Near Infrared Reflectance Spectroscopy Equations Research Articles

Prediction of the nutritive value of whole plants and morphological fractions of forage sunflower by near-infrared reflectance spectroscopy and empirical equations

This technical note sought to examine the ability of near-infrared reflectance spectroscopy (NIRS) to predict the chemical content and organic matter digestibility (OMD) of whole plants and the morphological components of forage sunflower. Empirical models for the prediction of OMD values from chemical components were developed, and their predictive ability vs. NIRS models was assessed. The total set of samples (n=147) was composed of whole plants (n=14) and morphological components (n=133) from different experiments performed at Galicia (Spain) and were scanned using a Foss NIR System 6500 instrument. The reference values of OMD corresponded to in vitro determinations (n=112 samples) from laboratory incubation tests using rumen fluid. The predictive capacity of the NIRS models was assessed by the coefficient of determination value in external validation (r2 ), showing good to excellent quality prediction of OMD and chemical components with values of r2 ≥0.88. However, the estimation of lignin did not show predictive utility (r2 =0.40). Using the NIRS models to predict the OMD of whole plants and morphological components of forage sunflower led to a decrease in the standard error in external validation, in contrast to the best empirical equation through the chemical components of samples (from ±8.25 to ±3.23%). This technical note showed that NIRS is a suitable technology, providing a rapid assessment of forage sunflower. However, these results should be considered preliminary, as they are based on a limited number of samples, and it is desirable to improve the performance of NIRS equations by increasing the dataset in future works.

Comparative Accuracy of In Vitro Rumen Fermentation and Enzymatic Methodologies for Determination of Undigested Neutral Detergent Fiber in Forages and Development of Predictive Equations Using NIRS

Undigested neutral detergent fiber (uNDF) is becoming more widely recognized as an important fiber fraction in forage quality assessment because it explains a portion of NDF that is inaccessible to digestion in the ruminant digestive system and is, thus, important in modeling the digestion kinetics of the potentially degradable component of NDF. In experiment 1, uNDF was determined in several forage species in order to compare the accuracy of two reference methods: (1) a long-term in vitro ruminal fermentation (240 h) using an Ankom DaisyII incubator and (2) a multi-step enzymatic method without ruminal fluid. The objective of experiment 2 was to construct predictive equations for uNDF estimation using acid detergent lignin (ADL) and near-infrared reflectance spectroscopy (NIRS) in a pool (n = 264) of alfalfa hay, timothy hay, and tall fescue straw, using the most accurate reference method selected in experiment 1. Partial least squares regression analysis was used to calibrate the reference values against NIRS spectra. Several indicators were used to assess the performance of validation results, including standard error of cross-validation (SECrV), coefficient of determination of cross-validation (R2CrV), and ratio percentage deviation (RPD). The findings of experiment 1 suggested that, relative to the in vitro ruminal methodology, the enzymatic approach overestimated uNDF concentration of forages. Repeatability coefficient was also greater when uNDF was determined using the in vitro versus enzymatic procedure, potentially disqualifying the enzymatic method for the uNDF analysis in forages. In experiment 2, a poor relationship was established between ADL and uNDF (R2 < 0.60), suggesting the inadequacy of ADL parameter to represent the uNDF pool size in these forages. The best predictive equation using NIRS was obtained for alfalfa hay (R2CrV = 0.92; SECrV = 1.16; RPD = 3.57), using the in vitro fermentation as a reference method. The predictive equations were moderately accurate for timothy hay (R2CrV = 0.80; SECrV = 1.31; RPD = 2.08) and tall fescue straw (R2CrV = 0.79; SECrV = 1.38; RPD = 2.18). Our findings suggested the inadequacy of the enzymatic procedure in accurately determining uNDF concentration of forages as compared with the in vitro rumen fermentation protocol. Although the NIRS equations developed using the alfalfa hay dataset were more accurate than that of timothy hay and tall fescue straw, the validation results verified applicability of the equations as a fast screening tool for qualitative prediction of uNDF in these forages, which is important in commercial settings.

