Visible Near-infrared Diffuse Reflectance Spectroscopy Research Articles

Determination of Ash Content in Solid Cattle Manure with Visible Near-Infrared Diffuse Reflectance Spectroscopy

Visible and near-infrared (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy (DRS) is increasingly being used to quantify constituents of organic matter both in the lab and in situ. However, it is unknown if DRS can be utilized as a tool for determining crude ash content of solid cattle manure. Ash content is a significant contributor to the suitability and value of manure for use both as a biofuel and soil fertilizer, but conventional ash analysis is time-consuming and labor-intensive. In this study, we explored the feasibility of VisNIR-DRS for the rapid prediction of ash content in solid manure from beef feedyards in the southern High Plains. Proportionally mixed samples of soil and manure (n = 201) were evaluated for ash content by conventional analysis and then used to calibrate a statistical model for prediction of ash content by VisNIR-DRS based on multivariate partial-least squares regression and random test-set validation. Two thirds of the samples were randomly selected to build a calibration model, and the remaining third was used for validation. The coefficient of determination (r2), root mean squared deviation (RMSD), and ratio of prediction to standard deviation (RPD) were calculated to assess the prediction model. The prediction model had an r2 of 0.94, an RMSD of 5% ash (d.b.), and an RPD of 4. The VisNIR-DRS model successfully predicted crude ash content within 5% of the observed ash content (d.b.) as determined by dry oxidation using the accepted ASTM standard E1755-01.

ANALYSIS OF SOLUBLE SOLID CONTENT IN NANFENG MANDARIN FRUIT WITH VISIBLE NEAR INFRARED SPECTROSCOPY

Soluble solids content(SSC) of Nanfeng mandarin fruit was studied by quantitative analysis technique based on visible/near infrared diffuse reflectance spectroscopy(Vis/NIR).Partial least squares(PLS) regression was carried out to analyze the Vis/NIR of Nanfeng mandarin fruit and its flesh.Calibration results for SSC were compared and discussed by using different spectral pretreatment methods.The result shows that the best calibration models,in the wavelength range of 350~1800nm,can be obtained by the first derivative spectrum(D1log(1/R)).The best prediction results for the mandarin fruit are 0.825 and 0.899 for correlation coefficient(rp) and root mean square errors of prediction(RMSEP) respectively.The best prediction results for its flesh are 0.893 and 0.749 for rp and RMSEP respectively.

Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra

In order to reduce costs and time in the analysis of soil properties, visible/near-infrared diffuse reflectance spectroscopy (VNIRS) has been proposed. Since various pre-processing transformations and calibration techniques are in use to analyze soil spectral data, much uncertainty still exists about predictive soil modeling. We investigated the feasibility of VNIRS to determine the concentration of carbon in soils collected in the Santa Fe River Watershed, Florida. A total of 554 soil samples (400 for calibration, and 154 for validation) were collected to a depth from 0 to 180 cm. Total carbon was measured by dry combustion, after sieving (2 mm), air-drying and ball-milling, and is reported in mg kg − 1 . Reflectance measurements from 350 nm to 2500 nm were collected in a controlled laboratory environment. Five multivariate techniques (stepwise multiple linear regression, principal components regression, partial least-squares regression, regression tree and committee trees) and thirty pre-processing transformations (including derivatives, normalization and non-linear transformations) of spectral data were compared with the aim of identifying the best combination to predict soil carbon. The coefficient of determination ( R 2), the root mean square error (RMSE), and the residual prediction deviation (RPD) were used to evaluate the models. The combination of multivariate technique and pre-processing transformation that provided the highest coefficient of determination for the validation set ( R v 2) and RPD, and lowest root mean square error for the validation set (RMSE v), was committee trees associated with Norris gap derivative with a search window of 7 measurements ( R v 2 = 0.86, RMSE v = 0.170, RPD = 2.68). When considering the overall results of the multivariate techniques across all tested pre-processing transformations, partial least-squares regression performed best (lowest average RMSE v across all pre-processing transformations), followed by stepwise multiple linear regression, and committee trees. In terms of pre-processing transformations, Savitzky–Golay derivatives consistently improved the models of soil carbon, being among the five best pre-processing transformations for all of the multivariate techniques tested. Norris gap derivative was the preferred data preparation for the tree-based techniques. Except for standard variate transformation, normalization techniques performed worse than expected. The RPD of the best VNIRS models were higher than 2.50, which suggest that the VNIRS models produced in this study are robust and stable enough to be applied for similar soils.

Use of Visible-Near Infrared Diffuse Reflectance Spectroscopy to Discriminate between Kiwifruit with Properties Altered by Preharvest Treatments

Visible-near infrared (NIR) spectra of kiwifruit berries were processed by discriminant analysis techniques to differentiate berries treated during on-vine development. Treatments applied were leaf removal or shading berries with aluminium foil through crop development, or dipping the berries in ethephon two weeks prior to harvest. In order to accentuate the treatment effects, the canes used for individual treatments were cinctured to cut the phloem layer near the central cordon. Diffuse reflectance visible-NIR spectra were measured for all berries at harvest and after storing at 0°C for 16 weeks, and in sound ripened berries, for which the mass, skin colour, soluble solids and dry matter content were also determined. Principal components (PCs) were calculated for the 550–990 nm region of the visible-NIR absorption spectra for 500 berries randomly selected from all treatments. Canonical variate analyses of the PC were used to distinguish the berries from the original treatment groups. At harvest and after storage, discriminant algorithms, based on training spectra, were applied to validation spectra sets and correctly classified 99% and 87% of the berries, respectively, by their on-vine treatment. In the sound ripened berries, discrimination based on the visible-NIR data was superior to that achieved using combinations of mass, skin colour, dry matter and soluble solids.