Identification of geographical origins of Gastrodia elata Blume based on multisource data fusion.

INTRODUCTION: Identifying the geographical origin of Gastrodia elata Blume contributes to the scientific and rational utilization of medicinal materials. In this study, infrared spectroscopy was combined with machine learning algorithms to distinguish the origin of G. elata BI. OBJECTIVE: Realization of rapid and accurate identification of the origin of G. elata BI. MATERIALS AND METHODS: Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectra and Fourier transform near-infrared (FT-NIR) spectra were collected for 306 samples of G. elata BI. SAMPLES: Firstly, a support vector machine (SVM) model was established based on the single-spectrum and the full-spectrum fusion data. To investigate whether feature-level fusion strategy can enhance the model's performance, the sequential and orthogonalized partial least squares discriminant analysis (SO-PLS-DA) model was established to extract and combine two types of spectral features. Next, six algorithms were employed to extract feature variables, SVM model was established based on the feature-level fusion data. To avoid complicated preprocessing and feature extraction processes, a residual convolutional neural network (ResNet) model was established after converting the raw spectral data into spectral images. RESULTS: The accuracy of the feature-level fusion model is better as compared to the single-spectrum model and the fusion model with full-spectrum, and SO-PLS-DA is simpler than feature-level fusion based on the SVM model. The ResNet model performs well in classification but requires more data to enhance its generalization capability and training effectiveness. CONCLUSION: Sequential and orthogonalized data fusion approaches and ResNet models are powerful solutions for identifying the geographic origin of G. elata BI.

Chidamide enhances cytotoxicity of doxorubicin by promoting autophagy and apoptosis in breast cancer.

BACKGROUND: Breast cancer (BC) is a prevalent disease that harms women's health, and in-depth investigations of the pathogenesis, treatment, and prevention of BC are the focus of many research programs. Chidamide (CHI) is a histone deacetylase suppressor that depresses histone deacetylase functions, thereby influencing cell growth through an epigenetic mechanism. However, CHI effects upon BC are largely unknown. Present research aimed to confirm the possibility of using CHI combined with chemotherapy drug doxorubicin (DOX) to prevent chemotherapeutic BC resistance in vivo and in vitro. METHODS: In this study, CCK8 (a plate colony formation assay) was applied to detect cell proliferation. Flow cytometry detection showed the apoptotic cell death of both T47D and MCF-7 cells. Nude mouse xenografts were used to detect tumor growth and pulmonary metastasis. High-throughput sequencing was used to detect expression of different genes. RESULTS: Our data showed that CHI treatment reduced BC cell proliferation, tumor growth, and cell invasion. CHI treatments stimulated BC cell apoptosis by promoting ULK2-mediated autophagy and increasing MCF-7 cell sensitivity to DOX, resulting in decreased tumor growth. CONCLUSION: Collectively, our results illustrated that CHI enhanced DOX cytotoxicity by promoting apoptosis and autophagy in BC cells, which advised that CHI could be a candidate drug for BC patient treatments.

Volatile compounds and aroma characteristics of mushrooms: a review.

Mushrooms are popular due to their rich medicinal and nutritional value. Of the many characteristics of mushrooms, aroma has received extensive attention and research as a key determinant of consumer preference. This paper reviews the production, role and contribution of common volatile compounds (VCs) in wild and cultivated mushrooms, and explores the methods used to characterize them and the factors influencing aroma. To date, more than 347 common VCs have been identified in mushrooms, such as aldehydes, ketones, alcohols and sulfur-containing compounds. Extraction and identification of VCs is a critical step and combining multiple analytical methods is an effective strategy in mushroom aroma studies. In addition, the VCs and the aroma of mushrooms are affected by a variety of factors such as genetics, growing conditions, and processing methods. However, the mechanism of influence is unknown. Further studies on the production mechanisms of VCs, their contribution to aroma, and the factors influencing their formation need to be determined in order to fully elucidate aroma and flavor of mushrooms.