Genetic variation for seed storage protein composition in rapeseed (Brassica napus) and development of near‐infrared reflectance spectroscopy calibration equations

AbstractRapeseed protein consists mainly of the seed storage protein cruciferin and napin. Cruciferin and napin have different nutritional values and techno‐functional properties. Shifting the cruciferin/napin ratio towards either more napin or more cruciferin could allow diversified applications. The objective of this study was to investigate the genetic variation of cruciferin and napin in modern winter rapeseed cultivars. Cruciferin and napin contents were analysed by SDS‐PAGE. Genetic variation for both protein fractions was highly significant. Heritabilities were high ranging from 74% for cruciferin to 82% for napin. Napin was positively correlated with glucosinolate (rS = .52**) and seed protein content (rS = .48**). Additional plant material with much larger trait variation was included to develop near‐infrared reflectance spectroscopical calibrations. The Near‐infrared reflectance spectroscopy (NIRS) equations showed high fractions of explained variance in cross and independent validation of around .9 for all traits, indicating that the NIRS equations can be applied in routine screening of plant material.

Short communication: A survey of grass-clover ley management and creation of a near infra-red reflectance spectroscopy equation to predict clover concentration

The purpose of the present study was, firstly, to examine current practice for the agronomy of grass-clover mixed swards used for silage-making in the UK, and secondly, to develop and validate a Near Infra-Red Reflectance Spectroscopy (NIRS) equation capable of predicting clover concentration (CC) in undried and unmilled grass-clover silage samples. A calibration set of 94 grass-clover (white, trifolium repens, and red, trifolium pratense) mixture silage samples were sourced from UK farms and an accompanying questionnaire was used to obtain information on the sward agronomy used to produce each sample. Questionnaire data highlighted that (i) reducing the use of fertiliser inputs (ii) increasing uptake of new varieties, and (iii) increasing the farmer’s ability to measure botanical composition as potential strategies for improving the utilisation of clover in grass swards. Botanical composition was measured by hand separation for each sample and a new NIRS equation to predict CC was created and assessed using blind validation with an independent set of 30 grass-clover samples. The relative standard error of cross validation (SECV, as a percentage of the measured mean) of the optimised equation produced was 36.8%, and, in an independent validation test, the ratio of standard error of prediction to the standard deviation of the reference data set (RPD) was 1.56. The equation could be improved by increasing accuracy at high CCs but showed promise as a simple tool to assist growers in sward management decisions.

Assessing the accuracy of current near infra-red reflectance spectroscopy analysis for fresh grass-clover mixture silages and development of new equations for this purpose

The purpose of this study was to ascertain whether Near Infra-Red Reflectance Spectroscopy (NIRS) prediction equations calibrated on grass silage samples, could accurately predict the chemical composition of mixed grass-clover silage samples, and furthermore, to develop and calibrate new grass-clover equations should the grass-based equations be insufficiently accurate for these silages. A set of 94 silage samples from mixed grass-clover swards (clover concentration (CC) ranging from 4 to 1000 g/kg as fed; determined manually) were analysed for chemical composition using reference laboratory techniques, in vivo digestible organic matter in the dry matter (DOMD, in sheep), and in situ degradability of dry matter and crude protein (in cows). The same samples were scanned fresh (undried and unmilled, as is standard practice for silage analysis within UK laboratories) using NIRS (at AFBI, Northern Ireland) and grass-based prediction equations applied. Predicted and observed results were compared. Of 15 chemical components that were tested for prediction accuracy, only volatile-corrected dry matter and nitrogen were well predicted (RPD values of 4.9 and 2.4 respectively, with low root mean square errors of prediction (RMSEP)). Neutral detergent fibre and DOMD showed low RPD values, however the predicted and observed datasets had no significant bias between them and were therefore also considered as fit for purpose. Variables with significant bias between predicted and observed datasets that were not considered suitably accurate included crude protein, acid detergent fibre, microbial dry matter yield and effective degradabile nitrogen. For many components, bias could be attributed at least in part to CC and changes in the fractionation of nutrients present. For some variables such as crude protein, grass-based equations were sufficiently accurate at low CCs but became inaccurate as CC increased, as expected. In response to inadequate prediction accuracy of certain nutrients, new grass-clover equations were calibrated using the obtained spectra. These were validated and results indicated that the grass-clover-based equations outperformed their grass-based counterparts. The adoption of new grass-clover equations, or alternatively, with further development, the use of a CC correction factor to the existing grass-based equations, is recommended for commercial laboratories offering undried and unmilled silage analysis on samples containing clover.

