Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning

Efficient conversion of tea residue nutrients: Screening and proliferation of edible fungi

Despite lignocellulose hindering the extraction of intracellular components, tea residue can serve as an excellent substrate for fungal fermentation owing to their lignocellulose-degrading abilities. Thus, the fermentation efficiencies of Lentinus edodes, Lentinus sajor-caju (Fr.), Flammulina filiformis, Hericium erinaceus, Pleurotus pulmonarius, and Monascus kaoliang B6 were evaluated using tea residue as a medium. P. pulmonarius and L. sajor-caju (Fr.) exhibited the fastest growth rates, with colony radii of 33.1 and 28.5 mm, respectively. M. kaoliang B6 demonstrated substantial degradation abilities for cellulose, hemicellulose, and lignin, with decolorization radii of 12.2, 0.9, and 8.5 mm, respectively. After a 9-days liquid fermentation, M. kaoliang B6 achieved the highest conversion efficiency at 27.8%, attributed to its high cellulase (191 U∙mL-1) and lignin peroxidase (36.9 U∙L-1) activities. P. pulmonarius and L. sajor-caju (Fr.) showed lower conversion rates of 8.6% and 3.8%, despite having high hemicellulase activities (67.1 and 70.9 U∙mL-1). Fermentation by M. kaoliang B6 resulted in a reduction of protein and total sugar content in the tea residue by 174 and 192 mg g-1, by which the mycelium's protein and total sugar content increased by 73 and 188 mg g-1. Co-fermentation of these three strains had little effect on the improvement of conversion efficiency, which might owe to the antagonistic interactions among the strains. Generally, utilizing tea residue for edible fungi fermentation is a sustainable process for bio-waste treatment, enabling efficient nutrient conversion under mild conditions without adding chemicals.

Machine learning-assisted structure annotation of natural products based on MS and NMR data

Covering: up to March 2023Machine learning (ML) has emerged as a popular tool for analyzing the structures of natural products (NPs). This review presents a summary of the recent advancements in ML-assisted mass spectrometry (MS) and nuclear magnetic resonance (NMR) data analysis to establish the chemical structures of NPs. First, ML-based MS/MS analyses that rely on library matching are discussed, which involves the utilization of ML algorithms to calculate similarity, predict the MS/MS fragments, and form molecular fingerprint. Then, ML assisted MS/MS structural annotation without library matching is reviewed. Furthermore, the cases of ML algorithms in assisting structural studies of NPs based on NMR are discussed from four perspectives: NMR prediction, functional group identification, structural categorization and quantum chemical calculation. Finally, the review concludes with a discussion of the challenges and the trends associated with the structural establishment of NPs based on ML algorithms.

Enhancing the classification metrics of spectroscopy spectrums using neural network based low dimensional space

Spectroscopy is a methodology for gaining knowledge of particles, especially biomolecules, by quantifying the interactions between matter and light. By examining the level of light absorbed, reflected or released by a specimen, its constituents, properties, and volume can be determined. Spectra obtained through spectroscopy procedures are quick, harmless and contactless; hence nowadays preferred in chemometrics. Due to the high dimensional nature of the spectra, it is challenging to build a robust classifier with good performance metrics. Many linear and nonlinear dimensionality reduction-based classification models have been previously implemented to overcome this issue. However, they lack in capturing the subtle details of the spectra into the low dimension space or cannot efficiently handle the nonlinearity present in the spectral data. We propose a graph-based neural network embedding approach to extract appropriate features into latent space and circumvent the spectrums' nonlinearity problem. Our approach performs dimensionality reduction into two phases: constructing a nearest neighbor graph and producing almost linear embedding using a fully connected neural network. Further, the low dimensional embedding is subjected to classification using the Random Forest algorithm. In this paper, we have implemented and compared our technique with four nonlinear dimensionality techniques widely used for spectral data analysis. In this study, we have considered five different spectral datasets belonging to specific applications. The various classification performance metrics of all the techniques are evaluated. The proposed approach is able to perform competitively well on six different low-dimensional spaces for each dataset with an accuracy score above 95% and Matthew's correlation coefficient value close to 1. The trustworthiness score of almost 1 show that the presented dimensionality reduction approach preserves the closest neighbor structure of high dimensional spectral inputs into latent space.

