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Plasma-activated water (PAW), obtained by exposing liquid to cold atmospheric plasma (CAP) for a period, has gained widespread attention for its potential as anti-bacterial, anti-infective, anti-cancer and other biological agents. It is important to understand the PAW behavior and express it in a ‘visualization’ form. Near-infrared spectroscopy (NIR) and aquaphotomics were introduced in this study to investigate the PAW spectra to visualize the water molecular species and try to analyze the production and changes of the active substances in PAW. Second-order derivative, PCA and PLS were applied to identify specific peaks to construct the aquagram and reference method for the ROS assay used to prove the spectral results. The results showed that a longer treatment time resulted in greater spectral changes which could be visualized with 12 water matrix coordinates (WAMACS) and the change trends were in accordance with the ROS concentration variations. Furthermore, during PAW sample storage, there were fluctuations in spectral changes, with a general trend of increase, and a gradual decrease in ROS concentration due to active substance reactions in PAW. In conclusion, this study presents a new perspective on examining the water behavior of PAW and offers a new method to explore cold plasma biomedical materials.

Variable (wavelength) selection is essential in the multivariate analysis of near-infrared spectra to improve model performance and provide a more straightforward interpretation. This paper proposed a new variable selection method named binning-normalized mutual information (B-NMI) based on information entropy theory. “Data binning” was applied to reduce the effects of minor measurement errors and increase the features of near-infrared spectra. “Normalized mutual information” was employed to calculate the correlation between each wavelength and the reference values. The performance of B-NMI was evaluated by two experimental datasets (ideal ternary solvent mixture dataset, fluidized bed granulation dataset) and two public datasets (gasoline octane dataset, corn protein dataset). Compared with classic methods of backward and interval PLS (BIPLS), variable importance projection (VIP), correlation coefficient (CC), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS), B-NMI not only selected the most featured wavelengths from the spectra of complex real-world samples but also improved the stability and robustness of variable selection results.

Confusing low-molecular-weight hyaluronic acid (LMWHA) from acid degradation and enzymatic hydrolysis (named LMWHA–A and LMWHA–E, respectively) will lead to health hazards and commercial risks. The purpose of this work is to analyze the structural differences between LMWHA–A and LMWHA–E, and then achieve a fast and accurate classification based on near-infrared (NIR) spectroscopy and machine learning. First, we combined nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, two-dimensional correlated NIR spectroscopy (2DCOS), and aquaphotomics to analyze the structural differences between LMWHA–A and LMWHA–E. Second, we compared the dimensionality reduction methods including principal component analysis (PCA), kernel PCA (KPCA), and t-distributed stochastic neighbor embedding (t-SNE). Finally, the differences in classification effect of traditional machine learning methods including partial least squares–discriminant analysis (PLS-DA), support vector classification (SVC), and random forest (RF) as well as deep learning methods including one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were compared. The results showed that genetic algorithm (GA)–SVC and RF were the best performers in traditional machine learning, but their highest accuracy in the test dataset was 90%, while the accuracy of 1D-CNN and LSTM models in the training dataset and test dataset classification was 100%. The results of this study show that compared with traditional machine learning, the deep learning models were better for the classification of LMWHA–A and LMWHA–E. Our research provides a new methodological reference for the rapid and accurate classification of biological macromolecules.

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.

Osteoarthritis (OA) is a degenerative disease, and there is currently no effective medicine to cure it. Early prevention and treatment can effectively reduce the pain of OA patients and save costs. Therefore, it is necessary to diagnose OA at an early stage. There are various diagnostic methods for OA, but the methods applied to early diagnosis are limited. Ordinary optical diagnosis is confined to the surface, while laboratory tests, such as rheumatoid factor inspection and physical arthritis checks, are too trivial or time-consuming. Evidently, there is an urgent need to develop a rapid nondestructive detection method for the early diagnosis of OA. Vibrational spectroscopy is a rapid and nondestructive technique that has attracted much attention. In this review, near-infrared (NIR), infrared, (IR) and Raman spectroscopy were introduced to show their potential in early OA diagnosis. The basic principles were discussed first, and then the research progress to date was discussed, as well as its limitations and the direction of development. Finally, all methods were compared, and vibrational spectroscopy was demonstrated that it could be used as a promising tool for early OA diagnosis. This review provides theoretical support for the application and development of vibrational spectroscopy technology in OA diagnosis, providing a new strategy for the nondestructive and rapid diagnosis of arthritis and promoting the development and clinical application of a component-based molecular spectrum detection technology.

Chondroitin sulfate (CS) plays important roles in the complement system. However, the CS structure is complicated due to different sources and the number and positions of sulfate groups. The objective of this study was to prepare different low molecular weight chondroitin sulfates (LMWCSs) and to investigate the biological activity in anti-complement capacity. A series of LMWCSs was prepared from different sources and characterized by ultraviolet-visible (UV) spectroscopy, high-performance liquid chromatography (HPLC), size exclusion chromatography-multiangle laser light scattering (SEC-MALLS) and nuclear magnetic resonance (NMR) spectroscopy. Hemolytic, anti-complement 3 deposition capacity and cell viability assays were carried out to investigate the biological activities in vitro. The results showed that LMWCS prepared from shark cartilage with the oxidative degradation method (LMWCS-S-O) had the best anti-complement capacity. LMWCS-S-O could inhibit the alternative pathway of the complement system and protect chondrocytes from cell death. The attenuating effect of LMWCS-S-O on Osteoarthritis (OA) was investigated by destabilization of the medial meniscus (DMM) model in vivo. Functional wind-up, histological and C5b-9 analyses were used to evaluate the treatment effect on the OA model. In vivo results showed that LMWCS-S-O could attenuate OA. LMWCS-S-O with a high content of ΔDi-2,6diS and ΔDi-6S could be used for attenuating OA through regulating the complement system.