Near-infrared Reflectance Spectroscopy Method Research Articles

Application of NIRS adaptive technology for prediction of in vitro digestibility parameters of feed ingredients from Fermented Cocoa Pods

Near Infrared Reflectance Spectroscopy (NIRS) is an alternative method that can be applied in the evaluation of feed ingredients. The purpose of this study was to evaluate the in vitro digestibility value of fermented cocoa pods using the NIRS method of feed analysis. This study used 18 samples of fermented cocoa shell waste, where the material used was fermented cocoa pod skin (KBK) then in vitro analysis was carried out. Samples were acquired spectrum by Thermo Integrative. Chemical analysis is intended to compare the data from chemical analysis and the NIRS method. The parameters analyzed were pH, dry matter digestibility (KCBK), and organic matter digestibility (KCBO). Spectrum data were processed using PLS with the pre-treatment methods of multiplicative scatter correction (MSC) and DeTrending (DT). Based on the analysis that has been carried out using the NIRS method with a prediction model that has been built on pH parameters, KCBK has a good predictive model with the DT pre-treatment method and KCBO has a good predictive model with the MSC method where (pH = LV: 8, r: 0 .79, R2: 0.62, RMSEC: 0.04 and RPD: 2.00; KCBK= LV: 8, r: 0.86, R2: 0.74, RMSEC: 1.30 and RPD: 2.02 ; KCBO= LV: 8, r: 0.88, R2: 0.78, RMSEC: 1.39 and RPD: 2.21). Rough predictions for pH were obtained with Non pre-treatment and MSC pre-treatment (pH and MSC = LV: 8, r: 0.74, R2: 0.55, RMSEC: 0.05, RPD 1.58) and rough predictions on KCBK were obtained with Non Preatment (KCBK=LV: 8, r: 0.83 R2:0.70, RMSEC: 1.30, and RPD 1.87).

Determination of crude protein and metabolized energy with near infrared reflectance spectroscopy (NIRS) in ruminant mixed feeds

This study aims to determine the crude protein and metabolizable energy values in ruminant mixed feeds based on the measurements obtained from Near Infrared Reflectance Spectroscopy (NIRS) device. The mixed feeds used in the study were produced in Balıkesir. Reference analyses were determined by making chemical analyses of mixed feed samples used in the study. Metabolic energy value of mixed feed samples was calculated by using the determined nutrient values in equations. Portable NIRS device was used in the study. The relationship between estimates obtained from NIRS device and reference values was statistically evaluated. In regression analysis, R2 value was found as 0.0064 for crude protein while it was 0.9397 for metabolizable energy. It has been demonstrated that the NIRS method is a fast, reliable and good estimation method for quantitatively determining the metabolic energy value in ruminant mixed feeds.

Non-destructive determination of the main chemical components of red dragon fruit peel flour by using Near-Infrared Reflectance Spectroscopy (NIRS)

Red dragon fruit peel is still not optimally utilized and only becomes waste. According to previous research, it is known that red dragon fruit peel contains 72.1% carbohydrate, 0.7% fat, 3.2% protein, and 46.7% food fiber and contains betacyanin of 186.90 mg / 100g dry weight and 53.71% antioxidant. One of the solutions to use red dragon fruit peel is to process it into flour. The accurate and efficient determination of the chemical content of red dragon fruit peel flour can be realized through the development of the Near-Infrared Reflectance Spectroscopy (NIRS) technology. The sample that used was made directly from the waste of red dragon fruit peel with a process of flouring, then measured the spectra with NIRS and the chemical content of the sample destructively. The reflectance spectra as output were transformed into absorbance spectra. Data calibration uses a partial least squares (PLS) method with several variations in the number of factors and pretreatment data in the form of normalization and smoothing Savitzky-Golay smoothing (SGs), resulting in the best calibration regression model in estimating water, ash, carbohydrate, and protein content. In contrast, the estimation of fat cannot be done non-destructively by using the NIRS method.

