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

Quantitative evaluation of composition and biomolecular mapping of macrofungi spores by Raman spectroscopy

Raman spectroscopy is a widely used technique for detecting various chemical compounds in organic matter and creating high-resolution, label-free maps on the level of individual cells. One of the main advantages of this technique is its ability to study samples in vivo without special pretreatment. However, it is rarely used to determine the ratios between different substances or to conduct in-depth quantitative analysis of the obtained spectra. In our study, we establish ratiometric equations that enable the estimation of mass concentrations of triacylglycerols, proteins, sugars, polysaccharides, and DNA. We demonstrate that it is possible to determine the average unsaturation and chain length from the spectra of lipids and concentrations of phenylalanine, tyrosine, and tryptophan from the spectra of proteins. We apply the derived equations to the Raman spectra of fungal spores from over 70 different species of macrofungi, providing a comprehensive characterization of the lipids, proteins, and polysaccharides present in the spores. We believe that our study not only contributes valuable fundamental knowledge to the field of mycology but also lays the groundwork for the spectral quantification of any organic material. So, our approach may be applicable in areas such as food diagnostics.

Constructing an origin discrimination model of japonica rice in Heilongjiang Province based on confocal microscopy Raman spectroscopy technology

An origin discrimination model for rice from five production regions in Heilongjiang Province was constructed based on the combination of confocal microscopy Raman spectroscopy and chemometrics. A total of 150 field rice samples were collected from the Fangzheng, Chahayang, Jiansanjiang, Xiangshui, and Wuchang production areas. The optimal sample processing conditions, instrument parameter settings, and spectrum acquisition techniques were identified by investigating the influencing factor. The Raman spectra of milled rice within the range of 100-3200 cm-1 were selected as the raw data, and the optimal preprocessing method combination consisting of normalization, Savitzky-Golay smoothing, and multivariate scatter correction was identified. Subsequently, the competitive adaptive reweighted sampling and discrete binary particle swarm optimization algorithms were employed to optimize the feature wavelength selection, resulting in the screening of 226 and 1899 feature wavelength variables, respectively. Using the full Raman spectrum data and feature wavelength data as inputs, partial least squares discriminant analysis, support vector machine and extreme learning machine origin discrimination models were constructed. The results indicated that the BPSO-GA-SVM model exhibited the best predictive ability, achieving a testing set accuracy of 86.67%.

Mushroom-Mediated Redox Reactions

The application of biocatalysts in organic synthesis has grown significantly in recent years, and both academia and industry are continuously searching for novel biocatalysts capable of performing challenging chemical reactions. Mushrooms are a rich source of ligninolytic and secondary metabolite biosynthetic enzymes, and therefore were considered promising biocatalysts for organic synthesis. This review focuses on the broad utilization potential of mushroom-based biocatalysts and highlights key advances in mushroom-mediated redox reactions. It mainly includes the reduction of ketones and carboxylic acids, hydroxylation of aromatic and aliphatic compounds, epoxidation of olefins, oxidative cleavage of alkenes, and other uncommon reactions catalyzed by the whole cells or purified enzymes of mushroom origin. Overall, a comprehensive overview of the applications of mushrooms as biocatalysts in organic synthesis is provided, which puts this versatile microorganism in the spotlight of further research.

Preventing mislabeling of organic white button mushrooms (Agaricus bisporus) combining NMR-based foodomics, statistical, and machine learning approach

Organic foods are among the most susceptible to fraud and mislabeling since the differentiation between organic and conventionally grown food relies on a paper-trail-based system. This study aimed to develop a differentiation model that combines nuclear magnetic resonance (NMR), statistical approach (principal component analysis - PCA and partial least square discriminant analysis - PLS-DA), and classification artificial neural network (cANN). The model was tested for hydrophilic and lipophilic extracts of Agaricus bisporus. As linear techniques, the PCA and PLS-DA analyses and cANN as a non-linear classification tool successfully discriminated organic from conventional samples regarding their NMR data. PLS-DA revealed higher similarity among the hydrophilic samples within the organic class and among the lipophilic samples within the conventional class. Both applied approaches demonstrated high statistical quality, but a higher level of classification confidence in the case of lipophilic extracts. The metabolites responsible for discrimination and observed (dis)similarities between classes were considered according to cultivation specificities.

