Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM)

FT-NIR Spectra of Different Dimensions Combined with Machine Learning and Image Recognition for Origin Identification: An Example of Panax notoginseng

Panax notoginseng (P. notoginseng) is a traditional medicinal plant with high medicinal and economic values. The authenticity of P. notoginseng often determines its quality, and the quality of geographical indication (GI)-producing areas is usually superior to that of other producing areas, which are exploited by unscrupulous traders and affect the market order. The aim of this study was to characterize and identify the geographic origin of P. notoginseng using Fourier transform near-infrared (FT-NIR) spectroscopy, with rapid detection combined with multivariate analysis. The use of principal component analysis and correlation spectral analysis enabled the initial differential characterization and identification of P. notoginseng from different production areas. Then, random forest (RF) and support vector machine (SVM) models were established, and the results show that the results showed that the second-order derivative preprocessing and successive projection algorithm feature extraction achieved 100% classification correctness and the model training time is the shortest. Further constructing the image recognition model, synchronous two-dimensional correlation spectroscopy (2DCOS) image combined with residual convolutional neural network achieved accurate classification (accuracy of 100%) and did not require complex preprocessing and artificial feature extraction process, to maximize the avoidance of errors caused by human factors. The recognition results of the externally validated set showed that the image recognition method has a strong generalization ability and has a high potential for application in the identification of P. notoginseng production areas.

Biological Properties of Boletus edulis Extract on Caco-2 Cells: Antioxidant, Anticancer, and Anti-Inflammatory Effects

Boletus edulis (BE) is a mushroom well known for its taste, nutritional value, and medicinal properties. The objective of this work was to study the biological effects of BE extracts on human colon carcinoma cells (Caco-2), evaluating parameters related to oxidative stress and inflammation. In this study, a hydroethanolic extract of BE was obtained by ohmic heating green technology. The obtained BE extracts are mainly composed of sugars (mainly trehalose), phenolic compounds (taxifolin, rutin, and ellagic acid), and minerals (K, P, Mg, Na, Ca, Zn, Se, etc.). The results showed that BE extracts were able to reduce cancer cell proliferation by the induction of cell cycle arrest at the G0/G1 stage, as well as cell death by autophagy and apoptosis, the alteration of mitochondrial membrane potential, and caspase-3 activation. The extracts modified the redox balance of the cell by increasing the ROS levels associated with a decrease in the thioredoxin reductase activity. Similarly, BE extracts attenuated Caco-2 inflammation by reducing both iNOS and COX-2 mRNA expression and COX-2 protein expression. In addition, BE extracts protected the intestine from the oxidative stress induced by H2O2. Therefore, this study provides information on the potential use of BE bioactive compounds as anticancer therapeutic agents and as functional ingredients to prevent oxidative stress in the intestinal barrier.

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.

Direct Identification and Quantitation of Protein Peptide Powders Based on Multi-Molecular Infrared Spectroscopy and Multivariate Data Fusion

Given that protein peptide powders (PPPs) from different biological sources were inherited with diverse healthcare functions, which aroused adulteration of PPPs. A high-throughput and rapid methodology, united multi-molecular infrared (MM-IR) spectroscopy with data fusion, could determine the types and component content of PPPs from seven sources as examples. The chemical fingerprints of PPPs were thoroughly interpreted by tri-step infrared (IR) spectroscopy, and the defined spectral fingerprint region of protein peptide, total sugar, and fat was 3600-950 cm^-1, which constituted MIR finger-print region. Moreover, the mid-level data fusion model was of great applicability in qualitative analysis, in which the F1-score reached 1 and the total accuracy was 100%, and a robust quantitative model was established with excellent predictive capacity (R_p: 0.9935, RMSEP: 1.288, and RPD: 7.97). MM-IR coordinated data fusion strategies to achieve high-throughput, multi-dimensional analysis of PPPs with better accuracy and robustness which meant a significant potential for the comprehensive analysis of other powders in food as well.

A mid-infrared spectroscopy-random forest system for the origin tracing of Chinese geographical indication Aconiti Lateralis Radix Praeparata

Reliable origin certification methods are essential for the protection of high-value genuine medicinal material with designated origins and geographical indication (GI) products. Aconiti Lateralis Radix Praeparata (Fuzi), one well-known traditional Chinese medicine and geographical indication products have remarkable efficacy and wide clinical application, with high demand in domestic and international markets. The efficacy and price of Fuzi from different origins vary, and it is difficult for the general public to accurately identify them through traditional experience. The mass spectrometry detection technology based on the plant metabolomics is tedious and lengthy in test sample preparation, complicated in operation, long in detection time, and low in reproducibility. As a sophisticated, green, fast, and low-loss detection technique, infrared spectroscopy is integrated by machine learning to bring new ways for quality regulation and control of traditional Chinese medicines. An analytical method based on mid-infrared spectroscopy combined with a random forest algorithm was developed to verify the geographical origin of authentic herbs and/or GI products. The method successfully predicted and classified three varieties of Chinese GI Fuzi and four varieties of non-GI Fuzi. In this study, an environment-friendly traceability strategy with fast analysis, low sample loss and high precision was used to provide a new strategy for identifying the origin of Fuzi.

