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Performance comparison between a miniaturized and a conventional near infrared reflectance (NIR) spectrometer for characterizing soil carbon and nitrogen
摘要： Miniaturized near infrared spectrometers are now available, at more affordable prices than conventional spectrometers, but their performances have been poorly studied to date. This paper aimed at comparing the performances of the JDSU MicroNIR 2200 spectrophotometer (weight < 0.1 kg) with those of a conventional bench-top instrument for predicting carbon and nitrogen contents in laboratory conditions, on a range of representative Malagasy soils. Though its noticeably narrower and less resolved spectra (1151–2186 nm at 8.15 nm step vs. 1100–2498 nm at 2 nm step), the microspectrometer yielded predictions in independent validation that were almost as accurate as those of the conventional instrument (standard errors of prediction were 4.6 vs. 3.4 gC kg?1 after bias correction, and 0.36 vs. 0.35 gN kg?1, respectively). Due to noisy features, the MicroNIR spectra needed mathematical pretreatment (e.g. standard normal variate SNV), and bias correction for C, for providing accurate predictions, while the raw absorbance spectra from the conventional instrument did not. Furthermore, building multivariate models with MicroNIR spectra required less latent variables than with their conventional counterparts, and these models were less prone to performance degradation when applied to independent validation samples. Fitting the spectra of the conventional instrument to those of the MicroNIR (1150–2182 nm at 2 or 8 nm step) showed that (moderately) less accurate MicroNIR predictions could be firstly attributed to narrower spectral range rather than to poorer resolution. Considering their performances, such microspectrometers could thus represent a cost-effective alternative to conventional spectrometers. They have now to be tested in field conditions.
关键词： Near infrared reflectance spectroscopy (NIRS),Soil organic carbon,Madagascar,Soil total nitrogen,Microspectrometer
更新于2025-09-23 15:22:29
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Development of Near-Infrared Reflectance Spectroscopy (NIRS) Calibrations for Traits Related to Ethanol Conversion from Genetically Variable Napier Grass (Pennisetum purpureum Schum.)
摘要： Napier grass (Pennisetum purpureum Schum.) is one of the highest-yielding feedstocks for bio-based products and biofuel in semi-tropical areas of the USA and the world. Thirty genetically diverse Napier grass accessions were selected from a germplasm nursery in Tifton, GA and analyzed for fiber, ash, nitrogen (N) concentration, and biochemical conversion to ethanol. A near-infrared reflectance spectroscopy (NIRS) calibration was developed from this material to predict ethanol production, xylans, N concentration, and ash by separating leaves and stems and correlating with wet chemistry analyses. The high diversity of material from dwarf material with high leaf and stem digestibility to taller and more productive Napier grass cultivars resulted in high correlations with predicted results for in vitro dry matter digestibility (2 = 0.93), neutral detergent fiber (r2 = 0.83), acid detergent fiber (r2 = 0.95), ethanol (r2 = 0.90), nitrogen (r2 = 0.99), and ash (r2 = 0.98). This information will allow faster evaluation of Napier grass biomass for use by industry or geneticists.
关键词： Biomass,Near-infrared reflectance spectroscopy (NIRS),Forage,Biofuels
更新于2025-09-23 15:21:21
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SSC prediction of cherry tomatoes based on IRIV-CS-SVR model and near infrared reflectance spectroscopy
摘要： As one of the most important indexes of internal quality testing of fruit, soluble solids content (SSC) is significant for its rapid and efficient nondestructive testing by using near infrared reflectance spectroscopy (NIRS). In this article, 126 cherry tomatoes were selected as the research object. Reflectance spectra data of 228 bands in cherry tomatoes were acquired by the near infrared spectrometer and SSC was measured by the hand-held refractometer. Savitzky–Golay (SG) combined with multiplicative scatter correction (MSC) was used to preprocess the spectral data to reduce the effects of light scattering and other noise. Then, the dimensions of spectral data were reduced by iteratively retaining informative variables (IRIV) algorithm and 10 characteristic wavelengths were obtained, which were 1,080.37, 1,113.62, 1,117.3, 1,297.57, 1,301.02, 1,538.32, 1,540.40, 1,590.72, 1,615.94, and 1,636.89 nm, respectively. Subsequently, support vector regression (SVR) and its two optimization models, PSO-SVR and CS-SVR, were respectively used to establish SSC prediction models based on full spectra and characteristic spectra. The experimental results showed the IRIV-CS-SVR model for SSC prediction achieved the accuracy with R2 C of 0.9845. Thus, it is feasible to use NIRS with IRIV-CS-SVR to make a rapid and efficient nondestructive SSC prediction of cherry tomatoes.
