticos modernos e eficientes. Dentre tais métodos destaca-se os métodos lineares e não lineares de calibração multivariada e mais recentemente as redes neurais 1 . Para uma melhor compreensão da álgebra envolvida nos modelos de tratamento de dados, foi utilizada a seguinte notação:n Vetores -letras minúsculas em negrito (ex: x); n Matrizes -letras maiúsculas em negrito (ex: X); n Escalares -letras minúsculas sem negrito (ex: l); n Matrizes diagonais -letras maiúsculas sem negrito (ex: S). Redes NeuraisRecentes avanços em neurofisiologia têm desvendado vários mecanismos sobre o fluxo e o processamento de informações que ocorrem no cérebro humano. Alguns destes mecanismos foram modelados matematicamente permitind...

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“…It is very obvious that factors such as experimental condition, instrument variation, and analyzed characteristics induce nonlinearities in the spectra. Generally, artificial neural network (ANN) is a usual choice to solve this problem (Despagne and Massart 1998;Mello et al 1999). Although this method is used frequently to deal with the nonlinear model, it does not satisfy researchers due to some difficulties such as the selection of hidden layer size, learning rate, and momentum.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Nitrogen Status in Rice by Least Squares Support Vector Machines and Reflectance Spectroscopy

Shao

Zhao

Bao

et al. 2009

Food Bioprocess Technol

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The estimation of nitrogen status non-destructively in rice was performed using canopy spectral reflectance with visible and near-infrared reflectance (Vis/NIR) spectroscopy. The canopy spectral reflectance of rice grown with different levels of nitrogen inputs was determined at several important growth stages. This study was conducted at the experiment farm of Zhejiang University, Hangzhou, China. The soil plant analysis development (SPAD) value was used as a reference data that indirectly reflects nitrogen status in rice. A total of 64 rice samples were used for Vis/NIR spectroscopy at 325-1075 nm using a field spectroradiometer, and chemometrics of partial least square (PLS) was used for regression. The correlation coefficient (r), root mean square error of prediction, and bias in prediction set by PLS were, respectively, 0.8545, 0.7628, and 0.0521 for SPAD value prediction in tillering stage, 0.9082, 0.4452, and −0.0109 in booting stage, and 0.8632, 0.7469, and 0.0324 in heading stage. Least squares support vector machine (LS-SVM) model was compared with PLS and back propagation neural network methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SPAD values of rice. Independent component analysis was executed to select several sensitive wavelengths (SWs) based on loading weights; the optimal LS-SVM model was achieved with SWs of 560, 575-580, 700, 730, and 740 nm for SPAD value prediction in booting stage. It is concluded that Vis/NIR spectroscopy combined with LS-SVM regression method is a promising technique to monitor nitrogen status in rice.

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Pruning neural network for architecture optimization applied to near-infrared reflectance spectroscopic measurements. Determination of the nitrogen content in wheat leaves

Cited by 15 publications

References 25 publications

Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications

Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications

Redes neurais e suas aplicações em calibração multivariada

Quantification of Nitrogen Status in Rice by Least Squares Support Vector Machines and Reflectance Spectroscopy

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