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

Detection and quantification of cocoa powder adulteration using Vis-NIR spectroscopy with chemometrics approach

The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of adulteration practices aimed at achieving economic benefits. This study aimed to detect and quantify cocoa powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat. The NIRS data could not be resolved using Savitzky-Golay smoothing. Nevertheless, the application of a random forest and support vector machine successfully classified the samples with 100% accuracy. Quantification of adulteration using partial least squares (PLS), Lasso, Ridge, elastic Net, and RF regressions provided R2 higher than 0.96 and root mean square error <2.6. Coupling PLS with the Boruta algorithm produced the most reliable regression model (R2 = 1, RMSE = 0.0000). Finally, an online application was prepared to facilitate the determination of adulterants in the cocoa powder.

Analysis of Whole-Genome facilitates rapid and precise identification of fungal species

Fungal identification is a cornerstone of fungal research, yet traditional molecular methods struggle with rapid and accurate onsite identification, especially for closely related species. To tackle this challenge, we introduce a universal identification method called Analysis of whole GEnome (AGE). AGE includes two key steps: bioinformatics analysis and experimental practice. Bioinformatics analysis screens candidate target sequences named Targets within the genome of the fungal species and determines specific Targets by comparing them with the genomes of other species. Then, experimental practice using sequencing or non-sequencing technologies would confirm the results of bioinformatics analysis. Accordingly, AGE obtained more than 1,000,000 qualified Targets for each of the 13 fungal species within the phyla Ascomycota and Basidiomycota. Next, the sequencing and genome editing system validated the ultra-specific performance of the specific Targets; especially noteworthy is the first-time demonstration of the identification potential of sequences from unannotated genomic regions. Furthermore, by combining rapid isothermal amplification and phosphorothioate-modified primers with the option of an instrument-free visual fluorescence method, AGE can achieve qualitative species identification within 30 min using a single-tube test. More importantly, AGE holds significant potential for identifying closely related species and differentiating traditional Chinese medicines from their adulterants, especially in the precise detection of contaminants. In summary, AGE opens the door for the development of whole-genome-based fungal species identification while also providing guidance for its application in plant and animal kingdoms.

Facile biosynthesis of Ag-ZnO nanocomposites using Launaea cornuta leaf extract and their antimicrobial activity

The quest to synthesize safe, non-hazardous Ag-ZnO nanoomposites (NCs) with improved physical and chemical properties has necessitated green synthesis approaches. In this research, Launaea cornuta leaf extract was proposed for the green synthesis of Ag-ZnO NCs, wherein the leaf extract was used as a reducing and capping agent. The antibacterial activity of the prepared nanoomposites was investigated against Escherichia coli and Staphylococcus aureus through the disc diffusion method. The influence of the synthesis temperature, pH, and precursor concentration on the synthesis of the Ag-ZnO NCs and antimicrobial efficacy were investigated. The nanoparticles were characterized by ATR-FTIR, XRD, UV-Vis, FESEM, and TEM. The FTIR results indicated the presence of secondary metabolites in Launaea cornuta which assisted the green synthesis of the nanoparticles. The XRD results confirmed the successful synthesis of crystalline Ag-ZnO NCs with an average particle size of 21.51 nm. The SEM and TEM images indicated the synthesized nanoparticles to be spherical in shape. The optimum synthesis conditions for Ag-ZnO NCs were at 70 °C, pH of 7, and 8% silver. Antibacterial activity results show Ag-ZnO NCs to have higher microbial inhibition on E. coli than on S. aureus with the zones of inhibition of 21 ± 1.08 and 19.67 ± 0.47 mm, respectively. Therefore, the results suggest that Launaea cornuta leaf extract can be used for the synthesis of Ag-ZnO NCs.

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.