Mushroom By-Products as a Source of Growth Stimulation and Biochemical Composition Added-Value of Pleurotus ostreatus, Cyclocybe cylindracea, and Lentinula edodes

Spent mushroom substrates (SMSs) and mushroom basal bodies (MBBs) are significant by-products because of their nutrient content even after harvesting. This study aimed to evaluate the effect of these two by-products, derived from Agaricus bisporus (Ab) and Cyclocybe cylindracea (Cc) cultivation, as potential growth and biochemical composition add-value enhancers of edible mushroom mycelia such as Pleurotus ostreatus, C. cylindracea, and Lentinula edodes. Fungal growth substrates enriched with SMS and MBB extracts significantly affected the growth of mushroom mycelia. In particular, on P. ostreatus, the MBBs Ab and Cc extracts determined an increase in mycelial weight by 89.5%. Also, by-products influenced mushrooms' mycelial texture, which appeared more floccose and abundant in growth. FT-IR analysis showed that L. edodes mycelium, grown on MBB substrates, showed the highest increase in bands associated with proteins and chitin. Results demonstrated that mushroom by-products enhance mycelial growth and confer an enrichment of compounds that could increase mycelial resistance to pathogens and make a nutraceutical improvement.

Yeasts volatile organic compounds (VOCs) as potential growth enhancers and molds biocontrol agents of mushrooms mycelia

Volatile organic compounds (VOCs) produced by yeasts can positively affect crops, acting as antifungals or biostimulants. In this study, Aureobasidium pullulans and Metschnikowia pulcherrima were evaluated as potential antagonists of Trichoderma spp., common fungal pathogen in mushroom cultivation. To assess the biocontrol ability and biostimulant properties of the selected yeast species, in vitro co-culture and VOCs exposure assays were conducted. In both assays, VOCs produced by Aureobasidium spp. showed the stronger antifungal activity with a growth inhibition up to 30 %. This result was further confirmed by the higher volatilome alcohol content revealed by solid phase microextraction-gas chromatography mass spectrometry (SPME/GC-MS). Overall, Aureobasidium strains can be potentially used as biocontrol agent in Pleorotus ostreatus and Cyclocybe cylindracea mycelial growth, without affecting their development as demonstrated by VOCs exposure assay and Fourier-transform infrared spectroscopy (FT-IR). Conversely, M. pulcherrima was characterized by a lower or absent antifungal properties and by a volatilome composition rich in isobutyl acetate, an ester often recognized as plant growth promoter. As confirmed by FT-IR, Lentinula mycelia exposed to M. pulcherrima VOCs showed a higher content of proteins and lipids, suggesting an improvement of some biochemical properties. Our study emphasizes that VOCs produced by specific yeast strains are potentially powerful alternative to synthetic fungicide in the vegetative growth of mushroom-forming fungi and also able to modify their biochemical composition.

Benefits of Mushroom-Based Supplements on Growth Performance, Immunocompetence, and Meat Quality in Poultry

The restriction on the use of antibiotics in poultry has led to an increase in the use of natural products that could serve as alternatives to antibiotics. Mushrooms contain bioactive compounds that exhibit antifungal, antiparasitic, antibacterial, antioxidant, antiviral, anti-inflammatory, and cytotoxic properties. Hence, they are being tested, revealing as performance-enhancing natural feed additives for livestock. This review focused on the role of different species of mushrooms commonly used in poultry on the performance, immunomodulatory actions, cholesterolemic properties, and meat quality of poultry birds. Different studies reviewed show that mushrooms could positively impact poultry production, improve growth performance, modulate immune response, exert tissue antioxidant activity, influence intestinal morphology, enhance gut microbiome, and improve lipid profile. The variations in their efficacy could be attributed to the variations in physicochemical properties of different species and dosage levels applied in the experiments. However, the use of mushrooms as a natural product supplement is in its infancy, and more basic, pilot and large-scale research is required to make it a viable approach for improving immune responses in the poultry industry.