Towards a global arctic-alpine model for Near-infrared reflectance spectroscopy (NIRS) predictions of foliar nitrogen, phosphorus and carbon content

Near-infrared spectroscopy (NIRS) is a high-throughput technology with potential to infer nitrogen (N), phosphorus (P) and carbon (C) content of all vascular plants based on empirical calibrations with chemical analysis, but is currently limited to the sample populations upon which it is based. Here we provide a first step towards a global arctic-alpine NIRS model of foliar N, P and C content. We found calibration models to perform well (R2validation = 0.94 and RMSEP = 0.20% for N, R2validation = 0.76 and RMSEP = 0.05% for P and R2validation = 0.82 and RMSEP = 1.16% for C), integrating 97 species, nine functional groups, three levels of phenology, a range of habitats and two biogeographic regions (the Alps and Fennoscandia). Furthermore, when applied for predicting foliar N, P and C content in samples from a new biogeographic region (Svalbard), our arctic-alpine NIRS model performed well. The precision of the resulting NIRS method meet international requirements, indicating one NIRS measurement scan of a foliar sample will predict its N, P and C content with precision according to standard method performance. The modelling scripts for the prediction of foliar N, P and C content using NIRS along with the calibration models upon which the predictions are based are provided. The modelling scripts can be applied in other labs, and can easily be expanded with data from new biogeographic regions of interest, building the global arctic-alpine model.

Identification of genetic variation in Brassica napus seeds for tocopherol content and composition using near‐infrared spectroscopy technique

AbstractTocopherol is an essential fat‐soluble nutrient for humans. Increasing the tocopherol content in Brassica napus seeds can add value to rapeseed vegetable oil; this has become an important breeding target. However, there is no efficient and non‐destructive method for selecting rapeseed accessions with high tocopherol contents. Here, we report the first near‐infrared reflectance spectroscopy (NIRS)‐based technique for predicting rapeseed tocopherol content. Individual seed tocopherol compositions were estimated from 373 rapeseed genotypes of different origins. This method and chemical methods produced comparable predicted values of the tocopherol constituents in the seeds. Three equations were generated for the prediction of tocopherol content by using a modified partial least squares (MPLS) model. The total tocopherol content for the determination coefficient of cross‐validation (R2cv) (0.74), determination coefficient (RSQ) (0.76) and one minus the ratio of unexplained variance to total variance (1‐VR) (0.65) values indicates a strong correlation between the calibration and validation sets. Overall, our model confirmed the NIRS method as feasible for predicting tocopherol content in rapeseed and as an efficient screening tool for future breeding programs.

Models of near infrared spectroscopy of fatty acid contents in rapeseed

AbstractContent of oleic acid varied greatly in rapeseed. When rapeseeds with broad range of oleic acid content were used for calibration, models of near‐infrared reflectance spectroscopy (NIRS) showed qualified accuracy. To further increase the accuracy of models, the present study narrowed the range of oleic content in calibration samples and only focused rapeseeds with oleic acid content ranging from 55 to 81%. Finally, external validation showed higher accuracy of the model for oleic acid than previous models, with the RSQ of 0.995. Meanwhile, models for linoleic and linolenic acids were also constructed, both showing high RSQ (0.929 and 0.907). These NIRS models could be used for fast determination of fatty acid contents in rapeseeds during agricultural and breeding practices.Practical applicationsThe NIRS method has advantages of rapidity, high efficiency, reliable results, no drug contamination, and no damage to materials. The models constructed in the present study could be used to determine contents of oleic, linoleic, and linolenic acids in rapeseeds. This method can determine several quality parameters with only a small amount of sample at the same time. It is of great significance to save manpower, materials and improve the analysis efficiency in plant breeding applications and rapeseed quality analysis. It is an effective detection method for screening rapeseed breeding materials, analyzing fatty acid components, and quality of rapeseeds.

