Near Infrared Reflectance Spectroscopy Technique Research Articles

Genetic variation for bioenergy traits within and among lowland switchgrass (Panicum virgatum L.) crosses

Cellulosic biomass derived from switchgrass can be an alternative source to produce renewable bioenergy. However, bioenergy production from switchgrass is not economically viable unless genotypes with high yield potential and acceptable biofuel quality are available. To identify switchgrass genotypes with good biofuel quality, ten high biomass yielding hybrid populations derived from crosses between two lowland cultivars, Alamo (A) and Kanlow (K), were evaluated in this study. The experiment was planted in 2018 in two field environments, the East Tennessee Research and Education Center, Knoxville, and the Plateau Research and Education Center, Crossville, in a randomized complete block design with two replications per location. Tillers were collected in the Fall of 2020 and 2021, and analyzed for bioenergy traits using the near-infrared reflectance spectroscopy technique. The crosses and their parents were different in bioenergy traits across the environments (locations and years) (P ≤ 0.05). The crosses’ total ethanol yield (EtOH) ranged from 63.5 to 70.4 mg g−1, and the total sugars yield varied from 725 to 737 mg g−1 across the environments. Average cellulose, hemicellulose, lignin, and ash content were 395.8, 333.9, 178.4, and 49.3 mg g−1, respectively. The estimated non-structural carbohydrates were found as good predictors of EtOH (R2 = 0.67) (P ≤ 0.05). The cross, 12A-259 × 12K-247, exhibited high EtOH, high hemicellulose, low lignin, and low ash. Crosses 12A-261 × 12K-245 and 12A-263 × 12K-250 exhibited high cellulose and sugar. These results indicate significant genetic heterogeneity for bioenergy traits within and between these two lowland switchgrass cultivars.

121 Non-invasive monitoring of hot-iron brands on growing cattle

Abstract Hot-iron branding is a traditional form of permanent cattle identification that produces a scar. There is a need for science-based determination of cattle brand age through non-invasive monitoring of the healing process. Near infrared reflectance spectroscopy (NIRS) has been used by medical forensic scientists to obtain such information. Healing of cutaneous injury involves inflammatory (1 to 3 d), proliferative (4 to 21 d), and remodeling (22 to 365 d) phases. Collagen changes as burn scars mature, i.e. there is an increase in the Type I/ Type III collagen ratio compared to normal skin. The altered ratio is evident in a transformation of collagen from a basketweave arrangement to small parallel bundles. During the remodeling phase, due largely to the Type I/III collagen ratio, scar tissue becomes visibly different than un-injured skin. Previous research has examined the differences in hot-iron brands applied to nursing (~30 d old) Bos taurus cross calves at 0, 33, and 153 d post-branding. Our objective was to continue this research by obtaining near infrared spectra (11001700nm) on hot-iron brands applied to 15 weaned (~ 270 d old; 238 ± 7kg) Bos taurus cross steers at 21, 32, and 51 d post-branding. Spectra were obtained on branded (n = 3) and unbranded (n = 3) skin tissue from each animal, each date. Hair was clipped to < 5mm on unbranded skin. Spectra were analyzed as log 1/reflectance with 1st derivative and scatter correction applied. Linear discriminant analysis and regression procedures were applied to distinguish between brand treatment and date post-branding. Spectra from branded and unbranded skin were correctly (P < 0.05) identified at ~95%. Brand age was predicted successfully (P < 0.05) but not accurately enough for forensic application (RSQ = 0.50; RMSE = 9.75 d). The NIRS technique can discriminate differences in the age of cattle brand scars but numerical prediction requires further investigation.

Rapid Determination of the Oil and Moisture Contents in Camellia gauchowensis Chang and Camellia semiserrata Chi Seeds Kernels by Near-infrared Reflectance Spectroscopy.

