Geographic identification of Boletus mushrooms by data fusion of FT-IR and UV spectroscopies combined with multivariate statistical analysis

Rapid discriminant analysis for the origin of specialty yam based on multispectral data fusion strategies

In order to achieve rapid and effective identification of Hebei yam, a qualitative discrimination model was constructed based on near infrared (NIR), mid infrared (MIR), and microscopic Raman spectra in combination with individual spectra and multispectral data fusion strategies. The results showed that the gray wolf optimizer-support vector machine (GWO-SVM) model constructed by mid-level fusion using the three feature spectra performed the best in distinguishing the geographic origin of the yam, with a prediction accuracy of 100.00% in both the training set and the test set, and an F1 score of 1.00. The results indicated that due to spectral complementarity, NIR, MIR and Raman combined with feature-level fusion can be used as a powerful, non-destructive, fast and feasible tool for geographic origin classification and brand protection of Hebei yam. This work is expected to be a potential method for origin identification analysis and quality monitoring in the food and pharmaceutical industries.

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.

Mid-Level Data Fusion Combined with the Fingerprint Region for Classification DON Levels Defect of Fusarium Head Blight Wheat

In this study, a method of mid-level data fusion with the fingerprint region was proposed, which was combined with the characteristic wavelengths that contain fingerprint information in NIR and FT-MIR spectra to detect the DON level in FHB wheat during wheat processing. NIR and FT-MIR raw spectroscopy data on normal wheat and FHB wheat were obtained in the experiment. MSC was used for pretreatment, and characteristic wavelengths were extracted by CARS, MGS and XLW. The variables that can effectively reflect fingerprint information were retained to build the mid-level data fusion matrix. LS-SVM and PLS-DA were applied to investigate the performance of the single spectroscopic model, mid-level data fusion model and mid-level data fusion with fingerprint information model, respectively. The experimental results show that mid-level data fusion with a fingerprint information strategy based on fused NIR and FT-MIR spectra represents an effective method for the classification of DON levels in FHB wheat samples.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

Biosynthesis, Spectrophotometric Follow-Up, Characterization, and Variable Antimicrobial Activities of Ag Nanoparticles Prepared by Edible Macrofungi

The biosynthesis of silver nanoparticles (Ag NPs) could play a significant role in the development of commercial antimicrobials. Herein, the biosynthesis of Ag NPs was studied using the edible mushroom Pleurotus floridanus, and following its formation, spectrophotometry was used to detect the best mushroom content, pH, temperature, and silver concentration. After that, the morphology was described via transmission electron microscopy (TEM), and nanoscale-size particles were found ranging from 11 to 13 nm. The best conditions of Ag content and pH were found at 1.0 mM and 11.0, respectively. In addition, the best mushroom extract concentration was found at 30 g/L. According to XRD analysis, the crystal structure of the formed amorphous Ag NPs is cubic with a space group of fm-3m and a space group number of 225. After that, the function groups at the surface of the prepared Ag NPs were studied via FTIR analysis, which indicated the presence of C=O, C-H, and O-H groups. These groups could indicate the presence of mushroom traces in the Ag NPs, which was confirmed via the amorphous characteristics of Ag NPs from the XRD analysis. The prepared Ag NPs have a high impact against different microorganisms, which could be attributed to the ability of Ag NPs to penetrate the cell bacterial wall.

Identification of Gentian-Related Species Based on Two-Dimensional Correlation Spectroscopy (2D-COS) Combined with Residual Neural Network (ResNet)

