Rapid discrimination of cultivated Codonopsis lanceolata in different ages by FT-IR and 2DCOS-IR

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.

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.

Machine learning and deep learning based on the small FT-MIR dataset for fine-grained sampling site recognition of Boletus tomentipes

This study proposed the necessity of identifying the sampling sites for Boletus tomentipes (B.tomentipes) in combination with cadmium content and environmental factors. Based on fourier transform mid-infrared spectroscopy (FT-MIR) preprocessing by 1st, 2nd, MSC, SNV and SG, five machine learning (ML) algorithms (NB, DT, KNN, RF, SVM) and three Gradient Boosting Machine (GBM) algorithms (XGBoost, LightGBM, CatBoost) were built. To avoid complex preprocessing, we construct BoletusResnet model, propose the concepts of 3DCOS, 3DCOS projected images, index images in addition to 2DCOS, and combine them with deep learning (DL) for classification for the first time. It shows that GBM has higher accuracy than ML and DL has better accuracy than GBM. The four DL models presented in this paper achieve fine-grained sampling sites recognition based on small samples with 100 % accuracy, and a computer application system was developed on them. Therefore, spectral image processing combined with DL is a rapid and efficient classification method which can be widely used in food identification.

A nondestructive solution to quantify monosaccharides by ATR-FTIR and multivariate regressions: A case study of Atractylodes polysaccharides

The quality evaluation of nature polysaccharides is a tough nut to crack because of its high M_w distributions and larger polarity property. It is well-known that infrared spectroscopy and multiple regression modeling have been used for quantitative examinations in multiple fields, but it has not been applied to the compositional analysis of polysaccharides. In this study, attenuated total reflectance-fourier transform infrared spectroscopy is used to simultaneously quantify aldoses, ketose and uronic acids in Atractylodes polysaccharides by a combination of multivariate regressions. After experience of different data processing pretreatments, the resulting spectrum contains maximum amount of information of monosaccharide contents in Atractylodes polysaccharides. In this case, different smoothing points, derivatives, SNV and MSC are used in the pre-modeling spectrum processing and VIP screening is used to reduce the number of variables to simplify the calculation of the model. All the most optimal prediction models have both good prediction ability (R² ≥ 0.9 and RPD > 3) and no over fitting (RMSEP/RMSEC < 3). This strategy has opened a new possibility for the nondestructive determination of complex monosaccharide compositions of natural polysaccharides in a short detection time, low equipment requirement and high experimental safety.

A Comparison of the Flavonoid Biosynthesis Mechanisms of Dendrobium Species by Analyzing the Transcriptome and Metabolome

Dendrobium huoshanense, Dendrobium officinale, and Dendrobium moniliforme, as precious Chinese medicinal materials, have a variety of medicinal properties. Flavonoids are important medicinal components of Dendrobium, but their accumulation rules and biosynthesis mechanisms remain unclear. To explore the similarities and differences of flavonoid accumulation and biosynthesis in these three Dendrobium species, we performed flavonoid content determination, widely-targeted metabolomics and transcriptome sequencing on 1-4 years old Dendrobium species. The results showed that in different growth years, D. huoshanense stems had the highest flavonoid content in the second year of growth, while D. officinale and D. moniliforme stems had the highest flavonoid content in the third year of growth. A total of 644 differentially accumulated metabolites (DAMs) and 10,426 differentially expressed genes (DEGs) were identified by metabolomic and transcriptomic analysis. It was found that DAMs and DEGs were not only enriched in the general pathway of "flavonoid biosynthesis", but also in multiple sub-pathways such as "Flavone biosynthesis", and "Flavonol biosynthesis" and "Isoflavonoid biosynthesis". According to a combined transcriptome and metabolome analysis, the expression levels of the F3'H gene (LOC110096779) and two F3'5'H genes (LOC110101765 and LOC110103762) may be the main genes responsible for the differences in flavonoid accumulation. As a result of this study, we have not only determined the optimal harvesting period for three Dendrobium plants, but also identified the key genes involved in flavonoid biosynthesis and provided a basis for further study of the molecular mechanism of flavonoid synthesis.

Integration of Transcriptome and Metabolome Provides New Insights to Flavonoids Biosynthesis in Dendrobium huoshanense

Dendrobium huoshanense is both a traditional herbal medicine and a plant of high ornamental and medicinal value. We used transcriptomics and metabolomics to investigate the effects of growth year on the secondary metabolites of D. huoshanense stems obtained from four different years of cultivation. In this study, a total of 428 differentially accumulated metabolites (DAMs) and 1802 differentially expressed genes (DEGs) were identified. The KEGG enrichment analysis of DEGs and DAMs revealed significant differences in "Flavonoid biosynthesis", "Phenylpropanoid biosynthesis" and "Flavone and flavonol biosynthesis". We summarize the biosynthesis pathway of flavonoids in D. huoshanense, providing new insights into the biosynthesis and regulation mechanisms of flavonoids in D. huoshanense. Additionally, we identified two candidate genes, FLS (LOC110107557) and F3'H (LOC110095936), which are highly involved in flavonoid biosynthesis pathway, by WGCNA analysis. The aim of this study is to investigate the effects of growth year on secondarily metabolites in the plant and provide a theoretical basis for determining a reasonable harvesting period for D. huoshanense.

