New data preprocessing trends based on ensemble of multiple preprocessing techniques

The total viable count (TVC) is an important index that reflects the freshness, contamination, and spoilage of dishes. Presently, near-infrared spectroscopy (NIR) is widely used for the assessment of TVC in raw meat, but there are few reports on the application of NIR spectroscopy to evaluate the TVC of Chinese meat dishes. Also, in the existing research, the modeling samples are industrial products, which makes

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“…Schematic and (b) photograph of near-infrared spectrum acquisition system and sensitivity (Mishra et al, 2020;Nicolai et al, 2007;Zhao, 2013).…”

Section: Spectral Pre-processingmentioning

confidence: 99%

Determination of the total viable count of Chinese meat dishes by near‐infrared spectroscopy: A predictive model

Cao

Wei

Xing

et al. 2021

J. Food Process. Preserv.

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“…Therefore, FT-IR spectroscopy, in combination with scatter correction techniques, has been widely applied, such as in reports involving wild Boletus edulis (Li et al, 2017;, Radix Astragali (Yang et al, 2021), and Panax notoginseng . Nevertheless, the data modeling in all these studies can easily become sub-optimal since only one single scatter correction technique could be selected from a shortlist of potential candidates (Mishra et al, 2020a;Roger et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

“…Sequential preprocessing through orthogonalization (SPORT), a novel ensemble approach to the preprocessing fusion technique, takes inspiration from sequential and orthogonalized partial least-squares (SO-PLS). In fact, SPORT could lead to a boosting procedure by using SO-PLS to fuse several scatter correction techniques, since data processed with different scatter correction methods carry at least partially complementary information (Mishra et al, 2020a(Mishra et al, , 2021c. Recent momentum tends to indicate that the use of traditional single scatter correction methods has fallen out of favor in the emerging ensemble approaches to preprocessing fusion, such as reports involving wheat kernels (Mishra et al, 2021b), minced pork meat (Mishra et al, 2021d) fresh fruits (Mishra et al, 2020c), and fuel (Mishra et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Preprocessing Integration Method for the Qualitative and Quantitative Assessment of Wild Medicinal Plants: Gentiana rigescens as an Example

et al. 2021

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Until now, the over-exploitation of wild resources has increased growing concern over the quality of wild medicinal plants. This led to the necessity of developing a rapid method for the evaluation of wild medicinal plants. In this study, the content of total secoiridoids (gentiopicroside, swertiamarin, and sweroside) of Gentiana rigescens from 37 different regions in southwest China were analyzed by high performance liquid chromatography (HPLC). Furthermore, Fourier transform infrared (FT-IR) was adopted to trace the geographical origin (331 individuals) and predict the content of total secoiridoids (273 individuals). In the traditional FT-IR analysis, only one scatter correction technique could be selected from a series of preprocessing candidates to decrease the impact of the light correcting effect. Nevertheless, different scatter correction techniques may carry complementary information so that using the single scatter correction technique is sub-optimal. Hence, the emerging ensemble approach to preprocessing fusion, sequential preprocessing through orthogonalization (SPORT), was carried out to fuse the complementary information linked to different preprocessing methods. The results suggested that, compared with the best results obtained on the scatter correction modeling, SPORT increased the accuracy of the test set by 12.8% in qualitative analysis and decreased the RMSEP by 66.7% in quantitative analysis.

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“…The biochemical sensitivity of IR spectroscopy usually needs to be leveraged by the application of advanced multivariate algorithms, which opens up a whole field of optimization of such approaches [ 24 , 25 , 26 , 27 ]. It is known that every specific data structure will change the optimal parameters of most optimizations; therefore, there is a need to perform optimization for a given problem [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. Here, we present the discrimination of five prostate cell lines: one RWPE-1 cell line derived from the peripheral zone of the histologically normal adult human prostate and four cancerous cell lines derived from prostate cancer (22Rv1), and its metastasis to lymph nodes (LNCaP), the brain (Du145), and bone (PC3).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging

Liberda

Pięta

Pogoda

et al. 2021

Cells

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Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler’s smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing.

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New data preprocessing trends based on ensemble of multiple preprocessing techniques

Cited by 198 publications

References 47 publications

Determination of the total viable count of Chinese meat dishes by near‐infrared spectroscopy: A predictive model

Determination of the total viable count of Chinese meat dishes by near‐infrared spectroscopy: A predictive model

A Novel Multi-Preprocessing Integration Method for the Qualitative and Quantitative Assessment of Wild Medicinal Plants: Gentiana rigescens as an Example

The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging

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