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Fast discrimination of bacteria using a filter paper–based SERS platform and PLS-DA with uncertainty estimation
摘要： Rapid and reliable identification of bacteria is an important issue in food, medical, forensic, and environmental sciences; however, conventional procedures are time-consuming and often require extensive financial and human resources. Herein, we present a label-free method for bacterial discrimination using surface-enhanced Raman spectroscopy (SERS) and partial least squares discriminant analysis (PLS-DA). Filter paper decorated with gold nanoparticles was fabricated by the dip-coating method and it was utilized as a flexible and highly efficient SERS substrate. Suspensions of bacterial samples from three genera and six species were directly deposited on the filter paper–based SERS substrates before measurements. PLS-DA was successfully employed as a multivariate supervised model to classify and identify bacteria with efficiency, sensitivity, and specificity rates of 100% for all test samples. Variable importance in projection was associated with the presence/absence of some purine metabolites, whereas confidence intervals for each sample in the PLS-DA model were calculated using a resampling bootstrap procedure. Additionally, a potential new species of bacteria was analyzed by the proposed method and the result was in agreement with that obtained via 16S rRNA gene sequence analysis, thereby indicating that the SERS/PLS-DA approach has the potential to be a valuable tool for the discovery of novel bacteria.
关键词： Chemometrics, partial least squares discriminant analysis,Surface-enhanced Raman spectroscopy,Reliability estimation,16S rRNA gene sequence analysis,Gold nanoparticles
更新于2025-09-04 15:30:14
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Rapid prediction of acid detergent fiber content in corn stover based on NIR-spectroscopy technology
摘要： Prediction of acid detergent fiber (ADF) content in corn stover depends on precise data and appropriate analytical methods. In this paper, the optimal PLSR-BPNN model was created for rapidly getting ADF content based on the optimal selection of crucial parameters and the combination of partial least squares regression (PLSR) and back propagation neural network (BPNN). Herein, Mahalanobis distance (MD) was proposed as a tool to recognize and remove outliers. Additionally, on the basis of the characteristic bands extracted by correlation coefficient method (CC), principal component analysis (PCA) was performed to select principal components (PCs) to further compress data of bands for obtaining few characteristic wavelengths. It turned out that the performance of PLSR calibration model based on the selected 10 wavelengths was best. The correlation coefficient (R2), root mean square error of prediction (RMSEP), residual predictive deviation (RPD) and relative standard deviation (RSD) of test set successively were 0.9936, 0.3765, 12.5869, and 0.0087. Besides, BPNN was proposed to cut down the nonlinear regression residual of PLSR model. Genetic algorithm (GA) was applied to avoid the problem of local minimum in network. When RMSEP decreased to the minimum value of 0.2181, PLSR-BPNN model was proven to further improve performance and reached for the best level. Finally, the result of external validation shown that the R2, RMSEP, RPD, RSD were 0.9856, 0.4590, 8.3264, 0.0110, respectively, the created model presented the best predictive performance. Hence, the proposed methods combining with NIR-spectroscopy technology can be used to determine ADF content in corn stover.
关键词： Principal component analysis,Corn stover,Acid detergent fiber,Back propagation neural network,Genetic algorithm,Partial least squares regression
更新于2025-09-04 15:30:14
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A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species
摘要： Species identification—of importance for most biological disciplines—is not always straightforward as cryptic species hamper traditional identification. Fibre-optic near-infrared spectroscopy (NIRS) is a rapid and inexpensive method of use in various applications, including the identification of species. Despite its efficiency, NIRS has never been tested on a group of more than two cryptic species, and a working routine is still missing. Hence, we tested if the four morphologically highly similar, but genetically distinct ant species Tetramorium alpestre, T. caespitum, T. impurum, and T. sp. B, all four co-occurring above 1,300 m above sea level in the Alps, can be identified unambiguously using NIRS. Furthermore, we evaluated which of our implementations of the three analysis approaches, partial least squares regression (PLS), artificial neural networks (ANN), and random forests (RF), is most efficient in species identification with our data set. We opted for a 100% classification certainty, i.e., a residual risk of misidentification of zero within the available data, at the cost of excluding specimens from identification. Additionally, we examined which strategy among our implementations, one-vs-all, i.e., one species compared with the pooled set of the remaining species, or binary-decision strategies, worked best with our data to reduce a multi-class system to a two-class system, as is necessary for PLS. Our NIRS identification routine, based on a 100% identification certainty, was successful with up to 66.7% of unambiguously identified specimens of a species. In detail, PLS scored best over all species (36.7% of specimens), while RF was much less effective (10.0%) and ANN failed completely (0.0%) with our data and our implementations of the analyses. Moreover, we showed that the one-vs-all strategy is the only acceptable option to reduce multi-class systems because of a minimum expenditure of time. We emphasise our classification routine using fibre-optic NIRS in combination with PLS and the one-vs-all strategy as a highly efficient pre-screening identification method for cryptic ant species and possibly beyond.
关键词： Random forests,Ants,Species identification tool,One-vs-all strategy,Formicidae,Neural networks,Cryptic-species complex,Partial least squares regression,Tetramorium
更新于2025-09-04 15:30:14
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[American Society of Agricultural and Biological Engineers 2017 Spokane, Washington July 16 - July 19, 2017 - ()] 2017 Spokane, Washington July 16 - July 19, 2017 - <i>Variety classification of maize kernels using near infrared (NIR) hyperspectral imaging</i>
摘要： Variety classification of maize kernels was evaluated using near infrared (NIR) hyperspectral imaging in this work. Firstly, NIR hyperspectral images of kernels of four widely used maize varieties were acquired within effective spectral range of 1000-2500 nm. Spectral math was used to compensate for minor lighting differences, and band math combined with threshold method was used to remove the background from images. Minimum noise fraction (MNF) was adopted to reduce noise. Texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation) as appearance character of each maize kernel were calculated and extracted to establish classification model combined with spectra data. Moving average smoothing and standard normal variate were applied on the raw spectra extracted from hyperspectral images. Four optimal wavelengths (1352.20 nm, 1615.50 nm, 1733.10 nm, and 2478.20 nm) were selected by competitive adaptive reweighted sampling (CARS) method. Partial least squares discriminant analysis (PLSDA) was employed to build varieties classification models, based on full wavelength data, the four wavelengths data, and combination of spectral and textural features at four wavelengths, respectively. Results demonstrated that PLSDA model based on combination of spectral and textural features had the best performance with accuracies of 0.89, 0.83 for calibration and prediction set, which indicated the hyperspectral imaging technique with combination of spectral and textural features had a potential of application for variety classification.
关键词： Variety classification,Maize kernel,NIR hyperspectral imaging,Partial least squares discriminant analysis (PLSDA),Competitive adaptive reweighted sampling (CARS) method
更新于2025-09-04 15:30:14