The potential of the tropical “miracle tree” Moringa oleifera and its desert relative Moringa peregrina as edible seed-oil and protein crops under Mediterranean conditions

The potential of the tropical Moringa oleifera Lam. and its desert relative Moringa peregrina (Forssk.) Fiori as edible seed-oil and seed-protein crops under Mediterranean conditions was evaluated. Initially, we developed a NIRS (Near Infrared Reflectance Spectroscopy) method for the analysis of seed weight, and seed-oil and protein contents. We found NIRS to be a relatively accurate method for estimating seed traits of both Moringa species.Comparative analysis of bloom phenology and reproductive success between M. oleifera and M. peregrina, grown under Mediterranean conditions, revealed for M. oleifera a shorter juvenility period and a lower variation in bloom and reproductive traits. Both species bloomed in summer and set fruits in autumn. M. peregrina also bloomed in late autumn and set fruits in spring the following year. Annually, M. oleifera trees produced significantly more flowers and set more fruits. Fruit-set for both species was extremely low (∼0.5–1.5%). M. oleifera pods were 45% longer, contained 23% more seeds that were 47% lighter with 11% lower oil concentration compared with M. peregrina, resulting in approximately six-fold higher oil and protein yields per plant.In conclusion, M. oleifera was better suited for oil and protein production under Mediterranean conditions as it produced more seeds and in a more predictable and uniform manner. However, since M. peregrina produced larger seeds with higher oil concentrations and was less susceptible to local diseases, future breeding efforts should be concentrated on producing interspecies hybrids resulting in elevated oil production and reduced susceptibility to diseases.

Comparison of innovative and non-invasive methods in estimating the fat content in pork trimmings

The purpose of research was to determine a possibility of application of computer vision systems (CVS) for estimation of fat content in pork trimmings in comparison with methods based on DXR (dual energy X-ray) and NIR (near-infrared reflectance spectroscopy). Research was conducted on 232 samples of pork trimmings. In order to verify the fat content determined by CVS, DXR, and NIR methods, fat content was also determined by the Soxhlet reference method. It was found that CVS can be used to estimate fat content in pork trimmings with a standard error of prediction between 4.9 and 5.6%. In order to achieve higher efficiency, it seems advisable to grind and standardise meat in a meat grinder with a kidney shaped plate.

Botanical Composition Prediction of Alfalfa–Grass Mixtures using NIRS: Developing a Robust Calibration

Botanical composition of alfalfa (Medicago sativa L.)–grass fresh and ensiled mixtures is a key parameter for assessing forage and diet quality as well as for managing mixed stands. Previous attempts to validate near‐infrared reflectance spectroscopy (NIRS) equations for estimating botanical composition have had mixed results. This study was conducted to develop a robust NIRS method to estimate botanical composition of binary alfalfa–grass mixtures. Alfalfa–grass samples were collected across New York State over four growing seasons, hand separated, and a subset were ensiled separately. Dry samples were coarsely ground, mixed in known proportions, and reground for analysis by NIRS at Dairy One Forage Laboratory, Ithaca, NY. Samples were mixed to range from 0 to 100% alfalfa for NIRS calibration, with a total of 741 individual samples from 3 yr used for calibration of three NIRS instruments and samples from a fourth year used for validation. Grass composition was predicted with good precision and accuracy showing biases of 2.49 and standard errors of prediction (SEP) of 5.06, with R2 of 0.972, using the equation developed across multiple instruments. With selection of a robust set of calibration samples over many environments, NIRS can be used to determine the botanical composition of fresh‐dried or ensiled‐dried alfalfa–grass samples, and replicate scans from multiple instruments can be combined to develop a single calibration that will perform with equal efficiency across different instruments.

Rapid authentication of starch adulterations in ultrafine granular powder of Shanyao by near-infrared spectroscopy coupled with chemometric methods

Near-infrared reflectance (NIR) spectroscopy combined with chemometric techniques was developed for classification and quantification of cheaper starches (corn and wheat starch) in ultrafine granular powder of Shanyao (UGPSY). By performing orthogonal partial least squares discrimination analysis (OPLS-DA), NIR could efficiently distinguish among authentic UGPSY and UGPSY adulterated with cornstarch and wheat starch. In addition, the starch content in adulterated UGPSY was determined by NIR coupled with an appropriate multivariate calibration method. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were performed comparatively to calibrate the regression model. Experimental results showed that the performance of the siPLS model is the best compared to PLS and iPLS. These results show that the combination of NIR spectroscopy and chemometric methods offers a simple, fast and reliable method for the classification and quantification of the ultrafine granular powder of the herb.

Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

Near infrared reflectance spectroscopy (NIRS), a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis (PLS-DA) to discriminate the transgenic (TCTP and mi166) and wild type (Zhonghua 11) rice. Furthermore, rice lines transformed with protein gene (OsTCTP) and regulation gene (Osmi166) were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000–8 000cm-1 and 4 000–10 000cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000–10 000cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000–10 000cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

Does species richness affect fine root biomass and production in young forest plantations?

