Identification of Chinese red wine origins based on Raman spectroscopy and deep learning.

In this study, we combined Raman spectroscopy with deep learning for the first time to establish an accurate, simple, and fast method to identify the origin of red wines. We collected Raman spectra from 200 red wine samples of the Cabernet Sauvignon variety from four different origins with a portable Raman spectrometer. The red wine samples, made in 2021, were from the same producer in China. Differences were found by analyzing the Raman spectra of red wine samples. These differences are mainly caused by ethanol, carboxylic acids, and polyphenols. After further analysis, for different origins, the different performances of these substances on the Raman spectrum are related to the climate and geographical conditions of the origin. The Raman spectra were analyzed by principal component analysis (PCA). The data with PCA dimensionality reduction were imported into an artificial neural network (ANN), multifeature fusion convolutional neural network (MCNN), GoogLeNet, and residual neural network (ResNet) to establish red wine origin identification models. The classification results of the model prove that climate, geography, and other conditions can provide support for the classification of red wine origin. The experiments showed that all four models performed well, among which MCNN performed the best with 93.2% classification accuracy, and the area under the curve (AUC) was 0.987. This study provides a new means to classify the origin of red wine and opens up new ideas for identifying origins in the food field.

A comparison of DNA sequencing and gene expression profiling to assist tissue of origin diagnosis in cancer of unknown primary.

Cancer of unknown primary (CUP) is a syndrome defined by clinical absence of a primary cancer after standardised investigations. Gene expression profiling (GEP) and DNA sequencing have been used to predict primary tissue of origin (TOO) in CUP and find molecularly guided treatments; however, a detailed comparison of the diagnostic yield from these two tests has not been described. Here, we compared the diagnostic utility of RNA and DNA tests in 215 CUP patients (82% received both tests) in a prospective Australian study. Based on retrospective assessment of clinicopathological data, 77% (166/215) of CUPs had insufficient evidence to support TOO diagnosis (clinicopathology unresolved). The remainder had either a latent primary diagnosis (10%) or clinicopathological evidence to support a likely TOO diagnosis (13%) (clinicopathology resolved). We applied a microarray (CUPGuide) or custom NanoString 18-class GEP test to 191 CUPs with an accuracy of 91.5% in known metastatic cancers for high-medium confidence predictions. Classification performance was similar in clinicopathology-resolved CUPs - 80% had high-medium predictions and 94% were concordant with pathology. Notably, only 56% of the clinicopathology-unresolved CUPs had high-medium confidence GEP predictions. Diagnostic DNA features were interrogated in 201 CUP tumours guided by the cancer type specificity of mutations observed across 22 cancer types from the AACR Project GENIE database (77,058 tumours) as well as mutational signatures (e.g. smoking). Among the clinicopathology-unresolved CUPs, mutations and mutational signatures provided additional diagnostic evidence in 31% of cases. GEP classification was useful in only 13% of cases and oncoviral detection in 4%. Among CUPs where genomics informed TOO, lung and biliary cancers were the most frequently identified types, while kidney tumours were another identifiable subset. In conclusion, DNA and RNA profiling supported an unconfirmed TOO diagnosis in one-third of CUPs otherwise unresolved by clinicopathology assessment alone. DNA mutation profiling was the more diagnostically informative assay. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Multispectral Portable Fibre-Optic Reflectometer for the Classification of the Origin of Chicken Eggshells in the Case of Mycoplasma synoviae Infections.

The proper classification of the origins of food products is a crucial issue all over the world nowadays. In this paper, the authors present a device-a multispectral portable fibre-optic reflectometer and signal processing patch-together with a machine-learning algorithm for the classification of the origins of chicken eggshells in the case of Mycoplasma synoviae infection. The sensor device was developed based on previous studies with a continuous spectrum in transmittance and selected spectral lines in reflectance. In the described case, the sensor is based on the integration of reflected spectral data from short spectral bands from the VIS and NIR region, which are produced by single-colour LEDs and introduced to the sample via a fibre bundle. The measurement is carried out in a sequence, and the reflected signal is pre-processed to be put in the machine learning algorithm. The support vector machine algorithm is used together with three different types of data normalization. The obtained results of the F-score factor for classification of the origins of samples show that the percentages of eggs coming from Mycoplasma synoviae infected hens are up to 87% for white and 96% for brown eggshells.

Multi-element Analysis of Honey from Amhara Region-Ethiopia for Quality, Bioindicator of Environmental Pollution, and Geographical Origin Discrimination.