A fast and effective determination method of different species of vegetable seeds oil is vital in the plant oil industry. The near-infrared reflectance spectroscopy (NIRS) method was developed in this study to analyze the oil and moisture contents of Camellia gauchowensis Chang and C. semiserrata Chi seeds kernels. Calibration and validation models were established using principal component analysis (PCA) and partial least squares (PLS) regression methods. In the prediction models of NIRS, the levels of accuracy obtained were sufficient for C. gauchowensis Chang and C. semiserrata Chi, the correlation coefficients of which for oil were 0.98 and 0.95, respectively, and those for moisture were 0.92 and 0.89, respectively. The near infrared spectrum of crush seeds kernels was more precise compared to intact kernels. Based on the calibration models of the two Camellia species, the NIRS predictive oil contents of C. gauchowensis Chang and C. semiserrata Chi seeds kernels were 48.71 ± 8.94% and 58.37 ± 7.39%, and the NIRS predictive moisture contents were 4.39 ± 1.08% and 3.49 ± 0.71%, respectively. The NIRS technique could determine successfully the oil and moisture contents of C. gauchowensis Chang and C. semiserrata Chi seeds kernels.

Prediction of Cortisol and Progesterone Concentrations in Cow Hair Using Near-Infrared Reflectance Spectroscopy (NIRS).

Concentrations of different steroid hormones have been used in cows as a measure of adrenal or gonadal activity and, thus, as indicators of stress or reproductive state. Detecting cortisol and progesterone in cow hair provides a long-term integrative value of retrospective adrenal or gonadal/placental activity, respectively. Current techniques for steroid detection require a hormone-extraction procedure that involves time, several types of equipment, management of reagents, and some assay procedures (which can also be time-consuming and can destroy the samples). In contrast, near-infrared reflectance spectroscopy (NIRS) is a multi-component predictor technique, characterized as rapid, nondestructive for the sample, and reagent-free. However, as a predictor technique, NIRS needs to be calibrated and validated for each matrix, hormone, and species. The main objective of this study was to evaluate the predictive value of the NIRS technique for hair cortisol and progesterone quantification in cows by using specific enzyme immunoassay as a reference method. Hair samples from 52 adult Friesian lactating cows from a commercial dairy farm were used. Reflectance spectra of hair samples were determined with a NIR reflectance spectrophotometer before and after trimming them. Although similar results were obtained, a slightly better relationship between the reference data and NIRS predicted values was found using trimmed samples. Near infrared reflectance spectroscopy demonstrated its ability to predict cortisol and progesterone concentrations with certain accuracy (R2 = 0.90 for cortisol and R2 = 0.87 for progesterone). Although NIRS is far from being a complete alternative to current methodologies, the proposed equations can offer screening capability. Considering the advantages of both fields, our results open the possibility for future work on the combination of hair steroid measurement and NIRS methodology.

Quantitative Analysis of Heave Mental Ion Based on Portable NIR Spectrometer

By virtue of the characters of rapid analysis and simple pretreatment, near infrared reflectance spectroscopy technique is widely used in agriculture, medicine, environment, petrochemical and other fields. To satisfy the rapid on-site identification and analysis, portable near-infrared spectrometers have gained more and more attention. Because near infrared reflectance spectroscopy technique also is a green tool for multi-component analysis, the paper aims at investigating the feasibility for simultaneous quantitative analysis of various heavy metal ions in dilute solution using portable near infrared spectrometer. First, amino modified polymerized starch was used as adsorbent to enrich nickel ions and copper ions in diluted solution. Then, the diffuse reflectance spectra of amino modified polymerized starch samples were measured directly by portable near-infrared spectrometer. Furthermore, with the help of spectral preprocessing methods and partial least-squares regression, quantitative models were built from the near infrared diffuse reflectance spectra of amino modified polymerized starch enriched with heavy metal ions and reference concentrations. At last, the stability of the models was proved through cross validation and external validation. The results show that nickel ions and copper ions in diluted solution can be efficiently enriched by amino modified polymerized starch in the presence of other interfering ions. The adsorption rates for nickel ions and copper ions are 99.5% and 99.8%, respectively. Two robust models can be achieved after spectra processing and partial least squares regression. The spectra processing methods contains Continuous wavelet transform and multiplicative scatter correction combined with Savitzky-Golay. The obtained corresponding correlation coefficients of the two robust models are 0.981 9 and 0.965 4, respectively. Thus, simultaneous quantitative analysis of nickel ions and copper ions in the mixed diluted solution was achieved, and the detectable concentrations of nickel ions and copper ions are both as low as 3.0 mg·L(-1). The method not only improves the sensitivity of near infrared reflectance spectroscopy technique, but also demonstrates the feasibility for simultaneous quantitative determination of various heavy metal ions by using portable near infrared spectrometer. Moreover, the method may be a useful exploration to further broaden the application of near infrared reflectance spectroscopy technique.