Technical note: Near infrared reflectance spectroscopy to predict fecal indigestible neutral detergent fiber for dairy cows

In vitro and in situ procedures performed to estimate indigestible neutral detergent fiber (iNDF) in forage or fecal samples are time consuming, costly, and limited by intrinsic factors. In contrast, near infrared reflectance spectroscopy (NIRS) has become widely recognized as a valuable tool for accurately determining chemical composition and digestibility parameters of forages. The aim of this study was to build NIRS calibrations and equations for fecal iNDF. In total, 1,281 fecal samples were collected to build a calibration data set, but only 301 were used to develop equations. Once dried, samples were ground and chemically analyzed for crude protein, ash, amylase and sodium sulfite-treated NDF corrected for ash residue (aNDFom), acid detergent fiber, acid detergent lignin, and in vitro digestion at 240 h to estimate iNDF (uNDF240). Each fecal sample was scanned using a NIRSystem 6500 instrument (Perstorp Analytical Inc., Silver Spring, MD). Spectra selection was performed, resulting in 301 sample spectra used to develop regression equations with good accuracy and low standard error of prediction. The standard error of calibration (SEC), cross validation (SECV), and coefficients of determination for calibration (R2) and for cross validation (1 - VR, where VR = variance ratio) were used to evaluate calibration and validation results. Moreover, the ratio performance deviation (RPD) and ratio of the range of the original data to SECV (range/SECV; range error ratio, RER) were also used to evaluate calibration and equation performance. Calibration data obtained on fiber fractions aNDFom (R2 = 0.92, 1 - VR = 0.87, SEC = 1.48, SECV = 1.89, RPD = 2.80, and RER = 20.19), uNDF240 (R2 = 0.92, 1 - VR = 0.86, SEC = 1.65, SECV = 2.24, RPD = 2.57, and RER = 14.30), and in vitro rumen aNDFom digestibility at 240 h (R2 = 0.90, 1 - VR = 0.85, SEC = 2.68, SECV = 3.43, RPD = 2.53, and RER = 14.0) indicated the predictive equations had good predictive value.

Large-Scale Screening of Intact Tomato Seeds for Viability Using Near Infrared Reflectance Spectroscopy (NIRS)

Near infrared reflectance spectroscopy (NIRS), a non-destructive and rapid analytical method, was used to examine the possibility of replacing a method for the large-scale screening of tomato seed viability. A total of 368 tomato seed samples were used for development and validation of an NIRS calibration model. The accelerating aging method (98 ± 2% R.H., 40 °C) was employed for preparation of a calibration set (n = 268) and a validation set (n = 100) with wider seed viability. Among the tomato NIRS calibration models tested, the modified partial least square (MPLS) regression produced the best equation model. Specifically, this model produced a higher RSQ (0.9446) and lower SEC (6.5012) during calibration and a higher 1-VR (0.9194) and lower SECV (7.8264) upon cross-validation compared to the other regression methods (PLS, PCR) tested in this study. Additionally, the SD/SECV was 3.53, which was greater than the criterion point of 3. External validation of this NIRS equation revealed a significant correlation between reference values and NIRS-estimated values based on the coefficient of determination (R2), the standard error of prediction (SEP (C)), and the ratio of performance to deviation (RPD = SD/SEP (C)), which were 0.94, 6.57, and 3.96, respectively. The external validation demonstrated that this model had predictive accuracy in tomato, indicating that it has the potential to replace the germination test.

