A parameter-free framework for calibration enhancement of near-infrared spectroscopy based on correlation constraint

Zhang, Jin; Hu, Yun; Zhou, Lifeng; Wang, Guoze; Guo, Guo; Zhang, Qinghai; Lei, Shicheng; Zhang, Aihua

doi:10.1016/j.aca.2020.11.006

Cited by 26 publications

(11 citation statements)

References 22 publications

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“…Bioactivity data can be retrieved from many specialized databases, such as the DBAAPS, APD3, DRAMP, and CAMP databases. Machine learning methods can effectively associate the peptide sequences with their bioactivity values, − and we have proposed many effective algorithms. − However, to the best of our knowledge, few complete works integrate a large-scale screening protocol, successful screening application, chemical synthesis, and bioactivity validation of antifungal peptides.…”

mentioning

confidence: 99%

Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure–Activity Relationship

Jin

Yang

Tian

et al. 2021

ACS Med. Chem. Lett.

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Antifungal peptides are effective, biocompatible, and biodegradable, and thus, they are promising to be the next generation of drugs for treating infections caused by fungi. The identification processes of highly active peptides, however, are still time-consuming and labor-intensive. Quantitative structure–activity relationships (QSARs) have dramatically facilitated the discovery of many bioactive drug molecules without a priori knowledge. In this study, we have established an effective QSAR protocol for screening antifungal peptides. The screening protocol integrates an accurate antifungal peptide classification model and four activity prediction models against specified target fungi. A demonstrative application was performed on more than three million candidate peptides, and three outstanding peptides were identified. The whole screening took only a few days, which was much faster than our previous experimental screening works. In conclusion, the protocol is useful and effective for reducing repetitive laboratory efforts in antifungal peptide discovery. The prediction server (antifungal Web server) is freely available at .

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confidence: 99%

Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure–Activity Relationship

Jin

Yang

Tian

et al. 2021

ACS Med. Chem. Lett.

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“…NS-PFCE has been applied to eliminate the calibration transfer between different instruments 46 and different mesh leaf samples. 36 These results show that the algorithms based on VCPA-IRIV-PLSR and NS-PFCE are suitable for the calibration transfer of near-infrared spectra between samples that exist in different states.…”

Section: Discussionmentioning

confidence: 72%

Feasibility of an NIR spectral calibration transfer algorithm based on optimized feature variables to predict tobacco samples in different states

Geng

Shen

et al. 2023

Anal. Methods

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“…Figure 6 b shows the spectra transferred by PDS and IPCA displayed different extent differences in the whole spectra range, with the largest difference in the 1400–1900 nm region. This suggests that the spectral variations brought by spectrometer differences could be systematic differences that vary from sample to sample [ 15 ]. The difference spectra also provide evidence that the IPCA may be more effective than PDS, as the difference spectra of IPCA were more stable in the whole spectral range, while the difference spectra of PDS varied along the wavelength.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, multi-level simultaneous component analysis (MSCA) [ 13 ] was developed to explore the underlying relationship of the multivariate data involving different factors [ 14 ]. The recently proposed parameter-free framework for calibration enhancement (PFCE) applies correlation constraints on the regression coefficients [ 15 ]. Alternatively, Tikhonov regularization [ 16 , 17 ] and domain-invariant partial least squares regression (di-PLS) [ 18 ] are both based on the original model coefficients.…”

Section: Introductionmentioning

confidence: 99%

Improved Principal Component Analysis (IPCA): A Novel Method for Quantitative Calibration Transfer between Different Near-Infrared Spectrometers

et al. 2023

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Given the labor-consuming nature of model establishment, model transfer has become a considerable topic in the study of near-infrared (NIR) spectroscopy. Recently, many new algorithms have been proposed for the model transfer of spectra collected by the same types of instruments under different situations. However, in a practical scenario, we need to deal with model transfer between different types of instruments. To expand model applicability, we must develop a method that could transfer spectra acquired from different types of NIR spectrometers with different wavenumbers or absorbance. Therefore, in our study, we propose a new methodology based on improved principal component analysis (IPCA) for calibration transfer between different types of spectrometers. We adopted three datasets for method evaluation, including public pharmaceutical tablets (dataset 1), corn data (dataset 2), and the spectra of eight batches of samples acquired from the plasma ethanol precipitation process collected by FT-NIR and MicroNIR spectrometers (dataset 3). In the calibration transfer for public datasets, IPCA displayed comparable results with the classical calibration transfer method using piecewise direct standardization (PDS), indicating its obvious ability to transfer spectra collected from the same types of instruments. However, in the calibration transfer for dataset 3, our proposed IPCA method achieved a successful bi-transfer between the spectra acquired from the benchtop and micro-instruments with/without wavelength region selection. Furthermore, our proposed method enabled improvements in prediction ability rather than the degradation of the models built with original micro spectra. Therefore, our proposed method has no limitations on the spectrum for model transfer between different types of NIR instruments, thus allowing a wide application range, which could provide a supporting technology for the practical application of NIR spectroscopy.

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A parameter-free framework for calibration enhancement of near-infrared spectroscopy based on correlation constraint

Cited by 26 publications

References 22 publications

Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure–Activity Relationship

Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure–Activity Relationship

Feasibility of an NIR spectral calibration transfer algorithm based on optimized feature variables to predict tobacco samples in different states

Improved Principal Component Analysis (IPCA): A Novel Method for Quantitative Calibration Transfer between Different Near-Infrared Spectrometers

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