Quantification of fatty acids in forages by near-infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was evaluated as a possible alternative to gas chromatography (GC) for the quantitative analysis of fatty acids in forages. Herbage samples from 11 greenhouse-grown forage species (grasses, legumes, and forbs) were collected at three stages of growth. Samples were freeze-dried, ground, and analyzed by GC and NIRS techniques. Half of the 195 samples were used to develop an NIRS calibration file for each of eight fatty acids, with the remaining half used as a validation data set. Spectral data, collected over a wavelength range of 1100-2498 nm, were regressed against GC data to develop calibration equations for lauric (C12:0), myristic (C14:0), palmitic (C16:0), stearic (C18:0), palmitoleic (C16:1), oleic (C18:1), linoleic (C18:2), and alpha-linolenic (C18:3) acids. Calibration equations had high coefficients of determination for calibration (0.93-0.99) and cross-validation (0.89-0.98), and standard errors of calibration and cross-validation were < 20% of the respective means. Simple linear regressions of NIRS results against GC data for the validation data set had r2 values ranging from 0.86 to 0.97. Regression slopes for C12:0, C14:0, C16:0, C18:0, C16:1, C18:2, and C18:3 were not significantly different (P = 0.05) from 1.0. The regression slope for C18:1 was 1.1. The ratio of standard error of prediction to standard deviation was > 3.0 for all fatty acids except C12:0 (2.6) and C14:0 (2.9). Validation statistics indicate that NIRS has high prediction ability for fatty acids in forages. Calibration equations developed using data for all plant materials accurately predicted concentrations of C16:0, C18:2, and C18:3 in individual plant species. Accuracy of prediction was less, but acceptable, for fatty acids (C12:0, C14:0, C18:0, C16:1, and C18:1) that were less prevalent.     

Rapid prediction of acid detergent fiber, neutral detergent fiber, and acid detergent lignin of rice materials by near-infrared spectroscopy

A rapid predictive method based on near-infrared spectroscopy (NIRS) was developed to measure acid detergent fiber (ADF), neutral detergent fiber (NDF), and acid detergent lignin (ADL) of rice stem materials. A total of 207 samples were divided into two subsets, one subset (approximately 136 samples) for calibration and cross-validation and the other subset for independent external validation to evaluate the calibration equations. Different mathematical treatments were applied to obtain the best calibration and validation results. The highest coefficient of determination for calibration (R2) and coefficient of determination for cross-validation (1-VR) were 0.968 and 0.949 for ADF, 0.846 and 0.812 for NDF, and 0.897 and 0.843 for ADL, respectively. Independent external validation still gave a high coefficient of determination for external validation (r2) and a low standard error of performance (SEP) for the three parameters; the best validation results were SEP = 0.933 and r2 = 0.959 for ADF, SEP = 2.228 and r2 = 0.775 for NDF, and SEP = 0.616 and r2 = 0.847 for ADL, indicating that NIR gave a sufficiently accurate prediction of ADF and ADL content of rice material but a less satisfactory prediction for NDF. This study suggested that routine screening for these forage quality parameters with large numbers of samples is possible with NIRS in early-generation selection in rice-breeding programs.     

Near-infrared analysis of fat, protein, and casein in cow's milk.

Fat, crude protein, true protein, and casein were determined in cow milks by near-infrared transmission spectroscopy (NIR). Partial and overall PLS calibrations were performed on two sets of samples: partial calibration included 76 unhomogenized samples, whereas overall calibration used 96 homogenized and unhomogenized samples. Standard errors of calibration were 0.12% for fat, 0.06% for crude protein, 0.04% for true protein, and 0.05% for casein in the overall calibration. Validation of the overall calibration with an independent set of samples gave standard errors of prediction of 0. 07% for fat, 0.06% for crude protein and casein, and 0.05% for true protein. Except for fat, all of the statistical parameters were better with overall than with partial calibrations, which indicates that homogenization has an effect on NIR fat determination. Despite the relatively small number of samples included in the calibration model, NIR transmission was found to be a reliable method for the determination of fat and nitrogenous constituents in milk.     

