Rapid identification of Lilium species and polysaccharide contents based on near infrared spectroscopy and weighted partial least square method

Nano effect fluorescence visual sensor based on Au-AgNCs: A novel strategy to identify the origin and growth year of Lilium bulbs

In this study, we developed a cost-effective fluorescence visual sensor strategy based on gold and silver nanocluster (Au-AgNCs) for the rapid identification of the origins and growth years of Lilium bulbs (LB). Au-AgNCs combined with catechins in LB produce aggregation-induced emission (AIE). The catechin content in LB of different origins and growth years varied, resulting in different fluorescence color responses of the sensor system. Furthermore, the RGB values of the fluorescent color were extracted, and the discriminant effect of visual visualisation was verified using the data-driven soft independent modelling of class analogy (DD-SIMCA) and partial least squares discriminant analysis (PLSDA) models. The results showed that the accuracy of DD-SIMCA for identifying LB origins and PLSDA for growth year identification was 100%. These results indicated that the established strategy could accurately identify the quality of LB, which has great potential for application in the rapid and visual identification of other foods.

Extraction, purification, structural characteristics, biological activities, and applications of polysaccharides from the genus Lilium: A review

The genus Lilium (Lilium) has been widely used in East Asia for over 2000 years due to its rich nutritional and medicinal value, serving as both food and medicinal ingredient. Polysaccharides, as one of the most important bioactive components in Lilium, offer various health benefits. Recently, polysaccharides from Lilium plants have garnered significant attention from researchers due to their diverse biological properties including immunomodulatory, anti-oxidant, anti-diabetic, anti-tumor, anti-bacterial, anti-aging and anti-radiation effects. However, the limited comprehensive understanding of polysaccharides from Lilium plants has hindered their development and utilization. This review focuses on the extraction, purification, structural characteristics, biological activities, structure-activity relationships, applications, and relevant bibliometrics of polysaccharides from Lilium plants. Additionally, it delves into the potential development and future research directions. The aim of this article is to provide a comprehensive understanding of polysaccharides from Lilium plants and to serve as a basis for further research and development as therapeutic agents and multifunctional biomaterials.

Auto-encoder design based on the 1D-VD-CNN model for the detection of honeysuckle from unknown origin

The disadvantages of the traditional one-dimensional convolution neural network (1D-CNN) model based on honeysuckle near-infrared spectral data (NIRS) include high parameter quantity, low efficiency, and inability to identify unknown categories effectively. In this paper, we propose a one-dimensional very deep convolution neural network (1D-VD-CNN) and design an auto-encoder mechanism for detecting honeysuckle from unexplored habitats. First, the 1D-VD-CNN model uses the efficient very deep (VD) structure to replace the hidden layer structure in the traditional 1D-CNN model. The model can be directly applied to analyze one-dimensional near-infrared spectral data (NIRS). Second, combining the reconstruction error of the auto-encoder, a honeysuckle identification method considering an unknown origin is designed, which can solve the problem of high confidence in convolution neural networks by using an auto-encoder and reconstruction errors of the samples to be tested. Whether the sample is an unknown variety can be determined by comparing the corrected confidence level with the preset threshold value. The results show that the accuracy of the 1D-VD-CNN training set and test set is 100%, and the loss value converges to 0.001. Compared with the traditional 1D-CNN model, the parameters and FLOPs are reduced by nearly 71% and 8%, respectively. At the same time, compared with the NIRS analysis and the PLS-DA method, the 1D-VD-CNN model has higher efficiency and better recognition performance for honeysuckle near-infrared spectral classification. Meanwhile, the accuracy rate of the auto-encoder for the category detection mechanism of honeysuckle from an unknown origin is 98%. The model can quickly and efficiently classify honeysuckle from different habitats and detect honeysuckle from unexplored habitats.

Rapid Determination of Polysaccharides in Cistanche Tubulosa Using Near-Infrared Spectroscopy Combined with Machine Learning

BACKGROUND

Cistanche tubulosa, as a homology of medicine and food, not only has a unique medicinal value but also is widely used in healthcare products. Polysaccharide is one of its important quality indicators.