New preservation and detection technologies for edible mushrooms: A review

Edible mushrooms are nutritious, tasty, and have medicinal value, which makes them very popular. Fresh mushrooms have a high water content and a crisp texture. They demonstrate strong metabolic activity after harvesting. However, they are prone to textural changes, microbial infestation, and nutritional and flavor loss, and they therefore require appropriate post-harvest processing and preservation. Important factors affecting safety and quality during their processing and storage include their quality, source, microbial contamination, physical damage, and chemical residues. Thus, these aspects should be tested carefully to ensure safety. In recent years, many new techniques have been used to preserve mushrooms, including electrofluidic drying and cold plasma treatment, as well as new packaging and coating technologies. In terms of detection, many new detection techniques, such as nuclear magnetic resonance (NMR), imaging technology, and spectroscopy can be used as rapid and effective means of detection. This paper reviews the new technological methods for processing and detecting the quality of mainstream edible mushrooms. It mainly introduces their working principles and application, and highlights the future direction of preservation, processing, and quality detection technologies for edible mushrooms. Adopting appropriate post-harvest processing and preservation techniques can maintain the organoleptic properties, nutrition, and flavor of mushrooms effectively. The use of rapid, accurate, and non-destructive testing methods can provide a strong assurance of food safety. At present, these new processing, preservation and testing methods have achieved good results but at the same time there are certain shortcomings. So it is recommended that they also be continuously researched and improved, for example through the use of new technologies and combinations of different technologies. © 2023 Society of Chemical Industry.

AUNet: a deep learning method for spectral information classification to identify inks

It is common to tamper with the contents of documents and forge contracts illegally. In this work, we propose a U-shaped network with attention modules (AUNet) and combine it with a hyperspectral system to effectively identify different inks. It provides an effective detection method for illegal tampering with documents and forging contract contents. First, the hyperspectral system obtains the spectral information of different pen inks without destroying the sample. Second, because the hyperspectral system's detection data have the characteristics of small samples, we introduce U-Net to conduct the deep fusion of multi-level spectral information to avoid feature degradation and fully mine the deep features hidden in the spectral information. Finally, spatial and channel attention modules are introduced to focus on the features affecting classification performance. The results show that AUNet effectively realizes the effective classification of ink spectral information and achieves 97.81% accuracy, 98.71% recall, 98.80% precision, and 98.71% F₁-score.

Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review

The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.

A multi-kernel channel attention combined with convolutional neural network to identify spectral information for tracing the origins of rice samples

Rice is a primary food consumed daily by many people, and different samples of rice often show disparate quality levels due to different production environments. In the rice market, it is common to sell low-quality rice with high-quality origin labels. As a nondestructive testing technology, spectral analysis has been widely used in food quality supervision. In this work, a deep learning method was developed and combined with a hyperspectral imaging system to achieve a quality-based identification of rice samples from different origins. First, the hyperspectral system was used to obtain spectral information of rice samples from five different origins. Then, a multi-kernel channel attention (MKCA) was proposed to focus on the deep features of the spectral information. Finally, based on the classical deep learning network, combined with MKCA, the spectral characteristics of rice samples from different origins were effectively identified. The results showed that MKCA combined with the LeNet-5 network structure achieved 97.40% accuracy, 97.63% precision, 97.78% recall, and 97.70% F₁-score. It provides an effective technical method for tracing rice.

Review of Recent Modern Analytical Technology Combined with Chemometrics Approach Researches on Mushroom Discrimination and Evaluation

Mushroom is a macrofungus with precious fruiting body, as a food, a tonic, and a medicine, human have discovered and used mushrooms for thousands of years. Nowadays, mushroom is also a "super food" recommended by the World Health Organization (WHO) and Food and Agriculture Organization (FAO), and favored by consumers. Discrimination of mushroom including species, geographic origin, storage time, etc., is an important prerequisite to ensure their edible safety and commodity quality. Moreover, the effective evaluation of its chemical composition can help us better understand the nutritional properties of mushrooms. Modern analytical technologies such as chromatography, spectroscopy and mass spectrometry, etc., are widely used in the discrimination and evaluation researches of mushrooms, and chemometrics is an effective means of scientifically processing the multidimensional information hidden in these analytical technologies. This review will outline the latest applications of modern analytical technology combined with chemometrics in qualitative and quantitative analysis and quality control of mushrooms in recent years. Briefly describe the basic principles of these technologies, and the analytical processes of common chemometrics in mushroom researches will be summarized. Finally, the limitations and application prospects of chromatography, spectroscopy and mass spectrometry technology are discussed in mushroom quality control and evaluation.