Near-infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fishmeal, meat meal products, and poultry meal

Near-infrared reflectance spectroscopy (NIRS) calibrations were developed to enable the accurate and fast prediction of the total contents of methionine, cystine, lysine, threonine, tryptophan, and other essential amino acids, protein, and moisture in the most important protein-rich feed ingredients. More than 1000 samples of global origin collected over four years were analyzed on amino acids following the official methods of the United States and the European Union. Detailed data and graphics are given to characterize the obtained calibration equations. NIRS was validated with independent samples for soy and meat meal products and compared to the amino acid predictions using linear crude protein regressions. With a few exceptions, validation showed that 85-98% of the amino acid variance in the samples could be explained using NIRS. NIRS predictions compared to reference results agree excellently, with relative mean deviations below 5%. Especially for meat and poultry meals, NIRS can predict amino acids much better than crude protein regressions. By enabling the amino acid analysis of many samples to be completed in a short time, NIRS can improve the accuracy of feed formulation and thus the quality and production costs of mixed feeds.     

Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. 2. Results for wheat,barley, corn,triticale, wheat bran/middlings,rice bran,and sorghum

Further NIRS calibrations were developed for the accurate and fast prediction of the total contents of methionine, cystine, lysine, threonine, tryptophan, and other essential amino acids, protein, and moisture in the most important cereals and brans or middlings for animal feed production. More than 1100 samples of global origin collected over five years were analyzed for amino acids following the Official Methods of the United States and European Union. Detailed data and graphics are given to characterize the obtained calibration equations. NIRS was validated with 98 independent samples for wheat and 78 samples for corn and compared to amino acid predictions using linear crude protein regression equations. With a few exceptions, validation showed that 70-98% of the amino acid variance in the samples could be explained using NIRS. Especially for lysine and methionine, the most limiting amino acids for farm animals, NIRS can predict contents in cereals much better than crude protein regressions. Through low cost and high speed of analysis NIRS enables the amino acid analysis of many samples in order to improve the accuracy of feed formulation and obtain better quality and lower production costs.     

Applicability of near infrared spectroscopy as an alternative to acid detergent analysis for cattle and swine manure compost

Abstract

Recently, acid detergent analysis has been reported to provide valid data to evaluate decomposition properties and to determine the available nitrogen (AVN) of organic materials, such as compost. However, this methodology requires complex procedures and creates considerable costs. As an alternative, near infrared spectroscopy (NIRS) was evaluated as a simple method to determine acid detergent fiber (ADF), acid detergent lignin (ADL) and acid-detergent-soluble organic matter (ADSOM), in order to evaluate the decomposition properties of cattle and swine manure compost. To establish an easy and accurate method of estimating AVN in cattle and swine manure compost, the accuracies of direct estimations of AVN by NIRS in incubation experiments and indirect estimations by NIRS based on acid-detergent-soluble nitrogen (ADSN) or total nitrogen (TN) were examined. The reflectance spectra of freeze-dried and milled compost samples were determined using a scanning monochromator. Second derivative spectra and multiple regression analysis were used to develop calibration equations for each constituent. The calibration equations for ADF, ADL and ADSOM were “successful” according to commonly applied criteria. Acid-detergent-soluble nitrogen was found to be more suitable than TN for estimating AVN in cattle and swine manure compost. As the accuracies of the estimations of ADSN and TN by NIRS were comparable, the estimation of AVN based on ADSN as determined by NIRS was more accurate than that based on TN determined by NIRS. The direct prediction of AVN through NIRS was not as accurate as the estimation of AVN based on ADSN determined by NIRS. We conclude that NIRS is a practicable alternative to the time-consuming acid detergent analysis of cattle and swine compost, and that ADSN as determined by NIRS is useful for estimating AVN in the compost.     

Applicability of near infrared spectroscopy as an alternative to acid detergent analysis for cattle and swine manure compost

Recently, acid detergent analysis has been reported to provide valid data to evaluate decomposition properties and to determine the available nitrogen (AVN) of organic materials, such as compost. However, this methodology requires complex procedures and creates considerable costs. As an alternative, near infrared spectroscopy (NIRS) was evaluated as a simple method to determine acid detergent fiber (ADF), acid detergent lignin (ADL) and acid-detergent-soluble organic matter (ADSOM), in order to evaluate the decomposition properties of cattle and swine manure compost. To establish an easy and accurate method of estimating AVN in cattle and swine manure compost, the accuracies of direct estimations of AVN by NIRS in incubation experiments and indirect estimations by NIRS based on acid-detergent-soluble nitrogen (ADSN) or total nitrogen (TN) were examined. The reflectance spectra of freeze-dried and milled compost samples were determined using a scanning monochromator. Second derivative spectra and multiple regression analysis were used to develop calibration equations for each constituent. The calibration equations for ADF, ADL and ADSOM were "successful" according to commonly applied criteria. Acid-detergent-soluble nitrogen was found to be more suitable than TN for estimating AVN in cattle and swine manure compost. As the accuracies of the estimations of ADSN and TN by NIRS were comparable, the estimation of AVN based on ADSN as determined by NIRS was more accurate than that based on TN determined by NIRS. The direct prediction of AVN through NIRS was not as accurate as the estimation of AVN based on ADSN determined by NIRS. We conclude that NIRS is a practicable alternative to the time-consuming acid detergent analysis of cattle and swine compost, and that ADSN as determined by NIRS is useful for estimating AVN in the compost.     

