Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective

SimpleNMR: An interactive graph network approach to aid constitutional isomer verification using standard 1D and 2D NMR experiments

Despite progress in computer automated solutions, constitutional isomer verification by NMR using one- and two-dimensional data sets is still, in the main, a manual, user-intensive activity that is challenging for a number of reasons. These include the problem of simultaneously keeping track of the information from a number of separate NMR experiments and the difficulty of another researcher subsequently verifying the assignments made without having to independently repeat the whole analysis. This paper describes a graphical interactive approach that overcomes some of these problems. By using concepts used to visualise graph networks, we have been able to represent the NMR data in a manner that highlights directly the link between the different NMR experiments and the molecule of interest. Furthermore, by making the graph networks interactive, a user can easily validate and correct the assignment and understand the decisions made in arriving at the solution. We have developed a usable proof-of-concept computer program, 'simpleNMR', written in Python to illustrate the ideas and approach.

Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy

Discriminant analysis of similar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine learning algorithms has become an extremely attractive approach to develop intelligent discrimination techniques. Feature spectral analysis can help researchers gain a deeper understanding of the data patterns in food quality discrimination. Herein, this work takes the discrimination of three brands of dairy products as an example to investigate the Raman spectral feature based on the support vector machines (SVM), extreme learning machines (ELM) and convolutional neural network (CNN) algorithms. The results show that there are certain differences in the optimal spectral feature interval corresponding to different machine learning algorithms. Selecting the appropriate spectral feature interval can maintain high recognition accuracy and improve the computational efficiency of the algorithm. For example, the SVM algorithm has a recognition accuracy of 100% in the 890-980 cm-1, 1410-1500 cm-1 fusion spectral range, which takes about 200 s. The ELM algorithm also has a recognition accuracy of 100% in the 890-980 cm-1, 1410-1500 cm-1 fusion spectral range, which takes less than 0.3 s. The CNN algorithm has a recognition accuracy of 100% in the 890-980 cm-1, 1050-1180 cm-1, 1410-1500 cm-1 fusion spectral range, which takes about 80 s. In addition, by analyzing the distribution of spectral feature intervals based on Euclidean distance, the distribution of experimental samples based on feature spectra is visually displayed. Through the spectral feature analysis process of similar samples, a set of analysis strategies is provided to deeply reveal the data foundation of classification algorithms, which can provide reference for the analysis of relevant discriminative research patterns.

Cross-Modal Retrieval Between 13C NMR Spectra and Structures Based on Focused Libraries

Library matching by comparing carbon-13 nuclear magnetic resonance (13C NMR) spectra with spectral data in the library is a crucial method for compound identification. In our previous paper, we introduced a deep contrastive learning system called CReSS, which used a library that contained more structures. However, CReSS has two limitations: there were no unknown structures in the library, and a redundant library reduces the structure-elucidation accuracy. Herein, we replaced the oversize traditional libraries with focused libraries containing a small number of molecules. A previously generative model, CMGNet, was used to generate focused libraries for CReSS. The combined model achieved a Top-10 accuracy of 54.03% when tested on 6,471 13C NMR spectra. In comparison, CReSS with a random reference structure library achieved an accuracy of only 9.17%. Furthermore, to expand the advantages of the focused libraries, we proposed SAmpRNN, which is a recurrent neural network (RNN). With the large focused library amplified by SAmpRNN, the structure-identification accuracy of the model increased in 70.0% of the 30 random example cases. In general, cross-modal retrieval between 13C NMR spectra and structures based on focused libraries (CFLS) achieved high accuracy and provided more accurate candidate structures than traditional libraries for compound identification.

Cr-Detector: A simple chemosensing system for onsite Cr (VI) detection in water

Due to the increase in urbanization and industrialization, the load of toxicants in the environment is alarming. The most common toxicants, including heavy metals and metalloids such as hexavalent Chromium, have severe pathophysiological impacts on humans and other aquatic biotas. Therefore, developing a portable rapid detection device for such toxicants in the aquatic environment is necessary. This work portrays the development of a field-portable image analysis device coupled with 3,3',5,5'-tetramethylbenzidine (TMB) as a sensing probe for chromium (VI) detection in the aquatic ecosystem. Sensor parameters, such as reagent concentration, reaction time, etc., were optimized for the sensor development and validation using a commercial UV-Vis spectrophotometer. The chemoreceptor integrated with a uniform illumination imaging system (UIIS) revealed the system's applicability toward Cr(VI) detection. The calibration curve using the R-value of image parameters allows Cr(VI) detection in the linear range of 25 to 600 ppb, which covers the prescribed permissible limit by various regulatory authorities. Furthermore, the adjusted R2 = 0.992 of the linear fit and correlation coefficients of 0.99018 against the spectrophotometric method signifies the suitability of the developed system. This TMB-coupled field-portable sensing system is the first-ever reported image analysis-based technology for detecting a wide range of Cr(VI) in aquatic ecosystems to our knowledge.