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

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

Synthesis and structural characterization of β -cyclodextrin butenate

β-cyclodextrin butenate was synthesized by using N, N'-Carbonyldiimidazole (CDI) activating reagent and 4-Dimethylaminopyridine (DMAP) as catalyst. The best preparation condition of β-CD butenate was described as below: reaction temperature was 25°C, concentration of 2-butenoic acid was 450 mmol/L, concentration of DMAP was 12.5 mmol/L and reaction time was 20 minutes and at this condition the yield of β-CD butenate was 0.83 mmol/g. According to the results of FT-IR spectrum, NMR spectroscopy and HPLC-QTof-mass spectrum of β-CD butenate, there were four types β-CD butenate synthesized, which were β-CD-2-butenoic acid monoester, β-CD-2-butenoic acid diester, β-CD-2-butenoic acid triester and β-CD-2-butenoic acid tetraester, respectively.

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.

Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS-DA

BACKGROUND

How to quickly identify poisonous mushrooms is a worldwide problem, because poisonous mushrooms and edible mushrooms have very similar appearances. Even some edible mushrooms must be processed further before they can be eaten. In addition, mushrooms from different geographical origins contain different levels of heavy metals. Eating frequent mushrooms with excessive heavy metal content can also cause food poisoning. This information is very important and needs to be informed to consumers in advance. Through the demand for the safety of porcini mushrooms in the Yunnan area we propose a hierarchical identification system based on Fourier-transform near-infrared (FT-NIR) spectroscopy to evaluate the edible safety of porcini species.

RESULTS

We found that deep learning is the most effective means to identify the edible safety of porcini, and the recognition accuracy was 100%, by comparing two pattern recognition tools, deep learning and partial least square discriminant analysis (PLS-DA). Although the accuracy of the PLS-DA test set is 96.10%, the poisonous porcini is not allowed to be wrongly judged. In addition, the cadmium (Cd) content of Leccinum rugosiceps in the Midu area exceeded the standard. Deep learning can trace Le. rugosiceps geographic origin with an accuracy of 100%.

CONCLUSION

The overall results show that deep learning methods based on FT-NIR can identify porcini that is at risk of being eaten. This has useful application prospects in food safety. © 2021 Society of Chemical Industry.

Identification techniques and detection methods of edible fungi species

Edible fungi have high nutritional value and great potential. Confusion among edible fungi species, and foodborne diseases due to toadstool poisoning or death induced by inadvertent consumption exist across the world. Therefore, edible fungi must be accurately identified. Based on different substances in edible fungi, there are different detection methods, and the same method can use different identification technology. Sensory identification methods include morphological and odor methods. Instrumental analysis methods based on chemical composition include chromatographic, mass spectrometry and spectral technology. Molecular biology identification methods based on nucleic acids include molecular marker technology, sequencing technology, isothermal amplification technology and endogenous reference gene method. Method is channel, and technology is the means. The principles, advantages, disadvantages and applications of various identification techniques and detection methods were discussed in this work to provide reference for the identification research of edible fungi and technical support for preventing food safety incidents caused by toadstools.

Infrared Fiber-Optic Spectroscopy Detects Bovine Articular Cartilage Degeneration

OBJECTIVE

Joint injuries may lead to degeneration of cartilage tissue and initiate development of posttraumatic osteoarthritis. Arthroscopic surgeries can be used to treat joint injuries, but arthroscopic evaluation of articular cartilage quality is subjective. Fourier transform infrared spectroscopy combined with fiber optics and attenuated total reflectance crystal could be used for the assessment of tissue quality during arthroscopy. We hypothesize that fiber-optic mid-infrared spectroscopy can detect enzymatically and mechanically induced damage similar to changes occurring during progression of osteoarthritis.

DESIGN

Bovine patellar cartilage plugs were extracted and degraded enzymatically and mechanically. Adjacent untreated samples were utilized as controls. Enzymatic degradation was done using collagenase and trypsin enzymes. Mechanical damage was induced by (1) dropping a weight impactor on the cartilage plugs and (2) abrading the cartilage surface with a rotating sandpaper. Fiber-optic mid-infrared spectroscopic measurements were conducted before and after treatments, and spectral changes were assessed with random forest, partial least squares discriminant analysis, and support vector machine classifiers.

