2DCOS combined with CNN and blockchain to trace the species of boletes

Frontiers of machine learning in smart food safety

Integration of machine learning (ML) technologies into the realm of smart food safety represents a rapidly evolving field with significant potential to transform the management and assurance of food quality and safety. This chapter will discuss the capabilities of ML across different segments of the food supply chain, encompassing pre-harvest agricultural activities to post-harvest processes and delivery to the consumers. Three specific examples of applying cutting-edge ML to advance food science are detailed in this chapter, including its use to improve beer flavor, using natural language processing to predict food safety incidents, and leveraging social media to detect foodborne disease outbreaks. Despite advances in both theory and practice, application of ML to smart food safety still suffers from issues such as data availability, model reliability, and transparency. Solving these problems can help realize the full potential of ML in food safety. Development of ML in smart food safety is also driven by social and industry impacts. The improvement and implementation of legal policies brings both opportunities and challenges. The future of smart food safety lies in the strategic implementation of ML technologies, navigating social and industry impacts, and adapting to regulatory changes in the AI era.

Novel Developments and Progress in Two-Dimensional Correlation Spectroscopy (2D-COS)

This first of the two-part series of the comprehensive survey review on the progress of the two-dimensional correlation spectroscopy (2D-COS) field during the period 2021-2022, covers books, reviews, tutorials, novel concepts and theories, and patent applications that appeared in the last two years, as well as some inappropriate use or citations of 2D-COS. The overall trend clearly shows that 2D-COS is continually growing and evolving with notable new developments. The technique is well recognized as a powerful analytical tool that provides deep insights into systems in many science fields.

Diverse Applications of Two-Dimensional Correlation Spectroscopy (2D-COS)

This second of the two-part series of a comprehensive survey review provides the diverse applications of two-dimensional correlation spectroscopy (2D-COS) covering different probes, perturbations, and systems in the last two years. Infrared spectroscopy has maintained its top popularity in 2D-COS over the past two years. Fluorescence spectroscopy is the second most frequently used analytical method, which has been heavily applied to the analysis of heavy metal binding, environmental, and solution systems. Various other analytical methods including laser-induced breakdown spectroscopy, dynamic mechanical analysis, differential scanning calorimetry, capillary electrophoresis, seismologic, and so on, have also been reported. In the last two years, concentration, composition, and pH are the main effects of perturbation used in the 2D-COS fields, as well as temperature. Environmental science is especially heavily studied using 2D-COS. This comprehensive survey review shows that 2D-COS undergoes continuous evolution and growth, marked by novel developments and successful applications across diverse scientific fields.

A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition

Lanxangia tsaoko's accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensional and three-dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT-NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS-DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.

Composition and structure analysis of different depths in the stratum corneum using confocal Raman microscopy combined with two-dimensional correlation spectroscopy

The chemical composition and structure of the stratum corneum (SC) play a crucial role in the skin barrier function. Therefore, accurately determining the SC thickness and studying the changes in lipid and keratin structure and distribution within it are key aspects of skin barrier research. Currently, there are limited analytical tools and data analysis methods available for real-time and online studies of SC composition and structural changes. In this study, we focus on depth as a perturbation and employ confocal Raman microscopy combined with moving-window two-dimensional correlation spectroscopy (MW2D) technique to investigate the SC thickness. Additionally, we employ confocal Raman microscopy combined with perturbation-correlation moving-window two-dimensional correlation spectroscopy (PCMW2D) to precisely characterize the stratification of the SC. Furthermore, the two-dimensional correlation spectroscopy (2DCOS) method is utilized to examine the content of various conformations in the keratin secondary structure within the SC, as well as the subtle interrelationships between lipid and keratin structures.

An integrated chemical characterization based on FT-NIR, and GC-MS for the comparative metabolite profiling of 3 species of the genus Amomum

BACKGROUND

The fruits and seeds of genus Amomum are well-known as medicinal plants and edible spices, and are used in countries such as China, India and Vietnam to treat malaria, gastrointestinal disorders and indigestion. The morphological differences between different species are relatively small, and technical characterization and identification techniques are needed.

RESULTS

Fourier transform near infrared spectroscopy (FT-NIR) and gas chromatography-mass spectrometry (GC-MS), combined with principal component analysis and two-dimensional correlation analysis were used to characterize the chemical differences of Amomum tsao-ko, Amomum koenigii, and Amomum paratsaoko. The targets and pathways for the treatment of diabetes mellitus in three species were predicted using network pharmacology and screened for the corresponding pharmacodynamic components as potential quality markers. The results of "component-target-pathway" network showed that (+)-Nerolidol, 2-Nonanol, α-Terpineol, α-Pinene, 2-Nonanone had high degree values and may be the main active components. Partial least squares-discriminant analysis (PLS-DA) was further used to select for differential metabolites and was identified as a potential quality marker, 11 in total. PLS-DA and residual network (ResNet) classification models were developed for the identification of 3 species of the genus Amomum, ResNet model is more suitable for the identification study of large volume samples.