Tree species diversity has been reported to increase forest ecosystem above-ground biomass and productivity, but little is known about below-ground biomass and production in diverse mixed forests compared to single-species forests. For testing whether species richness increases below-ground biomass and production and thus complementarity between forest tree species in young stands, we determined fine root biomass and production of trees and ground vegetation in two experimental plantations representing gradients in tree species richness. Additionally, we measured tree fine root length and determined species composition from fine root biomass samples with the near-infrared reflectance spectroscopy method. We did not observe higher biomass or production in mixed stands compared to monocultures. Neither did we observe any differences in tree root length or fine root turnover. One reason for this could be that these stands were still young, and canopy closure had not always taken place, i.e. a situation where above- or below-ground competition did not yet exist. Another reason could be that the rooting traits of the tree species did not differ sufficiently to support niche differentiation. Our results suggested that functional group identity (i.e. conifers vs. broadleaved species) can be more important for below-ground biomass and production than the species richness itself, as conifers seemed to be more competitive in colonising the soil volume, compared to broadleaved species.

Determination of plant silicon content with near infrared reflectance spectroscopy.

Silicon (Si) is one of the most common elements in the earth bedrock, and its continental cycle is strongly biologically controlled. Yet, research on the biogeochemical cycle of Si in ecosystems is hampered by the time and cost associated with the currently used chemical analysis methods. Here, we assessed the suitability of Near Infrared Reflectance Spectroscopy (NIRS) for measuring Si content in plant tissues. NIR spectra depend on the characteristics of the present bonds between H and N, C and O, which can be calibrated against concentrations of various compounds. Because Si in plants always occurs as hydrated condensates of orthosilicic acid (Si(OH)4), linked to organic biomolecules, we hypothesized that NIRS is suitable for measuring Si content in plants across a range of plant species. We based our testing on 442 samples of 29 plant species belonging to a range of growth forms. We calibrated the NIRS method against a well-established plant Si analysis method by using partial least-squares regression. Si concentrations ranged from detection limit (0.24 ppmSi) to 7.8% Si on dry weight and were well predicted by NIRS. The model fit with validation data was good across all plant species (n = 141, R2 = 0.90, RMSEP = 0.24), but improved when only graminoids were modeled (n = 66, R2 = 0.95, RMSEP = 0.10). A species specific model for the grass Deschampsia cespitosa showed even slightly better results than the model for all graminoids (n = 16, R2 = 0.93, RMSEP = 0.015). We show for the first time that NIRS is applicable for determining plant Si concentration across a range of plant species and growth forms, and represents a time- and cost-effective alternative to the chemical Si analysis methods. As NIRS can be applied concurrently to a range of plant organic constituents, it opens up unprecedented research possibilities for studying interrelations between Si and other plant compounds in vegetation, and for addressing the role of Si in ecosystems across a range of Si research domains.

Near Infrared Reflectance Spectroscopy (NIRS) for rapid determination of biochemical methane potential of plant biomass

Determination of biochemical methane potential (BMP) by fermentation tests is time-consuming and costly, and therefore not useful for operators of full-scale biogas digesters. A great advantage of NIRS in determining BMP is the reduction in measurement time from at least one month for chemical determination to a couple of minutes for production of near infrared spectroscopy (NIRS) spectra. An innovative NIRS method that can be used as an alternative to current BMP tests was developed in this study. We tested the Partial Least Squares (PLS) model for a rapid determination of BMP using NIRS by applying a series of pre-processing methods with caution. A total of 88 plant biomass samples of a wide variety were used for model prediction. The standard error of the best PLS model was 37 CH4 NLkg−1 VS, where BMP of the test set was between 136.2 and 477.9 CH4 NLkg−1 VS. Coefficient of determination (R2) and residual prediction deviation (RPD) were 0.84 and 2.49, respectively. This shows that the new NIRS model is moderately successful in application and could be an alternative modern tool to overcome the problems of current BMP methods.