Quantification of exopolysaccharide, lactic acid, and lactose concentrations in culture broth by near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) was used for the simultaneous prediction of exopolysaccharide (EPS; 0-3 g/L) and lactic acid (0-59 g/L) productions as well as lactose (0-68 g/L) concentration in supernatant samples from pH-controlled batch cultures of Lactobacillus rhamnosus RW-9595M in supplemented whey permeate medium. To develop calibration equations, the correlation between the second derivative of 164 NIRS transmittance spectra and concentration data obtained with reference methods was calculated at the wavelength between 1653-1770 and 2041-2353 nm, using a partial least-squares method (PLS). The lactic acid and lactose concentrations were measured by HPLC, and the EPS concentration was estimated by a new ultrafiltration method. The PLS correlation coefficient (R(2)) and the standard error of cross-validation for the calibrations were 91% and 0.26 g/L for EPS, 99% and 2.54 g/L for lactic acid, and 98% and 3.32 g/L for lactose, respectively. The calibration equations were validated with 45 randomly selected culture samples from 6 cultures that were not used for calibration. A high agreement between data of the reference methods and those of NIRS was observed, with correlation coefficients and standard errors of prediction of 99% and 1.64 g/L for lactic acid, 99% and 4.5 g/L for lactose, and 91% and 0.32 g/L for EPS. The results suggest that NIRS could be a useful method for rapid monitoring and control of EPS lactic fermentations.     

Ash and Mineral Contents in Leaves of Woody Species: Analysis by Near‐Infrared Reflectance Spectroscopy

Abstract

Near‐infrared reflectance spectroscopy (NIRS) was evaluated for its effectiveness to determine ash and mineral concentrations [potassium (K), magnesium (Mg), copper (Cu), iron (Fe), and zinc (Zn)], in a total of 182 leaf samples of 17 woody species located in the central‐western region of the Iberian Peninsula. Chemical analysis revealed great variability in all leaf mineral elements. This variability was mainly related to differences in leaf habit (deciduous versus evergreen) and to differences in mean leaf longevity and among leaf age classes within evergreen species. A set of samples including all 17 species and leaf age classes was used to develop the calibration equations using multiple linear regression (MLR) and partial‐least squares regression (PLSR). The set of samples that did not enter in the calibration was used for external validation. In general, the most satisfactory results were obtained using PLSR and derivative transformations. Despite the strong heterogeneity of the samples included in the study, the results showed that NIRS can be employed as an effective tool, alternative to the more time‐consuming standard methods. The best predictive model was obtained for ash content. Models with acceptable accuracy were obtained in the prediction of K and Mg contents. However, their applicability for the determination of trace elements was more limited.     

Use of near‐infrared reflectance spectroscopy to assess nitrogen concentration in different plant tissues of rapeseed

Abstract

An evaluation of the performance of near‐infrared reflectance spectroscopy (NIRS) in the analysis of nitrogen (N) concentration in different rapeseed (Brassica napus L.) tissues was made. A total of 228 samples from an N‐efficiency study corresponding to leaves and stems at flowering, fallen leaves, mature stems, and mature pod walls were oven dried, ground, and then analyzed by NIRS. The N concentration was determined by Dumas combustion. Two different calibration strategies were followed: (i) separate calibration equations were developed for each type of tissue, resulting in r² above 0.95 in crossvalidation for all tissues with the ratio of the standard error of crossvalidation (SECV) to the standard deviation of the population (SD) ranging from 0.10 to 0.22, and (ii) a NIRS calibration equation was developed from a set integrating 149 samples from the five groups of tissues. External . validation with a set containing 79 further samples from all the groups resulted in an r² of 0.99 and a ratio of the standard error of performance (SEP) to the SD of 0.08. External validation for each group separately resulted in r² from 0.91 to 0.99 and SEP/SD from 0.10 to 0.27. It was concluded that a universal NIRS calibration equation integrating samples from all the types of tissues is an adequate approach for the accurate analysis of N concentration in rapeseed. Based on our results, the NIRS technique can reliably replace the Kjeldahl or Dumas methods to determine the N concentration in investigations of the N efficiency in rapeseed.     

