Rapid Estimation of Wheat Straw Decomposition Constituents Using Near-Infrared Spectroscopy

Adoption of no-till systems in Eastern Washington has been slow due to the difficulty of managing wheat (Triticum aestivum L.) straw residue and the unknown decomposition potential of cultivars. We hypothesize that by analyzing wheat straw with near-infrared spectroscopy (NIRS), calibration models can be developed to accurately predict fiber and chemical constituents of wheat, determining straw decomposition potential. Straw from a panel of 480 soft winter wheat cultivars adapted to the Pacific Northwest are analyzed for neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), cellulose, hemicellulose, carbon (C), and nitrogen (N). Using modified partial least squares regression and cross validation techniques, specific environment and broad-based NIRS models are calibrated and predictive ability is validated. R2cal values from broad models are better than the specific models, and are 0.85 (NDF), 0.86 (ADF), 0.65 (ADL), 0.88 (cellulose), 0.42 (hemicellulose), 0.67 (C), and 0.73 (N). The corresponding SEP values are 1.68% (NDF), 1.54% (ADF), 0.62% (ADL), 1.14% (cellulose), 1.11% (hemicellulose), 1.23% (C), and 0.06% (N). A Finch × Eltan breeding population is used to further validate models and prediction accuracies for variety selection within a breeding program scenario. The broad NIRS models prove useful for estimating high and low ranges of NDF, ADF, and cellulose in wheat cultivars which translate into characteristics of slow and fast decomposition potential.

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Prediction of neutral detergent fiber content in corn stover using near-infrared spectroscopy technique

International Journal of Food Engineering ◽

10.1515/ijfe-2019-0192 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Xuyang Pan ◽

Laijun Sun ◽

Guobing Sun ◽

Panxiang Rong ◽

Yuncai Lu ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Prediction Model ◽

Spectral Data ◽

Corn Stover ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Cross Validation ◽

Fiber Content ◽

Partial Least Square ◽

Neutral Detergent Fiber

AbstractNeutral detergent fiber (NDF) content was the critical indicator of fiber in corn stover. This study aimed to develop a prediction model to precisely measure NDF content in corn stover using near-infrared spectroscopy (NIRS) technique. Here, spectral data ranging from 400 to 2500 nm were obtained by scanning 530 samples, and Monte Carlo Cross Validation and the pretreatment were used to preprocess the original spectra. Moreover, the interval partial least square (iPLS) was employed to extract feature wavebands to reduce data computation. The PLSR model was built using two spectral regions, and it was evaluated with the coefficient of determination (R2) and root mean square error of cross validation (RMSECV) obtaining 0.97 and 0.65%, respectively. The overall results proved that the developed prediction model coupled with spectral data analysis provides a set of theoretical foundations for NIRS techniques application on measuring fiber content in corn stover.

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Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

Agronomy ◽

10.3390/agronomy11040666 ◽

2021 ◽

Vol 11 (4) ◽

pp. 666

Author(s):

Rafael Font ◽

Mercedes del Río-Celestino ◽

Diego Luna ◽

Juan Gil ◽

Antonio de Haro-Bailón

Keyword(s):

Infrared Spectroscopy ◽

Least Squares ◽

Partial Least Squares ◽

Cicer Arietinum ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Cross Validation ◽

Neutral Detergent Fiber ◽

Acid Detergent Fiber ◽

Cicer Arietinum L

The near-infrared spectroscopy (NIRS) combined with modified partial least squares (modified PLS) regression was used for determining the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) fractions of the chickpea (Cicer arietinum L.) seed. Fifty chickpea accessions (24 desi and 26 kabuli types) and fifty recombinant inbred lines F5:6 derived from a kabuli × desi cross were evaluated for NDF and ADF, and scanned by NIRS. NDF and ADF values were regressed against different spectral transformations by modified partial least squares regression. The coefficients of determination in the cross-validation and the standard deviation from the standard error of cross-validation ratio were, for NDF, 0.91 and 3.37, and for ADF, 0.98 and 6.73, respectively, showing the high potential of NIRS to assess these components in chickpea for screening (NDF) or quality control (ADF) purposes. The spectral information provided by different chromophores existing in the chickpea seed highly correlated with the NDF and ADF composition of the seed, and, thus, those electronic transitions are highly influenced on model fitting for fiber.

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Near-Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures

AgriEngineering ◽

10.3390/agriengineering3010005 ◽

2021 ◽

Vol 3 (1) ◽

pp. 73-91

Author(s):