Development of NIRS Equations and Mass Evaluation of Crude Protein, Oil and Composition of Fatty Acid by Near Infrared Reflectance Spectroscopy (NIRS) in Soybean Landraces from Korea

Yu-Mi Choi*, Do Yoon Hyun, Sugyeong Lee, Myung-Chul Lee, Sejong Oh, Jeongro Lee, Hocheol Ko,Onsook Huh, and Munsup Yoon. Korean J. Breed. Sci. 2016;48:406-13. https://doi.org/10.9787/KJBS.2016.48.4.406

Use of near-infrared reflectance spectroscopy for the rapid determination of the digestible energy and metabolizable energy content of corn fed to growing pigs.

BackgroundThe ability of near-infrared reflectance spectroscopy (NIRS) to determine the digestible energy (DE) and metabolizable energy (ME) content of corn fed to growing pigs was tested. One hundred and seventeen corn samples, comprising different planting regions and varieties were collected from all over China in a three-year period. The samples were randomly split into a calibration set (n = 88) and a validation set (n = 29). The actual and calculated DE and ME content of the corn samples was determined by digestion-metabolism experiments and the prediction equations of Noblet and Perez (J Anim Sci. 71:3389–98,1993). The samples were then subjected to NIRS scanning and calibrations were performed by the modified partial least square (MPLS) regression method based on 77 different spectral pre-treatments. The NIRS equations based on the actually determined and calculated DE and ME were built separately and then validated using validation samples.ResultsThe NIRS equations obtained from actually determined DE, the coefficient of determination for calibration (RSQcal), cross-validation (R2CV), and validation (RSQv) were 0.89, 0.87 and 0.86, and these values for determined ME were 0.87, 0.86 and 0.86. For the NIRS equations built from calculated DE, the RSQcal, R2CV, and RSQv values were 0.88, 0.85 and 0.84, and these values for calculated ME were 0.86, 0.84 and 0.82. Except for the equation based on calculated ME (RPDv = 2.38, < 2.50), the other three equations built from actually determined energy and calculated DE produced good prediction performance (RPDv ranging from 2.53 to 2.69, > 2.50) when applied to validation samples.ConclusionThese results indicate that NIRS can be used as a quantitative method for the rapid determination of the available energy in corn fed to growing pigs, and the NIRS equations based on the actually determined energy produced better predictive performance than those built from calculated energy values.

Determination of Fatty Acid Composition in Peanut Seed by Near Infrared Reflectance Spectroscopy

This study was conducted to develop a fast and efficient screening method to determine the quantity of fatty acid in peanut oil for high oleate breeding program. A total of 329 peanut samples were used in this study, 227 of which were considered in the calibration equation development and 102 were utilized for validation, using near infrared reflectance spectroscopy (NIRS). The NIRS equations for all the seven fatty acids had low standard error of calibration (SEC) values, while high R2 values of 0.983 and 0.991 were obtained for oleic and linoleic acids, respectively in the calibration equation. Furthermore, the predicted means of the two main fatty acids in the calibration equation were very similar to the means based on gas chromatography (GC) analysis, ranging from 36.7 to 77.1% for oleic acid and 7.1 to 42.7% for linoleic acid. Based on the standard error of prediction (SEP), bias values, and statistics, the NIRS fatty acid equations were accurately predicted the concentrations of oleic and linoleic acids of the validation sample set. These results suggest that NIRS equations of oleic and linoleic acid can be used as a rapid mass screening method for fatty acid content analysis in peanut breeding program.

