Transfer of multivariate classification models applied to digital images and fluorescence spectroscopy data

Milanez, Karla Danielle Tavares de Melo; Nóbrega, Thiago César Araújo; Nascimento, Danielle Silva Do; Insausti, Matías; Pontes, Márcio José Coelho

doi:10.1016/j.microc.2017.03.004

Cited by 13 publications

(7 citation statements)

References 28 publications

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“…Based on the survey (see ESI Table 1†), most studies have employed the whole input region to produce a global model. 22–28,30–42 This practice is in line with a general belief that PLS-DA is capable of removing the analyte-irrelevant information by assigning relative low coefficient ( i.e. loading) to the uninformative region.…”

Section: Status Quo In Pls-da Classification Modelling Practice Strat...supporting

confidence: 60%

“…22,23,28–33 Overall, it is noted that PLS-DA was mostly used in modelling spectral-like data such as Raman and infrared spectra. Occasionally, data obtained using non-spectroscopic techniques like imaging 35,54 and chromatography 28,53 have also been used successfully in PLS-DA modelling.…”

Section: Status Quo In Pls-da Classification Modelling Practice Strat...mentioning

confidence: 99%

“…On the other hand, the favoured sampling algorithm and split ratio being employed in running the ET are also recorded. In overall, Kennard–Stone 35,48,57,59 and random sampling 22,23,28,47,54,55 are the two most used sampling algorithms and the favoured split ratios are 7 : 3 and 2/3 : 1/3. In addition, only a few studies have employed repeated random sampling.…”

Section: Status Quo In Pls-da Classification Modelling Practice Strat...mentioning

confidence: 99%

“…A total of 68 articles published since 2013 were reviewed 22–89 and the following information was recorded: (a) the nature and dimension of the dataset, (b) algorithm/acronym, (c) input data ( i.e. global or interval selection), (d) data pre-processing (DP) methods, (e) model validation; (f) decision rules (DRs), (g) approach to determine the optimum number of PLS components, and (h) figures of merits employed to describe the model performance.…”

Section: Introductionmentioning

confidence: 99%

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Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

Lee

Liong

Jemain

2018

Analyst

537

324

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Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. Over the past two decades, PLS-DA has demonstrated great success in modelling high-dimensional datasets for diverse purposes, e.g. product authentication in food analysis, diseases classification in medical diagnosis, and evidence analysis in forensic science. Despite that, in practice, many users have yet to grasp the essence of constructing a valid and reliable PLS-DA model. As the technology progresses, across every discipline, datasets are evolving into a more complex form, i.e. multi-class, imbalanced and colossal. Indeed, the community is welcoming a new era called big data. In this context, the aim of the article is two-fold: (a) to review, outline and describe the contemporary PLS-DA modelling practice strategies, and (b) to critically discuss the respective knowledge gaps that have emerged in response to the present big data era. This work could complement other available reviews or tutorials on PLS-DA, to provide a timely and user-friendly guide to researchers, especially those working in applied research.

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Section: Status Quo In Pls-da Classification Modelling Practice Strat...supporting

confidence: 60%

Section: Status Quo In Pls-da Classification Modelling Practice Strat...mentioning

confidence: 99%

Section: Status Quo In Pls-da Classification Modelling Practice Strat...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

Lee

Liong

Jemain

2018

Analyst

537

324

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“…Fluorescence spectral classification [6] is an important branch of fluorescence spectral analysis, and classification model for fluorescence spectra map the input objects (spectroscopy) to the desired outputs (class assignments) [7]. In general, the construction of a fluorescence spectral classification model consists of the following steps: preprocessing [8], dimensionality reduction (feature extraction) [9] and classification [10].…”