Complete mitochondrial genomes of edible mushrooms: features, evolution, and phylogeny

Edible mushrooms are an important food source with high nutritional and medicinal value. They are a useful source for studying phylogenetic evolution and species divergence. The exploration of the evolutionary relationships among these species conventionally involves analyzing sequence variations within their complete mitochondrial genomes, which range from 31,854 bp (Cordyceps militaris) to 197,486 bp (Grifolia frondosa). The study of the complete mitochondrial genomes of edible mushrooms has emerged as a critical field of research, providing important insights into fungal genetic makeup, evolution, and phylogenetic relationships. This review explores the mitochondrial genome structures of various edible mushroom species, highlighting their unique features and evolutionary adaptations. By analyzing these genomes, robust phylogenetic frameworks are constructed to elucidate mushrooms lineage relationships. Furthermore, the exploration of different variations of mitochondrial DNA presents novel opportunities for enhancing mushroom cultivation biotechnology and medicinal applications. The mitochondrial genomic features are essential for improving agricultural practices and ensuring food security through improved crop productivity, disease resistance, and nutritional qualities. The current knowledge about the mitochondrial genomes of edible mushrooms is summarized in this review, emphasising their significance in both scientific research and practical applications in bioinformatics and medicine.

Biological Activities of Secondary Metabolites from the Edible-Medicinal Macrofungi

Macrofungi are well-known as edible-medicinal mushrooms, which belong mostly to Basidiomycota, with a few from Ascomycota. In recent years, macrofungi have been recognized as a rich resource of structurally unique secondary metabolites, demonstrating a wide range of bioactivities, including anti-tumor, antioxidant, anti-inflammatory, antimicrobial, antimalarial, neuro-protective, hypoglycemic, and hypolipidemic activities. This review highlights over 270 natural products produced by 17 families of macrofungi covering 2017 to 2023, including their structures, bioactivities, and related molecular mechanisms.

Rapid identification of counterfeited beef using deep learning-aided spectroscopy: Detecting colourant and curing agent adulteration

The adulteration of meat products using colourants and curing agents has heightened concerns over food safety, thereby necessitating the development of advanced detection methods. This study introduces a deep-learning-based spectroscopic method for swiftly identifying counterfeit beef altered to appear fresh. The experiment involved 60 beef samples, half of which were artificially adulterated using a colouring solution. Despite meticulous analysis of the beef's colour attributes, no significant differences were observed between the fresh and adulterated samples. However, our method, utilising a 344-1040 nm spectral range, achieved a classification accuracy of 98.84%. To enhance practicality, we employed gradient-weighted class activation mapping and identified the 580-600 nm range as particularly influential for classification. Remarkably, even when we narrowed the input to the model to this spectral range, a high level of classification accuracy was maintained. To further validate the model's robustness and generalisability, we allocated 70 beef samples to an external validation set. Comparative performance analysis revealed that our model outperformed traditional machine learning algorithms, such as SVM and logistic regression, by 9.3% and 28.4%, respectively. Overall, this study offers invaluable insights for detecting counterfeited beef, thereby contributing to the preservation of meat product quality and integrity within the food industry.

Metabolite Profiling of Pleurotus ostreatus Grown on Sisal Agro-Industrial Waste Supplemented with Cocoa Almond Tegument and Wheat Bran

Pleurotus ostreatus is an edible fungus with high nutritional value that uses industrial and agricultural lignocellulosic residues as substrates for growth and reproduction. Understanding their growth metabolic dynamics on agro-industrial wastes would help to develop economically viable and eco-friendly biotechnological strategies for food production. Thus, we used UHPLC/MS/MS and GNPS as an innovative approach to investigate the chemical composition of two strains of P. ostreatus, coded as BH (Black Hirataki) and WH (White Hirataki), grown on sisal waste mixture (SW) supplemented with 20 % cocoa almond tegument (CAT) or 20 % of wheat bran (WB). Metabolite dereplication allowed the identification of 53 metabolites, which included glycerophospholipids, fatty acids, monoacylglycerols, steroids, carbohydrates, amino acids, and flavonoids. This is the first report of the identification of these compounds in P. ostreatus, except for the steroid ergosterol. Most of the metabolites described in this work possess potential biological activities, which support the nutraceutical properties of P. ostreatus. Thus, the results of this study provide essential leads to the understanding of white-rot fungi chemical plasticity aiming at developing alternative biotechnologies strategies for waste recycling.