PRIAS: An Intelligent Analysis System for Pesticide Residue Detection Data and Its Application in Food Safety Supervision

Pesticide residue is a prominent factor that leads to food safety problems. For this reason, many countries sample and detect pesticide residues in food every year, which generates a large amount of pesticide residue data. However, the way to deeply analyze and mine these data to quickly identify food safety risks is still an unresolved issue. In this study, we present an intelligent analysis system that supports the collection, processing, and analysis of detection data of pesticide residues. The system is first based on a number of databases such as maximum residue limit standards for the fusion of pesticide residue detection results; then, it applies a series of statistical methods to analyze pesticide residue data from multiple dimensions for quickly identifying potential risks; it uses the Apriori algorithm to mine the implicit association in the data to form pre-warning rules; finally, it applies Word document automatic generation technology to automatically generate pesticide residue analysis and pre-warning reports. The system was applied to analyze the pesticide residue detection results of 42 cities in mainland China from 2012 to 2015. Application results show that the system proposed in this study can greatly improve the depth, accuracy and efficiency of pesticide residue detection data analysis, and it can provide better decision support for food safety supervision.

An integrated chemical characterization based on FT-NIR, GC-MS and LC-MS for the comparative metabolite profiling of wild and cultivated agarwood

Agarwood is a well-known and precious traditional Chinese medicine, has been widely applied as drugs and spices for century. The large demand for this material has deeply stimulated the emergence of numerous cultivated products. However, it is difficult to distinguish wild agarwood from cultivated agarwood, and the chemical composition difference between them is not clear. In this study, an integrated method of Fourier transform near-infrared (FT-NIR), gas chromatography-mass spectrometry (GC-MS) and ultraperformance liquid chromatography Quadrupole-Exactive Orbitrap tandem mass spectrometry (UHPLC-Q-Exactive Orbitrap/MS) was developed to explore chemical variation between wild and cultivated agarwood in combination with multivariate statistical analysis. Twenty-four wild and cultivated agarwood samples were collected from different regions. FT-NIR profiles were used to obtain the holistic metabolic characterization in combination with principal component analysis (PCA). A total of seventy-six and seventy-nine metabolites, including volatile components and 2-(2-phenethyl) chromones derivatives, were successfully identified by GC-MS and UHPLC-Q-Exactive Orbitrap/MS, respectively. Thereafter, the orthogonal-partial least square method-discriminant analysis (OPLS-DA) and variable importance in the projection (VIP) were used to screen potential characteristic chemical components (VIP > 1) in wild and cultivated agarwood, respectively. Finally, eight key chemical markers were putatively identified by two techniques to distinguish agarwood from different origins, which can be found that sesquiterpenes, aromatics, terpenoids, 2-(2-phenylethyl) chromones of the flidersia type (FTPECs) and tetrahydro-2-(2-phenylethyl) chromones (THPECs) are the most important metabolites. Summary, this research presented a comprehensive metabolomic variation between wild and cultivated agarwood on the basis of a multi-technology platform, which laid a foundation for distinguishing the two ecotypes of agarwood and was conducive to the quality control of this resource.

A Novel Multi-Preprocessing Integration Method for the Qualitative and Quantitative Assessment of Wild Medicinal Plants: Gentiana rigescens as an Example

Until now, the over-exploitation of wild resources has increased growing concern over the quality of wild medicinal plants. This led to the necessity of developing a rapid method for the evaluation of wild medicinal plants. In this study, the content of total secoiridoids (gentiopicroside, swertiamarin, and sweroside) of Gentiana rigescens from 37 different regions in southwest China were analyzed by high performance liquid chromatography (HPLC). Furthermore, Fourier transform infrared (FT-IR) was adopted to trace the geographical origin (331 individuals) and predict the content of total secoiridoids (273 individuals). In the traditional FT-IR analysis, only one scatter correction technique could be selected from a series of preprocessing candidates to decrease the impact of the light correcting effect. Nevertheless, different scatter correction techniques may carry complementary information so that using the single scatter correction technique is sub-optimal. Hence, the emerging ensemble approach to preprocessing fusion, sequential preprocessing through orthogonalization (SPORT), was carried out to fuse the complementary information linked to different preprocessing methods. The results suggested that, compared with the best results obtained on the scatter correction modeling, SPORT increased the accuracy of the test set by 12.8% in qualitative analysis and decreased the RMSEP by 66.7% in quantitative analysis.