Neoadjuvant docetaxel plus carboplatin vs epirubicin plus cyclophosphamide followed by docetaxel in triple-negative, early-stage breast cancer (NeoCART): Results from a multicenter, randomized controlled, open-label phase II trial.

Previous studies have shown that the addition of carboplatin to neoadjuvant chemotherapy improved the pathologic complete response (pCR) rate in patients suffering from triple-negative breast cancer (TNBC) and patients who obtained a pCR could achieve prolonged event-free survival (EFS) and overall survival (OS). However, no studies have assessed the effects of the combination of docetaxel and carboplatin without anthracycline with taxane-based and anthracycline-based regimens. The NeoCART study was designed as a multicenter, randomized controlled, open-label, phase II trial to assess the efficacy and safety of docetaxel combined with carboplatin in untreated stage II-III TNBC. All eligible patients were randomly assigned, at a 1:1 ratio, to an experimental docetaxel plus carboplatin (DCb) for six cycles group (DCb group) or an epirubicin plus cyclophosphamide for four cycles followed by docetaxel for four cycles group (EC-D group). PCR (ypT0/is ypN0) was evaluated as the primary outcome. Between 1 September 2016 and 31 December 2019, 93 patients were randomly assigned and 88 patients were evaluated for the primary endpoint (44 patients in each group). In the primary endpoint analysis, 27 patients in the DCb group (61.4%, 95% CI 47.0-75.8) and 17 patients in the EC-D group achieved a pCR (38.6%, 95% CI 24.3-53.0; odds ratio 2.52, 95% CI 2.4-43.1; Pnoninferiority = .004). Noninferiority was met, and the DCb regimen was confirmed to be superior to the EC-D regimen (P = .044, superiority margin of 5%). At the end of the 37-month median follow-up period, OS and EFS rates were equivalent in both groups.

Ultrasound-assisted carbon nanoparticle suspension mapping versus dual tracer-guided sentinel lymph node biopsy in patients with early breast cancer (ultraCars): phase III randomized clinical trial.

BACKGROUND: Appropriate tracing methods for sentinel lymph node biopsy (SLNB) play a key role in accurate axillary staging. This prospective, non-inferiority, phase III RCT compared the feasibility and diagnostic performance of ultrasound-assisted carbon nanoparticle suspension (CNS) mapping with dual tracer-guided SLNB in patients with early breast cancer. METHODS: Eligible patients had primary breast cancer without nodal involvement (cN0), or had clinically positive lymph nodes (cN1) that were downstaged to cN0 after neoadjuvant chemotherapy. Patients were randomly assigned (1 : 1) to undergo either ultrasound-assisted CNS sentinel lymph node (SLN) mapping (UC group) or dual tracer-guided mapping with CNS plus indocyanine green (ICG) (GC group). The primary endpoint was the SLN identification rate. RESULTS: Between 1 December 2019 and 30 April 2021, 330 patients were assigned randomly to the UC (163 patients) or GC (167 patients) group. The SLN identification rate was 94.5 (95 per cent c.i. 90.9 to 98.0) per cent in the UC group and 95.8 (92.7 to 98.9) per cent in the GC group. The observed difference of -1.3 (-5.9 to 3.3) per cent was lower than the prespecified non-inferiority margin of 6 per cent (Pnon-inferiority = 0.024). No significant difference was observed in metastatic node rate (30.5 versus 24.4 per cent; P = 0.222), median number of SLNs harvested (3 (range 1-7) versus 3 (1-8); P = 0.181), or duration of surgery (mean(s.d.) 7.53(2.77) versus 7.63(3.27) min; P = 0.316) between the groups. Among the subgroup of patients who had undergone neoadjuvant treatment, the SLN identification rate was 91.7 (82.2 to 100) per cent in the UC group and 90.7 (81.7 to 99.7) per cent in the GC group. CONCLUSION: The diagnostic performance of ultrasound-assisted CNS mapping was non-inferior to that of dual tracer-guided SLN mapping with CNS plus ICG in patients with early breast cancer. REGISTRATION NUMBER: NCT04951245 (http://www.clinicaltrials.gov).

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.