Data fusion of middle-resolution NMR spectroscopy and low-field relaxometry using the Common Dimensions Analysis (ComDim) to monitor diesel fuel adulteration

Medium-resolution (MR-NMR) and time-domain NMR relaxometry (TD-NMR) using benchtop and low-field NMR instruments are powerful tools to tackle fuel adulteration issues. In this work, for the first time, we investigate the possibility of enhancing the low-field NMR capability on fuel analysis using data fusion of MR and TD-NMR. We used the ComDim (Common Dimensions Analysis) multi-block analysis to join the data, which allowed exploration, classification, and quantification of common adulterations of diesel fuel by vegetable oils, biodiesel, and diesel of different sources as well as the sulfur content. After data exploration using ComDim, classification (applying linear discriminant analysis, LDA), and regression (applying multiple linear regression, MLR), models were built using ComDim scores as input variables on the LDA and MLR analyses. This approach enabled 100% of accuracy in classifying diesel fuel source (refinery), sulfur content (S10 or S500), vegetable oil, and biodiesel source. Moreover, in the quantification step, all MLR models showed a root mean square error of prediction (RMSEP) and the residual prediction deviation (RPD) values comparable to the literature for determining diesel, vegetable oil, and biodiesel contents.

Effect of ultrasonic pretreatment monitored by real-time online technologies on dried preparation time and yield during extraction process of okra pectin

BACKGROUND

Ultrasonic pretreatment is a novel physical method that can be used in the extraction process of okra pectin. Real-time online monitoring technologies were introduced in time and frequency domains when okra was pretreated. Preparation time of dried okra and yield of okra pectin were studied; and physicochemical properties of okra pectin were analyzed at the optimum ultrasonic parameter.

RESULTS

Results showed that ultrasonic intensity of sweeping-frequency ultrasonic (SFU) pretreatment was stronger than that of fixed-frequency ultrasonic pretreatment (FFU). SFU pretreatment (60 ± 1 kHz) at 30 min had a strong ultrasonic voltage peak of 0.05387 V and signal power peak of -6.62 dBm. The preparation time of dried okra was 160 ± 14.14 min in the pretreated group, 44.83% lower than control without SFU pretreatment. The intercellular space was 56.03% higher than control. Water diffusion coefficient increased from 1.41 × 10^-9 to 2.14 × 10^-9  m²  s^-1 . Monobasic quadratic equations were developed for the monitored ultrasonic intensity and pectin yield. Compared to control, extraction yield (16.70%), pectin content (0.564 mg mg^-1 ), solubility (0.8187 g g^-1 ) and gel strength (30.91 g) were improved in the pretreated group. Viscosity decreased, and values of G' and G″ crossing at 63 rad s^-1 revealed the viscoelastic behavior and the beginning of viscous behavior with a sol state.

CONCLUSION

Decrement of dried preparation time and increment of yield were achieved by ultrasonic pretreatment during the extraction process of okra pectin, and the relationship of ultrasonic intensity monitored by real-time online technologies and yield was given. © 2021 Society of Chemical Industry.

Fast-forward approach of time-domain NMR relaxometry for solid-state chemistry of chitosan

Chitosans with different average degrees of acetylation and weight molecular weight were analyzed by time-domain NMR relaxometry using the recently proposed pulse sequence named Rhim and Kessemeier - Radiofrequency Optimized Solid-Echo (RK-ROSE) to acquire ¹H NMR signal of solid-state materials. The NMR signal decay was composed of faster (tenths of μs) and longer components, where the mobile-part fraction exhibited an effective relaxation transverse time assigned to methyl hydrogens from N-acetyl-d-glucosamine (GlcNAc) units. The higher intrinsic mobility of methyl groups was confirmed via DIPSHIFT experiments by probing the ¹H-¹³C dipolar interaction. RK-ROSE data were modeled by using Partial Least Square (PLS) multivariate regression, which showed a high coefficient of determination (R² > 0.93) between RK-ROSE signal profile and average degrees of acetylation and crystallinity index, thus indicating that time-domain NMR consists in a promising tool for structural and morphological characterization of chitosan.