Rapid discrimination of Curcuma longa and Curcuma xanthorrhiza using Direct Analysis in Real Time Mass Spectrometry and Near Infrared Spectroscopy

Recent application of direct analysis in real time mass spectrometry in plant materials analysis with emphasis on traditional Chinese herbal medicine

Direct analysis in real time (DART) represents a new generation of ionization techniques that are used to rapidly ionize small molecules under ambient environments. The combination of DART with various mass spectrometry (MS) instruments allows analyzing multiple plant materials, including traditional Chinese herbal medicines (TCHMs), under simple or no sample treatment conditions. This review discussed the DART principles, including devices, ionization mechanisms, and operation parameters. Typical spectra detected by DART-MS were exhibited and discussed. Numerous applications of DART-MS in the fields of plant material and TCHM analysis were reviewed, including compound identification, biomarker discovery, fingerprinting analysis, and quantification analysis. Besides, modifications and improvements of DART-MS, such as hyphenated application with other separation methods, laser-based desorption techniques, and online sampling configuration, were summarized as well.

Miniaturized Near-Infrared spectrophotometers in forensic analytical science - a critical review

The advent of miniaturized NIR instruments, also known as compact, portable, or handheld, is revolutionizing how technology can be employed in forensics. In-field analysis becomes feasible and affordable with these new instruments, and a series of methods has been developed to provide the police and official agents with objective, easy-to-use, tailored, and accurate qualitative and quantitative forensic results. This work discusses the main aspects and presents a comprehensive and critical review of compact NIR spectrophotometers associated with analytical protocols to produce information on forensic matters.

Combination of near-infrared spectroscopy with Wasserstein generative adversarial networks for rapidly detecting raw material quality for formula products

Near-infrared spectroscopy (NIRS) has emerged as a key technique for rapid quality detection owing to its fast, non-destructive, and eco-friendly characteristics. However, its practical implementation within the formulation industry is challenging owing to insufficient data, which renders model fitting difficult. The complexity of acquiring spectra and spectral reference values results in limited spectral data, aggravating the problem of low generalization, which diminishes model performance. To address this problem, we introduce what we believe to be a novel approach combining NIRS with Wasserstein generative adversarial networks (WGANs). Specifically, spectral data are collected from representative samples of raw material provided by a formula enterprise. Then, the WGAN augments the database by generating synthetic data resembling the raw spectral data. Finally, we establish various prediction models using the PLSR, SVR, LightGBM, and XGBoost algorithms. Experimental results show the NIRS-WGAN method significantly improves the performance of prediction models, with R2 and RMSE of 0.949 and 1.415 for the chemical components of sugar, respectively, and 0.922 and 0.243 for nicotine. The proposed framework effectively enhances the predictive capabilities of various models, addressing the issue caused by limited training data in NIRS prediction tasks.

Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy

Traditional soil nitrogen detection methods have the characteristics of being time-consuming and having an environmental pollution effect. We urgently need a rapid, easy-to-operate, and non-polluting soil nitrogen detection technology. In order to quickly measure the nitrogen content in soil, a new method for detecting the nitrogen content in soil is presented by using a near-infrared spectrum technique and random forest regression (RF). Firstly, the experiment took the soil by the Xunsi River in the area of Hubei University of Technology as the research object, and a total of 143 soil samples were collected. Secondly, NIR spectral data from 143 soil samples were acquired, and chemical and physical methods were used to determine the content of nitrogen in the soil. Thirdly, the raw spectral data of soil samples were denoised by preprocessing. Finally, a forecast model for the soil nitrogen content was developed by using the measured values of components and modeling algorithms. The model was optimized by adjusting the changes in the model parameters and Gini coefficient (∆Gini), and the model was compared with the back propagation (BP) and support vector machine (SVM) models. The results show that: the RF model modeling set prediction R²_C is 0.921, the RMSEC is 0.115, the test set R²_P is 0.83, and the RMSEP is 0.141; the detection of the soil nitrogen content can be realized by using a near-infrared spectrum technique and random forest algorithm, and its prediction accuracy is better than that of the BP and SVM models; using ∆ Gini to optimize the RF modeling data, the spectral information of the soil nitrogen content can be extracted, and the data redundancy can be reduced effectively.

In silico NIR spectroscopy - A review. Molecular fingerprint, interpretation of calibration models, understanding of matrix effects and instrumental difference

Quantum mechanical calculations are routinely used as a major support in mid-infrared (MIR) and Raman spectroscopy. In contrast, practical limitations for long time formed a barrier to developing a similar synergy between near-infrared (NIR) spectroscopy and computational chemistry. Recent advances in theoretical methods suitable for calculation of NIR spectra opened the pathway to modeling NIR spectra of various molecules. Accurate theoretical reproduction of NIR spectra of molecules reaching the size of long-chain fatty acids was accomplished so far. In silico NIR spectroscopy, where the spectra are calculated ab initio, provides substantial improvement in our understanding of the overtones and combination bands that overlap in staggering numbers and create complex lineshape typical for NIR spectra. This improves the comprehension of the spectral information enabling access to rich and detail molecular footprint, essential for fundamental research and useful in routine analysis by NIR spectroscopy and chemometrics. This review article summarizes the most recent accomplishments in the emerging field with examples of simulated NIR spectra of molecules reaching long-chain fatty acids and polymers. In addition to detailed NIR band assignments and new physical insights, simulated spectra enable innovative support in applications. Understanding of the difference in the performance observed between miniaturized NIR spectrometers and chemical interpretation of the chemometric models are noteworthy here. These new elements integrated into NIR spectroscopy framework enable a knowledge-based design of the analysis with comprehension of the processed chemical information.