An Automated Image Processing Module for Quality Evaluation of Milled Rice

The paper demonstrates a low-cost rice quality assessment system based on image processing and machine learning (ML) algorithms. A Raspberry-Pi based image acquisition module was developed to extract the structural and geometric features from 3081 images of eight different varieties of rice grains. Based on features such as perimeter, area, solidity, roundness, compactness, and shape factor, an automatic identification system is developed to segment the grains based on their types and classify them by using seven machine learning algorithms. These ML models are trained using the images and are compared using different ML models. ROC curves are plotted for each model for quantitative analysis to assess the model’s performance. It is concluded that the random forest classifier presents an accuracy of 77 percent and is the best-performing model for the classification of rice varieties. Furthermore, the same algorithm is efficiently employed to determine the price of adulterated rice samples based upon the market price of individual rice.

Determination on Tree Species Selection for Lingzhi or Reishi Medicinal Mushroom Ganoderma lucidum (Agaricomycetes) Cultivation by Fourier Transform Infrared and Two-Dimensional Infrared Correlation Spectroscopy

As a wood-degrading Agaricomycetes mushroom, Ganoderma lucidum can be cultivated on broad-leaf hardwoods. Generally, producers care about the yield, but not the quality of G. lucidum cultivated by different tree species. In this study, five broad-leaf hardwood tree species-Quercus variabilis Bl. (Qv), Castanea mollissima Bl. (Cm), Liquidambar formosana Hance (Lf), Dalbergia hupeana Hance (Dh), and Platycarya strobilacea Sieb. et Zucc. (Ps)-were selected for cultivating of G. lucidum. The chemical compositions of G. lucidum fruiting bodies produced by these tree species were determined by Fourier transform infrared and two-dimensional infrared correlation spectroscopy in order to select the most suitable tree species for cultivation. The overall spectra showed less discrimination of each peak variation detected and properly kept most of the primary metabolites. The second derivative unfolded the stagnation of the first spectrum and more base peaks were detected especially in the range of the first two sections. The protein content contained in G. lucidum cultivated on Ps was 92%, like that on Dh. On the other hand, only 27% similarity was determined in G. lucidum cultivated on Ps and Qv. Therefore, the correlation of this range for the protein content can help in tree species selection. The active sequence of 2DIR spectral could be determined by the active bonding of the component reacted to the perturbation. The result could provide a scientific basis for the selection of tree species and the comprehensive utilization of broad-leaf tree resources on G. lucidum cultivation.

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.

Effect of Electrohydrodynamic (EHD) on Drying Kinetics and Quality Characteristics of Shiitake Mushroom

The effect of an electrohydrodynamic (EHD) drying system on the drying kinetics, microstructure and nutritional composition of shiitake mushrooms was studied. Shiitake mushroom slices were dried at 0, 18, 22, 26, 30 and 34 kV. The results showed that the drying rate, effective moisture diffusion coefficient and shrinkage of the EHD treatment group were significantly higher than those of the control group. The 34 kV treatment group had the highest drying rate (0.24 g W/g DM × h) and the highest effective moisture diffusion coefficient (1.45 × 10−6 m2/s), which were 6.75 and 7.41 times higher than those of the control group, respectively. The control group had the highest rehydration ratio (7.72) and showed unsatisfactory color performance. The scanning electron microscopy (SEM) results showed that compared with the control group, the surface of samples dried by EHD exhibited different degrees of encrustation, and the area of encrustation increased with increasing voltage. After analysis by Fourier transform infrared (FTIR) spectroscopy, it was found that the samples of both the EHD-treated and control groups had similar absorption peak positions, but the intensity of the absorption peak of the EHD-dried samples was greater. Compared with the control group, the shiitake mushroom slices dried by EHD had a higher protein content and polysaccharide content. The polysaccharide content in 22 kV treatment group was the highest (4.67 g/100 g), and the protein content in 26 kV and 34 kV treatment groups was the highest (17.0 g/100 g). This study provides an experimental and theoretical basis for an in-depth study of the drying kinetics of shiitake mushrooms and provides theoretical guidance and clues for the wider application of EHD drying technology.