Application of Identification and Evaluation Techniques for Edible Mushrooms: A Review

Edible mushrooms are healthy food with high nutritional value, which is popular with consumers. With the increase of the problem of mushrooms being confused with the real and pollution in the market, people pay more and more attention to food safety. More than 167 articles of edible mushroom published in the past 20 years were reviewed in this paper. The analysis tools and data analysis methods of identification and quality evaluation of edible mushroom species, origin, mineral elements were reviewed. Five techniques for identification and evaluation of edible mushrooms were introduced and summarized. The macroscopic, microscopic and molecular identification techniques can be used to identify species. Chromatography, spectroscopy technology combined with chemometrics can be used for qualitative and quantitative study of mushroom and evaluation of mushroom quality. In addition, multiple supervised pattern-recognition techniques have good classification ability. Deep learning is more and more widely used in edible mushroom, which shows its advantages in image recognition and prediction. These techniques and analytical methods can provide strong support and guarantee for the identification and evaluation of mushroom, which is of great significance to the development and utilization of edible mushroom.

Discovery of Quality Markers of Nucleobases, Nucleosides, Nucleotides and Amino Acids for Chrysanthemi Flos From Different Geographical Origins Using UPLC-MS/MS Combined With Multivariate Statistical Analysis

Nucleobases, nucleosides, nucleotides and amino acids, as crucial nutrient compositions, play essential roles in determining the flavor, function and quality of Chrysanthemi Flos. The quality of Chrysanthemi Flos from different geographical origins is uneven, but there have been no reports about the screening of their quality markers based on nutritional ingredients. Here, we developed a comprehensive strategy integrating ultra performance liquid chromatography coupled with triple-quadrupole linear ion-trap tandem mass spectrometry (UPLC-MS/MS) and multivariate statistical analysis to explore quality markers of Chrysanthemi Flos from different geographical origins and conduct quality evaluation and discrimination of them. Firstly, a fast, sensitive, and reliable UPLC-MS/MS method was established for simultaneous quantification 28 nucleobases, nucleosides, nucleotides and amino acids of Chrysanthemi Flos from nine different regions in China. The results demonstrated that Chrysanthemi Flos from nine different cultivation regions were rich in the above 28 nutritional contents and their contents were obvious different; however, correlation analysis showed that altitude was not the main factor for these differences, which required further investigation. Subsequently, eight crucial quality markers for nine different geographical origins of Chrysanthemi Flos, namely, 2'-deoxyadenosine, guanosine, adenosine 3',5'-cyclic phosphate (cAMP), guanosine 3',5'-cyclic monophosphate (cGMP), arginine, proline, glutamate and tryptophan, were screened for the first time using partial least squares discriminant analysis (PLS-DA) and the plot of variable importance for projection (VIP). Moreover, a hierarchical clustering analysis heat map was employed to intuitively clarify the distribution of eight quality markers in the nine different regions of Chrysanthemi Flos. Finally, based on the contents of selected eight quality markers, support vector machines (SVM) model was established to predict the geographical origins of Chrysanthemi Flos, which yielded excellent prediction performance with an average prediction accuracy of 100%. Taken together, the proposed strategy was suitable to discover the quality markers of Chrysanthemi Flos and could be used to discriminate its geographical origin.

Method Superior to Traditional Spectral Identification: FT-NIR Two-Dimensional Correlation Spectroscopy Combined with Deep Learning to Identify the Shelf Life of Fresh Phlebopus portentosus

The taste of fresh mushrooms is always appealing. Phlebopus portentosus is the only porcini that can be cultivated artificially in the world, with a daily output of up to 2 tons and a large sales market. Fresh mushrooms are very susceptible to microbial attacks when stored at 0-2 °C for more than 5 days. Therefore, the freshness of P. portentosus must be evaluated during its refrigeration to ensure food safety. According to their freshness, the samples were divided into three categories, namely, category I (1-2 days, 0-48 h, recommended for consumption), category II (3-4 days, 48-96 h, recommended for consumption), and category III (5-6 days, 96-144 h, not recommended). In our study, a fast and reliable shelf life identification method was established through Fourier transform near-infrared (FT-NIR) spectroscopy combined with a machine learning method. Deep learning (DL) is a new focus in the field of food research, so we established a deep learning classification model, traditional support-vector machine (SVM), partial least-squares discriminant analysis (PLS-DA), and an extreme learning machine (ELM) model to identify the shelf life of P. portentosus. The results showed that FT-NIR two-dimensional correlation spectroscopy (2DCOS) combined with the deep learning model was more suitable for the identification of fresh mushroom shelf life and the model had the best robustness. In conclusion, FT-NIR combined with machine learning had the advantages of being nondestructive, fast, and highly accurate in identifying the shelf life of P. portentosus. This method may become a promising rapid analysis tool, which can quickly identify the shelf life of fresh edible mushrooms.