AMP-activated protein kinase: a potential therapeutic target for triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive subset of breast carcinomas that lack expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2). Unlike other breast cancer subtypes, targeted therapy is presently unavailable for patients with TNBC. In spite of initial responses to chemotherapy, drug resistance tends to develop rapidly and the prognosis of metastatic TNBC is poor. Hence, there is an urgent need for novel-targeted treatment methods or development of safe and effective alternatives with recognized mechanism(s) of action. AMP-activated protein kinase (AMPK), an energy sensor, can regulate protein and lipid metabolism responding to alterations in energy supply. In the past 10 years, interest in AMPK has increased widely since it appeared as an attractive targeting molecule for cancer therapy. There has been a deep understanding of the possible role of abnormal AMPK signaling pathways in the regulation of growth and survival and the development of drug resistance in TNBC. The increasing popularity of using AMPK regulators for TNBC-targeted therapy is supported by a considerable development in ascertaining the molecular pathways implicated. This review highlights the available evidence for AMPK-targeted anti-TNBC activity of various agents or treatment strategies, with special attention placed on recent preclinical and clinical advances in the manipulation of AMPK in TNBC. The elaborative analysis of these AMPK-related signaling pathways will have a noteworthy impact on the development of AMPK regulators, resulting in efficacious treatments for this lethal disease.

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

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

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

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

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

BACKGROUND: Boletaceae mushrooms are wild-grown edible mushrooms that have high nutrition, delicious flavor and large economic value distributing in Yunnan Province, China. Traceability is important for the authentication and quality assessment of Boletaceae mushrooms. In this study, UV-visible and Fourier transform infrared (FTIR) spectroscopies were applied for traceability of 247 Boletaceae mushroom samples in combination with chemometrics. RESULTS: Compared with a single spectroscopy technique, data fusion strategy can obviously improve the classification performance in partial least square discriminant analysis (PLS-DA) and grid-search support vector machine (GS-SVM) models, for both species and geographical origin traceability. In addition, PLS-DA and GS-SVM models can provide 100.00% accuracy for species traceability and have reliable evaluation parameters. For geographical origin traceability, the accuracy of prediction in the PLS-DA model by data fusion was just 64.63%, but the GS-SVM model based on data fusion was 100.00%. CONCLUSION: The results demonstrated that the data fusion strategy of UV-visible and FTIR combined with GS-SVM could provide a higher synergic effect for traceability of Boletaceae mushrooms and have a good generalization ability for the comprehensive quality control and evaluation of similar foods. © 2017 Society of Chemical Industry.

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

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

[Study on the Discrimination of Boletus Edulis from Different Years and Origins with FTIR].

In order to establish a rapid method for discriminating Boletus edulis mushroom, Fourier transform infrared spectroscopy combined with multivariate statistical analysis were used to study B. edulis which were collected from different origins and different years. The original infrared spectra of all the 152 B. edulis samples collected from 2011 to 2014 and 26 different areas of Yunnan Province were optimized with orthogonal signal correction and wavelet compression (OSCW) method. The spectral data that before and after being preprocessed with OSCW were analyzed with partial least squares discriminant analysis (PLS-DA). The classification results of PLS-DA were compared. Then the 152 B. edulis samples were randomly divided into a training set (120) and a validation set (32) to establish the PLS classification prediction model. The results showed that, after OSCW processing, the classification result of PLS-DA was significantly better than the other one which was not processed by OSCW. Principal component score plot can accurately distinguish B. edulis samples collected from different years and different origins. It indicated that OSCW can effectively eliminate the noise of spectra and reduce the unrelated interference information about the dependent variables to improve the accuracy and calculation speed of spectral analysis. Before OSCW preprocessed, the R2 and RMSEE of PLS model of the training set were 0.790 1 and 21.246 5 respectively while R2 and RMSEP of the model of validation set were 0.922 5 and 14.429 2. After OSCW pretreatment, R2 and RMSEE of the training set were 0.852 3 and 17.238 1 while R2 and RMSEP of validation set were 0.845 4 and 20.87. It suggested that OSCW could improve the predictive effect of the training set, but the over-fitting of OSCW-PLS may reduce the predictive ability of validation set. Therefore, it was unsuitable to establish a model with OSCW combined with PLS. In a conclusion, OSCW combined with PLS-DA can eliminate a large amount of spectrum interference information. This method could accurately distinguish B. edulis samples collected from different years and different origins. It could provide a reliable basis for the discrimination and classification of wild edible fungi.