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.

Rapid Determination of β-Glucan Content of Hulled and Naked Oats Using near Infrared Spectroscopy Combined with Chemometrics

The quantification of β-glucan in oats is of immense importance for plant breeders and food scientists to develop plant varieties and food products with a high quantity of β-glucan. However, the chemical analysis of β-glucan is time consuming, destructive, and laborious. In this study, near-infrared (NIR) spectroscopy in conjunction with Chemometrics was employed for rapid and non-destructive prediction of β-glucan content in oats. The interval Partial Least Square (iPLS) along with correlation matrix plots were employed to analyze the NIR spectrum from 700-1300 nm, 1300-1900 nm, and 1900-2500 nm for the selection of important wavelengths for the prediction of β-glucan. The NIR spectral data were pre-treated using Savitzky Golay smoothening and normalization before employing partial least square regression (PLSR) analysis. The PLSR models were established based on the selection of wavelengths from PLS loading plots that present a high correlation with β-glucan content. It was observed that wavelength region 700-1300 nm is sufficient for the satisfactory prediction of β-glucan of hulled and naked oats with R²c of 0.789 and 0.677, respectively, and RMSE < 0.229.

Experimental Treatment of Hazardous Ash Waste by Microbial Consortium Aspergillus niger and Chlorella sp.: Decrease of the Ni Content and Identification of Adsorption Sites by Fourier-Transform Infrared Spectroscopy

Despite the negative impact on the environment, incineration is one of the most commonly used methods for dealing with waste. Besides emissions, the production of ash, which usually shows several negative properties, such as a higher content of hazardous elements or strongly alkaline pH, is problematic from an environmental viewpoint as well. The subject of our paper was the assessment of biosorption of Ni from ash material by a microbial consortium of Chlorella sp. and Aspergillus niger. The solid substrate represented a fraction of particles of size <0.63 mm with a Ni content of 417 mg kg-1. We used a biomass consisting of two different organisms as the sorbent: a non-living algae culture of Chlorella sp. (an autotrophic organism) and the microscopic filamentous fungus A. niger (a heterotrophic organism) in the form of pellets. The experiments were conducted under static conditions as well as with the use of shaker (170 rpm) with different modifications: solid substrate, Chlorella sp. and pellets of A. niger; solid substrate and pellets of A. niger. The humidity-temperature conditions were also changed. Sorption took place under dry and also wet conditions (with distilled water in a volume of 30-50 ml), partially under laboratory conditions at a temperature of 25°C as well as in the exterior. The determination of the Ni content was done using inductively coupled plasma optical emission spectrometry (ICP-OES). The removal of Ni ranged from 13.61% efficiency (Chlorella sp., A. niger with the addition of 30 ml of distilled water, outdoors under static conditions after 48 h of the experiment) to 46.28% (Chlorella sp., A. niger with the addition of 30 ml of distilled water, on a shaker under laboratory conditions after 48 h of the experiment). For the purpose of analyzing the representation of functional groups in the microbial biomass and studying their interaction with the ash material, we used Fourier-transform infrared (FTIR) spectroscopy. We observed that the amount of Ni adsorbed positively correlates with absorbance in the spectral bands where we detect the vibrations of several organic functional groups. These groups include hydroxyl, aliphatic, carbonyl, carboxyl and amide structural units. The observed correlations indicate that, aside from polar and negatively charged groups, aliphatic or aromatic structures may also be involved in sorption processes due to electrostatic attraction. The correlation between absorbance and the Ni content reached a maximum in amide II band (r = 0.9; P < 0.001), where vibrations of the C=O, C-N, and N-H groups are detected. The presented results suggest that the simultaneous use of both microorganisms in biosorption represents an effective method for reducing Ni content in a solid substrate, which may be useful as a partial process for waste disposal.