RESULTS

All models had excellent classification performance for detecting the different enzymatic and mechanical damage on cartilage matrix. Random forest models achieved accuracies between 90.3% and 77.8%, while partial least squares model accuracies ranged from 95.8% to 84.7%, and support vector machine accuracies from 91.7% to 80.6%.

CONCLUSIONS

The results suggest that fiber-optic Fourier transform infrared spectroscopy attenuated total reflectance spectroscopy is a viable way to detect minor and major degeneration of articular cartilage. Objective measures provided by fiber-optic spectroscopic methods could improve arthroscopic evaluation of cartilage damage.

Study on the identification and evaluation of growth years for Paris polyphylla var. yunnanensis using deep learning combined with 2DCOS

Paris polyphylla var. yunnanensis, as perennial plants, its quality is closely related to growth period. Different harvest years determine the dry matter accumulation of its medicinal parts and the dynamic accumulation of active ingredients, as well as its economic value and medicinal value. Therefore, it is necessary to establish a systematic evaluation method for the identification and evaluation of P. polyphylla var. yunnanensis with different growth years. Deep learning has a powerful ability in recognition. This study extends it to the identification analysis of medicinal plants from the perspective of spectrum. For the first time, two-dimensional correlation spectroscopy (2DCOS) based on the attenuated total reflection Fourier transformed infrared spectroscopy (ATR-FTIR) combined with residual neural network (Resnet) was used to identify growth years. 525 samples were collected, 4725 2DCOS images were drawn, and the dry matter accumulation in rhizomes of different growth years and different sampling sites were briefly analyzed. The results show that the eight-year-old P. polyphylla var. yunnanensis in Dali has higher economic value and medicinal value. The synchronous 2DCOS models based on ATR-FTIR can realize the identification of growth years with accuracy of 100%. Synchronous 2DCOS are more suitable for the identification of medicinal plants with complex systems. 2DCOS images with different colors and second derivative processing cannot optimize the modeling results. In summary, the method we established is innovative and feasible. It not only solved the identification of growth years, expanded the application field of deep learning, but could also be extended to further research on other medicinal plants.

Validation and analysis of the geographical origin of Angelica sinensis (Oliv.) Diels using multi-element and stable isotopes

Background

Place of origin is an important factor when determining the quality and authenticity of Angelica sinensis for medicinal use. It is important to trace the origin and confirm the regional characteristics of medicinal products for sustainable industrial development. Effectively tracing and confirming the material’s origin may be accomplished by detecting stable isotopes and mineral elements.

Methods

We studied 25 A. sinensis samples collected from three main producing areas (Linxia, Gannan, and Dingxi) in southeastern Gansu Province, China, to better identify its origin. We used inductively coupled plasma mass spectrometry (ICP-MS) and stable isotope ratio mass spectrometry (IRMS) to determine eight mineral elements (K, Mg, Ca, Zn, Cu, Mn, Cr, Al) and three stable isotopes (δ¹³C, δ¹⁵N, δ¹⁸O). Principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to verify the validity of its geographical origin.

Results

K, Ca/Al, δ¹³C, δ¹⁵N and δ¹⁸O are important elements to distinguish A. sinensis sampled from Linxia, Gannan and Dingxi. We used an unsupervised PCA model to determine the dimensionality reduction of mineral elements and stable isotopes, which could distinguish the A. sinensis from Linxia. However, it could not easily distinguish A. sinensis sampled from Gannan and Dingxi. The supervised PLS-DA and LDA models could effectively distinguish samples taken from all three regions and perform cross-validation. The cross-validation accuracy of PLS-DA using mineral elements and stable isotopes was 84%, which was higher than LDA using mineral elements and stable isotopes.