Milling quality assessment of Khao Dok Mali 105 milled rice by near-infrared reflectance spectroscopy technique

The objective of this study was to assess milling quality of Khao Dok Mali 105 milled rice. The physicochemical properties of milled rice can be analyzed in values of milling quality as degree of milling (DOM), surface lipid content (SLC), color as L*, a*and b*, and the whiteness index using various kinds of measuring instrument. However in this study, the calibration models were developed using only the near infrared reflectance spectroscopy (NIRS) to determine the various physicochemical properties at wavelengths between 1,100 and 2,500 nm. The signal pretreatment and partial least square (PLS) regression were used to validate the models for the measurement of DOM, SLC, L*, a*, b* and whiteness index. Six calibration models of these properties were optimized based on the cross validation correlations (Rcv), standard error of cross validation (SECV), the external validation correlations (Rv) and standard error of prediction (SEP). It was found that the Rcv for these six models were 0.98, 0.99, 0.98, 0.95, 0.85 and 0.98 while the Rv for those were 0.99, 0.99, 0.98, 0.96, 0.88 and 0.99, the correlation were closed to 1 which shown that the agreement between data and the models were satisfied. In the meantime, the SECV and the SEP of these six calibration models shew less fluctuation of data and models. As the results, the calibration models developed in this study can be used to predict the milling quality of KDML 105 milled rice. The relationships between DOM and the five parameters were also investigated in this study.

A Review on the Use of Near-Infrared Spectroscopy for Analyzing Feed Protein Materials

: Due to the strong link between feed and food, the quality and safety of feed protein materials are of public concern. Firstly, this article summarizes the recent advances in near-infrared reflectance spectroscopy (NIRS) techniques applied to the chemical content and traceability analyses of feed protein materials. The results show the potential of NIRS as an efficient first-line screening tool for monitoring the quality and safety of feed protein materials. Finally, future prospects and the need to increase the feasibility of industrial applications and improve the limit of detection of NIRS techniques for feed protein materials are discussed.

NIRS determination of non-structural carbohydrates, water soluble carbohydrates and other nutritive quality traits in whole plant maize with wide range variability

The aim of this work was to study the potential of near-infrared reflectance spectroscopy (NIRS) to predict non-structural carbohydrates (NSC), water soluble carbohydrates (WSC), in vitro organic dry matter digestibility (IVOMD), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and starch in samples of whole plant maize with a wide range of variability. The samples were analyzed in reflectance mode by a spectrophotometer FOSS NIRSystems 6500. Four hundred and fifty samples of wide spectrum from different origin were selected out of 3000 scanned for the calibration set, whereas 87 independent random samples were used in the external validation. The goodness of the calibration models was evaluated using the following statistics: coefficient of determination (R2), standard error of cross-validation (SECV), standard error of prediction for external validation (SEP) and the RPDCV and RPDP indexes [ratios of standard deviation (SD) of reference analysis data to SECV and SEP, respectively]. The smaller the SECV and SEP and the greater the RPDCV and RPDP, the predictions are better. Trait measurement units were g/100g of dry matter (DM), except for IVOMD (g/100g OM). The SECV and RPDCV statistics of the calibration set were 1.34 and 3.2 for WSC, 2.57 and 3 for NSC and 2.3 and 2.2 for IVOMD, respectively. The SEP and RPDP statistics for external validation were 0.74 and 4.7 for WSC, 2.14 and 2.5 for NSC and 1.68 and 1.6 for IVOMD respectively. It can be concluded that the NIRS technique can be used to predict WSC and NSC with good accuracy, whereas prediction of IVOMD showed a lesser accuracy. NIRS predictions of OM, CP, NDF, ADF and starch also showed good accuracy.

