Author
Ana Garrido-Varo
Other affiliations: University College London, Texas A&M University
Bio: Ana Garrido-Varo is an academic researcher from University of Córdoba (Spain). The author has contributed to research in topics: Hyperspectral imaging & Partial least squares regression. The author has an hindex of 29, co-authored 127 publications receiving 2519 citations. Previous affiliations of Ana Garrido-Varo include University College London & Texas A&M University.
Papers published on a yearly basis
Papers
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TL;DR: The spectral pre-treatments known as standard normal variate (SNV) and multiplicative scatter correction (MSC) often give very similar results, and are widely regarded as exchangeable as discussed by the authors.
187 citations
TL;DR: This overview of the most widely used non-linear algorithms in the management of NIRS data addresses the most common strategies and algorithms used in the generation of prediction equations and their applications.
108 citations
TL;DR: In this paper, a combination of NIR hyperspectral imaging technique and spectral similarity analyses was used for detecting low levels (1.0%) of melamine particles in milk powders.
100 citations
TL;DR: In this paper, the ability of NIR technology to classify individual strawberries as a function of variety was tested using partial least squares discriminant analysis (PLS-DA), which yielded percentages of correctly classified samples ranging from 57% for the Camarosa variety to 78% for Antilla Fnm.
93 citations
TL;DR: In this paper, the feasibility of using NIRS technology to predict internal quality parameters in individual tomatoes was examined using new-generation diode-array instruments, which can be adapted for on-site and online measurements.
90 citations
Cited by
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
6,278 citations
2,730 citations
TL;DR: In this paper, the authors investigated the critical points to be aware of when accuracy of NIR-based measurements is assessed and proposed a new index based on the quartiles of the empirical distribution.
Abstract: Near-infrared (NIR) and mid-IR spectroscopy applied to soil compositional analysis started to develop markedly in the 1990s, taking advantage of earlier advances in instrumentation and chemometrics for agricultural products. Today, NIR spectroscopy is envisioned as replacing laboratory analysis in certain applications (e.g., soil-carbon-credit assessment at the farm level). However, accuracy is still unsatisfactory compared with standard laboratory procedures, leading some authors to think that such a challenge will never be met.
This article investigates the critical points to be aware of when accuracy of NIR-based measurements is assessed. First is the decomposition of the standard error of prediction into components of bias and variance, only the latter being reducible by averaging. This decomposition is not used routinely in the soil-science literature. Contrarily, a log-normal distribution of reference values is very often encountered with soil samples [e.g., elemental concentrations (e.g., carbon)] with numerous small or zero values. These very skewed distributions make us take precautions when using inverse regression methods (e.g., principal component regression or partial least squares), which force the predictions towards the centre of the calibration set, leading to negative effects on the standard error prediction – and therefore on prediction accuracy – especially when log-normal distributions are encountered. Such distributions, which are very common for soil components, also make the ratio of performance to deviation a useless, even hazardous, tool, leading to erroneous conclusions.
We propose a new index based on the quartiles of the empirical distribution – ratio of performance to inter-quartile distance – to overcome this problem.
668 citations
TL;DR: The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks, and can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.
Abstract: Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.
663 citations