Traceability of Boletaceae mushrooms using data fusion of UV-visible and FTIR combined with chemometrics methods

Authenticity and species identification of Fritillariae cirrhosae: a data fusion method combining electronic nose, electronic tongue, electronic eye and near infrared spectroscopy

This paper focuses on determining the authenticity and identifying the species of Fritillariae cirrhosae using electronic nose, electronic tongue, and electronic eye sensors, near infrared and mid-level data fusion. 80 batches of Fritillariae cirrhosae and its counterfeits (including several batches of Fritillaria unibracteata Hsiao et K.C. Hsia, Fritillaria przewalskii Maxim, Fritillaria delavayi Franch and Fritillaria ussuriensis Maxim) were initially identified by Chinese medicine specialists and by criteria in the 2020 edition of Chinese Pharmacopoeia. After obtaining the information from several sensors we constructed single-source PLS-DA models for authenticity identification and single-source PCA-DA models for species identification. We selected variables of interest by VIP value and Wilk's lambda value, and we subsequently constructed the three-source fusion model of intelligent senses and the four-source fusion model of intelligent senses and near-infrared spectroscopy. We then explained and analyzed the four-source fusion models based on the sensitive substances detected by key sensors. The accuracies of single-source authenticity PLS-DA identification models based on electronic nose, electronic eye, electronic tongue sensors and near-infrared were respectively 96.25%, 91.25%, 97.50% and 97.50%. The accuracies of single-source PCA-DA species identification models were respectively 85%, 71.25%, 97.50% and 97.50%. After three-source data fusion, the accuracy of the authenticity identification of the PLS-DA identification model was 97.50% and the accuracy of the species identification of the PCA-DA model was 95%. After four-source data fusion, the accuracy of the authenticity of the PLS-DA identification model was 98.75% and the accuracy of the species identification of the PCA-DA model was 97.50%. In terms of authenticity identification, four-source data fusion can improve the performance of the model, while for the identification of the species the four-source data fusion failed to optimize the performance of the model. We conclude that electronic nose, electronic tongue, electronic eye data and near-infrared spectroscopy combined with data fusion and chemometrics methods can identify the authenticity and determine the species of Fritillariae cirrhosae. Our model explanation and analysis can help other researchers identify key quality factors for sample identification. This study aims to provide a reference method for the quality evaluation of Chinese herbs.

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.

Geographical origin discrimination of Agaricus bisporus produced by the complete medium: A pilot study in South Korea

The complete medium used for mushroom cultivation is important for reliable crop production. We aimed to identify how the origin of Agaricus bisporus grown in Korea was affected by complete media produced in different geographical regions using stable isotope ratios (SIRs). We found that the SIR features of A. bisporus significantly depended on the complete media origin used; in particular, it appeared the high δ³⁴S in the Chinese complete medium, low δ³⁴S in the Dutch complete medium, and high δ¹⁵N in the Korean complete medium (P < 0.05). The support vector machine method appeared better geo-origin classification of A. bisporus by the complete media compared to a linear discriminant analysis. A large-scale study should be conducted to establish a reliable origin identification model for A. bisporus grown in complete media to improve the global mushroom marketplace.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review

The release of the FDA's guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

Nutritional Profile and Health Benefits of Ganoderma lucidum "Lingzhi, Reishi, or Mannentake" as Functional Foods: Current Scenario and Future Perspectives

Ganoderma lucidum has a long history of medicinal uses in the Far East countries of more than 2000 years due to its healing properties. Recently, G. lucidum has come under scientific scrutiny to evaluate its content of bioactive components that affect human physiology, and has been exploited for potent components in the pharmacology, nutraceuticals, and cosmetics industries. For instance, evidence is accumulating on the potential of this mushroom species as a promising antiviral medicine for treating many viral diseases, such as dengue virus, enterovirus 71, and recently coronavirus disease of 2019 (COVID-19). Still, more research studies on the biotherapeutic components of G. lucidum are needed to ensure the safety and efficiency of G. lucidum and promote the development of commercial functional foods. This paper provides an extensive overview of the nutraceutical value of Ganoderma lucidum and the development of commercial functional food. Moreover, the geo-origin tracing strategies of this mushroom and its products are discussed, a highly important parameter to ensure product quality and safety. The discussed features will open new avenues and reveal more secrets to widely utilizing this mushroom in many industrial fields; i.e., pharmaceutical and nutritional ones, which will positively reflect the global economy.

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.

A fast determination of insecticide deltamethrin by spectral data fusion of UV-vis and NIR based on extreme learning machine

Spectral data fusion strategies combined with the extreme learning machine (ELM) algorithm was applied to determine the active ingredient in deltamethrin formulation. Ultraviolet-visible spectroscopy (UV-vis) is a rapid and sensitive detection method for specific components that are sensitive to ultraviolet irradiation. Alternatively, near-infrared spectroscopy (NIR) technology can be applied over a broader range. To determine a feasible method with a higher sensitivity and broader application range, the active ingredient of deltamethrin formulation was comprehensively investigated by combining the spectral data fusion strategy with ELM by employing UV-vis, NIR and fusion strategies, individually. Consequently, the results demonstrated that the low-level fusion strategy exhibited better predictive ability (lower RMSEP of 0.0645% and higher R² of 0.9978) than mid-level fusion and individual methods. ELM combined with data fusion is proved to be an efficient method for the rapid analysis of deltamethrin formulations. Furthermore, this study provides a potential approach for pesticide quality control as well as on-site monitoring.