근적외선분광분석기를 이용한 황금(Scutellaria baicalensis)의 baicalin 및 baicalein 함량 분석

Near infrared reflectance spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. This study was conducted to measure baicalin, baicalein, and wogonin contents in Scutellaria baicalensis by using NIRS system. Total 63 samples previously were analyzed by HPLC, which showed baicalin, baicalein, and wogonin contents ranging 4.56 to 13.59%, 0.28 to 5.54%, and 0.50 to 1.63% with an average of 9.66%, 2.09% and 0.52%, respectively. Each sample was scanned by NIRS and calculated for calibration and validation equation. A calibration equation calculated by modified partial least squares(MPLS) regression technique was developed in which the coefficient of determination for baicalin, baicalein, and wogonin content was 0.958, 0.944, and 0.709, respectively. Each calibration equation was applied to validation set that was performed with the remaining samples not included in the calibration set, which showed high positive correlation both in baicalin and baicalein content file. In case of wogonin, the prediction model was needed more accuracy because of low value in validation set. These results demonstrate that the developed NIRS equation can be practically used as a rapid screening method for quantification of baicalin and baicalein contents in Scutellaria baicalensis.

근적외 분광분석법을 이용한 국내 유통 식품 함유 탄수화물, 단백질 및 지방의 정량 분석

Foods contain various nutrients such as carbohydrates, protein, oil, vitamins, and minerals. Among them, carbohydrates, protein, and oil are the main constituents of foods. Usually, these constituents are analyzed by the Kjeldahl and Soxhlet method and so on. However, these analytical methods are complex, costly, and time-consuming. Thus, this study aimed to rapidly and effectively analyze carbohydrate, protein, and oil contents with near-infrared reflectance spectroscopy (NIRS). A total of 517 food samples were measured within the wavelength range of 400 to 2,500 nm. Exactly 412 food calibration samples and 162 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of carbohydrates, the most accurate equation was obtained under 1, 4, 5, 1 (1st derivative, 4 nm gap, 5 points smoothing, and 1 point second smoothing) math treatment conditions using the weighted MSC (multiplicative scatter correction) scatter correction method with MPLS (modified partial least square) regression. In the case of protein and oil, the best equation were obtained under 2, 5, 5, 3 and 1, 1, 1, 1 conditions, respectively, using standard MSC and standard normal variate only scatter correction methods with MPLS regression. Calibrations of these NIRS equations showed a very high coefficient of determination in calibration (R²: carbohydrates, 0.971; protein, 0.974; oil, 0.937) and low standard error of calibration (carbohydrates, 4.066; protein, 1.080; oil, 1.890). Optimal equation conditions were applied to a validation set of 162 samples. Validation results of these NIRS equations showed a very high coefficient of determination in prediction (r²: carbohydrates, 0.987; protein, 0.970; oil, 0.947) and low standard error of prediction (carbohydrates, 2.515; protein, 1.144; oil, 1.370). Therefore, these NIRS equations can be applicable for determination of carbohydrates, proteins, and oil contents in various foods.

Near‐infrared spectroscopy for analysis of oil content and fatty acid profile in almond flour

AbstractAlmond kernels show large variability for oil content and fatty acid profile. The objective of this research was to evaluate the potential of near infrared (NIR) reflectance spectroscopy (NIRS) for the analysis of these traits in almond flour. Ground kernels of 181 accessions collected in 2009 were used for developing calibration equations for oil content and concentrations of individual fatty acids. Calibration equations were developed using second derivative transformation and modified partial least squares regression. They were validated with samples from 179 accessions collected in 2010. The accuracy of calibration equations was measured through the coefficient of determination (r2) in external validation and the ratio of the SD in the validation set to the standard error of prediction (RPD). Both r2 and RPD were high for oil content (r2 = 0.99; RPD = 9.24) and concentrations of oleic (r2 = 0.97; RPD = 5.37) and linoleic acids (r2 = 0.98; RPD = 7.35), revealing that calibration equations for these traits are highly accurate. Conversely, the accuracy of the calibration equations for palmitic (r2 = 0.54; RPD = 1.41) and stearic acids (r2 = 0.52; RPD = 1.44) was too low for allowing their application in practice. NIRS discrimination of oil content and concentrations of oleic and linoleic acids was mainly based on the spectral region from 2240 to 2380 nm.Practical applications: NIRS is a high‐throughput analytical technique that allows fast measurement of several traits in a single analysis without using chemical reagents. We evaluated the feasibility of analyzing oil content and concentrations of palmitic, stearic, oleic, and linoleic acids in almond flour using fruits collected during 2 years from a world germplasm collection. The fruits collected in 2009 were used for NIRS calibration, whereas the fruits collected in 2010 were used for validation. NIRS equations were highly accurate for measuring oil content and concentrations of oleic and linoleic acids, which are important traits defining the quality of almond flour for specific uses in the food industry. These results have applications both in the research laboratory and the food industry, where NIRS is becoming a widely used technique for quality control.