Honey is a widely utilized sweetener containing mainly sugars with many other minor ingredients such as metallic elements. The analysis aimed to develop a chemometric model for tracing the geographical origin, evaluating nutritional quality, assessing pollution effect, and searching for marker metals for the region's honey. Forty-seven honey samples were collected directly from the apiarists at seven administrative zones. The contents of 14 metals were analyzed using inductively coupled plasma optical emission spectrometry after standard sample digestion. The findings showed us the major elements ranged from 24.8 to 1996 mg/kg of the honey sample with K > Ca > Na > Mg. The minimum and maximum values for the trace metals were 2.35 mg/kg and 163 mg/kg, respectively, in the order of Fe > Cr > Zn > Ni > Mn > Cu > Co. From this data, the region's honey has its own contribution as a source of major and trace elements. Furthermore, mean values for the toxic heavy metals were 0.57 to 1.85 for Pb, 1.03 to 1.21 for Cd, and 2.85 to 6.21 for As in mg/kg. Thus, the pollution level in the environment seems to be at an alarming rate. Using principal components analysis (PCA), the first four principal components explained 80.16% of the total variation. The region's honey was best classified into five major clusters using linear discriminant analysis (LDA) with an average discrimination power of 89.91%. The LDA sorting model was verified by the cross-validation method. The verification revealed that the model has 92.11% recognition power and 93.33% prediction ability.

Molecular Subgroups of Diffuse Large B Cell Lymphoma: Biology and Implications for Clinical Practice.

PURPOSE OF REVIEW: Genomic analyses have immensely advanced our conception of the heterogeneity of diffuse large B cell lymphoma (DLBCL), resulting in subgroups with distinct molecular profiles. In this review, we summarize our current knowledge of the biology of DLBCL complexity and discuss the potential implications for precision medicine. RECENT FINDINGS: During the last two decades, gene expression profiling, copy number analysis, and high throughput sequencing enabled the identification of molecular subclasses of DLBCL that are biologically and clinically meaningful. The resulting classifications provided novel prospects of diagnosis, prognostication, and therapeutic strategies for this aggressive disease. The molecular characterization of DLBCL offers unprecedented insights into the biology of these lymphomas that can guide precision medicine. The knowledge of the molecular setup of an individual DLBCL patients enables prognostication of patients and will be useful to stratify patients in clinical trials. Future direction should focus to implement the molecular classifications of DLBCL in the clinical practice to evaluate their significance and scope using real-world data.

[Identification of Armeniacae Semen Amarum and Persicae Semen from different origins based on near infrared hyperspectral imaging technology].

In order to establish a rapid and non-destructive evaluation method for the identification of Armeniacae Semen Amarum and Persicae Semen from different origins, the spectral information of Armeniacae Semen Amarum and Persicae Semen in the range of 898-1 751 nm was collected based on hyperspectral imaging technology. Armeniacae Semen Amarum and Persicae Semen from different origins were collected as research objects, and a total of 720 Armeniacae Semen Amarum samples and 600 Persicae Semen samples were used for authenticity discrimination. The region of interest(ROI) and the average reflection spectrum in the ROI were obtained, followed by comparing five pre-processing methods. Then, partial least squares discriminant analysis(PLS-DA), support vector machine(SVM), and random forest(RF) method were established for classification models, which were evaluated by the confusion matrix of prediction results and receiver operating characteristic curve(ROC). The results showed that in the three sample sets, the se-cond derivative pre-processing method and PLS-DA were the best model combinations. The classification accuracy of the test set under the 5-fold cross-va-lidation was 93.27%, 96.19%, and 100.0%, respectively. It was consistent with the confusion matrix of the predicted results. The area under the ROC curve obtained the highest values of 0.992 3, 0.999 6, and 1.000, respectively. The study revealed that the near-infrared hyperspectral imaging technology could accurately identify the medicinal materials of Armeniacae Semen Amarum and Persicae Semen from different origins and distinguish the authentication of these two varieties.

Cocoa origin classifiability through LC-MS data: A statistical approach for large and long-term datasets.