Prediction of chemical composition of South African Medicago sativa L. hay from a near infrared reflectance spectroscopy spectrally structured sample population

The near infrared reflectance spectroscopy (NIRS) to predict chemical and digestibility parameters was investigated. Samples (n = 168) representing the spectral characteristics of the South African Medicago sativa L. hay population were chemically analysed for the development of calibration equations. Values for r² and ratio of prediction to deviation (RPD) used as estimates of calibration accuracy for these parameters were classified as follows: good for dry matter (DM) (r² = 0.97; RPD = 4.84), crude protein (CP) (r² = 0.97; RPD = 4.57), acid detergent fibre (ADF) (r² = 0.95; RPD = 3.97), neutral detergent fibre (NDF) (r² = 0.95; RPD = 3.99), lignin (r² = 0.94; RPD = 3.61), ash (r² = 0.93; RPD = 3.12) and chloride (Cl) (r² = 0.95; RPD = 3.74); intermediate for NDF-crude protein (NDF-CP) (r² = 0.91; RPD = 2.96), sugar (r² = 0.91; RPD = 2.82), in vitro organic matter digestibility at 24 hr (IVOMD24) (r² = 0.90; RPD = 2.84) and 48 hr (IVOMD48) (r² = 0.89; RPD = 2.70); and low (RPD <2.31) for soluble protein (SP), ADF-crude protein (ADF-CP), fat, starch, NDF digestibility (NDFD) and the macro minerals (Ca, P, Mg, P, Na and S). The results recorded in the present study indicated that the NIRS technique is acceptable for DM, CP, ADF, NDF, lignin, ash and Cl analysis and for inclusion in quality models.

The potential of near infrared reflectance spectroscopy (NIRS) for the estimation of agroindustrial compost quality

Composting is an environmentally friendly alternative for the recycling of organic wastes and its use is increasing in recent years. An exhaustive monitoring of the composting process and of the final compost characteristics is necessary to certify that the values of compost characteristics are within the limits established by the legislation in order to obtain a safe and marketable product. The analysis of these parameters on each composting batch in the commercial composting plant is time-consuming and expensive. So, their estimation in the composting facilities based on the use of near infrared reflectance spectroscopy (NIRS) could be an interesting approach in order to monitor compost quality. In this study, more than 300 samples from 20 different composting procedures were used to calibrate and validate the NIRS estimation of compost properties (pH, electrical conductivity (EC), total organic matter (TOM), total organic carbon (TOC), total nitrogen (TN) and C/N ratio, macronutrient contents (N, P, K) and potentially pollutant element concentrations (Fe, Cu, Mn and Zn)). The composts used were elaborated using different organic wastes from agroindustrial activities (GS: grape stalk; EGM: exhausted grape marc; GM: grape marc; V: vinasse; CJW: citrus juice waste; Alpeorujo: olive-oil waste; AS: almond skin; EP: exhausted peat; TSW: tomato soup waste; SMS: spent mushroom substrate) co-composted with manures (CM: cattle manure; PM: poultry manure) or urban wastes (SS: sewage sludge) The estimation results showed that the NIRS technique needs to be fitted to each element and property, using specific spectrum transformations, in order to achieve an acceptable accuracy in the prediction. However, excellent prediction results were obtained for TOM and TOC, successful calibrations for pH, EC, Fe and Mn, and moderately successful estimations for TN, C/N ratio, P, K, Cu and Zn.