A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose

Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.

Geographical traceability and multielement analysis of edible and medicinal fungi: Taking Wolfiporia cocos (F.A. Wolf) Ryvarden and Gilb. as an example

Different geographical environment has a certain influence on the accumulation of fungi elements and chemical components. However, our knowledge is limited to elucidate the fungi elements in response to heterogeneous environmental and the quality differences among different habitats. Here, multielement analysis, FTIR spectrum, and feature-level fusion technique combined with chemometrics were used to study Wolfiporia cocos from different geographical areas, different sampling sites and different altitude sources. From the results, (1) there is significant difference in element content of samples from different sampling sites and no positive correlation with geographical ranges. (2) There is a correlation between elevation and elements, and relatively low elevation (<1,800 m) is conducive to the enrichment of elements. (3) From the perspective of elements, the W. cocos in Yuxi have relatively better quality. (4) FTIR and feature-level models can well realize origin identification. The SVM models are better than the PLS-DA models, and the feature-level model is better than the single FTIR models. In summary, this study demonstrated that the developed method was reliable and could realize the genuineness evaluation and origin identification of W. cocos. The results have implications for the establishment of the technology system of geographical traceability and the development of high-quality geographical indication products of W. cocos.

Purification and characterization of a novel protein with activity against non-small-cell lung cancer in vitro and in vivo from the edible mushroom Boletus edulis

A new anti-tumor protein (designated as Boletus edulis or in short BEAP) was isolated from dried fruit bodies of the edible bolete mushroom Boletus edulis. The purification protocol employed comprised fast ion exchange chromatography on a Hitrap Q column and ion exchange chromatography on a DEAE-52 cellulose column. Superdex G75 gel filtration and SDS-PAGE analysis revealed that BEAP was a protein with a molecular weight of 16.7 KD. The protein exhibited potent anti-cancer activity on A549 cells both in vitro and in vivo. With the use of AO/EB staining, annexin V-FITC/PI, and Western blotting, it was demonstrated in vitro that the cytotoxicity of BEAP was mediated by induction of apoptosis and arrest of A549 cells in the G1 phase of the cell cycle. BEAP significantly suppressed the growth of A549 solid tumors in vivo. These results prove that BEAP is a new multifunctional protein with anti-tumor and anti-metastasis capabilities.

Effects of caprolactam content on curdlan-based food packaging film and detection by infrared spectroscopy

In this study, we report a rapid statistical approach used in determining the caprolactam (CPL) content in curdlan packaging films, which is based on the spectral data observed in the near-infrared (NIR) and Mid-infrared (MIR) regions. At the first stage of the study, the CPL content was added into the curdlan films prepared by controlling the concentration, and then the effect of the CPL concentration on the measured mechanical properties of the produced films were evaluated. At the next stage, the NIR and MIR spectra of the curdlan films with different CPL concentrations were recorded by using the FT-NIR and FT-IR spectroscopy technique, and the spectral data to be used in the regression models in our quantitative analyses were carefully selected. It was observed that the curdlan film with 5% CPL exhibited the best mechanical properties. The obtained best correlation parameters which are used in evaluation of CPL content through the observed NIR and MIR spectral data are Rp = 0.9552, RMSEP = 1.2506 (NIR); Rp = 0.9092 and RMSEP = 1.9136 (MIR), respectively. These optimal values support the expectation that our statistical approach based on NIR and MIR data can provide a rapid, accurate and nondestructive way of determining CPL content in curdlan packaging films.

Rapid Geographical Origin Identification and Quality Assessment of Angelicae Sinensis Radix by FT-NIR Spectroscopy

Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.

Geographical origin traceability and identification of refined sugar using UPLC-QTof-MS analysis

Authentication of geographical origin is essential to the food safety of refined sugar. This study aimed to determine the geographical origin traceability and authenticity of refined sugar in China. Ultra performance liquid chromatography-quadrupole time-of-flight Mass Spectrometry (UPLC-QTof-MS), instead of conventional stable isotope ratio mass spectrometer (IRMS), was used to detect the mass fragment ratios (Rδ-sucrose and Rδ-glucose) of refined sugar. These ratios could reflect the cultivation practice and environmental conditions. A total of 108 batches of samples were collected from six regions in China, and additional 72 samples were verified with support vector machines (SVM) model, in order to evaluate the accuracy of origin identification and composition prediction. Our results showed that 83.3% of the refined sugar was correctly classified based on the geographical region of origin under different environmental conditions. These findings indicate that the specified mass fragment ratio may be a promising approach for assessing the traceability and authenticity of refined sugar.