Gentian is a traditional Chinese herb with heat-clearing, damp-drying, inflammation-alleviating and digestion-promoting effects, which is widely used in clinical practice. However, there are many species of gentian. According to the pharmacopoeia, Gentiana manshurica Kitag, Gentiana scabra Bge, Gentiana triflora Pall and Gentianarigescens Franch are included. Therefore, accurately identifying the species of gentian is important in clinical use. In recent years, with the advantages of low cost, convenience, fast analysis and high sensitivity, infrared spectroscopy (IR) has been extensively used in herbal identification. Unlike one-dimensional spectroscopy, a two-dimensional correlation spectrum (2D-COS) can improve the resolution of the spectrum and better highlight the details that are difficult to detect. In addition, the residual neural network (ResNet) is an important breakthrough in convolutional neural networks (CNNs) for significant advantages related to image recognition. Herein, we propose a new method for identifying gentian-related species using 2D-COS combined with ResNet. A total of 173 gentian samples from seven different species are collected in this study. In order to eliminate a large amount of redundant information and improve the efficiency of machine learning, the extracted feature band method was used to optimize the model. Four feature bands were selected from the infrared spectrum, namely 3500-3000 cm^-1, 3000-2750 cm^-1, 1750-1100 cm^-1 and 1100-400 cm^-1, respectively. The one-dimensional spectral data were converted into synchronous 2D-COS images, asynchronous 2D-COS images, and integrative 2D-COS images using Matlab (R2022a). The identification strategy for these three 2D-COS images was based on ResNet, which analyzes 2D-COS images based on single feature bands and full bands as well as fused feature bands. According to the results, (1) compared with the other two 2D-COS images, synchronous 2D-COS images are more suitable for the ResNet model, and (2) after extracting a single feature band 1750-1100 cm^-1 to optimize ResNet, the model has the best convergence performance, the accuracy of training, test and external validation is 1 and the loss value is only 0.155. In summary, 2D-COS combined with ResNet is an effective and accurate method to identify gentian-related species.

Machine learning and deep learning based on the small FT-MIR dataset for fine-grained sampling site recognition of Boletus tomentipes

This study proposed the necessity of identifying the sampling sites for Boletus tomentipes (B.tomentipes) in combination with cadmium content and environmental factors. Based on fourier transform mid-infrared spectroscopy (FT-MIR) preprocessing by 1st, 2nd, MSC, SNV and SG, five machine learning (ML) algorithms (NB, DT, KNN, RF, SVM) and three Gradient Boosting Machine (GBM) algorithms (XGBoost, LightGBM, CatBoost) were built. To avoid complex preprocessing, we construct BoletusResnet model, propose the concepts of 3DCOS, 3DCOS projected images, index images in addition to 2DCOS, and combine them with deep learning (DL) for classification for the first time. It shows that GBM has higher accuracy than ML and DL has better accuracy than GBM. The four DL models presented in this paper achieve fine-grained sampling sites recognition based on small samples with 100 % accuracy, and a computer application system was developed on them. Therefore, spectral image processing combined with DL is a rapid and efficient classification method which can be widely used in food identification.

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.

Rapid Identification of Wild Gentiana Genus in Different Geographical Locations Based on FT-IR and an Improved Neural Network Structure Double-Net

Gentiana Genus, a herb mainly distributed in Asia and Europe, has been used to treat the damp heat disease of the liver for over 2000 years in China. Previous studies have shown significant differences in the compositional contents of wild Gentiana Genus samples from different geographical origins. Therefore, the traceable geographic locations of the wild Gentiana Genus samples are essential to ensure practical medicinal value. Over the last few years, the developments in chemometrics have facilitated the analysis of the composition of medicinal herbs via spectroscopy. Notably, FT-IR spectroscopy is widely used because of its benefit of allowing rapid, nondestructive measurements. In this paper, we collected wild Gentiana Genus samples from seven different provinces (222 samples in total). Twenty-one different FT-IR spectral pre-processing methods that were used in our experiments. Meanwhile, we also designed a neural network, Double-Net, to predict the geographical locations of wild Gentiana Genus plants via FT-IR spectroscopy. The experiments showed that the accuracy of the neural network structure Double-Net we designed can reach 100%, and the F1_score can reach 1.0.

Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview

Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.