Prediction of δ13C and δ15N in plant tissues with near‐infrared reflectance spectroscopy

Isotope measurements associated with critical plant resources, such as carbon and nitrogen, have helped deepen the ecological understanding of plant resource acquisition and plant interactions. In this study, we tested the appropriateness of near-infrared reflectance spectroscopy for the estimation of stable isotope ratios for nitrogen and carbon of plant tissues. delta(13)C and delta(15)N, as well as total carbon (Ct) and nitrogen (Nt), in leaf tissues of a heterogeneous set of 72 samples of seven bog species from southern Patagonia were determined. Near-infrared reflectance spectroscopy calibrations were developed using partial least-squares regressions and tested by a cross-validation procedure. For each variable, three calibrations were calculated: one with nontransformed data and two with transformations (first and second derivative). Ct and Nt, as well as delta(13)C and delta(15)N, were well predicted by our calibration models. The correlation coefficients of predicted vs actual values of the best calibration models were as follows: 0.95 (Ct), 0.99 (Nt), 0.89 (delta(13)C) and 0.99 (delta(15)N). The cross-validation procedure confirmed the high estimation quality of the calibrations. The results obtained underpin the great potential of the near-infrared reflectance spectroscopy technique in ecological studies as an alternative to more expensive and time-consuming standard methods.

AUTHENTICATION OF WILD AND REARED SEA BASS BY INFRARED SPECTROSCOPY NIRs (NEAR INFRARED REFLECTANCE SPECTROSCOPY)

The aim of this study was to evaluate NIRs (Near Infrared Reflectance Spectroscopy) performances in the prediction of Farmed vs.Wild production method in European sea bass. Samples collected (n=39) were submitted to analysis in order to assess chemical composition and fatty acids profile of fillets. Aliquots of wet and ground freeze-dried minced samples were scanned in duplicates (1100 to 2498 nm; 2 nm intervals) in reflectance mode using a monochromator NIRsystem 5000. NIRs technique showed a satisfactory accurateness in predicting Protein, Lipids and Fatty acids profile in raw samples. Sample lyophilisation increased some predicting values (r2: coefficient of determination on cross-validation range from 0,671 to 0,992; SECV: standard error of cross-validation range from 0,864 to 2,981). Results showed that NIRs technique was able to discriminate between Wild (94,7% samples recognized) and Farmed (100% samples recognized) using wet muscles, and 100% for both classes on ground freeze-dried fillet.

Prediction of fibre components in oat hulls by near infrared reflectance spectroscopy

AbstractThe fibre fractions of oat (Avena sativa L.) hulls, i.e. neutral detergent fibre (NDF), acid detergent fibre (ADF) and acid detergent lignin (ADL), were evaluated, using a specifically developed near infrared reflectance spectroscopy (NIRS) equation model, in a set of cultivars grown in various locations across Italy. The results indicated that the NIRS technique can accurately predict the fibre composition of oat hulls. Statistical analysis of variance underlined the strong impact of cultivar on the accumulation of fibre components, whereas location generally played a minor role. The largest variability among cultivars was observed in ADL content, whereas the range of variation in NDF and ADF contents appeared to be reduced. The description of the chemical characteristics of oat hulls could be helpful in determining the most suitable end‐use for specific cultivars. Copyright © 2007 Society of Chemical Industry

Theory and application of near infrared reflectance spectroscopy in determination of food quality

Near infrared reflectance spectroscopy (NIRS) is a non-destructive and rapid technique applied increasingly for food quality evaluation in recent years. It provides us more information to research the quality of food products. This review intends to give an overview of the type of information that can be obtained based on some developed theory and food research of NIRS. It includes the principle of NIRS technique, the specific techniques with chemometrics for data pre-processing methods, qualitative and quantitative analysis and model transfer, and the wide applications of NIRS in food science. In addition, the promise of NIRS technique for food quality evaluation is demonstrated, and some problems which need to be solved or investigated further are also discussed.

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.