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.

Assessment of Biotechnologically Important Filamentous Fungal Biomass by Fourier Transform Raman Spectroscopy

Oleaginous filamentous fungi can accumulate large amount of cellular lipids and biopolymers and pigments and potentially serve as a major source of biochemicals for food, feed, chemical, pharmaceutical, and transport industries. We assessed suitability of Fourier transform (FT) Raman spectroscopy for screening and process monitoring of filamentous fungi in biotechnology. Six Mucoromycota strains were cultivated in microbioreactors under six growth conditions (three phosphate concentrations in the presence and absence of calcium). FT-Raman and FT-infrared (FTIR) spectroscopic data was assessed in respect to reference analyses of lipids, phosphorus, and carotenoids by using principal component analysis (PCA), multiblock or consensus PCA, partial least square regression (PLSR), and analysis of spectral variation due to different design factors by an ANOVA model. All main chemical biomass constituents were detected by FT-Raman spectroscopy, including lipids, proteins, cell wall carbohydrates, and polyphosphates, and carotenoids. FT-Raman spectra clearly show the effect of growth conditions on fungal biomass. PLSR models with high coefficients of determination (0.83-0.94) and low error (approximately 8%) for quantitative determination of total lipids, phosphates, and carotenoids were established. FT-Raman spectroscopy showed great potential for chemical analysis of biomass of oleaginous filamentous fungi. The study demonstrates that FT-Raman and FTIR spectroscopies provide complementary information on main fungal biomass constituents.

Biological activities of Pleurotus spp. polysaccharides: A review

Mushrooms are consumed for their nutrients and therapeutic bioactive compounds and are used medicinally in Chinese and Japanese medicine traditions since time immemorial. Members of the genus Pleurotus form a heterogeneous group of edible species with outstanding nutritional profiles rich in fiber, vitamins (thiamine, riboflavin, ascorbic acid, ergosterine, and niacin), micro and macro-elements (phosphorus and iron), and carbohydrates. Pleurotus is one of the most diversified medicinal and edible mushrooms related to the composition of chemical structures such as polysaccharides, glycoproteins, and secondary metabolites such as alkaloids and betalains. The cultivation of Pleurotus spp. on lignocellulosic wastes represents one of the most economically and cost-effective organic recycling processes, especially for the utilization of different feasible and cheap recyclable residues. Also, several Pleurotus spp. have the ability to remove phenolic compounds from wastewater with the action of phenoloxidase activity. Here, we have reviewed the chemistry of such polysaccharides and their reported biological activities, namely, anti-inflammatory, immunomodulatory, anti-diabetic, anti-tumor, antioxidant, etc. The mechanism of action and effects of novel polysaccharides extracted from various species of Pleurotus have been studied. The current study will be beneficial for guiding future research projects on the above concept and investigating more deeply the health of human beings. PRACTICAL APPLICATIONS: Mushrooms are one of the most delicious foods around the globe and have many medicinal properties for decades. Various Pleurotus species have been in focus in recent years because of their palatability and medicinal importance too. It contains many bioactive compounds among which polysaccharides are valued to a great extent. Many biological activities are exerted by polysaccharides derived from the Pleurotus spp., namely, anti-tumor, antioxidant, and many more. They are responsible for significant physiological responses in animals, animal-alternative in vitro models, and humans. Their important physicochemical characteristics benefit their use in the food industry as well. So, the biological activities of these Pleurotus spp. polysaccharides will provide an insight to develop Pleurotus spp. as functional foods, because of their nutritional value and presence of bioactive components.