Correlations between chemical assays and near-infrared reflectance spectroscopy for nutrient components and correlations between nutrients and color scores of distillers dried grains with solubles

SUMMARY The objective of this research was to evaluate the correlations between chemical assay and near-infrared reflectance spectroscopy (NIRS) for the nutrient content of distillers dried grains with solubles (DDGS) samples. The samples were analyzed by NIRS or wet chemical procedures. Color [CIE lightness (L*), redness (a*), and yellowness (b*)] characteristics of the samples were measured in triplicates with a chroma meter. Highest CV were observed for fiber fragments in wheat DDGS and for fat in corn DDGS among the nutrients tested. Correlation coefficients showed good agreement between NIRS and wet methods for the determination of selected nutrients except DM. Strong negative correlations were found between ash and ADF (−0.811), ash and NDF (−0.649), and ash and CF (−0.757) when determined by wet analyses. Phosphorus and gross energy had negative correlations with fiber fragments. The values for a* were found to be inversely correlated to the CF, ADF, and NDF contents found in both methods. On the contrary, there was a positive correlation between a* values and ash content in both methods. Positive correlations also were found between fat content and L* and b* values of the ingredients. Negative correlations were found between b* value and CP, Arg, Ile, Gly, and Pro levels. Based on these results, NIRS is a reliable method depending on the calibration quality, and the correlations found among nutrients and between color and nutrients must be taken into consideration to predict nutrient content of DDGS. No antibiotic residue was found in the samples.

Solvent-free determination of TPH in soil by near-infrared reflectance spectroscopy

Public awareness of environmental concerns are on the increase, and thus, safety standards have become more stringent, and far more analyses are required. Due to extensive oil exploitation, refi ning and transportation, oil pollution has become a major source of water and soil contamination. Although a number of standard procedures exist to quantify total petroleum hydrocarbons (TPHs) in soil, they all require time- and labor-intensive sample preparation, and most use per-halogenated solvents. Therefore, a feasibility study was undertaken to test the possibility of using near-infrared refl ectance spectroscopy (NIRS) for direct determination of oil contamination in soil. Based on a set of no. 43 samples spiked with known and independently confi rmed TPH contamination of 0.05 to 2.19 wt% (using liquid-solid extraction and US-EPA method 418.1) a chemometric model was developed. The regression model fulfi lled the following criteria for the reference data: validation coeffi cient r 2 ≥0.929 and root-mean-square error of validation ≤0.177. A set of no. 26 fi samples contaminated with weathered crude oil (≈0.1-5%) were analyzed by NIRS and conventional methods (i.e., extraction and US-EPA method 418.1). A correlation factor of r 2 ≥0.928 with a standard deviation of the absolute differences between true and predicted values of ≤0.251 was obtained.

Prediction of the Chemical Composition of Fresh Whole Crop Barley Silages by Near Infrared Spectroscopy

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages and feedstuff. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of fresh whole crop barley silages. A representative population of 284 fresh whole crop barley silages was used as a database for studying the possibilities of NIRS to predict chemical composition. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data were recorded as log 1/Reflectance (log 1/R) and were scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh whole crop barley silages provided accurate predictions of moisture, acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP) and pH, as well as lactic acid content with correlation coefficients of cross-validation (<TEX>$R^2cv$</TEX>) of 0.96, 0.81, 0.79, 0.84, 0.72 and 0.78, respectively, and standard error of cross-validation (SECV) of 1.26, 2.83, 2.18, 1.19, 0.13 and 0.32% DM, respectively. Results of this experiment showed the possibility of the NIRS method to predict the chemical parameters of fresh whole crop barley silages as a routine analysis method in feeding value evaluation and for farmer advice.

A Near-Infrared Reflectance Spectroscopy Method for Direct Analysis of Several Chemical Components and Properties of Fruit, for Example, Chinese Hawthorn

Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of Chinese hawthorn (Crataegus pinnatifida Bge. var. major) fruit from three geographical regions as well as for the estimation of the total sugar, total acid, total phenolic content, and total antioxidant activity. Principal component analysis (PCA) was used for the discrimination of the fruit on the basis of their geographical origin. Three pattern recognition methods, linear discriminant analysis, partial least-squares-discriminant analysis, and back-propagation artificial neural networks, were applied to classify and compare these samples. Furthermore, three multivariate calibration models based on the first derivative NIR spectroscopy, partial least-squares regression, back-propagation artificial neural networks, and least-squares-support vector machines, were constructed for quantitative analysis of the four analytes, total sugar, total acid, total phenolic content, and total antioxidant activity, and validated by prediction data sets.