SIGNIFICANCE

This study characterizes the differences between the three species in a visual way and also provides a reliable technique for their identification, while demonstrating the ability of FT-NIR spectroscopy for fast, easy and accurate species identification. The results of this study lay the foundation for quality evaluation studies of genus Amomum and provide new ideas for the development of new drugs for the treatment of diabetes mellitus.

A new effective method for identifying boletes species based on FT-MIR and three dimensional correlation spectroscopy projected image processing

This study proposed the necessity of identifying the species for boletes in combination with the medicinal value, nutritional value and the problems existing in the industrial development of boletes. Based on the preprocessing of Fourier transform mid-infrared spectroscopy (FT-MIR) by 1st, 2nd, SNV, 2nd + MSC and 2nd + SG, Multilayer Perceptron (MLP) and CatBoost models were established. To avoid complex preprocessing and feature extraction, we try deep learning modeling methods based on image processing. In this paper, the concept of three-dimensional correlation spectroscopy (3DCOS) projection image was proposed, and 9 datasets of synchronous, asynchronous and integrative images are generated by computer method. In addition, 18 deep learning models were established for 9 image datasets with different sizes. The results showed that the accuracy of the three types of synchronous spectral models reached 100%, while the accuracy of the asynchronous spectral and integrative spectral models of 3DCOS projection images were 96.97% and 97.98% in the case of big datasets, which overcame the defects of poor modeling effect of asynchronous spectral and integrative spectral in previous two-dimensional correlation spectroscopy (2DCOS) studies. In conclusion, the modeling results of 3DCOS projection images are perfect, and we can apply this method to other identification fields in the future.

Quantitative analysis method of Panax notoginseng based on thermal perturbation terahertz two-dimensional correlation spectroscopy

This paper proposes a Panax notoginseng (P. notoginseng) quantitative analysis based on terahertz time-domain spectroscopy and two-dimensional correlation spectroscopy (2DCOS). By imposing temperature perturbation combined with 2DCOS, the one-dimensional absorbance spectra were transformed into 2DCOS synchronous spectra, which reflected the differences in characteristic information between different P. notoginseng contents more clearly. Then, the feature information of P. notoginseng contents was extracted from the 2DCOS synchronous spectra by a competitive adaptive reweighted sampling (CARS) method and was used to build a quantitative model combined with a support vector regression machine (SVR), called 2DCOS-CARS-SVR. We obtained a more accurate analysis result than the commonly used principal component analysis (PCA)-partial least squares regression (PLSR) and PCA-SVR. The prediction set correlation coefficient and root mean square error reached 0.9915% and 0.8160%, respectively.

Application of spectral image processing with different dimensions combined with large-screen visualization in the identification of boletes species

Boletes are favored by consumers because of their unique flavor, rich nutrition and delicious taste. However, the different nutritional values of each species lead to obvious price differences, so shoddy products appear on the market, which affects food safety. The aim of this study was to find a rapid and effective method for boletes species identification. In this paper, 1,707 samples of eight boletes species were selected as the research objects. The original Mid-Infrared (MIR) spectroscopy data were adopted for support vector machine (SVM) modeling. The 11,949 spectral images belong to seven data sets such as two-dimensional correlation spectroscopy (2DCOS) and three-dimensional correlation spectroscopy (3DCOS) were used to carry out Alexnet and Residual network (Resnet) modeling, thus we established 15 models for the identification of boletes species. The results show that the SVM method needs to process complex feature data, the time cost is more than 11 times of other models, and the accuracy is not high enough, so it is not recommended to be used in data processing with large sample size. From the perspective of datasets, synchronous 2DCOS and synchronous 3DCOS have the best modeling results, while one-dimensional (1D) MIR Spectrum dataset has the worst modeling results. After comprehensive analysis, the modeling effect of Resnet on the synchronous 2DCOS dataset is the best. Moreover, we use large-screen visualization technology to visually display the sample information of this research and obtain their distribution rules in terms of species and geographical location. This research shows that deep learning combined with 2DCOS and 3DCOS spectral images can effectively and accurately identify boletes species, which provides a reference for the identification of other fields, such as food and Chinese herbal medicine.

Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives

Herbal medicines (HMs) have been utilized to prevent and treat human ailments for thousands of years. Especially, HMs have recently played a crucial role in the treatment of COVID-19 in China. However, HMs are susceptible to various factors during harvesting, processing, and marketing, affecting their clinical efficacy. Therefore, it is necessary to conclude a rapid and effective method to study HMs so that they can be used in the clinical setting with maximum medicinal value. Non-targeted analytical technology is a reliable analytical method for studying HMs because of its unique advantages in analyzing unknown components. Based on the extensive literature, the paper summarizes the benefits, limitations, and applicability of non-targeted analytical technology. Moreover, the article describes the application of non-targeted analytical technology in HMs from four aspects: structure analysis, authentication, real-time monitoring, and quality assessment. Finally, the review has prospected the development trend and challenges of non-targeted analytical technology. It can assist HMs industry researchers and engineers select non-targeted analytical technology to analyze HMs' quality and authenticity.