Predicting Soil Phosphorus‐Related Properties Using Near‐Infrared Reflectance Spectroscopy

Near‐infrared reflectance spectroscopy (NIRS) is a rapid, inexpensive, and accurate analysis technique for a wide variety of materials, and it is increasingly used in soil science. The objectives of our study were to examine the potential of NIRS to predict (i) soil P extracted by two methods [Mehlich 3 (M3P) and water (Cp)], soil total P (TP), annual crop P‐uptake, and annual P‐budget, and (ii) other soil chemical properties [total C (TC), total N (TN), pH, and K, Al, Fe, Ca, Mg, Mn, Cu, and Zn extracted by Mehlich 3]. Soil samples (n = 448) were taken over a 7‐yr period from an experimental site in Lévis (Québec, Canada) where timothy (Phleum pratense L.) was grown under four combinations of P and N fertilizer. The NIRS equations were developed using 80% of the samples for calibration and 20% for validation. The predictive ability of NIRS was evaluated using the coefficient of determination of validation (Rv2) and the ratio of standard error of prediction to standard deviation (RPD). Results show that M3P, Cp, crop annual P‐uptake, and annual P‐budget were not accurately predicted by NIRS (Rv2 &lt; 0.70 and RPD &lt; 1.75). Similar results were found for K and Cu. However, NIRS predictions were moderately useful for TP, TN, Fe, and Zn (0.70 ≤ Rv2 &lt; 0.80 and 1.75 ≤ RPD &lt; 2.25), moderately successful for TC and Al (0.80 ≤ Rv2 &lt; 0.90 and 2.25 ≤ RPD &lt; 3.00), successful for pH and Mg (0.90 ≤ Rv2 ≤ 0.95 and 3.00 ≤ RPD ≤ 4.00), and excellent for Ca and Mn (Rv2 &gt; 0.95 and RPD &gt; 4.00). The NIRS predictive ability of several soil properties appears to be related to their relationship with soil organic C. Although NIRS can predict several soil properties, prediction of total P was the only soil P‐related property, correlated to soil C, that was moderately useful.

Nutritional evaluation of Jambo forage using near infrared reflectance spectroscopy and comparison with wet chemistry analysis

A study was undertaken for the nutritional evaluation of Jambo forage by using Near Infrared Reflectance Spectroscopy (NIRS) and compare with the values obtained from wet chemistry analysis. Near infra-red reflectance spectrum of ground forage samples were obtained in duplicate (scanningnumber 32, resolution 8) with an FT-NIRS (Bruker, MPA, Germany) systems monochromator (700-2400 nm) using a Qurtz cup sampling device. For the development of local calibration equations, multivariate analysis was performed by a commercial analysis program Optical User Software (OPUS) and OpusLab to relate the spectral data and corresponding concentration values for each nutrient component of forage. The108 Jambo forage samples were collected from 108 cultivated experimental plots and groundthrough 2.0 mm screen for analysis the proximate components (Moisture, CP, CF, NFE and ash). The value for each component was placed into calibration group for NIRS equation development. The root mean square error of estimation (RMSEE) for the determination of CP, CF, NFE and total ash of Jambo forage was 0.33, 0.51, 1.14 and 0.39% respectively with correlation coefficient (r2) of 79.18, 82.04, 87.92 and 84.37 respectively. After cross validation, the root mean square error cross validation (RMSECV) for the CP, CF, NFE and total ash of Jambo forage were 0.37, 0.58, 1.41 and 0.48% respectively with correlation coefficient (r2) of 72.42, 73.85, 78.87 and 73.78 respectively. The mean predicted values of CP, CF, NFE and total ash by NIRS are close to the mean laboratory values determined by wet chemistry analysis. It can be concluded that NIRS could potentially be used to predict the nutritional quality of Jambo forage.DOI: http://dx.doi.org/10.3329/bjas.v40i1-2.10790Bang. J. Anim. Sci. 2011. 40 (1-2): 46-50