Classification of food samples based upon their countries of origin is an important task in food industry for quality assurance and development of fine flavor products. Liquid chromatography -mass spectrometry (LC-MS) provides a fast technique for obtaining in-depth information about chemical composition of foods. However, in a large dataset that is gathered over a period of few years, multiple, incoherent and hard to avoid sources of variations e.g., experimental conditions, transportation, batch and instrumental effects, etc. pose technical challenges that make the study of origin classification a difficult problem. Here, we use a large dataset gathered over a period of four years containing 297 LC-MS profiles of cocoa sourced from 10 countries to demonstrate these challenges by using two popular multivariate analysis methods: principal component analysis (PCA) and linear discriminant analysis (LDA). We show that PCA provides a limited separation in bean origin, while LDA suffers from a strong non-linear dependence on the set of compounds. Further, we show for LDA that a compound selection criterion based on Gaussian distribution of intensities across samples dramatically enhances origin clustering of samples thereby suggesting possibilities for studying marker compounds in such a disparate dataset through this approach. In essence, we show and develop a new approach that maximizes, avoiding overfitting, the utility of multivariate analysis in a highly complex dataset.

Optical spectroscopy methods combined with multivariate statistical analysis for the classification of Cretan thyme, multi-floral and honeydew honey.

BACKGROUND: The botanical origin of honey attracts both commercial and research interest. Consumers' preferences and medicinal uses of particular honey types drive the demand for the determination of their authenticity with regard to their botanical origin. This study presents the discrimination of thyme, multi-floral. and honeydew honeys by Fourier-transform infrared (FTIR) and ultraviolet (UV) absorption spectroscopy combined with multivariate statistical analysis. UV absorption spectroscopy was applied without any dilution of the sample using a custom-made cuvette. FTIR and UV absorption spectroscopic data were processed by means of the orthogonal partial least squares discriminant analysis. RESULTS: The optimal classification of floral and honeydew honeys was accomplished with UV spectroscopy with a successful estimation of 92.65% for floral honey and 91.30% for honeydew honey. The discrimination of thyme versus the multi-floral honey was best achieved with FTIR, with a correct classification of 95.56% and 100% for multi-floral and thyme honey respectively. Furthermore, our findings revealed the region of 2400-4000 cm-1 of the FTIR spectra as the most significant for this discrimination. CONCLUSION: This work demonstrates that optical spectroscopic techniques in combination with multivariate statistical analysis can be a rapid, low-cost, easy-to-use approach for the determination of the botanical origin of honey without sample pretreatment. © 2021 Society of Chemical Industry.

Identification of Armeniacae Semen Amarum and Persicae Semen from different origins based on near infrared hyperspectral imaging technology / 中国中药杂志

In order to establish a rapid and non-destructive evaluation method for the identification of Armeniacae Semen Amarum and Persicae Semen from different origins, the spectral information of Armeniacae Semen Amarum and Persicae Semen in the range of 898-1 751 nm was collected based on hyperspectral imaging technology. Armeniacae Semen Amarum and Persicae Semen from different origins were collected as research objects, and a total of 720 Armeniacae Semen Amarum samples and 600 Persicae Semen samples were used for authenticity discrimination. The region of interest(ROI) and the average reflection spectrum in the ROI were obtained, followed by comparing five pre-processing methods. Then, partial least squares discriminant analysis(PLS-DA), support vector machine(SVM), and random forest(RF) method were established for classification models, which were evaluated by the confusion matrix of prediction results and receiver operating characteristic curve(ROC). The results showed that in the three sample sets, the se-cond derivative pre-processing method and PLS-DA were the best model combinations. The classification accuracy of the test set under the 5-fold cross-va-lidation was 93.27%, 96.19%, and 100.0%, respectively. It was consistent with the confusion matrix of the predicted results. The area under the ROC curve obtained the highest values of 0.992 3, 0.999 6, and 1.000, respectively. The study revealed that the near-infrared hyperspectral imaging technology could accurately identify the medicinal materials of Armeniacae Semen Amarum and Persicae Semen from different origins and distinguish the authentication of these two varieties.

Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning.

Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP). The resulting interactive map organizes molecules by physico-chemical properties and compound families such as peptides and glycosides. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin.

MYC rearrangement and MYC/BCL2 double expression but not cell-of-origin predict prognosis in R-CHOP treated diffuse large B-cell lymphoma.