Application of Near‐Infrared Reflectance Spectroscopy to Estimate Chemical Constituents in Broiler Litter

Abstract

Near‐infrared reflectance spectroscopy (NIRS) has potential to provide rapid estimates of phosphorus (P) and nitrogen (N) concentrations in broiler litter to assist managers in establishing application rates of litter to grazing lands that fall within productive and environmentally safe levels. An experiment was conducted to determine accuracy of NIRS estimates of moisture, P, N, and acid detergent fiber (ADF) concentrations in broiler litter. Broiler litter samples were collected from various farms to develop sample sets that were either with or without bedding material, and each sample set was subdivided into processed (i.e., dried and ground) and unprocessed samples to develop local equations for each constituent. Equations were developed by using all samples from each set and using samples following random removal of 20% of total for equation validation. Moisture was determined to be accurately measured by using NIRS based on a high R² (≥0.96), low SEC (<10 g kg^?1), and high s_x/SECV (>5.0). ADF also had a high R² (0.96), but the S_x/SEC (3.00) value was too low for the equation to be considered truly accurate. Estimations of P and N by calibrations that included all samples had a moderate to high R² values, but estimations for the validation set were relatively low in R² (≤0.78) and S_x/SEC (≤2.00). Concentrations of P and N were not estimated by NIRS with a high degree of accuracy, but other methodologies could enhance the usefulness of this technology to rapidly provide these nutrient measures.     

精料补充料中肉骨粉含量的近红外光谱检测

为了保证饲料安全,精料补充料中肉骨粉的检测是十分必要的。该文探讨了精料补充料中肉骨粉含量的近红外光谱分析方法,123个样品作为校正集,采用偏最小二乘法(PLS),分别对光谱进行散射校正和卷积平滑、一阶微分、二阶微分预处理建立校正模型,以最大的决定系数(R²)和最小的标准差(RMSEC)为选择依据,通过比较,以多元散射校正和卷积平滑处理与二阶微分相结合的处理效果最好,其预测值与测量值的决定系数(R²)和标准差(RMSEC)分别为0.9751和0.437。34个样品作为检验集进行外部验证,决定系数(r²)和标准差(RMSEP)分别为0.9749和0.420,平均绝对误差和相对误差分别为0.326和13.89％。结果表明,利用近红外分析技术可以检测精料补充料中肉骨粉的含量。     

Potential of Near- and Mid-infrared Spectroscopy in Biofuel Production

For many decades, near-infrared spectroscopy (NIRS) has been used to determine the composition of animal feedstuffs and grains. More recently, mid-infrared spectroscopy (mid-IR) has also been examined for similar determinations. These spectroscopic methods offer the potential for rapid and accurate determination of organic constituents, such as fiber components and protein, of forages, by-products, and grains at reduced cost and greatly increased speed (minutes instead of hours or days). Because they are nonchemical in nature, they result in a large reduction (90% or more) in the chemical wastes associated with standard chemical-based assay methods. The same components of interest for biofuel production (cellulose, hemicellulose, lignin, starch, protein, oil, etc.) are those that have already been determined by NIRS/mid-IR for evaluating grains and animal feedstuffs. Therefore, these techniques would appear to be a natural match for evaluating feedstocks for biofuels, and the literature shows that efforts in this direction are being successfully tested and instituted. For this discussion, an overview of where such efforts are and the potential for NIRS/mid-IR in producing biofuels will be covered. For example, while there are similarities between the needs of the biofuels industry and the analysis of animal feedstuffs, there are also both practical and technical differences between the two that will likely impact how NIRS/mid-IR is developed for biofuels. As an example, grain analysis for protein is performed on a large scale by government agencies such as the Canadian Grain Commission and U.S. Grain Inspection Service, while at least in the United States, animal feedstuff analysis is performed by state or independent laboratories for individual farmers. For biofuels, this might well result in most analysis being performed by the large corporations converting the feedstocks to biofuels, as opposed to the individual producer having analysis performed at an independent laboratory. Similarly for animal feedstuffs, measurements of fiber (neutral detergent fiber or NDF, acid detergent fiber or ADF, and lignin) and protein are carried out. These fiber measurements often consist of more than one type of fiber component with some being computed by difference (hemicellulose = NDF – ADF) and are empirical at best. Whether such empirical estimates will be sufficient for assessing biofuels or whether new spectroscopic methods for directly measuring the components of interest (cellulose, etc.) will need to be developed is a question to be answered when components other than starch for ethanol or oil for biodiesel become common.     