OBJECTIVE

In this study, an analytical model based on near-infrared (NIR) spectroscopy combined with machine learning was established to predict the polysaccharide content of C. tubulosa.

METHODS

The polysaccharide content in the samples determined by the phenol-sulfuric acid method was used as a reference value, and machine learning was applied to relate the spectral information to the reference value. Dividing the samples into a calibration set and a prediction set using the Kennard-Stone algorithm. The model was optimized by various preprocessing methods, including Savitzky-Golay (SG), standard normal variate (SNV), multiple scattering correction (MSC), first-order derivative (FD), second-order derivative (SD), and combinations of them. Variable selection was performed through the successive projections algorithm (SPA) and stability competitive adaptive reweighted sampling (sCARS). Four machine learning models were used to build quantitative models, including the random forest (RF), partial least-squares (PLS), principal component regression (PCR), and support vector machine (SVM). The evaluation indexes of the model were the coefficient of determination (R2), root-mean-square error (RMSE), and residual prediction deviation (RPD).

RESULTS

RF performs best among the four machine learning models. R2c （calibration set coefficient of determination） and RMSEC （root mean square error of the calibration set）, %, were 0.9763. and 0.3527 for calibration, respectively. R2p （prediction set coefficient of determination）, RMSEP （root mean square error of the prediction set）, %, and RPD were 0.9230, 0.5130, and 3.33 for prediction, respectively.

CONCLUSION

The results indicate that NIR combined with the RF is an effective method applied to the quality evaluation of the polysaccharides of C. tubulosa.

HIGHLIGHTS

Four quantitative models were developed to predict the polysaccharide content in C. tubulosa, and good results were obtained. The characteristic variables were basically determined by the sCARS algorithm, and the corresponding characteristic groups were analyzed.

Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy

Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.

Systematic evaluation on the physicochemical characteristics of a series polysaccharides extracted from different edible lilies by ultrasound and subcritical water

A series polysaccharide samples extracted from three edible lilies (Lilium davidii var. willmottiae, Lilium brownii var. viridulum, and Lilium lancifolium) by subcritical water and ultrasound-assisted extraction were systematically compared. The results showed that extraction method was a more important factor than lily species. Subcritical water extracted lily polysaccharides (S-LP) with higher yield, molecular weight, neutral glucose and uronic acid content as well as apparent viscosity. Ultrasound-assisted extracted lily polysaccharides (U-LP) with higher reducing sugars and protein content. Moreover, due to the degradation of glycosidic bonds, ultrasonic extraction was easier to obtain lower molecular weight polysaccharides. In addition, the extraction method significantly affected the monosaccharide proportion of polysaccharides, but had no effect on type. Glucose was the main component in S-LP, and glucose and mannose were the main components in U-LP. The micromorphology of different polysaccharide samples was similar, and the scanning electron microscope (SEM) images showed regular/irregular particle clusters with different particle sizes. Overall, the relationships between extraction methods, lily species and polysaccharide properties were preliminarily elucidated, providing a reference for the targeted extraction of specific lily polysaccharides (LP).

Accurate identification of the geographical origins of lily using near-infrared spectroscopy combined with carbon dot-tetramethoxyporphyrin nanocomposite and chemometrics

Near-infrared spectroscopy technique is a prevailing tool for quality control of foods and traditional Chinese medicines. However, it usually faced the problems of severe peak overlap, low classification accuracy and poor specificity. In this work, the potential of carbon dot-tetramethoxyporphyrin nanocomposite-based nano-effect near-infrared spectroscopy sensor combined with chemometric method was investigated for the accurate identification lily from different geographical origins. Partial least squares-discriminant analysis (PLS-DA) was used for differentiating geographical origins of lily based on the collected traditional and nano-effect near-infrared spectroscopy. Compared with traditional near-infrared spectroscopy, the nano-effect near-infrared spectroscopy obtains superior classification performance with 100% accuracy on the training and test set. The results showed that the proposed method based on near-infrared spectroscopy combined with nanocomposites and chemometrics could be considered as a promising tool for rapid discrimination of the authenticity of food and traditional Chinese medicine in the future.

