Raman spectroscopy for esophageal tumor diagnosis and delineation using machine learning and the portable Raman spectrometer.

Esophageal cancer is one of the leading causes of cancer-related deaths worldwide. The identification of residual tumor tissues in the surgical margin of esophageal cancer is essential for the treatment and prognosis of cancer patients. But the current diagnostic methods, either pathological frozen section or paraffin section examination, are laborious, time-consuming, and inconvenient. Raman spectroscopy is a label-free and non-invasive analytical technique that provides molecular information with high specificity. Here, we report the use of a portable Raman system and machine learning algorithms to achieve accurate diagnosis of esophageal tumor tissue in surgically resected specimens. We tested five machine learning-based classification methods, including k-Nearest Neighbors, Adaptive Boosting, Random Forest, Principal Component Analysis-Linear Discriminant Analysis, and Support Vector Machine (SVM). Among them, SVM shows the highest accuracy (88.61 %) in classifying the esophageal tumor and normal tissues. The portable Raman system demonstrates robust measurements with an acceptable focal plane shift of up to 3 mm, which enables large-area Raman mapping on resected tissues. Based on this, we finally achieve successful Raman visualization of tumor boundaries on surgical margin specimens, and the Raman measurement time is less than 5 min. This work provides a robust, convenient, accurate, and cost-effective tool for the diagnosis of esophageal cancer tumors, advancing toward Raman-based clinical intraoperative applications.

Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage.

Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.

Heavy metal Hg stress detection in tobacco plant using hyperspectral sensing and data-driven machine learning methods.

Accurate detection of heavy metal stress on the growth status of plants is of great concern for agricultural production and management, food security, and ecological environment. A proximal hyperspectral imaging (HSI) system covered the visible/near-infrared (Vis/NIR) region of 400-1000 nm coupled with machine learning methods were employed to discriminate the tobacco plants stressed by different concentration of heavy metal Hg. After acquiring hyperspectral images of tobacco plants stressed by heavy metal Hg with concentration solutions of 0 mg·L-1 (non-stressed groups), 1, 3, and 5 mg·L-1 (3 stressed groups), regions of interest (ROIs) of canopy in tobacco plants were identified for spectra processing. Meanwhile, tobacco plant's appearance and microstructure of mesophyll tissue in tobacco leaves were analyzed. After that, clustering effects of the non-stressed and stressed groups were revealed by score plots and score images calculated by principal component analysis (PCA). Then, loadings of PCA and competitive adaptive reweighted sampling (CARS) algorithm were employed to pick effective wavelengths (EWs) for discriminating non-stressed and stressed samples. Partial least squares discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) were utilized to estimate the stressed tobacco plants status with different concentrations Hg solutions. The performances of those models were evaluated using confusion matrixes (CMes) and receiver operating characteristics (ROC) curves. Results demonstrated that PLS-DA models failed to offer relatively good result, and this algorithm was abandoned to classify the stressed and non-stressed groups of tobacco plants. Compared to LS-SVM model based on full spectra (FS-LS-SVM), the LS-SVM model established EWs selected by CARS (CARS-LS-SVM) carried 13 variables provided an accuracy of 100%, which was promising to achieve the qualitative discrimination of the non-stressed and stressed tobacco plants. Meanwhile, for revealing the discrepancy between 3 stressed groups of tobacco plants, the other FS-LS-SVM, PCA-LS-SVM, and CARS-LS-SVM models were setup and offered relatively low accuracies of 55.56%, 51.11% and 66.67%, respectively. Performance of those 3 LS-SVM discriminative models was also poorly performing to differentiate 3 stressed groups of tobacco plants, which might be caused by low concentration of heavy metal and similar canopy (especially in fresh leaves) of plant. The achievements of the research indicated that HSI coupled with machine learning methods had a powerful potential to discriminate tobacco plant stressed by heavy metal Hg.

Differences in carbon and nitrogen metabolism between male and female Populus cathayana in response to deficient nitrogen.

Sexual dimorphism occurs regarding carbon and nitrogen metabolic processes in response to nitrogen supply. Differences in fixation and remobilization of carbon and allocation and assimilation of nitrogen between sexes may differ under severe defoliation. The dioecious species Populus cathayana was studied after two defoliation treatments with two N levels. Males had a higher capacity of carbon fixation because of higher gas exchange and fluorescence traits of leaves after severe long-term defoliation under deficient N. Males had higher leaf abscisic acid, stomatal conductance and leaf sucrose phosphate synthase activity increasing transport of sucrose to sinks. Males had a higher carbon sink than females, because under N-deficient conditions, males accumulated >131.10% and 90.65% root starch than males in the control, whereas females accumulated >40.55% and 52.81%, respectively, than females in the control group. Males allocated less non-protein N (NNon-p) to leaves, having higher nitrogen use efficiency (photosynthetic nitrogen use efficiency), higher glutamate dehydrogenase (GDH) and higher leaf GDH expression, even after long-term severe defoliation under deficient N. Females had higher leaf jasmonic acid concentration and NNon-p. The present study suggested that females allocated more carbon and nitrogen to defense chemicals than males after long-term severe defoliation under deficient N.

Potential of vibrational spectroscopy for rapid and accurate determination of the hydrogen peroxide treatment of plant leaves.

Detection and characterization of interactions between crop plants and hydrogen peroxide (H2O2) is significant for the exploration of the mechanisms in plant pathology. The objective of this research is to estimate spectral characteristics of rapeseed leaves (Brassica napus L.) during treatment with different H2O2 concentrations (0, 0.5, 1.0, and 3.0 mmol/L) by using Raman spectroscopy (RS) (800-1800 cm-1) and hyperspectral imaging (HSI) (400-1000 nm). Cluster analysis of RS and HSI data between the control and treated samples was conducted using kernel principal component analysis (KPCA) and principal component analysis (PCA), respectively. Characteristic Raman shifts at 1012, 1163, and 1530 cm-1 and hyperspectral featured wavelengths at 452, 558, 655, and 703 nm were selected for discriminating control and treated samples. The one-way analysis of variance (ANOVA) was applied to demonstrate the significant difference in spectral signatures of samples, and results showed that 452 nm is promising to assess the control and treated samples at the p < 0.05 level. The featured Raman shifts and hyperspectral wavelengths were employed to establish least squares-support vector machine (LS-SVM) discriminative models. The approach of multiple-level data fusion of 1163 cm-1 combined with 452 nm produced the best recognize rate (RR) of 81.7% to detect the control and treated leaves than other models. Therefore, the results encouraged multiple sensor fusion to improve models for better model performance and to detect plant treatment situations with H2O2 solutions.