Generic combustion method for determination of crude protein in feeds: collaborative study

Nine laboratories participated in a collaborative study on determination of crude protein in animal feeds to compare a generically described combustion method with the AOAC mercury catalyst Kjeldahl method (7.015). The combustion method was written in general terms of method principle, apparatus specifications, and performance requirements. The sample set comprised closely matched pairs of feed ingredients and mixed products ranging from 10 to 90% protein. Ten pairs ground to 0.5 mm were the focus of the study; 4 pairs were ground to 1.0 mm for comparison. Nicotinic acid and lysine monohydrochloride were included as standards. Collaborators were instructed to report their results for performance checks using materials supplied. Only one laboratory failed to meet the proposed limits. Seven laboratories used the LECO Model FP-228 analyzer and 2 used the LECO CHN 600 analyzer. For the 0.5 mm pairs, repeatability standard deviations (Sr) ranged from 0.09 to 0.58 for the Kjeldahl method and from 0.14 to 0.33 for the combustion method, with a pooled Sr value of 0.28 and relative standard deviation (RSDr) of 0.59%. Reproducibility standard deviations (Sg) ranged from 0.23 to 0.86 (Kjeldahl) and from 0.30 to 0.61 (combustion), with a pooled Sg value of 0.52 and RSDg of 1.10%. Grand means for the samples ground to 0.5 mm were 47.65% protein by the combustion method and 47.41% protein by the Kjeldahl method. For samples ground to 1.0 mm, corresponding values were 31.82 and 31.50% protein. The generic combustion method has been approved interim official first action.     

Development of NIRS equations for food grain quality traits through exploitation of a core collection of cultivated sorghum

A sorghum core collection representing a wide range of genetic diversity and used in the framework of a sorghum breeding and genetics program was evaluated by near-infrared reflectance spectroscopy (NIRS) to predict food grain quality traits: amylose content (AM), protein content (PR), lipid content (LI), endosperm texture (ET), and hardness (HD). A total of 278 sorghum samples were scanned as whole and ground grain to develop calibration equations. Laboratory analyses were performed on NIRS sample subsets that preserved the core collection racial distribution. Principal component analysis performed on NIRS spectra evidenced a level of structure following known sorghum races, which underlined the importance of using a wide range of genetic diversity. Performances of calibration equations were evaluated by the coefficient of determination, bias, standard error of laboratory (SEL), and ratio of performance deviation (RPD). Ground grain spectra gave better calibration equations than whole grain. PR equation (RPD of 5.7) can be used for quality control. ET, LI, and HD equations (RPD of 2.9, 2.6, and 2.6, respectively) can be used for screening steps. Even with a small SEL in whole sample analysis, a RPD of 1.8 for AM confirmed that this variable is not easy to predict with NIRS.     

Acid detergent fiber analysis in oilseed Brassicas by near-infrared spectroscopy

The potential of near infrared spectroscopy (NIRS) for determining the acid detergent fiber (ADF) in the seed of oilseed Brassica (fam. Brassicaceae) was assessed. One hundred and fifty accessions belonging to the species Indian mustard (Brassica juncea L. Czern.& Coss.), Ethiopian mustard (B. carinata A. Braun) and rapeseed (B. napus L.) were scanned by NIRS as intact and ground seed, and their ADF values were regressed against different spectra transformations by modified partial least squares regression. The coefficients of determination in the external validation (r(2)) for intact and ground seed were 0.83 and 0.85, respectively. The standard deviation to standard error of prediction ratio and range to standard error of prediction ratio were 2.40 and 10.75 for intact seed and 2.62 and 11.76 for ground seed. No significant differences in the prediction were found for both sample presentations. Effects of the C-H and O-H groups of lipids and water, respectively, as well as protein and chlorophyll, were most important in modeling these equations.     

