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Understanding the origins and hotspots of suspended sediments is crucial for safeguarding the biotic communities and for management of water quality in river‐reservoir systems. This study is an attempt to develop a new analytical framework to deconvolve the suspended sediment sources contribution using a novel index (i.e., erosion susceptibility index, ESI). The ESI is computed using sediment fingerprinting information, sediment yield, and the areal coverage of the sediment sources (land use). We have used X‐ray diffraction (XRD) and Partial least square regression (PLSR) based spectral fingerprinting technique to apportion the land use classes as sources of sediments in a representative catchment of Chotanagpur Plateau (CP), India (Konar reservoir catchment). PLSR model on the XRD spectra has performed well with R2 > 0.88 and identified agricultural areas as the major contributor of suspended sediments by delivering 28%–43%, varying seasonally (in the period of 2018–2019). However, based on the ESI values barren lands and human settlements were found to be the most crucial land use classes (with highest ESI value of 1.31 and 1.08 respectively) in terms of sediment generation (22%–29% and 14%–23% respectively) with only 14% and 12% areal coverage. The results have demonstrated that the state of critical conservation urgency of different land uses can be quantified more effectively when combining spectral tracers based knowledge with ESI. Plain Language Summary: It is crucial to identify the areas where soil erosion is most prevalent in order to reduce the amount of sediments carried by runoff into the reservoir. One method used to identify the most significant sedimentary contributions into a watershed is sediment fingerprinting. The purpose of this research is to improve our comprehension of sediment dynamics by using a new spectral fingerprinting technology (i.e., XRD‐PLSR) to classify sediment sources according to land use and further use this knowledge to develop a novel index (Erosion Susceptibility Index) to enhance the understanding on sediment dynamics more critically. The primary result of this research is a categorization of land uses according to their conservation priority for mitigating reservoir sediment inputs. Key Points: Effective X‐ray powder diffraction spectral fingerprinting identified agricultural land use as the primary sediment sourceLinked sediment source apportionment and source weightage through a novel indexErosion Susceptibility Index (ESI) has shown the state of critical conservation urgency is more for barren lands than agricultural areas [ABSTRACT FROM AUTHOR]

Biofilm‐related infections occur quite frequently in hospital settings and require rapid diagnostic identification as they are recalcitrant to antibiotic therapy and make special treatment necessary. One of the standard microbiological in vitro tests is the crystal violet test. It indirectly determines the amount of biofilm by measuring the optical density (OD) of the crystal violet‐stained biofilm matrix and cells. However, this test is quite time‐consuming, as it requires bacterial cultivation up to several days. In this study, we correlate fast Raman spectroscopic read‐out of clinical Staphylococcus aureus isolates from 47 patients with different disease background with their biofilm‐forming characteristics. Included were low (OD < 10), medium (OD ≥ 10 and ≤20), and high (OD > 20) biofilm performers as determined by the crystal violet test. Raman spectroscopic analysis of the bacteria revealed most spectral differences between high and low biofilm performers in the fingerprint region between 750 and 1150 cm−1. Using partial least square regression (PLSR) analysis on the Raman spectra involving the three categories of biofilm formation, it was possible to obtain a slight linear correlation of the Raman spectra with the biofilm OD values. The PLSR loading coefficient highlighted spectral differences between high and low biofilm performers for Raman bands that represent nucleic acids, carbohydrates, and proteins. Our results point to a possible application of Raman spectroscopy as a fast prediction tool for biofilm formation of bacterial strains directly after isolation from the infected patient. This could help clinicians make timely and adapted therapeutic decision in future. [ABSTRACT FROM AUTHOR]

The potential of Raman spectroscopy has been utilized for the diagnosis of hepatitis B virus (HBV) infection in blood plasma. Raman spectra of 24 diseased and 10 healthy samples were used to develop distinct types of support vector machine (SVM) models, including linear, quadratic, and radial basis function (RBF) using multivariate method of principal component analysis (PCA) to reduce the dimensions of the obtained datasets. To assess the diagnostic power of these algorithms, developed models were tested on independent dataset. RBF‐based PCA‐SVM model achieved the best performance and yielded accuracy of 98.82%, sensitivity of 98.89%, and specificity of 98.80%. The performance of the SVM models was compared with rerated chemometric method of partial least square regression (PLSR), which has been developed by using the same dataset. The PLSR model attained the diagnostic accuracy of 88%, sensitivity of 93%, and specificity of 78% for same dataset. Our developed model has established promising results compared with state‐of‐the‐art approaches. The results reveal the improved performance of the developed chemometric techniques and clinical prediction potential of HBV by PCA‐SVMs in conjunction with Raman spectroscopy. [ABSTRACT FROM AUTHOR]