OBJECTIVE: Diffuse large B-cell lymphoma (DLBCL) can be classified as germinal center B cell-like (GCB) or activated B cell-like (ABC)/non-GCB based on cell-of-origin (COO) classification. This study evaluated the prognostic significance of COO classification in 250 patients diagnosed with de novo DLBCL who received R-CHOP therapy. We also assessed whether the genomic status of MYC, BCL2, or MYC/BCL2 double expression (DE) could provide additional prognostic information for DLBCL patients. METHODS: The clinicopathologic features and outcome of patients with GCB DLBCL were compared to patients with non-GCB DLBCL using Fisher's exact test. The prognostic significance of COO, MYC-R, and MYC/BCL2 DE were studied using multivariate Cox proportional hazard analysis. RESULTS: There were 162 men and 88 women with a median age of 62 years (range, 18-86). Forty-five of 250 (18%) cases harbored MYC rearrangement (R). The frequency of MYC-R was much higher in GCB than in non-GCB tumors (40/165, 24% vs 5/85, 6%) (P = .0001). MYC/BCL2 DE was observed in 53 of 125 (42%) cases. COO classification failed to predict overall survival (OS) in DLBCL patients, either those patients with MYC-R were included (P = .10) or not (P = .27). In contrast, MYC-R and MYC/BCL2 DE significantly correlated with inferior OS (P = .0001 and P = .001, respectively). In multivariate analysis, MYC-R and MYC/BCL2 DE were still independent prognostic factors in DLBCL patients. CONCLUSIONS: MYC-R and MYC/BCL2 DE are independent prognostic factors for DLBCL patients treated with R-CHOP. In this cohort, COO classification failed to stratify patient outcome.

High Ki67 proliferation index but not cell-of-origin subtypes is associated with shorter overall survival in diffuse large B-cell lymphoma.

BACKGROUND: CD10, BCL6, and MUM1 are commonly used immunohistochemical stains for classifying diffuse large B-cell lymphoma (DLBCL), which is useful in predicting outcome. Conflicting reports of the prognostic value of other markers such as BCL2, CD23, and Ki67 proliferation index have been reported. Our objective was to correlate these immunostains and Hans classification with response to therapy and overall survival. MATERIALS AND METHODS: A retrospective study of patients diagnosed with DLBCL from 2008-2014 at a tertiary-care cancer hospital. The slides with the IHC stains were reviewed by two independent pathologists. The clinical outcomes--assessed independently--were response to therapy and overall survival. The treatment response evaluation was based on the new Lugano classification. Statistical analyses were conducted using the Fisher's exact test and Kaplan-Meier survival curves. Significance was set at P < 0.05. RESULTS: Forty-one patients were included in the study with a known Hans classification, available clinical data, and at least 5-year follow-up. CD10 immunostain was reported in all patients, whereas CD23 was the least reported in only four patients. No significant association was observed between CD10, BCL6, MUM1, BCL2, and both Response to therapy and overall survival. Owing to few cases reported CD23 immunostain, further analysis of association is not reported. High Ki67 proliferative index of >80% was statistically significantly associated with shorter overall survival and not statistically significant associated with no response to therapy. Hans classification subtypes were not predictive in regard to therapy response. CONCLUSION: High Ki67 expression (>80%) was associated with shorter overall survival in DLBCL. Hans classification subtypes were not predictive.

Historical evolution and current status analysis of commodity specification and grade of Curcumae Radix / 中草药

The commodity specification and grade of Curcumae Radix is an important indicator for evaluating the quality of medicinal materials. It is the basis for realizing the "price according to quality" of medicinal materials, and plays an extremely important role in regulating the market transactions and ensuring the clinical efficacy of Curcumae Radix. This paper makes a textual research on the historical evolution of the commodity specification and grade of Curcumae Radix, and sorts out the current status of the standard. At the same time, it investigates the current status of the market and origin of Curcumae Radix. It was found that the traditional classification and the quality of Curcumae Radix were mainly classified according to the indicators of origin and shape, color and other traits; In the "76 kinds of medicinal materials commodity specification standards" and "Chinese medicinal materials commodity specifications (225 kinds)", Curcumae Radix is mainly graded by traditional traits indicators, and the grading index is single; At present, Curcumae Radix is in a spontaneously grading market, and the market classification is not uniform; Curcumae Radix of origin are sold to major pharmaceutical markets and pharmaceutical factories in a unified way without grading of origin. Therefore, based on the results of the research and investigation of the specification and grade, it is proposed to improve and upgrade the specification and grade of Curcumae Radix in order to promote the formation of high-quality and high-priced trading systems. And at the same time to provide reference for the formulation of national or industrial standards for commodity specification and grade of Curcumae Radix.

A Hyperspectral Imaging Approach for Classifying Geographical Origins of Rhizoma Atractylodis Macrocephalae Using the Fusion of Spectrum-Image in VNIR and SWIR Ranges (VNIR-SWIR-FuSI).

