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

ATR-FTIR spectroscopy combined with metabolomics to analyze the taste components of boletus bainiugan at different drying temperatures

Boletus bainiugan has a unique flavor profile, its quality is correlated with metabolites. Herein, ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) is utilized to characterize the free amino acid and organic acid of Boletus bainiugan at different drying temperatures. Attenuated total internal reflectance Fourier transform infrared (ATR-FTIR) spectroscopy is employed to identify Boletus bainiugan with various treatment and to predicted compounds. The metabolome includes 72 amino acids and 64 organic acids, wherein, 11 important taste components are analyzed the changes with drying temperatures. The residual convolutional neural network (ResNet) model achieves 100 % accuracy for Boletus bainiugan with distinct treatment. The partial least squares regression (PLSR) model accurately predicted the contents of 11 compounds with an optimal R2 P of 0.975 and a best residual predictive deviation (RPD) of 4.404. The ATR-FTIR spectroscopy coupled with metabolomics can be used as a good tool to estimate the taste enhancers of Boletus bainiugan.

Rapid and accurate identification of Gastrodia elata Blume species based on FTIR and NIR spectroscopy combined with chemometric methods

Different varieties of Gastrodia elata Blume (G. elata Bl.) have different qualities and different contents of active ingredients, such as polysaccharide and gastrodin, and it is generally believed that the higher the active ingredients, the better the quality of G. elata Bl. and the stronger the medicinal effects. Therefore, effective identification of G. elata Bl. species is crucial and has important theoretical and practical significance. In this study, first unsupervised PCA and t-SNE are established for data visualisation, follow by traditional machine learning (PLS-DA, OPLS-DA and SVM) models and deep learning (ResNet) models were established based on the fourier transform infrared (FTIR) and near infrared (NIR) spectra data of three G. elata Bl. species. The results show that PLS-DA, OPLS-DA and SVM models require complex preprocessing of spectral data to build stable and reliable models. Compared with traditional machine learning models, ResNet models do not require complex spectral preprocessing, and the training and test sets of ResNet models built based on raw NIR and low-level data fusion (FTIR + NIR) spectra reach 100 % accuracy, the external validation set based on low-level data fusion reaches 100 % accuracy, and the external validation set based on NIR has only one sample classification error and no overfitting.

Feasibility study on discrimination of Polygonatum kingianum origins by NIR and MIR spectra data

Most existing studies have focused on identifying the origin of species with protected geographical indications while neglecting to determine the proximate geographical origin of different species. In this study, we investigated the feasibility of using near- and mid-infrared spectroscopy to identify the origin of 156 Polygonatum kingianum samples from six regions in Yunnan, China. In this work, spectral images of different modes reveal more information about the P. kingianum. Comparing the performance of traditional machine learning models according to single spectrum and data fusion, the middle-level data fusion-principal component model has the best performance, and its sensitivity, specificity, and accuracy are all 1, and the model has the least number of variables. The residual convolutional neural network (ResNet) model constructed in the 1050-850 cm-1 band confirms that fewer variables are beneficial in improving the accuracy of the model. In conclusion, this study verifies the feasibility of the proposed strategy and establishes a practical model to determine the source of P. kingianum.

Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes

The taste and aroma of edible mushrooms, which is a criterion of judgment for consumer purchases, are influenced by amino acids and their metabolites. Sixty-eight amino acids and their metabolites were identified using liquid chromatography mass spectrometry (LC-MS), and 16 critical marker components were screened. The chemical composition of different species of boletes was characterized by two-dimensional correlation spectroscopy (2DCOS) to determine the sequence of molecular vibrations or group changes. Identification of boletes species based on partial least squares discrimination (PLS-DA) combined with Fourier transform near-infrared spectroscopy (FT-NIR) and Fourier transform infrared spectroscopy (ATR-FTIR), residual convolutional neural network (ResNet) combined with three-dimensional correlation spectroscopy (3DCOS) was performed with 100% accuracy. Partial least squares regression (PLSR) analysis showed that FT-NIR and ATR-FTIR spectra were highly correlated with the amino acids and their metabolites detected by LC-MS. All models had achieved an R2p of 0.911 and an RPD >3.0. The results show that FT-NIR and ATR-FTIR spectroscopy in combination with chemometrics methods can be used for rapid species identification and estimation of amino acids and their metabolites content in boletes. This study provides new techniques and ideas for the authenticity of species information and the quality assessment of boletes.

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.

Optimization of the selection of suitable harvesting periods for medicinal plants: taking Dendrobium officinale as an example

BACKGROUND

Dendrobium officinale is a medicinal plant with high commercial value. The Dendrobium officinale market in Yunnan is affected by the standardization of medicinal material quality control and the increase in market demand, mainly due to the inappropriate harvest time, which puts it under increasing resource pressure. In this study, considering the high polysaccharide content of Dendrobium leaves and its contribution to today's medical industry, (Fourier Transform Infrared Spectrometer) FTIR combined with chemometrics was used to combine the yields of both stem and leaf parts of Dendrobium officinale to identify the different harvesting periods and to predict the dry matter content for the selection of the optimal harvesting period.

RESULTS

The Three-dimensional correlation spectroscopy (3DCOS) images of Dendrobium stems to build a (Split-Attention Networks) ResNet model can identify different harvesting periods 100%, which is 90% faster than (Support Vector Machine) SVM, and provides a scientific basis for modeling a large number of samples. The (Partial Least Squares Regression) PLSR model based on MSC preprocessing can predict the dry matter content of Dendrobium stems with Factor = 7, RMSE = 0.47, R2 = 0.99, RPD = 8.79; the PLSR model based on SG preprocessing can predict the dry matter content of Dendrobium leaves with Factor = 9, RMSE = 0.2, R2 = 0.99, RPD = 9.55.

CONCLUSIONS

These results show that the ResNet model possesses a fast and accurate recognition ability, and at the same time can provide a scientific basis for the processing of a large number of sample data; the PLSR model with MSC and SG preprocessing can predict the dry matter content of Dendrobium stems and leaves, respectively; The suitable harvesting period for D. officinale is from November to April of the following year, with the best harvesting period being December. During this period, it is necessary to ensure sufficient water supply between 7:00 and 10:00 every day and to provide a certain degree of light blocking between 14:00 and 17:00.

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.