In a quest to use Raman spectroscopy as an optical diagnostic tool, we recorded Raman spectra of 32 dengue virus (DENV)-infected and 28 healthy sera samples in the near-infrared spectral range (540 to 1700 cm−1) using laser at 785 nm as the excitation source. We observed clear differences in the Raman spectra of DENV-infected sera as compared with those of healthy individuals. Here, as a result of our study, we report 12 unique Raman bands associated with DENV-infected sera that are not reported earlier. After applying analysis of variance and t-test ( p < 0.05) on these 12 bands, six Raman bands at 630 ( N-acetylglucosamine), 883 (in-plane bending (ring) of deoxyribose), 1218 (amide III- β conformation from C6H5-C stretching vibrations of tryptophan and phenylalanine), 1273 (amide-III), 1623 (tryptophan) and 1672 cm−1 (ceramide) were found only in the DENV-infected sera. The remaining six Raman bands at 716 (lipids), 780 (Uracil-based ring breathing mode), 828 (ring breathing tyrosine), 840 ( α-anomers), 1101 (ν(C-N) of lipids and DNA) and 1150 cm−1(glycogen/carotenoids) were only found in healthy sera. Two types of classification models, principal component analysis and linear discriminant analysis, were employed to develop principal component analysis-linear discriminant analysis model that has provided diagnostic accuracy 96.50%, sensitivity 93.44%, and specificity 100%. This indicates that these 12 Raman bands have the potential to be used as biomarkers for optical diagnosis of DENV infection. This study provides a new insight for future research in the field of optical diagnosis using Raman spectroscopy. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

BACKGROUND Ochratoxin A ( OTA) contamination is highly prevalent in a variety of agricultural products including the commercially important coffee bean. As such, rapid and accurate detection methods are considered necessary for the identification of OTA in green coffee beans. The goal of this research was to apply Fourier transform near infrared spectroscopy to detect and classify OTA contamination in green coffee beans in both a quantitative and qualitative manner. RESULTS PLSR models were generated using pretreated spectroscopic data to predict the OTA concentration. The best model displayed a correlation coefficient ( r) of 0.814, a standard error of prediction ( SEP and bias of 1.965 µg kg−1 and 0.358 µg kg−1, respectively. Additionally, a PLS-DA model was also generated, displaying a classification accuracy of 96.83% for a non- OTA contaminated model and 80.95% for an OTA contaminated model, with an overall classification accuracy of 88.89%. CONCLUSION The results demonstrate that the developed model could be used for detecting OTA contamination in green coffee beans in either a quantitative or qualitative manner. © 2016 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Caffeine is an important component that determines the quality of tea, and its rapid estimation is very much needed for the industry. In this pursuit, a near‐infrared (NIR) spectroscopy‐based technique for the estimation of caffeine is developed and presented in this paper. On the basis of responses of the different bonds present in caffeine, four specific wavelength windows—(a) 1075 to 1239.5 nm (C―H stretch second overtone); (b) 1339.25 to 1440.75 nm (C―H stretch and C―H deformation); (c) 1640.25 to 1700 nm (C―H stretch first overtone, ═CH & amp; ―CH3 asymmetric); and (d) 900 to 1700 nm (whole range of the spectrometer)—were analyzed in details for model development and to obtain the effective wavelength (EW). Five different preprocessing techniques followed by two regression techniques—(a) the partial least‐squares (PLS) and (b) the support vector regression (SVR) were implemented on raw data for analysis. Comparing all the models, the wavelength band of 1075 to 1239.5 nm and 1339.25 to 1440.75 nm were found to produce satisfactory results. The best discrimination result was obtained using the combination of standard normal variate (SNV) preprocessing with SVR at the 1075 to 1239.5 nm wavelength region. The SVR regression with 105 samples in the training set and 15 samples in the testing set resulted in the performance parameters as RMSECV = 0.134, RMSEP = 0.069, rcv2 = 0.869, rp2 = 0.65, and RPD = 5.626 at 1075 to 1239.5 nm, whereas the PLS model produced the best RMSECV = 0.287, RMSEP = 0.077, rcv2 = 0.637, rp2 = 0.675, and RPD = 5.218 at 1339.25 to 1440.75‐nm wavelength band. Caffeine is an important component that determines the quality of tea and in this paper, anear‐infrared (NIR)spectroscopy‐based technique coupled with preprocessing and regression is presented for the estimation of caffeine in green tea leaves. On the basis of responses of different bonds present in caffeine, four specific wavelength windows were analyzed. The best discrimination result was obtained using the combination of standard normal variate (SNV) preprocessing with support vector regression at the 1075 to 1239.5–nm wavelength region. [ABSTRACT FROM AUTHOR]