Hyperspectral data processing technique has gained increasing interests in the field of chemical and biomedical analysis. However, appropriate approaches to fusing features of hyperspectral data-cube are still lacking. In this paper, a new data fusion approach was proposed and applied to discriminate Rhizoma Atractylodis Macrocephalae (RAM) slices from different geographical origins using hyperspectral imaging. Spectral and image features were extracted from hyperspectral data in visible and near-infrared (VNIR, 435-1042 nm) and short-wave infrared (SWIR, 898-1751 nm) ranges, respectively. Effective wavelengths were extracted from pre-processed spectral data by successive projection algorithm (SPA). Meanwhile, gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) were employed to extract textural variables. The fusion of spectrum-image in VNIR and SWIR ranges (VNIR-SWIR-FuSI) was implemented to integrate those features on three fusion dimensions, i.e., VNIR and SWIR fusion, spectrum and image fusion, and all data fusion. Based on data fusion, partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were utilized to establish calibration models. The results demonstrated that VNIR-SWIR-FuSI could achieve the best accuracies on both full bands (97.3%) and SPA bands (93.2%). In particular, VNIR-SWIR-FuSI on SPA bands achieved a classification accuracy of 93.2% with only 23 bands, which was significantly better than those based on spectra (80.9%) or images (79.7%). Thus it is more rapid and possible for industry applications. The current study demonstrated that hyperspectral imaging technique with data fusion holds the potential for rapid and nondestructive sorting of traditional Chinese medicines (TCMs).

The cell-of-origin classification of diffuse large B cell lymphoma in a Korean population by the Lymph2Cx assay and its correlation with immunohistochemical algorithms.

Cell-of-origin (COO) classification of diffuse large B cell lymphoma (DLBCL) is increasingly important due to its prognostic significance and the development of subtype-specific therapeutics. We compared the clinical utility of the Lymph2Cx assay against four widely used immunohistochemical algorithms in 150 R-CHOP-treated DLBCL patients using archival tissue. In contrast to the predominance of germinal center B cell-like (GCB) subtype in Western populations, Lymph2Cx assay classified more than half of the Korean cases as the activated B cell-like (ABC) subtype (ABC, 83/150 [55.3%]; GCB, 51/150 [34.0%]; unclassifiable, 16/150 [10.7%]). Predominance of ABC subtype tended to be more pronounced in the nodal lymphomas than in the extranodal lymphomas. However, among the primary extranodal sites, ABC subgroups predominated in primary testicular, breast, and adrenal gland lymphomas. The classification of COO by Lymph2Cx assay did not show any significant association with clinical parameters. The overall concordance rates of the immunohistochemical algorithms with the Lymph2Cx ranged from 78.0 to 84.3%. However, 47.1-66.7% of the cases of the Lymph2Cx-defined GCB subgroup were misclassified as the non-GCB class by the IHC algorithms. The survival of Lymph2Cx-classified COO subtypes was not significantly different in the present cohort. In conclusion, ABC subtype predominated over GCB in Korean patients. There are significant discrepancies between the immunohistochemistry and Lymph2Cx classifications, especially in GCB subtype.

Rapid and direct volatile compound profiling of black and green teas (Camellia sinensis) from different countries with PTR-ToF-MS.

Volatile profiles of 63 black and 38 green teas from different countries were analysed with Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-ToF-MS) both for tea leaves and tea infusion. The headspace volatile fingerprints were collected and the tea classes and geographical origins were tracked with pattern recognition techniques. The high mass resolution achieved by ToF mass analyser provided determination of sum formula and tentative identifications of the mass peaks. The results provided successful separation of the black and green teas based on their headspace volatile emissions both from the dry tea leaves and their infusions. The volatile fingerprints were then used to build different classification models for discrimination of black and green teas according to their geographical origins. Two different cross validation methods were applied and their effectiveness for origin discrimination was discussed. The classification models showed a separation of black and green teas according to geographical origins the errors being mostly between neighbouring countries.

Classification of illicit heroin by UPLC-Q-TOF analysis of acidic and neutral manufacturing impurities.

The illicit manufacture of heroin results in the formation of trace levels of acidic and neutral manufacturing impurities that provide valuable information about the manufacturing process used. In this work, a new ultra performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF) method; that features high resolution, mass accuracy and sensitivity for profiling neutral and acidic heroin manufacturing impurities was developed. After the UPLC-Q-TOF analysis, the retention times and m/z data pairs of acidic and neutral manufacturing impurities were detected, and 19 peaks were found to be evidently different between heroin samples from "Golden Triangle" and "Golden Crescent". Based on the data set of these 19 impurities in 150 authentic heroin samples, classification of heroin geographic origins was successfully achieved utilizing partial least squares discriminant analysis (PLS-DA). By analyzing another data set of 267 authentic heroin samples, the developed discrimiant model was validated and proved to be accurate and reliable.