Discrimination of Adulterated Ginkgo Biloba Products Based on 2T2D Correlation Spectroscopy in UV-Vis Range

Ginkgo biloba is a popular medicinal plant widely used in numerous herbal products, including food supplements. Due to its popularity and growing economic value, G. biloba leaf extract has become the target of economically motivated adulterations. There are many reports about the poor quality of ginkgo products and their adulteration, mainly by adding flavonols, flavonol glycosides, or extracts from other plants. In this work, we developed an approach using two-trace two-dimensional correlation spectroscopy (2T2D COS) in UV-Vis range combined with multilinear principal component analysis (MPCA) to detect potential adulteration of twenty G. biloba food supplements. UV-Vis spectral data are obtained for 80% methanol and aqueous extracts in the range of 245–410 nm. Three series of two-dimensional correlation spectra were interpreted by visual inspection and using MPCA. The proposed relatively quick and straightforward approach successfully differentiated supplements adulterated with rutin or those lacking ginkgo leaf extract. Supporting information about adulteration was obtained from the difference between the DPPH radical scavenging capacity of both extracts and from chromatographic (HPLC-DAD) fingerprints of methanolic samples.

Forensic examination of thermal papers using Video Spectral Comparator (VSC) and ATR-FTIR spectroscopy coupled with chemometrics: Non-destructive approach

Thermal papers are replacing the conventional form of printing and are being extensively used across the globe. This study encompasses a non-destructive approach to examine thermal papers by using ATR-FTIR spectroscopy and Video Spectral Comparator (VSC), where the former technique helps in characterizing and discriminating different samples and the latter helps in deciphering the faded prints on thermal paper. The qualitative analysis of the spectroscopic data based on peak to peak comparison and quantitative analysis using chemometrics has been done to obtain high discriminating power. Multivariate analysis using HCA gave a discriminating power of 83.82% and PCA showed a variance of 95.64%. The strength of the study is portrayed through the decipherment of artificially and naturally faded thermal papers using VSC and analyzing the effect of different storing conditions on their rate of fading.

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.

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

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

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.

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

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

Rapid discrimination of Notoginseng powder adulteration of different grades using FT-MIR spectroscopy combined with chemometrics

Panax Notoginseng is a kind of herb material with high medicinal value, which requires adaptive planting environment, and not can be continuously cultivated in the same ground. Those reasons lead to a large number of low-grade Notoginseng appears in the market. The objective of this study is to discriminate adulterant of Notoginseng of different grades by FT-MIR spectroscopy couple with chemometrics. In the experiment, high-grade Notoginseng was adulterated with 14 blend ratios: 0%, 1%, 3%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of low-grade Notoginseng. All samples were scanned in the range of 4000-400 cm^-1 by FT-MIR spectra instrument in absorption mode. Baseline, standard normal variate (SNV), multiplicative scatter correction (MSC), orthogonal signal correction (OSC), first derivative (D1) with 11-points smoothing and second derivative (D2) with 11-points smoothing were used to preprocess the spectral data, in which Baseline combined with SNV and D1 with 11-points performed best. The spectral data in the range of 1485-405 cm^-1 were selected by interval partial least squares (iPLS) for modeling. Then, Support vector machine (SVM) and linear discriminant analysis (LDA) were applied for modeling analysis. The best result was achieved by SVM, as the classification accuracy was 100%, which indicated that FT-MIR spectroscopy combined with chemometrics was an effective approach to identify Notoginseng powder adulteration. It could detect the blend ratio of 5% (w/w) as well as the blend ratio of over 5%.

An additional data fusion strategy for the discrimination of porcini mushrooms from different species and origins in combination with four mathematical algorithms

Porcini are a source of popular food products with many beneficial functions and the internal quality of these mushrooms is largely determined by many factors. An additional data fusion strategy based on low-level data fusion for two portions (cap and stipe) and mid-level data fusion for two spectroscopic techniques (UV and FTIR) was developed to discriminate porcini mushrooms from different species and origins. Based on a finally obtained data array, four mathematical algorithms including PLS-DA, k-NN, SVM and RF were comparatively applied to build classification models. Each calibrated model was developed after selecting the best debug parameters and then a test set was used to validate the established model. The results showed that the SVM algorithm based on a GA procedure searching for parameters had the best performance for discriminating different porcini samples with the highest cross-validation, specificity, sensitivity and accuracy of 100.00%. Our study proved the feasibility of two spectroscopic techniques for the discrimination of porcini mushrooms originated from different species and origins. This proposed method can be used as an alternative strategy for the quality detection of porcini mushrooms.