Rapid quantification of cannabidiol from oils by direct analysis in real time mass spectrometry

This work is the first to describe the use of Direct Analysis in Real Time Mass Spectrometry (DART-MS) for the rapid quantification of cannabidiol (CBD) in CBD oils. For this study, self-prepared samples spiked with CBD in hemp seed oil as well as commercial CBD oils from the Austrian market with different CBD contents were analyzed. CBD concentrations were between 5 and 30% (m/m) for the spiked samples as well as between 5 and 15% (m/m) for the real samples. The performance of quantification by means of DART-MS was assessed against a validated liquid chromatography-mass spectrometry (LC-MS) method. The correlation of the quantification results of both methods was high with a correlation factor greater than 0.98 and a maximum bias of 9.8%. Furthermore, the relative standard deviation values of the DART-MS measurments were below the tolerable limit of 12%. These results demonstrate that quantification of CBD by DART-MS is reliable and hence suitable as a rapid and cost-effective alternative method for quality control of CBD content in CBD oils.

A Comparative Study of Benchtop and Portable NIR and Raman Spectroscopic Methods for the Quantitative Determination of Curcuminoids in Turmeric Powder

Turmeric consumption is continually increasing worldwide. Curcuminoids are major active constituents in turmeric and are associated with numerous health benefits. A combination of spectroscopic methods and chemometrics shows the suitability of turmeric for food quality control due to advantages such as speed, versatility, portability, and no need for sample preparation. Five calibration models to quantify curcuminoids in turmeric were proposed using benchtop and portable devices. The most remarkable results showed that Raman and NIR calibration models present an excellent performance reporting RMSEP of 0.44% w/w and 0.41% w/w, respectively. In addition, the five proposed methods (FT-IR, Raman, and NIR) were compared in terms of precision and accuracy. The results showed that benchtop and portable methods were in good agreement and that there are no significant differences between them. This study aims to foster the use of portable devices for food quality control in situ by demonstrating their suitability for the purpose.

Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm

Presently, the quality of cherries in the market is uneven, because human senses are used to distinguish cherry quality, which consumes a lot of time and energy and does not achieve good results in terms of accuracy. If the internal quality indices, such as the PH value, sugar–acid ratio, and vitamin C content, of cherries are extracted using chemical methods, the detection speed will decrease. With the development of artificial intelligence (AI), image processing by AI algorithms has attracted broad attention. The YOLOv5 model in the YOLO series has many advantages, such as high detection accuracy, fast speed, small size, and so on, and has been used in face recognition, image recognition and other fields. However, owing to the influence of seasonal weather, the environment and other factors, the dataset used in the training model decreases the accuracy of image recognition. To improve the accuracy, a large amount of data must be used for model training, but this will decrease the model training speed. Because it is impossible to use all data in training, there will inevitably be recognition errors in the detection process. In this study, the cherry images in a dataset were extracted by the flooding filling algorithm. The extracted cherry images were used as a new dataset for training and recognition, and the results were compared to those obtained with non-extracted images. The dataset generated by the flooding filling algorithm was used for model training. After 20 training epochs, the accuracy rate reached 99.6%. Without using the algorithm to extract images, the accuracy rate was only 78.6% after 300 training epochs.

Applications of ambient ionization mass spectrometry in 2021: An annual review

Ambient ionization mass spectrometry (AIMS) has revolutionized the field of analytical chemistry, enabling the rapid, direct analysis of samples in their native state. Since the inception of AIMS almost 20 years ago, the analytical community has driven the further development of this suite of techniques, motivated by the plentiful advantages offered in addition to traditional mass spectrometry. Workflows can be simplified through the elimination of sample preparation, analysis times can be significantly reduced and analysis remote from the traditional laboratory space has become a real possibility. As such, the interest in AIMS has rapidly spread through analytical communities worldwide, and AIMS techniques are increasingly being integrated with standard laboratory operations. This annual review covers applications of AIMS techniques throughout 2021, with a specific focus on AIMS applications in a number of key fields of research including disease diagnostics, forensics and security, food safety testing and environmental sciences. While some new techniques are introduced, the focus in AIMS research is increasingly shifting from the development of novel techniques toward efforts to improve existing AIMS techniques, particularly in terms of reproducibility, quantification and ease-of-use.