Estimation of mineral content in forages by near-infrared reflectance spectroscopy

In 100 dried samples of forages, harvested three or four times during 1997 and 1999 in different morphological stages the macro- (Ca, P, Mg, K and Na) and trace- (Zn, Mn and Se) mineral contents were determined by chemical methods and by near infrared reflectance spectroscopy (NIRS). The accuracy calibration equations of NIRS for macrominerals Ca, P, Mg and K showed a high degree of accuracy, with an R2 of 0.963, 0.884, 0.892 and 0.939, respectively. These results suggest that these macrominerals are probably closely associated with the organic components, such as plant cell wall (Ca and Mg), chlorophyll (Mg), phytates, phospholipids, phosphoproteins and nucleic acids (P) and organic acids, such as malate (K). On the contrary, NIRS did not estimate Na content very accurately (R2 = 0.52; coefficient of variability of cross validation (CVSECV) = 35.5%), suggesting that Na is not closely associated with organic functional groups or that the amount of the mineral is below the level of NIRS detection. Similar conlusions are also valid for trace minerals, where the R2 was 0.642 (SECV = 4.4) for Zn, 0.427 (SECV = 55.6) for Mg and only 0.186 (SECV = 6.5) for Se. The CVSECV for trace minerals always exceeded 20%. Despite the insensibility of NIRS to accurately determine Na and certain trace minerals, the NIRS technique may provide an acceptable prediction of macro mineral contents.

Monitoring powder blending in pharmaceutical processes by use of near infrared spectroscopy

Obtaining completely uniform distribution of the active principle and excipients in a pharmaceutical preparation is essential with a view to ensuring correct dosage. Uniformity in pharmaceutical formulations has usually been controlled by collecting samples at different stages of the process in order to determine the active principle using a chromatographic or UV–Visible spectroscopic method. In this work, near infrared reflectance spectroscopy (NIRS) was used to monitor blending in order to ensure uniformity in a mixture consisting of three typical pharmaceutical excipients and one active principle. To this end, a method for calculating the Mean Square of Differences between two consecutive spectra was developed with a view to expeditiously identifying the time mixture homogeneity was attained. The performance of the proposed method was compared with that of two others routinely employed to monitor blending by use of the NIRS technique and the results were found to be quite consistent.

The Use of Near-Infrared Reflectance Spectroscopy in the Prediction of the Chemical Composition of Goose Fatty Liver

The use of near-infrared reflectance spectroscopy (NIRS) on a meat product is described in this report. The aim of the study was to develop calibration equations to predict the chemical composition of goose fatty liver (foie gras) with lipid contents greater than 40% of the fresh pate. Spectra of 52 foie gras samples were collected in the visible and NIR region (400 to 2,498 nm). Calibration equations were computed for DM, CP, lipids and fatty acids using modified partial least-squares regression. R2 values were high for the total lipid content (0.805) and DM (0.908) but were low for ash (0.151) and relatively low for protein content (0.255). For the major fatty acids, R2 ranged from 0.886 for palmitic acid to 0.988 for oleic acid. Oleic acid, the main fatty acid of the liver, and the stearic acid had higher R2 values than the less represented fatty acids. This study suggests that the NIRS technique can be used to predict lipid content and the fatty acid composition of goose fatty livers, but calibration must be built on a larger number of samples to generate accurate predictions.

Characterization of different strains of broiler chicken by carcass measurements, chemical and physical parameters and NIRS on breast muscle

The aims of the work were: to investigate the effect of chicken strain on chemical composition and colour parameters of fresh breast muscle; to evaluate the results obtained using different illuminants (A, C and F); to verify the possibility of predicting some meat features by near infrared reflectance spectroscopy (NIRS) of freeze-dried breast muscle. The trial was carried out on 39 chicken broilers from three strains, differing in body size, slaughtered at 30 days of age. Fresh breast samples were different only in ash content ( P<0.01). Illuminant effect was highly significant for all colour variables, depending on the major wavelength characteristics of each illuminant. Significant differences among strains were detected in redness values. The NIRS technique was very accurate to predict fat content of muscle (R 2c=0.98, SEC=0.20). These findings may have practical importance to improve instrumental procedures for on-line evaluation of poultry meat quality.

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

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.