ICP-AES Determination of Mineral Content in Boletus tomentipes Collected from Different Sites of China.

P, Na, Ca, Cu, Fe, Mg, Zn, As, Cd, Co, Cr and Ni, contents have been examined in caps and stipes of Boletus tomentipes collected from different sites of Yunnan province, southwest China. The elements were determined using inductively coupled plasma atomic emission spectroscopy (ICP-AES) with microwave digestion. P, Ca, Mg, Fe, Zn and Cu were the most abundant amongst elements determined in Boletus tomentipes. The caps were richer in P, Mg, Zn and Cd, and the stipes in Ca, Co and Ni. Cluster analysis showed a difference between Puer (BT7 and BT8) and other places. The PCA explained about 77% of the total variance, and the minerals differentiating these places were P (PC1) together with Ca, Cu, Fe, Mg, As and Ni, Na (PC2) together with Cd, and Zn (PC3). The results of this study imply that element concentrations of a mushroom are mutative when collected from the different bedrock soil geochemistry.

[Effect of 0.1 Gy X-ray irradiation on gene expression profiles in normal human lymphoblastoid cells].

OBJECTIVE: To investigate the effect of 0.1 Gy X-ray irradiation on the gene expression profiles in normal human lymphoblastoid cells using gene microarray and to explore the possible mechanism of the biological effect of low-dose irradiation. METHODS: The NimbleGen 12×135 K microarray corresponding to 45033 genes was used to analyze the gene expression profiles in AHH-1 cells cultured for 6 h and 20 h after 0.1 Gy X-ray irradiation. A gene was identified as the differentially expressed gene if the ratio between its expression levels in irradiation group and control group was higher than 2 or lower than 0.5. RT-PCR and real-time PCR were used to confirm some differentially expressed genes. RESULTS: There were 760 up-regulated genes and 1222 down-regulated genes in the cells at 6 h after 0.1 Gy X-ray irradiation, while there were 463 up-regulated genes and 753 down-regulated genes at 20 h after 0.1 Gy X-ray irradiation; there were 92 differentially expressed genes in common. The expression of GADD45A, CDKN2A, and Cx43 measured using gene microarray was confirmed by RT-PCR and real-time PCR. CONCLUSION: Low-dose irradiation can affect the expression of many functional genes, which provides a basis for the research on the mechanism of radiation damage.

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.

Rapid and Accurate Authentication of Porcini Mushroom Species Using Fourier Transform Near-Infrared Spectra Combined with Machine Learning and Chemometrics.

Porcini mushrooms have high nutritional value and great potential, but different species are easily confused, so it is essential to identify them rapidly and precisely. The diversity of nutrients in stipe and cap will lead to differences in spectral information. In this research, Fourier transform near-infrared (FT-NIR) spectral information about imparity species of porcini mushroom stipe and cap was collected and combined into four data matrices. FT-NIR spectra of four data sets were combined with chemometric methods and machine learning for accurate evaluation and identification of different porcini mushroom species. From the results: (1) improved visualization level of t-distributed stochastic neighbor embedding (t-SNE) results after the second derivative preprocessing compared with raw spectra; (2) after using multiple pretreatment combinations to process the four data matrices, the model accuracies based on support vector machine and partial least-square discriminant analysis (PLS-DA) under the best preprocessing method were 98.73-99.04% and 98.73-99.68%, respectively; (3) by comparing the modeling results of FT-NIR spectra with different data matrices, it was found that the PLS-DA model based on low-level data fusion has the highest accuracy (99.68%), but residual neural network (ResNet) model based on the stipe, cap, and average spectral data matrix worked better (100% accuracy). The above results suggest that distinct models should be selected for dissimilar spectral data matrices of porcini mushrooms. Additionally, FT-NIR spectra have the advantages of being nondevastate and fast; this method is expected to be a promising analytical tool in food safety control.