Determination of Cultivation Regions and Quality Parameters of Poria cocos by Near-Infrared Spectroscopy and Chemometrics

Poria cocos (PC) is an important fungus with high medicinal and nutritional values. However, the quality of PC is heavily dependent on multiple factors in the cultivation regions. Traditional methods are not able to perform quality evaluation for this fungus in a short time, and a new method is needed for rapid quality assessment. Here, we used near-infrared (NIR) spectroscopy combined with chemometric method to identify the cultivation regions and determine PC chemical compositions. In our study, 138 batches of samples were collected and their cultivation regions were distinguished by combining NIR spectroscopy and random forest method (RFM) with an accuracy as high as 92.59%. In the meantime, we used partial least square regression (PLSR) to build quantitative models and measure the content of water-soluble extract (WSE), ethanol-soluble extract (ASE), polysaccharides (PSC) and the sum of five triterpenoids (SFT). The performance of these models were verified with correlation coefficients (R²_cal and R²_pre) above 0.9 for the four quality parameters and the relative errors (RE) of PSC, WSE, ASE and SFT at 4.055%, 3.821%, 4.344% and 3.744%, respectively. Overall, a new approach was developed and validated which is able to distinguish PC production regions, quantify its chemical contents, and effectively evaluate PC quality.

Molecular cloning, characterization and expression analysis of three key starch synthesis-related genes from the bulb of a rare lily germplasm, Lilium brownii var. giganteum

Starch is the predominant compound in bulb scales, and previous studies have shown that bulblet development is closely associated with starch enrichment. However, how starch synthesis affects bulbification at the molecular level is unclear. In this study, we demonstrate that Lilium brownii var. giganteum, a wild lily with a giant bulb in nature, and L. brownii, the native species, have different starch levels and characteristics according to cytological and ultra-structural observations. We cloned the complete sequence of three key gene-encoding enzymes (LbgAGPS, LbgGBSS, andLbgSSIII) during starch synthesis by rapid amplification of 5' and 3' complementary DNA (cDNA) ends (RACE) technology. Bioinformatics analysis revealed that the proteins deduced by these genes contain the canonical conserved domains. Constructed phylogenetic trees confirmed the evolutionary relationships with proteins from other species, including monocotyledons and dicotyledons. The transcript levels of various tissues and time course samples obtained during bulblet development uncovered relatively high expression levels in bulblets and gradual increase expression accompanying bulblet growth. Moreover, a set of single nucleotide polymorphisms (SNPs) was discovered in the AGPS genes of four lily genotypes, and a purifying selection fashion was predicted according to the non-synonymous/synonymous (Ka/Ks) values. Taken together, our results suggested that key starch-synthesizing genes might play important roles in bulblet development and lead to distinctive phenotypes in bulblet size.

Improved 1D convolutional neural network adapted to near-infrared spectroscopy for rapid discrimination of Anoectochilus roxburghii and its counterfeits

Anoectochilus roxburghii (Wall.) Lindl. (Orchidaceae) is a rare traditional Chinese medicine. For seeking high profit, some traditional Chinese medicine sellers usually adulterated A. roxburghii with Goodyera Schlechtendaliana and Ludisia discolor or directly fake A. roxburghii using Anoectochilus formosanus. These counterfeits with similar appearance greatly influence the prescription efficacy. Therefore, there is an urgent need for an effective and fast authentication method to identify A. roxburghii and its counterfeits. In this paper, the near-infrared spectroscopy (NIRS) data of A. roxburghii and its counterfeits are mearsured. Then, an improved inception architecture based 1-dimensional convolutional neural network (Improved 1D-Inception-CNN) is designed for processing the NIRS data and identifying A. roxburghii and its counterfeits. The Improved 1D-Inception-CNN has less parameters and high calculation efficiency which makes the identification model more practical. The experimental results show that compared with traditional structured CNN models, the complexity of the Improved 1D-Inception-CNN is reduced by 40 %, the parameters are reduced by 50 % and the performances are improved by 1.01 %. Therefore, the Improved 1D-Inception-CNN model based on NIRS technology can effectively and quickly identify A. roxburghii and its counterfeits.