Lectins purified from medicinal and edible mushrooms: Insights into their antiviral activity against pathogenic viruses

For thousands of years, fungi have been a valuable and promising source of therapeutic agents for treatment of various diseases. Mushroom is a macrofungus which has been cultivated worldwide for its nutritional value and medicinal applications. Several bioactive molecules were extracted from mushroom such as polysaccharides, lectins and terpenoids. Lectins are carbohydrate-binding proteins with non-immunologic origin. Lectins were classified according to their structure, origin and sugar specificity. This protein has different binding specificity with surface glycan moiety which determines its activity and therapeutic applications. A wide range of medicinal activities such as antitumor, antiviral, antimicrobial, immunomodulatory and antidiabetic were reported from sugar-binding proteins. However, glycan-binding protein from mushroom is not well explored as antiviral agent. The discovery of novel antiviral agents is a public health emergency to overcome the current pandemic and be ready for the upcoming viral pandemics. The mechanism of action of lectin against viruses targets numerous steps in viral life cycle such as viral attachment, entry and replication. This review described the history, classification, purification techniques, structure-function relationship and different therapeutic applications of mushroom lectin. In addition, we focus on the antiviral activity, purification and physicochemical characteristics of some mushroom lectins.

Application of vibrational spectroscopic techniques for determination of thermal degradation of frying oils and fats: a review

Deep fried foods are popular among consumers due to their unique taste and texture. During the process of deep-frying, oil is subjected to a high temperature that results into the generation of harmful compounds. The repeated usage of frying oil is a common exercise and associated with various health hazards. Thus, determination of frying oil quality is a critical practice to follow. The chemical methods employed to determine the quality of frying oil are destructive and require large amount of harmful chemical, thus researchers are exploring the application of various vibrational spectroscopic techniques for this purpose. The first part of this review provides a detailed insight into fundamental theoretical aspects of two main vibrational spectroscopic techniques (infrared and Raman spectroscopy) and chemical alteration in frying oils under thermal stress. While in the following parts, the application of near-infrared (NIR) and Fourier transform infrared (FTIR) and Raman spectroscopy for evaluating the quality of various frying oils and fats under thermal stress has been discussed. It is anticipated that this review paper can serve as a reference source for impending research in this field.

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.

Evaluation of the Cultivated Mushroom Pleurotus ostreatus Basidiocarps Using Vibration Spectroscopy and Chemometrics

Fruiting bodies (basidiocarps) of the cultivated mushroom Pleurotus ostreatus (16 strains) were characterized by vibration spectroscopy and chemometrics. According to organic elemental analysis and Megazyme assay, the basidiocarps contained ~6.2–17.5% protein and ~18.8–58.2% total glucans. The neutral sugar analysis confirmed that glucose predominated in all the samples (~71.3–94.4 mol%). Fourier-transformed (FT) mid- and near-infrared (FT MIR, FT NIR) and FT Raman spectra of the basidiocarps were recorded, and the characteristic bands of proteins, glucans and chitin were assigned. The samples were discriminated based on principal component analysis (PCA) of the spectroscopic data in terms of biopolymeric composition. The partial least squares regression (PLSR) models based on first derivatives of the vibration spectra were obtained for the prediction of the macromolecular components, and the regression coefficients R2 and root mean square errors (RMSE) were calculated for the calibration (cal) of proteins (R2cal 0.981–0.994, RMSEcal ~0.3–0.5) and total glucans (R2cal 0.908–0.996, RMSEcal ~0.6–3.0). According to cross-validation (CV) diagnosis, the protein models were more precise and accurate (R2cv 0.901–0.970, RMSEcv ~0.6–1.1) than the corresponding total glucan models (R2cv 0.370–0.804, RMSEcv ~4.7–8.5) because of the wide structural diversity of these polysaccharides. Otherwise, the Raman band of phenylalanine ring breathing vibration at 1004 cm−1 was used for direct quantification of proteins in P. ostreatus basidiocarps (R ~0.953). This study showed that the combination of vibration spectroscopy with chemometrics is a powerful tool for the evaluation of culinary and medicinal mushrooms, and this approach can be proposed as an alternative to common analytical methods.

