Improving discrimination of Raman spectra by optimising preprocessing strategies on the basis of the ability to refine the relationship between variance components

Martyna, Agnieszka; Menżyk, Alicja; Damin, Alessandro; Michalska, Aleksandra; Martra, Gianmario; Alladio, Eugenio; Zadora, Grzegorz

doi:10.1016/j.chemolab.2020.104029

Cited by 29 publications

(17 citation statements)

References 39 publications

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“…Roger et al ( 2020 ) reported the utilization of sequential and orthogonal PLS regression for pre-processing NIR spectral data of wheat grains, tablet and meat samples. Martyna et al ( 2020 ) applied genetic algorithm to Raman spectral data. The deployed genetic algorithm assessed the pre-processing technique by calculating variance ratios and validated it by applying it on forensic Raman spectral data.…”

Section: Ai and Automation In Analytical Chemistrymentioning

confidence: 99%

Navigating with chemometrics and machine learning in chemistry

Joshi¹

2023

Artif Intell Rev

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Chemometrics and machine learning are artificial intelligence-based methods stirring a transformative change in chemistry. Organic synthesis, drug discovery and analytical techniques are incorporating machine learning techniques at an accelerated pace. However, machine-assisted chemistry faces challenges while solving critical problems in chemistry due to complex relationships in data sets. Even with increasing publishing volumes on machine learning, its application in areas of chemistry is not a straightforward endeavour. A particular concern in applying machine learning in chemistry is data availability and reproducibility. The present review article discusses the various chemometric methods, expert systems, and machine learning techniques developed for solving problems of organic synthesis and drug discovery with selected examples. Further, a concise discussion on chemometrics and ML deployed in analytical techniques such as, spectroscopy, microscopy and chromatography are presented. Finally, the review reflects the challenges, opportunities and future perspectives on machine learning and automation in chemistry. The review concludes by pondering on some tough questions on applying machine learning and their possibility of navigation in the different terrains of chemistry.

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Section: Ai and Automation In Analytical Chemistrymentioning

confidence: 99%

Navigating with chemometrics and machine learning in chemistry

Joshi¹

2023

Artif Intell Rev

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“…To reduce background influence and improves classification accuracy, we looked into the methodology for data ranging, smoothing, and baseline removal that are mainly used to preprocess spectra. 5,6 Assuming the applied data as reference (Ref. ), this paper proposes a feature processing methodology.…”

Section: P-59 / Y-w Limmentioning

confidence: 99%

P‐59: In‐Fab Raman Spectroscopy for Defect Analysis of Random Failures

Lim

Kim²,

Jang

et al. 2022

Symp Digest of Tech Papers

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Random defects, which account for more than 90% of electronic product manufacturing defects, are defects caused by foreign substances. Although the detection ability has been improved through various efforts using optical inspection and image features, the ultimate enhancement to improve quality and yield cannot be achieved just by detecting defects. In particular, there was a limit to classifying and specifying the failure source because the ingredients were not known, and it was often impossible to eliminate defects even after spending a lot of time. We developed a world first in‐fab Raman spectroscopy microscopy to be applied to the analysis of foreign substances in the entire area of the 8.5th generation glass substrate and applied it in real time between manufacturing. As a result, it is possible to detect defective sources in real time and to improve quality and yield through process control and ultimate improvement.

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“…However, morphology, size, and spacing of NPs are difficult to control, which affects physical and chemical enhancement for SERS detection (Lin et al., 2009). In SERS spectra acquisition, physical factors, such as instrument temperature fluctuation, excitation laser power variation, and laser spot size, can be incorporated to the spectral information (Martyna et al., 2020). Therefore, a standardized preparation method is key to effectively obtaining the enhanced substrate with good performance (Moshahary & Mishra, 2020; Zheng & He, 2014).…”

Section: Introductionmentioning

confidence: 99%

Detection of nitrofurans residues in honey using surface‐enhanced Raman spectroscopy

Yan

Peng

et al. 2022

Journal of Food Science

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Residues of veterinary antibiotics in honey may be damaging to human health. Surface-enhanced Raman scattering spectroscopy (SERS) is an emerging technology widely applied in food safety. SERS has advantages of enabling fingerprint identification and fast detection, as well as does not require complex pretreatment. Considering the overuse of nitrofurans in honeybee breeding, SERS combined with spectral preprocessing was used to detect nitrofurantoin in honey. By using standardized experimental procedures and improved spectral correction methods, the lowest detection limit of nitrofurantoin was 0.1321 mg/kg. A good linear relationship in the partial least squares regression model was found among spiked samples, which allowed prediction of nitrofurantoin content in honey sample (𝑅 2 𝐶 = 0.9744; 𝑅 2 𝑃 = 0.976; RMSECV = 1.0353 mg/kg; RMSEP = 0.9987 mg/kg). Collectively, these results reliably demonstrated that quantification is more accurate when spectral preprocessing is better controlled. Therefore, this study indicates that SERS could be further implemented in fast and onsite detection of nitrofurantoin in honey for improved food safety.

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Improving discrimination of Raman spectra by optimising preprocessing strategies on the basis of the ability to refine the relationship between variance components

Cited by 29 publications

References 39 publications

Navigating with chemometrics and machine learning in chemistry

Navigating with chemometrics and machine learning in chemistry

P‐59: In‐Fab Raman Spectroscopy for Defect Analysis of Random Failures

Detection of nitrofurans residues in honey using surface‐enhanced Raman spectroscopy

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