关键词： IRIV-CS-SVR model,SSC prediction,cherry tomatoes,near infrared reflectance spectroscopy
更新于2025-09-23 15:21:01
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Discrimination of wood species using laser-induced breakdown spectroscopy and near-infrared reflectance spectroscopy
摘要： A new method is proposed for the discrimination of wood species by combining near-infrared reflectance spectroscopy (NIRS) and laser-induced breakdown spectroscopy (LIBS) and using chemometrics for data analysis. The method was applied to the analysis of 42 samples from six different species: Amburana cearensis, Copaifera lucens, Phyllocarpus riedelii, Cariniana legalis, Bowdichia virgilioides, and Aspidosperma pyricollum. The spectra from both techniques were merged on a single data matrix and pretreated by standard normal variate (SNV) and Savitzky– Golay first derivative with smoothing. Principal component analysis was applied to the exploratory data analysis and showed a clear formation of sample groups according to the wood species only when the data from both analytical techniques and the data pretreatment were used. Sample discrimination using partial least squares discriminant analysis was proved possible, but with an average misclassification of about 10%. Sample grouping and discrimination were shown to be probably related to different concentrations of iron, copper, zinc, and/or sodium (affecting the LIBS spectra) and lignin, water, cellulose, and/or hemicellulose (affecting the NIRS spectra).
关键词： wood species discrimination,laser-induced breakdown spectroscopy,near-infrared reflectance spectroscopy,chemometrics
更新于2025-09-12 10:27:22
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Effect of pH Adjustment on Hydrothermal Synthesis of Aluminum Doped Zinc Oxide
摘要： Aluminum doped zinc oxide (AZO) was synthesized with various pH adjustments reagent (ammonia, TMAH, TEAH and TPAH) by hydrothermal reaction. Regardless of the pH adjustment, the main product of the synthesized powders with 5?mol% Al was zinc oxide. However, the diffraction peaks of gahnite (ZnAl2O4) were detected in the sample prepared from the precursor solutions with TMAH, TEAH and TPAH as pH adjustment. The excellent absorption property in near-infrared (NIR) region was obtained in the synthesized powder with about 2?mol% Al using ammonia. Based on these results, the initial state of Zn and Al ion in precursor solution is thought to affect the crystal phase of the product in hydrothermal synthesis. The NIR absorption property was highly enhanced by using urea as the pH-shift reagent during hydrothermal reaction process.
关键词： pH adjustment,Near-infrared reflectance,Hydrothermal synthesis,Aluminum doped zinc oxide
更新于2025-09-10 09:29:36
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Determination of Nitrogen Concentration in Fresh Pear Leaves by Visible/Near-Infrared Reflectance Spectroscopy
摘要： A rapid and reliable method is required to determine the N status of pear (Pyrus communis L.) leaves during the growing season for timely fertilization to improve the yields and fruit quality. In the present study, we evaluated visible and near-infrared reflectance (Vis/NIR) spectra of fresh pear leaves using partial least squares (PLS) regression to determine the N concentration of fresh pear leaves. In addition, we studied the performance of modified spectra generated using different preprocessing techniques. A total of 450 leaf samples were collected from 6-yr-old pear trees of two cultivars, and randomly separated into two subsets (calibration subset [294 samples] and validation subset [180 samples]) after excluding outliers by using principle component analysis. Results showed that the model built using full spectra performed better than that developed using characteristic wavelength segments. In addition, we found that original spectral proved to provide better accuracy than derivative spectra. Among the studied preprocessing techniques, moving average smoothing (MAS) technique improved accuracy the most. Overall results suggested that PLS regression with preprocessing of full spectra using MAS is optimal method for modeling N concentration of fresh pear leaves which yielded 0.961 and 0.953 coefficient of determination (R2) for calibration and cross-validation, respectively. The validation of this method resulted high R2 value (0.847) and low mean relative error (4.48%). In conclusion, this model could provide a rapid and more reliable method to determine the total N concentration in fresh pear leaves and could be useful for fertilization management in pear orchards.
关键词： partial least squares regression,preprocessing techniques,pear leaves,Nitrogen concentration,visible/near-infrared reflectance spectroscopy
更新于2025-09-09 09:28:46