The effect of developmental and environmental factors on secondary metabolites in medicinal plants

Secondary metabolites (SMs) of medicinal plants are the material basis of their clinically curative effects. They are also important indicators for evaluating the quality of medicinal materials. However, the synthesis and accumulation of SMs are very complex, which are affected by many factors including internal developmental genetic circuits (regulated gene, enzyme) and by external environment factors (light, temperature, water, salinity, etc.). Currently, lots of literatures focused on the effect of environmental factors on the synthesis and accumulation of SMs of medicinal plants, the effect of the developmental growth and genetic factors on the synthesis and accumulation of SMs still lack systematic classification and summary. Here, we have given the review base on our previous works on the morphological development of medicinal plants and their secondary metabolites, and systematically outlined the literature reports how different environmental factors affected the synthesis and accumulation of SMs. The results of our reviews can know how developmental and environmental factors qualitatively and quantitatively influence SMs of medicinal plants and how these can be integrated as tools to quality control, as well as on the improvement of clinical curative effects by altering their genomes, and/or growth conditions.

Traceability the provenience of cultivated Paris polyphylla Smith var. yunnanensis using ATR-FTIR spectroscopy combined with chemometrics

The conventional procedures, based on attenuated total reflectance-Fourier transform infrared spectrometry (ATR-FTIR), have been developed for the origins traceability of cultivated Paris polyphylla Smith var. yunnanensis (PPY) samples with the help of partial least square discriminant analysis (PLS-DA) and random forest. In this study, a set of 219 batch cultivated PPY samples, containing the cultivation years of 5, 6 and 7, and covering the municipal districts of Chuxiong, Dali, Honghe, Lijiang and Yuxi in Yunnan Province, China, were used to build the discrimination models. Firstly, a visualized analysis was carried out by t-distributed stochastic neighbor embedding (t-SNE) to reduce each data point in a two-dimensional map and make a knowledge of the sample distribution tendency. Secondly, the single spectra data sets of Paridis rhizome and leaf tissues, and the combination of these two data sets with variable selection (mid-level data fusion strategy), were used to establish PLS-DA and random forest models, and parallelly compared the model performance. Results demonstrated that the discrimination ability of PLS-DA preceded the random forest model, and the classification performance was remarkably improved after mid-level data fusion. These results verified each other by 5-, 6- and 7-year old Paridis samples and indicated that the model performance established in the present study was reliable. Besides, five agronomic characters, including the plant height, dry weight of rhizome and leaf tissues, and the allocation of rhizome and leaf were determined and analyzed, results of which indicated that the dry weight and their allocation was significantly different among various origins and fluctuated with the cultivation years. This study was using a comprehensive and green analytical method to discriminate the cultivated Paridis according to their provenances, which was simultaneously benefited for the appropriate cultivation areas selection based on the dry weight of rhizome tissues.

Rapid and simple determination of polyphyllin I, II, VI, and VII in different harvest times of cultivated Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz by UPLC-MS/MS and FT-IR

Paris Polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz ("Dian Chonglou" in Chinese) is a famous herbal medicine in China, which is usually well known for activities of anti-cancer, hemolysis, and cytotoxicity. In this study, Fourier transform infrared (FT-IR) spectroscopy coupled with principal component analysis (PCA) and partial least-squares regression (PLSR) was applied to discriminate samples of P. polyphylla var. yunnanensis harvested in different years and determine the content of polyphyllin I, II, VI, and VII in P. polyphylla var. yunnanensis. Meanwhile, ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to study the dynamic changes of P. polyphylla var. yunnanensis harvested in different years (4, 5, 7, 8, 9, 12, and 13 years old). According to the UPLC-MS/MS result, the optimum harvest time of P. polyphylla var. yunnanensis is 8 years, due to the highest yield of four active components. By the PCA model, P. polyphylla var. yunnanensis could be exactly discriminated, except that two 8-year-old samples were misclassified as 9-year-old samples. For the prediction of polyphyllin I, II, VI, and VII, the quantitative results are satisfactory, with a high value for the determination coefficient (R ²) and low values for the root-mean-square error of estimation (RMSEE), root-mean-square error of cross-validation (RMSECV), and root-mean-square error of prediction (RMSEP). In conclusion, FT-IR combined with chemometrics is a promising method to accurately discriminate samples of P. polyphylla var. yunnanensis harvested in different years and determine the content of polyphyllin I, II, VI, and VII in P. polyphylla var. yunnanensis.

Rapid determination and chemical change tracking of benzoyl peroxide in wheat flour by multi-step IR macro-fingerprinting

BPO is often added to wheat flour as flour improver, but its excessive use and edibility are receiving increasing concern. A multi-step IR macro-fingerprinting was employed to identify BPO in wheat flour and unveil its changes during storage. BPO contained in wheat flour (<3.0 mg/kg) was difficult to be identified by infrared spectra with correlation coefficients between wheat flour and wheat flour samples contained BPO all close to 0.98. By applying second derivative spectroscopy, obvious differences among wheat flour and wheat flour contained BPO before and after storage in the range of 1500-1400 cm(-1) were disclosed. The peak of 1450 cm(-1) which belonged to BPO was blue shifted to 1453 cm(-1) (1455) which belonged to benzoic acid after one week of storage, indicating that BPO changed into benzoic acid after storage. Moreover, when using two-dimensional correlation infrared spectroscopy (2DCOS-IR) to track changes of BPO in wheat flour (0.05 mg/g) within one week, intensities of auto-peaks at 1781 cm(-1) and 669 cm(-1) which belonged to BPO and benzoic acid, respectively, were changing inversely, indicating that BPO was decomposed into benzoic acid. Moreover, another autopeak at 1767 cm(-1) which does not belong to benzoic acid was also rising simultaneously. By heating perturbation treatment of BPO in wheat flour based on 2DCOS-IR and spectral subtraction analysis, it was found that BPO in wheat flour not only decomposed into benzoic acid and benzoate, but also produced other deleterious substances, e.g., benzene. This study offers a promising method with minimum pretreatment and time-saving to identify BPO in wheat flour and its chemical products during storage in a holistic manner.