근적외선 분광법을 이용한 이탈리안 라이그라스 사일리지의 품질 평가

본 연구는 근적외선분광법의 현장 이용성 및 이탈리안 라이그라스 원물 사일리지의 신속한 품질평가를 위한 근적외선 검량식을 개발하기 위하여 전국에서 이탈리안 라이그라스 사일리지 약 450여 점을 수집하였다. 각각의 시료는 근적외선 분광기를 이용하여 스펙트럼을 측정한 후 측정된 스펙트럼과 실험실 분석값 간에 상관관계를 이용한 다변량회귀분석법을 통하여 검량식을 유도한 다음 각 성분별로 예측 정확성을 평가하였다. 이탈리안 라이그라스 원물 사일리지의 수분 함량에 대한 검량식 개발 결과는 검량식 작성시 표준오차(SEC)가 0.94% (<TEX>$R^2$</TEX>=0.99)로 매우 우수한 정확성을 보였으며 개발된 검량식의 상호검증(SECV) 결과는 1.27% (<TEX>$R^2$</TEX>=0.98)로 나타났다. ADF, NDF 및 조단백질 함량 평가를 위해 개발된 검량식의 상호검증(SECV) 결과는 각각 1.26% (<TEX>$R^2$</TEX>=0.88), 2.0% (<TEX>$R^2$</TEX>=0.84) 및 0.96% (<TEX>$R^2$</TEX>=0.93)으로 나타났으며 조회분함량 평가에 대한 검량식 개발 결과는 다소 낮은 정확성(SECV 0.72%, <TEX>$R^2$</TEX>=0.62)을 나타내었다. 사일리지의 발효품질의 주요 평가항목인 pH와 젖산함량 평가에 대한 검량식 개발결과(SEC)는 각각 0.41 (<TEX>$R^2$</TEX>=0.85), 0.18% (<TEX>$R^2$</TEX>=0.92)로 나타났으며 개발된 검량식의 상호검증 결과(SECV)는 각각 0.56 (<TEX>$R^2$</TEX>=0.78), 0.31%(0.81)로 나타났다. Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of Italian ryegrass silages. A population of 267 Italian ryegrass silages representing a wide range in chemical parameters and fermentative characteristics was used in this investigation. Samples of silage were scanned at 2 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of the highest coefficients of determination in cross validation (<TEX>$R^2$</TEX>) and the lowest standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The <TEX>$R^2$</TEX> and SECV were 0.98 (SECV 1.27%) for moisture, 0.88 (SECV 1.26%) for ADF, 0.84 (SECV 2.0%), 0.93 (SECV 0.96%) for CP and 0.78 (SECV 0.56), 0.81 (SECV 0.31%), 0.88 (SECV 1.26%) and 0.82 (SECV 4.46) for pH, lactic acid, TDN and RFV on a dry matter (%), respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation quality of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

Fourier transform near-infrared spectroscopy (FT-NIRS) application to estimate Brazilian soybean [Glycine max (L.) Merril] composition

This study examined the ability of near-infrared reflectance spectroscopy method (FT-NIRS) and multivariate calibration to estimate the concentration of moisture, protein, lipid, ash and carbohydrate of Brazilian soybeans. The spectra obtained in the range of 4000 to 10,000cm−1 were preprocessed by several combinations of mathematical treatments: MSC (multiplicative scatter correction), SNV (standard normal variate) or first and second derivative and all data were mean centered before the calibration, for which was used the PLS method (partial least squares). The best calibration models found in this study were the ones used to determine protein and moisture contents (R2=0.81, RMSEP=1.61% and R2=0.80, RMSEC=1.55%, respectively). However, the technique shows high predictability for all parameters, including lipids, ashes and carbohydrates, with RMSECV of 0.40 to 2.30% and RMSEP of 0.38 to 3.71%. This result shows the viability of using NIR in controlling the quality parameters of soybeans.