Conclusions

The PLS-DA and LDA models provide a theoretical basis for tracing the origin of A. sinensis in three regions (Linxia, Gannan and Dingxi). This is significant for protecting consumers’ health, rights and interests.

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.

Identification and characterization of Aspergillus species of fruit rot fungi using microscopy, FT-IR, Raman and UV-Vis spectroscopy

During the investigation of fungal isolation from fruit, the major genera were Aspergillus, Penicillium, cladosporium, Alternaria, fusarium, Colletotrichum were found. Among them Aspergillus (15 species) was found major dominant on different fruits. Fifteen different Aspergillus species viz. Aspergillus brasiliensis, Aspergillus phoenicis, Aspergillus carbonarius, four Aspergillus flavus, Aspergillus acidus, two Aspergillus awamori, Aspergillus aculeatus, Aspergillus eucalypticola, Aspergillus oryzae and two Aspergillus Spp. have been differentiate and identify using morphology (microscopic technique), Fourier Transforms Infrared spectroscopy (FTIR), Raman Spectroscopy (RS) and UV-visible spectrophotometry (UV-vis). The fungal mass in powder form was used in present study. In FTIR the finger print region is important for the characterization of Aspergillus because this region is unique and contains peaks indicating the presence of DNA. From the results were found Fourier transform infrared (FTIR) technique and Raman spectroscopy a useful tool, sensitive, fast, economical, accurate, not require sample preparation and successfully used to identify fungi.

Isaria tenuipes Peck, an entomopathogenic fungus from Darjeeling Himalaya: Evaluation of in-vitro antiproliferative and antioxidant potential of its mycelium extract

BACKGROUND

Isaria tenuipes is one of the potent species in the members of the genus Isaria, which is well reported to possess multiple bioactive substances of therapeutic importance. Therefore, an in vitro experimental study was carried to evaluate the bioactivities of the crude methanolic extract from the mycelium of this fungus.

METHODS

The fungus was authenticated through morphological characters and the species discrepancy was resolved using the nuclear rDNA ITS sequence. The methanolic extract was fingerprinted by FTIR. The antioxidant components in terms of total phenols and flavonoids were determined as gallic acid and quercetin equivalents respectively. Antioxidant activities of the methanolic extract was assessed using 1, 1-diphenyl-2-picrylhydrazyl (DPPH), 2, 2/-azinobis-(3-ethylbenzthiazoline-6-sulphonic acid) radical cation (ABTS0+), Fe2+chelating activity, and hydroxyl radical scavenging assays. Cytotoxicity of the extract was determined by [3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide] (MTT) assay on three cancer cell lines: HeLa, HepG2, and PC3. Apoptosis was further studied by propidium iodide (PI) and Annexin-V/PI staining flow cytometric analysis. Anti-proliferation capacity was studied by colony-forming assay.

RESULTS

In the present study total phenol content of the dried methanol extract was 148.09 ± 3.51μg gallic acid equivalent/mg and flavonoid was 9.02±0.95 μg quercetin/mg. The antioxidant activities of methanol-water extract (8:2 v/v) from cultured mycelia of I. tenuipes investigated and evaluated with 1, 1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging assay revealed IC50 value of 5.04mg/ml with an inhibition rate of 74.77% at 10mg/ml and with an iron-chelating assay the chelating ability was recorded to be 86.76% where the IC50 value was 4.43 mg/ml. In comparison among the antioxidant assays, 2,2/-azinobis-(3-ethylbenzthiazoline-6-sulphonic acid) radical cation (ABTS0+) and hydroxyl assay exhibited radical scavenging rate of 44.42% and 49.82% respectively at a concentration of 10 mg/ml. The IC50 value of the extract in MTT assay was 43.45μg/ml with HeLa cells, 119.33μg/ml with PC3 cells, and 125.55μg/ml with HepG2 cells.

CONCLUSION

In this study, it can be concluded that the crude methanolic extract exhibited potent antioxidant and antiproliferative activities suggesting natural antioxidative and antiproliferative agents.

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.