近红外光谱技术定量分析玉米杂交种纯度

摘要：应用近红外光谱分析技术结合定量偏最小二乘法对先玉335杂交种纯度进行了定量分析,将不同年份和来源的杂交种和其母本种子粉碎后混合,按0.5%的梯度获得纯度80～100%范围内的样本123份（每梯度按年份和来源设置3个重复）后采集光谱。结果表明：采用散射校正预处理,4 000～8 000 cm-1光谱范围时建模效果较适宜（建模集∶检验集=3∶1）,建模集内部交叉决定系数达96.06%,校正标准差1.18%,平均相对误差1.03%;检验集的决定系数均达到95.02%,校正标准差1.28%,平均相对误差1.12%。采用不同比例的建模样品和检验样品时,建模集和检验集的决定系数均在94%以上,证明了近红外光谱技术定量测定玉米杂交种纯度的可行性以及所建模型的稳定性。     

Authentication of Rice Using Near‐Infrared Reflectance Spectroscopy

The authentication of rice (Korean domestic rice vs. foreign rice) has been attempted using near‐infrared spectroscopy (NIRS). Two sample sets (n₁ = 280 and n₂ = 200) were used to obtain calibration equations and the spectral regions used for this study were 500–600 nm, 700–900nm, and 980–2,498 nm. Modified partial least square (MPLS) regression was used to develop the prediction model. The standard error of cross validation (SECV) and the r² were 0.165 and 0.91 respectively for 1st calibration set and 0.165 and 0.93 for 2nd calibration set respectively. The results of the independent validation (n₃ = 80) showed that all of 80 samples were identified correctly. Even though authentication of rice was performed successfully using NIRS, the calibration statistics in this study showed that further effort is needed for implementation of NIRS for authentication of rice for industry purposes.     

Quantitative Analysis of Chlorophyll and Protein in Alfalfa Leaves Using Fiber‐Optic Near‐Infrared Spectroscopy

The performance of near‐infrared (NIR) spectroscopy as a rapid technique for the estimation of chlorophyll and protein contents in alfalfa (Medicago sativa L.) was investigated. A fiber‐optic probe was employed directly on a total of 198 fresh leaves to measure spectra between 1100 and 2200 nm. Partial least squares (PLS) regression models were developed with a calibration set of 120 samples spanning a concentration range of 5.20–158.5 for the chlorophyll content index (CCI), 0.39–4.60 mg g^?1 (fresh weight) for the chlorophyll extracted with dimethylsulfoxide (DMSO), and 9.92–45.32% (dry matter) for protein content. The models obtained were validated with 78 independent samples. Standard errors of prediction of 12.49 were obtained for the CCI, 0.24 mg g^?1 for DMSO‐extracted chlorophyll, and 3.27% for the protein content. These results support the use of NIRS equipped with a fiber‐optic probe to monitor and assess the composition and quality of forages in a nondestructive way.     

Nondestructive determination of lignans and lignan glycosides in sesame seeds by near infrared reflectance spectroscopy

Sesame (Sesamum indicum L.) contains abundant lignans including lipid-soluble lignans (sesamin and sesamolin) and water-soluble lignan glycosides (sesaminol triglucoside and sesaminol diglucoside) related to antioxidative activity. In this study, near infrared reflectance spectroscopy (NIRS) was used to develop a rapid and nondestructive method for the determination of lignan contents on intact sesame seeds. Ninety-three intact seeds were scanned in the reflectance mode of a scanning monochromator. This scanning procedure did not require the pulverization of samples, allowing each analysis to be completed within minutes. Reference values for lignan contents were obtained by high-performance liquid chromatography analysis. Calibration equations for lignans (sesamin and sesamolin) and lignan glycosides (sesaminol triglucoside and sesaminol diglucoside) contents were developed using modified partial least squares regression with internal cross-validation (n = 63). The equations obtained had low standard errors of cross-validation and moderate R2 (coefficient of determination in calibration). The prediction of an external validation set (n = 30) showed significant correlation between reference values and NIRS predicted values based on the SEP (standard error of prediction), bias, and r2 (coefficient of determination in prediction). The models developed in this study had relatively higher values (more than 2.0) of SD/SEP(C) for all lignans and lignan glycosides except for sesaminol diglucoside, which had a minor amount, indicating good correlation between the reference and the NIRS estimate. The results showed that NIRS, a nondestructive screening method, could be used to rapidly determine lignan and lignan glycoside contents in the breeding programs for high quality sesame.