Species discrimination and total polyphenol prediction of porcini mushrooms by fourier transform mid-infrared (FT-MIR) spectrometry combined with multivariate statistical analysis

The plateau specialty agricultural products, wild porcini mushrooms, have great value both as a superb cuisine and as a potential medication. Due to quality different between species added with the fraud behavior in sales process, make poor quality or poisonous sample inflow into the market, which pose a health risk for consumers, but also disrupted the mushroom market. Traditional analysis way is time-consuming and laborious. Therefore, the aim of this study is to develop a way using fourier transform mid-infrared (FT-MIR) spectrometry and data fusion strategies for the fast and accurate species discrimination and predict amount of total polyphenol in four porcini mushrooms. The t-distributed stochastic neighbor embedding based on mid-level data fusion showed two species of Boletus edulis and B. umbriniporus have been identified. The order of correct rate of PLS-DA models was mid-level data fusionq (100%) > mid-level data fusione (97.06%) = mid-level data fusionv (97.06%) = stipes (97.06%) > low-level data fusion (94.12%) > caps (91.18%). The order of correct rate of grid-search support vector machine models was low-level data fusion (100%) > caps (94.12%) > stipes (91.18%), and the order of particle swarm optimization support vector machine was low-level data fusion (100%) > caps (97.06%) > stipes (88.24%). The mid-level data fusionq and low-level data fusion had best discrimination accuracy (100%) allowing each mushroom classed into its real species, which could be used for accurate discrimination of samples. B. edulis mushrooms had highest total polyphenol, with 14.76 mg/g dw and 17.33 in caps and stipes mg/g dw, respectively. The phenols were easier to accumulate in the caps in Leccinum rugosiceps (1.03) and B. tomentipes (1.19), and the opposite phenomenon is observed in B. edulis (0.85) and B. umbriniporus (0.95). The correlation coefficient and residual predictive deviation of best prediction model were 86.76% and 2.40%, respectively, indicating that that there is good relevance between FT-MIR and total polyphenol content, which could be used to predict roughly polyphenols content in mushrooms.

An integrated spectroscopic strategy to trace the geographical origins of emblic medicines: Application for the quality assessment of natural medicines

Emblic medicine is a popular natural source in the world due to its outstanding healthcare and therapeutic functions. Our preliminary results indicated that the quality of emblic medicines might have an apparent regional variation. A rapid and effective geographical traceability system has not been designed yet. To trace the geographical origins so that their quality can be controlled, an integrated spectroscopic strategy including spectral pretreatment, outlier diagnosis, feature selection, data fusion, and machine learning algorithm was proposed. A featured data matrix (245 × 220) was successfully generated, and a carefully adjusted RF machine learning algorithm was utilized to develop the geographical traceability model. The results demonstrate that the proposed strategy is effective and can be generalized. Sensitivity (SEN), specificity (SPE) and accuracy (ACC) of 97.65%, 99.85% and 97.63% for the calibrated set, as well as 100.00% predictive efficiency, were obtained using this spectroscopic analysis strategy. Our study has created an integrated analysis process for multiple spectral data, which can achieve a rapid, nondestructive and green quality detection for emblic medicines originating from seventeen geographical origins.

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.

Data fusion strategies for performance improvement of a Process Analytical Technology platform consisting of four instruments: An electrospinning case study

The aim of this work was to develop a PAT platform consisting of four complementary instruments for the characterization of electrospun amorphous solid dispersions with meloxicam. The investigated methods, namely NIR spectroscopy, Raman spectroscopy, Colorimetry and Image analysis were tested and compared considering the ability to quantify the active pharmaceutical ingredient and to detect production errors reflected in inhomogeneous deposition of fibers. Based on individual performance the calculated RMSEP values ranged between 0.654% and 2.292%. Mid-level data fusion consisting of data compression through latent variables and application of ANN for regression purposes proved efficient, yielding an RMSEP value of 0.153%. Under these conditions the model could be validated accordingly on the full calibration range. The complementarity of the PAT tools, demonstrated from the perspective of captured variability and outlier detection ability, contributed to model performance enhancement through data fusion. To the best of the author's knowledge, this is the first application of data fusion in the field of PAT for efficient handling of big-analytical-data provided by high-throughput instruments.

Fusion of near-infrared and fluorescence spectroscopy for untargeted fraud detection of Chinese tea seed oil using chemometric methods

BACKGROUND

This paper investigated the feasibility of data fusion of near-infrared (NIR) and fluorescence spectroscopy for rapid analysis of cheap vegetable oils in Chinese Camellia oleifera Abel. (COA) oil. Because practical frauds usually involve adulterations of multiple known and unknown cheap oils, traditional analytical methods aimed at detecting one or more known adulterants are insufficient to identify adulterated COA oil. Therefore, untargeted analysis was performed by developing class models of pure COA oil using robust one-class partial least squares (OCPLS).

RESULTS

The most accurate OCPLS model was obtained with fusion of standard normal variate (SNV)-NIR and SNV-fluorescence spectra with sensitivity of 0.954 and specificity of 0.91. Robust OCPLS could detect adulterations with 2% (w/w) or more cheap oils, including rapeseed oil, sunflower seed oil, corn oil and peanut oil.

CONCLUSION

Fusion of NIR and fluorescence data and chemometrics provided enhanced capacity for rapid and untargeted analysis of multiple adulterations in Chinese COA oils. © 2018 Society of Chemical Industry.

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.