Evaluation of Millet (Panicum miliaceum subsp. miliaceum) Germplasm For Seed Fatty Acids Using Near-Infrared Reflectance Spectroscopy

The objective of this study was to rapidly evaluate fatty acids in a collection of millet (Panicum miliaceum subsp. miliaceum) of different origins so that this information could be disseminated to breeders to advance germplasm use and breeding. To develop the calibration equations for rapid and nondestructive evaluation of fatty acid content, near-infrared reflectance spectroscopy (NIRs) spectra (1104-2494 nm) of samples ground into flour (<TEX>$n$</TEX>=100) were obtained using a dispersive spectrometer. A modified partial least-squares model was developed to predict each component. For foxtail millet germplasm, our models returned coefficients of determination (<TEX>$R^2$</TEX>) of 0.89, 0.89, 0.89, and 0.92 for palmitic acid, oleic acid, linoleic acid, and total fatty acids, respectively. The prediction of the external validation set (n=10) showed significant correlation between references values and NIRs values (<TEX>$r^2$</TEX>=0.64, 0.90, 0.79, and 0.89 for palmitic acid, oleic acid, linoleic acid, and total fatty acids, respectively). Standard deviation/standard errors of cross-validation (SD/SECV) values were close to 3 (2.62, 2.40, 1.85, and 2.23 for palmitic acid, oleic acid, linoleic acid, and total fatty acids, respectively). These results indicate that these NIRs equations are functional for the mass screening and rapid quantification of the oleic and total fatty acids characterizing millet germplasm. Among the samples, IT153514 showed an especially high content of fatty acids (<TEX>$48.14mg\;g^{-1}$</TEX>), whereas IT123909 had a very low content (<TEX>$34.44mg\;g^{-1}$</TEX>).

Phenotypic characterization of sweet potato [Ipomoea batatas (L.) Lam.] genotypes in relation to prediction of chemical quality constituents by NIRS equations

With 5 figures and 3 tables AbstractThis study evaluates the potential of NIRS (near‐infrared reflectance spectroscopy) to predict the major constituents (starch, sugars, cellulose, proteins and minerals) of the sweet potato root. Overall, 240 accessions were morphologically described, chemically analysed and their NIR spectra recorded. No correlations were observed between aerial and underground traits, and between morphological traits and major constituents. Calibration equations, developed on 190 accessions, showed high explained variances in cross‐validation (r2cv) for starch (0.82), sugars (0.91), proteins (0.89) and minerals (0.74) but no response for cellulose (0.21). The predictions were tested on an independent set of 50 randomly selected accessions. The r2pred values for starch, sugars and proteins were, respectively, of 0.71, 0.82 and 0.87 with ratios of performance to deviation (RPD) of 2.11, 2.29 and 2.93. New calibration equations developed on 240 accessions showed improved RPD values for starch (2.65), sugars (2.75), cellulose (1.58), proteins (3.47) and minerals (2.34), indicating that larger sets could improve prediction. NIRS could be used in sweet potato breeding programmes to predict starch, sugars and proteins contents in the roots.