mentioning

confidence: 99%

PCANet: A Common Solution for Laser-Induced Fluorescence Spectral Classification

Zhou

Yan

et al. 2019

IEEE Access

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Laser-induced fluorescence (LIF) technology is an advanced optical detection method, which has the advantages of fast, high precision and nondestructive testing, and is widely used in many fields. The general pattern recognition method for fluorescence spectral classification is highly dependent on pretreatment and dimension reduction. Specific pretreatment and dimension reduction methods are required for specific substances. Deep learning, especially the convolutional neural network (CNN), has the advantage of low dependence on data preprocessing and dimensionality reduction process, which makes it perform well in spectral classification. However, learning a useful CNN model for classification depends crucially on the expertise of parameter tuning and some ad hoc tricks, which is not suitable for chemometrics researchers. This paper presents a novel chemometrics tool for fluorescence spectra, principal component analysis network (PCANet), and more specifically a PCANet model with the optimized hyper-parameters (only optimized once). A two-stage cascaded PCANet model is constructed based on the liquor dataset, and the hyper-parameters are optimized and determined, which can make PCANet recognition model with the highest accuracy. Comparing the CNN model with two convolutional layers, the PCANet model is less affected by the size of the input image and the number of samples in the training set. At the same time, the performance of the two models on the mine water dataset is analyzed, and PCANet has higher recognition accuracy. That is to say, the PCANet is more accurate than CNN in fluorescence spectral classification, and its ability to expand application is stronger than that of the CNN model. The successful application of PCANet model with the optimized hyper-parameters (only optimized once) in the liquor dataset and the mine water dataset has important reference significance for the classification of fluorescence spectra of other substances in the future. INDEX TERMS Laser-induced fluorescence, spectral classification, principal component analysis network, hyper-parameter. I. INTRODUCTION Laser-induced fluorescence (LIF) [1], [2] is a spectroscopic method in which an atom or molecule is excited to a higher energy level by the absorption of laser light followed by spontaneous emission of light [3]. It was first reported by Zare [4], [5] and coworkers in 1968. The technology has many advantages, such as fast, high precision, high sensitivity, and nondestructive testing, so it is widely used in many fields like medicine, environment, biology, chemical industry, as well as food science. The associate editor coordinating the review of this manuscript and approving it for publication was Sungroh Yoon.

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Nondestructive discrimination of Potentilla anserina L. from different production areas based on near‐infrared spectroscopy

Ma,

Hai,

Zhang

et al. 2024

Phytochemical Analysis

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IntroductionThe traditional Chinese medicine (TCM) Potentilla anserina L. can use both as food and medicine. At present, the market mainly depends on experience to identify the species and determine the production areas of P. anserina. To ensure the quality of P. anserina, it is essential to improve the level of quality control.ObjectiveWe aimed to establish a rapid and nondestructive discrimination model to identify P. anserina from different production areas by near‐infrared spectroscopy.MethodsThe spectra of complete P. anserina medicinal materials and their powder of the same variety from four production areas were collected, and principal component analysis discriminant analysis and partial least squares discriminant analysis (PLS‐DA) were conducted based on different pretreatment methods and band selection methods. Then, the spectra of complete medicinal materials were converted into the spectra of medicinal powder for nondestructive identification.ResultsThe correct recognition rate (CRR) of the PLS‐DA discriminant model was the best after spectral preprocessing using autoscaling and competitive adaptive reweighted sampling for band selection. The CRRs of the calibration set and validation set were 100%, the CRRs of the external test set were 95%, 90%, 82%, and 88%, respectively, and the CRRs of the transfer external test set were 84%, 80%, 82%, and 86%, respectively.ConclusionWe realized the nondestructive and effective identification of P. anserina from different origins and laid a foundation for the industrialization and upgrading of TCM.

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Transfer of multivariate classification models applied to digital images and fluorescence spectroscopy data

Cited by 13 publications

References 28 publications

Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

PCANet: A Common Solution for Laser-Induced Fluorescence Spectral Classification

Nondestructive discrimination of Potentilla anserina L. from different production areas based on near‐infrared spectroscopy

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