João Serrano ◽

Shakib Shahidian ◽

Ângelo Carapau ◽

Ana Elisa Rato

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Optical Sensors ◽

Near Infrared ◽

Production Systems ◽

External Validation ◽

Neutral Detergent Fiber ◽

Reference Laboratory ◽

Pasture Quality ◽

Animal Grazing

Dryland pastures provide the basis for animal sustenance in extensive production systems in Iberian Peninsula. These systems have temporal and spatial variability of pasture quality resulting from the diversity of soil fertility and pasture floristic composition, the interaction with trees, animal grazing, and a Mediterranean climate characterized by accentuated seasonality and interannual irregularity. Grazing management decisions are dependent on assessing pasture availability and quality. Conventional analytical determination of crude protein (CP) and fiber (neutral detergent fiber, NDF) by reference laboratory methods require laborious and expensive procedures and, thus, do not meet the needs of the current animal production systems. The aim of this study was to evaluate two alternative approaches to estimate pasture CP and NDF, namely one based on near-infrared spectroscopy (NIRS) combined with multivariate data analysis and the other based on the Normalized Difference Vegetation Index (NDVI) measured in the field by a proximal active optical sensor (AOS). A total of 232 pasture samples were collected from January to June 2020 in eight fields. Of these, 96 samples were processed in fresh form using NIRS. All 232 samples were dried and subjected to reference laboratory and NIRS analysis. For NIRS, fresh and dry samples were split in two sets: a calibration set with half of the samples and an external validation set with the remaining half of the samples. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantifying pasture quality parameters, with greater accuracy in dry samples (R2 = 0.936 and RPD = 4.01 for CP and R2 = 0.914 and RPD = 3.48 for NDF) than fresh samples (R2 = 0.702 and RPD = 1.88 for CP and R2 = 0.720 and RPD = 2.38 for NDF). The NDVI measured by the AOS shows a similar coefficient of determination to the NIRS approach with pasture fresh samples (R2 = 0.707 for CP and R2 = 0.648 for NDF). The results demonstrate the potential of these technologies for estimating CP and NDF in pastures, which can facilitate the farm manager’s decision making in terms of the dynamic management of animal grazing and supplementation needs.

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Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds

Animal Production Science ◽

10.1071/an17240 ◽

2019 ◽

Vol 59 (6) ◽

pp. 1190 ◽

Cited By ~ 2

Author(s):

A. Bahri ◽

S. Nawar ◽

H. Selmi ◽

M. Amraoui ◽

H. Rouissi ◽

...

Keyword(s):

Principal Component Analysis ◽

Infrared Spectroscopy ◽

Random Forest ◽

Near Infrared Spectroscopy ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Principal Component ◽

Least Squares Regression ◽

Random Forest Regression ◽

Milk Samples

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.

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Evaluation of the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using near infrared spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033517728733 ◽

2017 ◽

Vol 25 (5) ◽

pp. 301-310 ◽

Cited By ~ 8

Author(s):

Jetsada Posom ◽

Panmanas Sirisomboon

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Model Development ◽

Volatile Matter ◽

Ash Content ◽

Least Squares Regression ◽

Fixed Carbon ◽

Bomb Calorimetry ◽

Heating Value

This research aimed to determine the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using Fourier transform near infrared spectroscopy as an alternative to bomb calorimetry and thermogravimetry. Bamboo culms used in this study had circumferences ranging from 16 to 40 cm. Model development was performed using partial least squares regression. The higher heating value, volatile matter, fixed carbon and ash content were predicted with coefficients of determination (r2) of 0.92, 0.82, 0.85 and 0.51; root mean square error of prediction (RMSEP) of 122 J g−1, 1.15%, 1.00% and 0.77%; ratio of the standard deviation to standard error of validation (RPD) of 3.66, 2.55, 2.62 and 1.44; and bias of 14.4 J g−1, −0.43%, 0.03% and −0.11%, respectively. This report shows that near infrared spectroscopy is quite successful in predicting the higher heating value, and is usable with screening for the determination of fixed carbon and volatile matter. For ash content, the method is not recommended. The models should be able to predict the properties of bamboo samples which are suitable for achieving higher efficiency for the biomass conversion process.

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Power plant intake quantification of wheat straw composition for 2nd generation bioethanol optimization – A Near Infrared Spectroscopy (NIRS) feasibility study

Bioresource Technology ◽

10.1016/j.biortech.2009.09.027 ◽

2010 ◽

Vol 101 (4) ◽

pp. 1199-1205 ◽

Cited By ~ 24

Author(s):

Carina J. Lomborg ◽

Mette H. Thomsen ◽

Erik Steen Jensen ◽

Kim H. Esbensen

Keyword(s):

Infrared Spectroscopy ◽

Power Plant ◽

Wheat Straw ◽

Feasibility Study ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

2Nd Generation

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Rapid Diagnosis of HIV-1 virus by Near Infrared Spectroscopy: based on Partial least squares regression

E3S Web of Conferences ◽

10.1051/e3sconf/202127103067 ◽

2021 ◽

Vol 271 ◽

pp. 03067

Author(s):

Xiaohong He ◽

Zhihong Song ◽

Haifei Shang ◽

Silang Yang ◽

Lujing Wu ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

False Negative ◽

Cost Effective ◽

Rapid Diagnosis ◽

Enzyme Linked Immunosorbent Assay ◽

Least Squares Regression ◽

Early Screening ◽

Hiv 1

Currently, the laboratory diagnostic tests available for HIV-1 viral infection are mainly based on serological testing which relies on enzyme-linked immunosorbent assay (ELISA) for blood HIV antigen detection and reverse transcription polymerase chain reaction (RT-PCR) for HIV specific RNA sequence identification. However, these methods are expensive and time-consuming, and suffer from false positive and/or false negative results. Thus, there is an urgent need for developing a cost effective, rapid and accurate diagnostic method for HIV-1 infection. In order to reduce the barriers for effective diagnosis, a near-infrared spectroscopy (NIR) method was used to detect the HIV-1 virus in human serum, specifically, three absorption peaks with dose-dependent at 1582nm, 1810nm and 2363nm were found by multiple FBiPLSR test analysis for HIV-nano and HIV-EGFP, but not for MLV. Therefore, we recommend the use of 1582nm, 1810nm and 2363nm as the characteristic spectrum peak, for early screening and rapid diagnosis of serum HIV.

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