Fermentation characteristics of novel Coriolus versicolor and Lentinus edodes kombucha beverages and immunomodulatory potential of their polysaccharide extracts

Medicinal mushrooms, Coriolus versicolor and Lentinus edodes are extremely attractive as nutraceuticals. Here we used fruiting bodies to prepare novel kombucha beverage. Microbiological, physicochemical and chemical properties were monitored for eleven days, while the immunological properties of kombucha polysaccharide extracts were determined in peripheral blood mononuclear cell (PBMC) cultures. FTIR analysis of polysaccharide extracts showed dominant presence of polysaccharides, in addition to phenols, lipids and proteins. C. versicolor kombucha extract displayed more complex polysaccharides, and a higher content of total polysaccharides, phenols and flavonoids compared to L. edodes kombucha extract. The extracts were not cytotoxic for PBMC in vitro up to 500 µg/ml, while immunomodulatory effects depended on their chemical compositions. The most prominent effect was on the reduction of Th2 cytokines and IL-10 in PBMC cultures. Based on these results, novel kombucha products could be recommended as functional beverages or nutraceuticals with potentially beneficial immunomodulatory effects in allergies.

Applying microwave vacuum pyrolysis to design moisture retention and pH neutralizing palm kernel shell biochar for mushroom production

Microwave vacuum pyrolysis of palm kernel shell was examined to produce engineered biochar for application as additive in agriculture application. The pyrolysis approach, performed at 750 W of microwave power, produced higher yield of porous biochar (28 wt%) with high surface area (270 cm²/g) compared to the yield obtained by conventional approach (<23 wt%). Addition of the porous biochar in mushroom substrate showed increased moisture content (99%) compared to the substrate without biochar (96%). The mushroom substrate added with biochar (150 g) was optimal in shortening formation, growth, and full colonization of the mycelium within one month. Using 2.5% of the biochar in mushroom substrate desirably maintained the optimum pH level (6.8-7) during the mycelium colonization period, leading to high mycelium growth (up to 91%) and mushroom yield (up to 280 g). The engineered biochar shows great potential as moisture retention and neutralizing agent in mushroom cultivation.

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.

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.

A Modified Moving-Window Partial Least-Squares Method by Coupling with Sampling Error Profile Analysis for Variable Selection in Near-Infrared Spectral Analysis

In this study, a new variable selection method, named moving-window partial least-squares coupled with sampling error profile analysis (SEPA-MWPLS), is developed. With a moving window, moving-window partial least-squares (MWPLS) is used to find window intervals which show low residual sums of squares (RSS) of a calibration set. Sampling error profile analysis (SEPA) is a useful method based on Monte-Carlo Sampling and profile analysis for cross validation (CV). By combining MWPLS with SEPA, we can obtain more stable and reliable results. Besides, we simplify the plot of the RSS line so that it is easier to determine the informative intervals. In addition, a backward elimination strategy is used to optimize the combination of subintervals. The performance of SEPA-MWPLS was tested with two near-infrared (NIR) spectra datasets and was compared with PLS, MWPLS and Monte Carlo uninformative variable elimination (MC-UVE). The results show that SEPA-MWPLS can improve model performances significantly compared with MWPLS in the number of variables, root-mean-squared errors of CV, calibration and prediction (RMSECVs, RMSECs and RMSEPs). Meanwhile it also exhibits better performances than MC-UVE.

Evaluation of the analytical performances of two Raman handheld spectrophotometers for pharmaceutical solid dosage form quantitation

This paper addresses the issue of pharmaceutical solid dosage form quantitation using handheld Raman spectrophotometers. The two spectrophotometers used are designed with different technologies: one allows getting a more representative sampling with the Orbital Raster Scanning technology and the other one allows setting acquisition parameters. The goal was to evaluate which technology could provide the best analytical results. Several parameters were optimized to get the lowest prediction error in the end. The main objective of this study was to evaluate if this kind of instrument would be able to identify substandard medicines. For that purpose, two case-study were explored. At first, a full ICH Q2 (R1) compliant validation was performed for moderate Raman scatterer active pharmaceutical ingredient (API) in a specific formulation. It was successfully validated in the ±15% relative total error acceptance limits, with a RMSEP of 0.85% (w/w). Subsequently, it was interesting to evaluate the influence of excipients when the API is a high Raman scatterer. For that purpose, a multi-formulation model was developed and successfully validated with a RMSEP of 2.98% (w/w) in the best case. These two studies showed that thanks to the optimization of acquisition parameters, Raman handheld spectrophotometers methods were validated for two different case-study and could be applied to identify substandard medicines.