Building deep learning and traditional chemometric models based on Fourier transform mid-infrared spectroscopy: Identification of wild and cultivated Gastrodia elata.

To identify wild and cultivated Gastrodia elata quickly and accurately, this study is the first to apply three-dimensional correlation spectroscopy (3DCOS) images combined with deep learning models to the identification of G. elata. The spectral data used for model building do not require any preprocessing, and the spectral data are converted into three-dimensional spectral images for model building. For large sample studies, the time cost is minimized. In addition, a partial least squares discriminant analysis (PLS-DA) model and a support vector machine (SVM) model are built for comparison with the deep learning model. The overall effect of the deep learning model is significantly better than that of the traditional chemometric models. The results show that the model achieves 100% accuracy in the training set, test set, and external validation set of the model built after 46 iterations without preprocessing the original spectral data. The sensitivity, specificity, and the effectiveness of the model are all 1. The results concluded that the deep learning model is more effective than the traditional chemometric model and has greater potential for application in the identification of wild and cultivated G. elata.

Review of postharvest processing of edible wild-grown mushrooms.

Edible wild-grown mushrooms, plentiful in resources, have excellent organoleptic properties, flavor, nutrition, and bioactive substances. However, fresh mushrooms, which have high water and enzymatic activity, are not protected by cuticles and are easily attacked by microorganisms. And wild-grown mushroom harvesting is seasonal the harvest of edible wild-grown mushrooms is subject to seasonality, so their market availability is challenging. Many processing methods have been used for postharvest mushroom processing, including sun drying, freezing, packaging, electron beam radiation, edible coating, ozone, and cooking, whose effects on the parameters and composition of the mushrooms are not entirely positive. This paper reviews the effect of processing methods on the quality of wild and some cultivated edible mushrooms. Drying and cooking, as thermal processes, reduce hardness, texture, and color browning, with the parallel that drying reduces the content of proteins, polysaccharides, and phenolics while cooking increases the chemical composition. Freezing, which allows mushrooms to retain better hardness, color, and higher chemical content, is a better processing method. Water washing and ozone help maintain color by inhibiting enzymatic browning. Edible coating facilitates the maintenance of hardness and total sugar content. Electrolytic water (EW) maintains total phenol levels and soluble protein content. Pulsed electric field and ultrasound (US) inhibit microbial growth. Frying maintains carbohydrates, lipids, phenolics, and proteins. And the mushrooms processed by these methods are safe. They are the focus of future research that combines different methods or develops new processing methods, molecular mechanisms of chemical composition changes, and exploring the application areas of wild mushrooms.

A new effective method for identifying boletes species based on FT-MIR and three dimensional correlation spectroscopy projected image processing.

This study proposed the necessity of identifying the species for boletes in combination with the medicinal value, nutritional value and the problems existing in the industrial development of boletes. Based on the preprocessing of Fourier transform mid-infrared spectroscopy (FT-MIR) by 1st, 2nd, SNV, 2nd + MSC and 2nd + SG, Multilayer Perceptron (MLP) and CatBoost models were established. To avoid complex preprocessing and feature extraction, we try deep learning modeling methods based on image processing. In this paper, the concept of three-dimensional correlation spectroscopy (3DCOS) projection image was proposed, and 9 datasets of synchronous, asynchronous and integrative images are generated by computer method. In addition, 18 deep learning models were established for 9 image datasets with different sizes. The results showed that the accuracy of the three types of synchronous spectral models reached 100%, while the accuracy of the asynchronous spectral and integrative spectral models of 3DCOS projection images were 96.97% and 97.98% in the case of big datasets, which overcame the defects of poor modeling effect of asynchronous spectral and integrative spectral in previous two-dimensional correlation spectroscopy (2DCOS) studies. In conclusion, the modeling results of 3DCOS projection images are perfect, and we can apply this method to other identification fields in the future.

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.