Classification of multicategory edible fungi based on the infrared spectra of caps and stalks

As a characteristic edible fungus with a high nutritional value and medicinal effect, the Bachu mushroom has a broad market. To distinguish among Bachu mushrooms with high value and other fungi effectively and accurately, as well as to explore a universal identification method, this study proposed a method to identify Bachu mushrooms by Fourier Transform Infrared Spectroscopy (FT-IR) combined with machine learning. In this experiment, two kinds of common edible mushrooms, Lentinus edodes and club fungi, were selected and classified with Bachu mushrooms. Due to the different distribution of nutrients in the caps and stalks, the caps and stalks were studied in this experiment. By comparing the average normalized infrared spectra of the caps and stalks of the three types of fungi, we found differences in their infrared spectra, indicating that the latter can be used to classify and identify the three types of fungi. We also used machine learning to process the spectral data. The overall steps of data processing are as follows: use partial least squares (PLS) to extract spectral features, select the appropriate characteristic number, use different classification algorithms for classification, and finally determine the best algorithm according to the classification results. Among them, the basis of selecting the characteristic number was the cumulative variance interpretation rate. To improve the reliability of the experimental results, this study also used the classification results to verify the feasibility. The classification algorithms used in this study were the support vector machine (SVM), backpropagation neural network (BPNN) and k-nearest neighbors (KNN) algorithm. The results showed that the three algorithms achieved good results in the multivariate classification of the caps and stalks data. Moreover, the cumulative variance explanation rate could be used to select the characteristic number. Finally, by comparing the classification results of the three algorithms, the classification effect of KNN was found to be the best. Additionally, the classification results were as follows: according to the caps data classification, the accuracy was 99.06%; according to the stalks data classification, the accuracy was 99.82%. This study showed that infrared spectroscopy combined with a machine learning algorithm has the potential to be applied to identify Bachu mushrooms and the cumulative variance explanation rate can be used to select the characteristic number. This method can also be used to identify other types of edible fungi and has a broad application prospect.

Functional principal component analysis for near-infrared spectral data: a case study on Tricholoma matsutakeis

Tricholoma matsutakeis (TM) is the most expensive edible fungi in China. Given its price and exclusivity, some dishonest merchants will sell adulterated TM by combining it with cheaper fungi in an attempt to earn more profits. This fraudulent behavior has broken food laws and violated consumer trust. Therefore, there is an urgent need to develop a rapid, accurate, and nondestructive tool to discriminate TM from other edible fungi. In this work, a novel detection algorithm combined with near-infrared spectroscopy (NIR) and functional principal component analysis (FPCA) is proposed. Firstly, the raw NIR data were pretreated by locally weighted scatterplot smoothing (LOWESS) and multiplication scatter correction (MSC). Then, FPCA was used to extract valuable information from the preprocessed NIR data. Then, a classifier was designed by using the least-squares support-vector machine (LS-SVM) to distinguish categories of edible fungi. Furthermore, the one-versus-one (OVO) strategy was included and the binary LS-SVM was extended to a multi-class classifier. The 166 samples of four varieties of fungi were used to validate the proposed method. The results show that the proposed method has great capability in near infrared spectra classification, and the average accurate of FPCA-LSSVM is 97.3% which is greater than that of PCA-LSSVM (93.5%).

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

Pleurotus Mushrooms Content in Glucans and Ergosterol Assessed by ATR-FTIR Spectroscopy and Multivariate Analysis

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy was used to monitor the infrared absorption spectra of 79 mushroom samples from 29 Pleurotus ostreatus, P. eryngii and P. nebrodensis strains cultivated on wheat straw, grape marc and/or by-products of the olive industry. The spectroscopic analysis provided a chemical insight into the mushrooms examined, while qualitative and quantitative differences in regions related to proteins, phenolic compounds and polysaccharides were revealed among the species and substrates studied. Moreover, by using advanced chemometrics, correlations of the recorded mushrooms’ spectra versus their content in glucans and ergosterol, commonly determined through traditional analytical techniques, allowed the development of models predicting such contents with a good predictive power (R²: 0.80–0.84) and accuracy (low root mean square error, low relative error and representative to the predicted compounds spectral regions used for the calibrations). Findings indicate that FTIR spectroscopy could be exploited as a potential process analytical technology tool in the mushroom industry to characterize mushrooms and to assess their content in bioactive compounds.