The prediction of apparent metabolisable energy content of oil seeds and oil seed by-products for poultry from its chemical components, in vitro analysis or near-infrared reflectance spectroscopy

Regression models including chemical composition, in vitro digestibility and near-infrared reflectance spectroscopy (NIRS) were compared to predict the energy content of several feed ingredients for poultry. The nitrogen-corrected apparent metabolisable energy content (AMEn) and its proportion on total gross energy (AMEn/GE) were determined in 52 batches of six protein concentrates; samples were randomly obtained from a poultry feed manufacturing plant throughout a 3-year period. A preliminary trial was also designed to adapt in vitro methods for prediction of in vivo energy values for poultry. Mean concentrations of AMEn of the ingredients studied ranged from 5.29 to 14.7 MJ/kg DM and those of AMEn/GE from 0.260 to 0.609. The most precise model for prediction of AMEn and AMEn/GE values for the whole data analysed was that based on NIRS equations ( R cv 2 = 0.952 and 0.926 , respectively). The best single chemical predictor was the concentration of neutral detergent fibre (NDF) in each ingredient ( R 2 = 0.721 and 0.736, respectively). Further inclusion of ether extract content increased R 2 to 0.945 and 0.902 for AMEn and AMEn/GE, respectively. A model including linear and quadratic effects of in vitro organic matter digestibility (ivOMd) provided a slightly inferior prediction of AMEn/GE values ( R 2 = 0.865). However, the prediction of AMEn from ivOMd was clearly lower ( R 2 = 0.756), as variations among batches of GE concentration (from 19.0 to 24.9 kcal/kg DM) were poorly related to in vitro digestibility values. All the indirect methods used were useful to explain part of the variability of the energy value of the ingredients studied, although the best results were generally obtained from the NIRS equations. Otherwise, the significance of the prediction depended directly on the variability of the different data set studied.

A comparison of the prediction of apparent metabolisable energy content of starchy grains and cereal by-products for poultry from its chemical components, in vitro analysis or near-infrared reflectance spectroscopy

Regression models including chemical composition, in vitro digestibility and near infrared reflectance spectroscopy (NIRS) were compared in order to predict the energy value of several feed ingredients for poultry. The nitrogen-corrected apparent metabolisable energy content (AMEn) in cockerels and its proportion on total gross energy (AMEn/GE) were determined in 94 batches from six starchy grains and six cereal byproducts. Two preliminary trials were also designed to adapt in vitro methods for prediction of in vivo energy values for poultry. Mean concentrations of AMEn of the ingredient studied ranged from 2,464 to 3,595 kcal/kg DM, and those of AMEn/GE from 53.7 to 80.0%. The most precise model of prediction of AMEn and AMEn/GE values was that based on NIRS equations (R2cv = 0.823 and 0.861, respectively). The best single chemical predictor of these energy values was the neutral detergent fibre concentration (R2 = 0.616 and 0.736, respectively). Further inclusion of ether extract and ash contents in the AMEn model and those of starch and ether extract in the AMEn/GE model allowed increasing coefficients of determination up to 0.791 and 0.839, respectively. A model including linear and quadratic effects of in vitro organic matter digestibility (IVOMd) provided a similar prediction of AMEn/GE values (R2 = 0.833). However the prediction of AMEn from IVOMd was worse (R2 = 0.62), as variations among batches of GE concentration (from 4,225 to 5,896 kcal/kg DM) were little related to in vitro digestibility values.

Carbohydrates in alfalfa-timothy mixtures predicted with near infrared reflectance spectroscopy equations developed for single species

Our objective was to evaluate the feasibility of using near infrared reflectance spectroscopy (NIRS) equations previously developed with a calibration set that included samples of both timothy and alfalfa to predict carbohydrate fractions in mixed samples of both species. Timothy and alfalfa mixed samples were prepared with the alfalfa proportion ranging from 0 to 100%, with increments of 4%. With previously developed NIRS equations based on samples of single species of timothy and alfalfa, concentrations of total ethanol soluble carbohydrates (TESC), starch, and neutral detergent soluble carbohydrates (NDSC) of the mixed samples were predicted successfully, but concentrations of organic acids (OA) and neutral detergent soluble fiber (NDSF) were unsuccessfully predicted. Adding 13 mixed samples to the initial calibration set of around 110 samples of pure timothy and alfalfa samples improved the accuracy of already successful predictions for TESC, starch, and NDSC, and resulted in a successful prediction for NDSF in timothy and alfalfa mixtures.Key words: Near infrared reflectance spectroscopy, sugars, Phleum pratense, Medicago sativa