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000-4000 cm^-1) and Fourier transform mid-infrared (MIR: 4000-600 cm^-1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen's kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

Investigation of a Medical Plant for Hepatic Diseases with Secoiridoids Using HPLC and FT-IR Spectroscopy for a Case of Gentiana rigescens

Secoiridoids could be used as a potential new drug for the treatment of hepatic disease. The content of secoiridoids of G. rigescens varied in different geographical origins and parts. In this study, a total of 783 samples collected from different parts of G. rigescens in Yunnan, Sichuan, and Guizhou Provinces. The content of secoiridoids including gentiopicroside, swertiamarin, and sweroside were determined by using HPLC and analyzed by one-way analysis of variance. Two selected variables including direct selected and variable importance in projection combined with partial least squares regression have been used to establish a method for the determination of secoiridoids using FT-IR spectroscopy. In addition, different pretreatments including multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative and second derivative (SD), and orthogonal signal correction (OSC) were compared. The results indicated that the sample (root, stem, and leaf) with total secoiridoids, gentiopicroside, swertiamarin, and sweroside from west Yunnan had higher content than samples from the other regions. The sample from Baoshan had more total secoiridoids than other samples for the whole medicinal plant. The best performance using FT-IR for the total secoiridoid was with the direct selected variable method involving pretreatment of MSC+OSC+SD in the root and stem, while in leaf, of the best method involved using original data with MSC+OSC+SD. This method could be used to determine the bioactive compounds quickly for herbal medicines.

A new strategy of applying modeling indicator determined method to high-level fusion for quantitative analysis

A novel method, named as modeling indicator determined (MID) method, based on two model evaluation parameters i.e., root mean square error of prediction (RMSEP) and ratio performance deviation (RPD), is proposed to employ high-level fusion for quantitative analysis. The two MID methods of root mean square error of prediction weighted (RMSEPW) method and ratio performance deviation weighted (RPDW) method are put forward on the basis of the model evaluation indicators from the individual models. Performance of RMSEPW method and RPDW method are evaluated in terms of the predictive ability of root mean square error of prediction for fusion (RMSEP_f) through the fused models. The two MID methods are applied to UV-visible (UV-vis), near infrared (NIR) and mid-infrared (MIR) spectral data of active ingredient in pesticide, and gas chromatography-mass spectrometer (GC-MS) and NIR spectral data of n-heptane in chemical complex for high-level fusion. Moreover, the results are compared with the individual methods. As a result, the overall results show that the two MID methods are promising with significant improvement of predictive performance for high-level fusion when executing quantitative analysis.

Species and Geographical Origins Discrimination of Porcini Mushrooms Based on FT-IR Spectroscopy and Mineral Elements Combined with Sparse Partial Least Square-Discriminant Analysis

Misrecognition and toxic elements are two of several reasons responsible for food poisoning even death in the summer, a time when a great deal of edible mushrooms is celebrated in Southwestern China featured as complex environment conditions. It is highly important to identify the difference of chemical constituents in edible mushrooms at the regional-scale. In this study, Fourier transform infrared (FT-IR) spectroscopy and inductively coupled plasma mass spectrometry were applied to investigate organic matters and 18 mineral elements in porcini mushrooms of six species collected from 17 sampling sites in nine Yunnan cities. Classification models on the species, regions, and part levels were established using sparse partial least square-discriminant analysis and principal component analysis. At the species level and region level accuracies of greater than 92.1% and 92.8% was achieved, respectively, whereas on the part level caps and stipes were classified with 96.7% accuracy. One of the most popular mushrooms is Boletus edulis characterized by polysaccharide, lipid, and ribonucleic acid as well as several phenolic compounds. Temperature and precipitation show possible influences on accumulations of polysaccharides and ribonucleic acid. Furthermore, the most important elements of caps contributed the difference between two parts are copper (Cu), zinc (Zn), and phosphorus (P), whereas stipes instead by manganese (Mn) and cobalt (Co). These results demonstrated that FT-IR spectroscopy and elements contents provide information sufficient for classifying different porcini mushroom samples, which might be helpful for controlling food security and quality assessment of edible mushrooms.

Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Combined with Chemometrics Methods for the Classification of Lingzhi Species

Due to the existence of Lingzhi adulteration, there is a growing demand for species classification of medicinal mushrooms by various techniques. The objective of this study was to explore a rapid and reliable way to distinguish between different Lingzhi species and compare the influence of data pretreatment methods on the recognition results. To this end, 120 fresh fruiting bodies of Lingzhi were collected, and all of them were analyzed by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). Random forest (RF), support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) classification models were established for raw and pretreated second derivative (SD) spectral matrices to authenticate different Lingzhi species. The results of multivariate statistical analysis indicated that the SD preprocessing method displayed a higher classification ability, which may be attributed to the analysis of powder samples that requires removal of overlapping peaks and baseline shifts. Compared with RF, the results of the SVM and PLS-DA methods were more satisfying, and their accuracies for the test set were both 100%. Among SVM and PLS-DA, the training set and test set accuracy of PLS-DA were both 100%. In conclusion, ATR-FTIR spectroscopy data pretreated by SD combined with PLS-DA is a simple, rapid, non-destructive and relatively inexpensive method to discriminate between mushroom species and provide a good reference to quality assessment.

Application of vibrational spectroscopy for classification, authentication and quality analysis of mushroom: A concise review

Chemical compositions of mushrooms are greatly dependent on the geographical region, and also the different parts of the same mushroom have different chemical constitutions. Several chemical methods are employed for quality control of mushrooms. However, these methods are destructive, require skilled personnel and are time consuming. To overcome these limitations researchers are aiming for vibrational spectroscopic techniques. This review is focused on various studies related to the application of vibrational spectroscopy for classification, authentication and quality analysis of mushrooms. It was concluded that vibrational spectroscopy could be efficiently employed for assessing the quality, authenticity and geographical origin of the mushrooms. Fourier-transform infrared (FTIR) and near infrared (NIR) spectroscopy were the most explored, whereas, Raman spectroscopy is the least explored technique in this field. Compact and cost-effective spectrometers based on the selective wavelengths have to be designed and installed at commercial and industrial level for rapid quality control of mushrooms.

Determination of mineral contents of wild Boletus edulis mushroom and its edible safety assessment

This study aimed to determine the contents of main mineral elements of wild Boletus edulis and to assess its edible safety, which may provide scientific evidence for the utilization of this species. Fourteen mineral contents (Ba, Ca, Cd, Co, Cr, Cu, Fe, Mg, Mn, Na, Ni, Sr, V and Zn) in the caps and stipes of B. edulis as well as the corresponding surface soils collected from nine different geographic regions in Yunnan Province, southwest China were determined. The analyses were performed using inductively coupled plasma atomic emission spectrometer (ICP-AES) after microwave digestion. Measurement data were analyzed using variance and Pearson correlation analysis. Edible safety was evaluated according to the provisionally tolerable weekly intake (PTWI) of heavy metals recommended by United Nations Food and Agriculture Organization and World Health Organization (FAO/WHO). Mineral contents were significantly different with the variance of collection areas. B. edulis showed relative abundant contents of Ca, Fe, Mg and Na, followed by Ba, Cr, Cu, Mn and Zn, and the elements with the lower content less were Cd, Co, Ni, Sr and V. The elements accumulation differed significantly in caps and stipes. Among them, Cd and Zn were bioconcentrated (BCF > 1) while others were bioexcluded (BCF < 1). The mineral contents in B. edulis and its surface soil were positively related, indicating that the elements accumulation level was related to soil background. In addition, from the perspective of food safety, if an adult (60 kg) eats 300 g fresh B. edulis per week, the intake of Cd in most of tested mushrooms were lower than PTWI value whereas the Cd intakes in some other samples were higher than this standard. The results indicated that the main mineral contents in B. edulis were significantly different with respect to geographical distribution, and the Cd intake in a few of regions was higher than the acceptable intakes with a potential risk.

Feature Fusion of ICP-AES, UV-Vis and FT-MIR for Origin Traceability of Boletus edulis Mushrooms in Combination with Chemometrics

Origin traceability is an important step to control the nutritional and pharmacological quality of food products. Boletus edulis mushroom is a well-known food resource in the world. Its nutritional and medicinal properties are drastically varied depending on geographical origins. In this study, three sensor systems (inductively coupled plasma atomic emission spectrophotometer (ICP-AES), ultraviolet-visible (UV-Vis) and Fourier transform mid-infrared spectroscopy (FT-MIR)) were applied for the origin traceability of 192 mushroom samples (caps and stipes) in combination with chemometrics. The difference between cap and stipe was clearly illustrated based on a single sensor technique, respectively. Feature variables from three instruments were used for origin traceability. Two supervised classification methods, partial least square discriminant analysis (FLS-DA) and grid search support vector machine (GS-SVM), were applied to develop mathematical models. Two steps (internal cross-validation and external prediction for unknown samples) were used to evaluate the performance of a classification model. The result is satisfactory with high accuracies ranging from 90.625% to 100%. These models also have an excellent generalization ability with the optimal parameters. Based on the combination of three sensory systems, our study provides a multi-sensory and comprehensive origin traceability of B. edulis mushrooms.

Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose

Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables’ behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively.