Near Infrared Reflectance Spectroscopy Models Research Articles

Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato.

Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQexternal), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQexternal values of 0.937, 0.892, and 0.759 were obtained for protein, dry matter, and total phenols, respectively. Higher RPD values were found for protein (3.982), followed by dry matter (3.041) and total phenolics (2.000), which indicates the excellent predictability of the models. A paired t-test confirmed that the differences between laboratory and predicted values are non-significant. This study presents the first multi-trait NIRS prediction model for Indian potato germplasm. The developed NIRS model effectively predicted the remaining genotypes in this study, demonstrating its broad applicability. This work highlights the rapid screening potential of NIRS for potato germplasm, a valuable tool for identifying trait variations and refining breeding strategies, to ensure sustainable potato production in the face of climate change.

Genetic parameters and genomic prediction for nutritional quality-related traits of Pacific abalone (Haliotis discus hannai)

The Pacific abalone (Haliotis discus hannai) is one of the most widely cultivated shellfish species in China. Nutritional quality-related traits are a major objective in aquatics as they determine the value of products and consumer preference. Estimating genetic parameters and the potential of genomic selection is significant in designing breeding programs, which has not been performed in abalone. In the study, we developed near infrared reflectance spectroscopy (NIRS) models for collagen content (CC) and taurine content (TC) in Pacific abalone, estimated the genetic parameters for glycogen content (GLY), total protein content (TPC), CC, and TC, and assessed the prediction ability of GBLUP and three Bayesian methods for the four nutritional traits. The genetic analysis included all 274 individuals and 69,530 high quality SNPs. The prediction correlation of the NIRS models were 0.9702 and 0.9289 for CC and TC, respectively. The heritability of GLY, TPC, CC, and TC was 0.33 ± 0.13, 0.42 ± 0.14, 0.02 ± 0.10 and 0.32 ± 0.13, respectively, while the heritability of growth traits in our study ranged from 0.34 ± 0.14 to 0.47 ± 0.14. Negative correlations were observed between GLY and both TPC (−0.96 ± 0.03) and TC (−0.90 ± 0.07), whereas moderate correlations were found between GLY and growth traits. The prediction ability for GLY, TPC, and TC was 0.21 ± 0.04 to 0.22 ± 0.03, 0.27 ± 0.02 to 0.29 ± 0.03, 0.20 ± 0.03 to 0.24 ± 0.02, respectively, using four genomic selection (GS) models (GBLUP, BayesA, BayesB, and BayesCpi). The results were encouraging and showed comparable performance for the predictability of genomic estimated breeding values, demonstrating the potential for GS in selection breeding of the nutritional quality-related traits in Pacific abalone.

Near-infrared reflectance spectroscopy model predictive of cadmium concentration in peanut kernels

Near-infrared reflectance spectroscopy model predictive of cadmium concentration in peanut kernels

Development of near-infrared reflectance spectroscopy (NIRS) model and genome-wide association study for glycogen and protein content in Pacific abalone

Quality traits have recently become the key objective in aquaculture selection breeding recently. The Pacific abalone (Haliotis discus hannai) is the most widely farmed abalone species in China. In this study, we investigated the potential of the near-infrared reflectance spectroscopy (NIRS) model to predict the glycogen and protein content of Pacific abalone. Additionally, we performed a genome-wide association study (GWAS) analysis to identify related SNPs and candidate genes for these two quality traits. Our results showed that the prediction accuracy of the NIRS model was 0.9929 and 0.9808 for glycogen and protein content, respectively. Moreover, the external cross-validation showed an accuracy above 0.95, indicating the reliability and robustness of the models. A total of 274 individuals were predicted by the NIRS model and genotyped with 69,530 high quality SNPs. Fourteen and fifteen genes were identified for glycogen and protein content, respectively, including slit2, fgfr2, gria4, hnrnpm, and megf11. These genes may affect glycogen and protein traits through biological processes such as energy metabolism, transmembrane transport, immune regulation, and signal transduction. This study not only constructed a rapid and reliable NIRS prediction model, but also helped us better understand the genetics of glycogen and protein content of Pacific abalone, which will improve the quality of Pacific abalone.

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.

Estimation of in vitro digestibility and fermentation of elephant grass by near infrared spectroscopy

AbstractThe methods used to evaluate kinetic parameters of ruminant feeds can be in situ and in vitro. For both methods, it is necessary to maintain cannulated animals in order to collect the inoculum from the rumen, which has been under strong pressure from society, in terms of animal welfare policies, to avoid this type of practices. This work aimed to evaluate the estimation of in vitro digestibility and fermentation of elephant grass forage by near infrared reflectance spectroscopy (NIRS), as a fast and noninvasive alternative to the in vitro method. The experimental design was in randomized blocks with three repetitions. The treatments were arranged in a split‐plot scheme, with 13 genotypes in the plot and 15 regrowth ages in the subplot. Crude protein (CP) and neutral detergent fiber (NDF) contents; in vitro dry matter (IVDMD) and NDF (IVNDFD) digestibilities; and total volume (Vt), gas production rate (μ) and half‐life conventional analyses were determined for pre‐dried elephant grass forage samples. For the evaluation of NIRS models performance, coefficient of determination (R2cv) and standard error of cross validation (SECV) were used. As the regrowth age advanced, there was a linear increase in the NDF content and a linear reduction in the IVDMD, IVNDFD and Vt values. The NIRS estimates proved to be adequate for IVDMD (R2cv = .95), IVNDFD (R2cv = .85), Vt (R2cv = .81) and gas production at 48 (R2cv = .82), 72 (R2cv = .85) and 96 h (R2cv = .84), confirming as an alternative to the in vitro methods that dispense with the use of cannulated animals.

Prediction of protein and amino acid contents in whole and ground lentils using near-infrared reflectance spectroscopy

Lentil is an important source of plant-based protein, and the protein and amino acid contents have a significant influence on its nutritional quality and value. In this study, near-infrared reflectance spectroscopy (NIRS) models were developed by partial least squares (PLS) regression to predict the crude protein and 18 amino acid contents of lentil seeds. The effects of sample status (whole and ground), type of spectrometer (PerkinElmer DA 7250 and FT 9700), and amino acid/protein correlation on model performance were analyzed and evaluated. The DA 7250 models and FT 9700 models of protein and 14 amino acids, except histidine, tyrosine, methionine and cysteine, showed good statistical results with coefficients of determination for calibration (R2C) higher than 0.652 and residual predictive deviation (RPD) values higher than 1.57. The DA 7250 models achieved similar accuracy for the determination of compositions in whole and ground samples. Two spectrometers had no significant difference (p > 0.05) for measurement in ground lentils. NIRS models could perform better for certain amino acids when they were highly correlated to protein. Overall, NIRS had a significant potential for rapid, accurate and simultaneous prediction of protein and most amino acid contents in lentils.

Comparison of smartphone and lab-grade NIR spectrometers to measure chemical composition of lamb and beef

Context Near-infrared reflectance spectroscopy (NIRS) has been extensively investigated for non-destructive and rapid determination of pH and chemical composition of meat including water, crude protein, intramuscular fat (IMF) and stable isotopes. Smaller, cheaper NIRS sensors that connect to a smartphone could enhance the accessibility and uptake of this technology by consumers. However, the limited wavelength range of these sensors could restrict the accuracy of predictions compared with benchtop laboratory NIRS models. Aims To compare the precision and accuracy metrics of predicting pH, water, crude protein and IMF of three sample presentations and two sensors. Methods Fresh intact (FI) store-bought beef and lamb steak samples (n = 43) were ground and freeze-dried (FD), and then oven-dried to create freeze-dried oven-dried (FDOD) samples. All three forms of sample presentation (FI, FD, FDOD) were scanned using the smartphone and benchtop NIRS sensors. Key results The IMF was the best predicted trait in FD and FDOD forms by the smartphone NIRS (R2 >0.75; RPD >1.40) with limited differences between the two sensors. However, predictions on FI meat were poorer for all traits regardless of the NIRS scanner used (R2 ≤ 0.67; RPD ≤ 1.58) and not suitable for use in research or industry. Conclusion The smartphone NIRS sensor showed accuracy and precision comparable to benchtop NIRS to predict meat composition. However, these preliminary results found that neither of the two sensors reliably predicted quality attributes for industry or consumer applications. Implications Miniaturised NIRS sensors connected to smartphones could provide a practical solution to measure some meat quality attributes such as IMF, but the accuracy depends on sample presentation.

Use of Visible and Near-Infrared Reflectance Spectroscopy Models to Determine Soil Erodibility Factor (K) in an Ecologically Restored Watershed

This study aimed to assess the ability of using visible and near-infrared reflectance (Vis–NIR) spectroscopy to quantify soil erodibility factor (K) rapidly in an ecologically restored watershed. To achieve this goal, we explored the performance and transferability of the developed spectral models in multiple land-use types: woodland, shrubland, terrace, and slope farmland (the first two types are natural land and the latter two are cultivated land). Subsequently, we developed an improved approach by combining spectral data with related topographic variables (i.e., elevation, watershed location, slope height, and normalized height) to estimate K. The results indicate that the calibrated spectral model using total samples could estimate K factor effectively (R2CV = 0.71, RMSECV = 0.0030 Mg h Mj−1 mm−1, and RPDCV = 1.84). When predicting K in the new samples, models performed well in natural land soils (R2P = 0.74, RPDP = 1.93) but failed in cultivated land soils (R2P = 0.24, RPDP = 0.99). Furthermore, the developed models showed low transferability between the natural and cultivated land datasets. The results also indicate that the combination of spectral data with topographic variables could slightly increase the accuracies of K estimation in total and natural land datasets but did not work for cultivated land samples. This study demonstrated that the Vis–NIR spectroscopy could be used as an effective method in predicting K. However, the predictability and transferability of the calibrated models were land-use type dependent. Our study also revealed that the coupling of spectrum and environmental variable is an effective improvement of K estimation in natural landscape region.

Near Infrared Reflectance Spectroscopy and Multivariate Analyses for Fast and Non-Destructive Prediction of Corn Seed Germination

The application of near-infrared reflectance spectroscopy (NIRS) and multivariate analysis for determining the seed germination rate of corn genotypes was assessed. Seed samples about 90 gr belong to commercial and local corn varieties at various ages were scanned with FT-NIRS on the reflectance mode from 1000 to 2500 nm wavelength. Filter paper technique showed the seed germination rates varied between 18-100% depending on the genotypes after 7 days at ±25°C. Partial least squares regression (PLSR) was applied to the reference values corresponding to the spectra. The best statistical results obtained from the pre-treatment combinations of Smooth Savitzky-Golay 9 Points (sg9), MSC full and normalization to unit length (nle). The regression coefficient of calibration (R2C) and prediction (R2P) of the created NIRS calibration via chemometric software NIRCal are realized 0.97 and 0.98 respectively for the property of corn germination rate. The standard error of both calibration (SEC) and prediction (SEP) were almost overlapping (4.17%, 4.61% respectively). The prediction accuracy of the final NIRS model was quite reasonable with the acceptable root mean standard error of prediction (RMSEP) as 8.88%. According to the residual predictive deviation (RPD) index (4.18), the accuracy of the NIRS model regarded as in the best category. Therefore, the NIRS model developed here is sufficient to predict the corn seed germination rate very fast and non-destructively without using any regents.

How well can near infrared reflectance spectroscopy (NIRS) measure sediment organic matter in multiple lakes?

Loss-on-ignition (LOI) is the most widely used measure of organic matter in lake sediments, a variable related to both climate and land-use change. The main drawback for conventional measurement methods is the processing time and hence high labor costs associated with high-resolution analyses. On the other hand, broad-based near infrared reflectance spectroscopy (NIRS) is a time and cost efficient method to measure organic carbon and organic matter content in lacustrine sediments once predictive methods are developed. NIRS-based predictive models are most robust when applied to sediments with properties that are already included in the calibration dataset. To test the potential for a broad applicability of NIRS models in samples foreign to the calibration model using linear corrections, sediment cores from six lakes (537 samples, LOI range 1.03–85%) were used as reference samples to develop a predictive model. The applicability of the model was assessed by sequentially removing one lake from the reference dataset, developing a new model and then validating it against the removed lake. Results indicated that NIRS has a high predictive power (RMSEP < 4.79) for LOI with the need for intercept and slope correction for new cores measured by NIRS. For studies involving many samples, NIRS is a cost and time-efficient method to estimate LOI on a range of lake sediments with only linear bias adjustments for different records.

Rapid prediction of Apparent Amylose, total starch, and crude protein by near‐infrared reflectance spectroscopy for foxtail millet (Setaria italica)

AbstractBackground and objectivesThe aim of this study was to construct near‐infrared reflectance spectroscopy (NIRS) models to test the contents of apparent amylose, total starch, and crude protein for both foxtail millet grain and dehusked millet grain. The construction of models is based on chemical values and optimized spectral data of 111 core foxtail millet germplasm materials after analyzation of partial least square (PLS) method with cross‐validation.FindingsModel calibration showed that there was better fitting degree of dehusked millet grain than that of foxtail millet. The coefficient determination for calibration (Rc2) of former is &gt;0.843, while that of latter is &gt;0.823. The coefficient determination of prediction (Rp2) of each component ranged from 0.794 to 0.888 for foxtail millet grain and 0.833 to 0.970 for dehusked millet grain.ConclusionsThe constructed NIRS models of both samples can be used to rapidly test foxtail millet quality, and the nondestructive nature of foxtail millet grain‐based test (which maintains seed vigor) better meets the requirements of the breeding, although the accuracy needs to be further improved.Significance and noveltyThis study developed a rapid method to detect the main nutritional quality for foxtail millet using two different samples, which can be used in germplasm resource innovation and contribute to the acceleration of cereal breeding.

Using near infrared reflectance spectroscopy for estimating nutritional quality of Brachiaria humidicola in breeding selections

AbstractBrachiaria humidicola (BH) (syn. Urochloa humidicola) is an important forage grass in the tropics due to its capacity to grow in nutrient‐deficient soils, tolerate waterlogging, and inhibit soil nitrification. A major objective of BH breeding is to improve its nutritional quality. Therefore, a rapid and low‐cost method is needed to assess main quality parameters such as neutral detergent fiber (NDF), acid detergent fiber (ADF), in vitro dry matter digestibility (IVDMD), and crude protein (CP). This study developed models using near infrared reflectance spectroscopy (NIRS) to predict concentrations of these parameters toward breeding. Samples were collected from BH trials located in different regions of Colombia, scanned for NIRS (400–2,500 nm), analyzed with wet chemistry as reference values, and used to build the chemometric models. Results from wet chemistry showed wide variability in terms of dry matter percentage for NDF (51.6–76.2%), ADF (26.1–46.1%), IVDMD (41.5–78.3%), and CP (2.8–12.8%). The NIRS models were validated using an independent set of samples and have coefficients of determination (R2) and one minus the variance ratio (1 – VR) values in the range of .9 and .95, suggesting a good correlation between reference‐lab and NIRS‐predicted values. The standard errors of cross validation (SECV) for IVDM, NDF, ADF, and CP were 1.59, 1.18, 0.74, and 0.53%, respectively. Prediction efficiency (ratio of performance to standard deviation, RPD) for all parameters was above 3.0, except for CP (2.6). Calibrations obtained present an adequate adjustment and predictive tendency, making them suitable for selection and BH breeding.

NIRs calibration models for chemical composition and fatty acid families of raw and freeze-dried beef: A comparison

The aim of this study was to develop near-infrared reflectance spectroscopy (NIRS) calibrations for estimation of chemical components (protein, fat, ash and moisture) and the principal fatty acid (FA) groups (SFA, MUFA, PUFA, n−3, n−6) in beef meat. Beef samples (n = 207) of three different muscles (Longissimus dorsi, Rectus abdominis and Semitendinosus) were used for the study. The calibration models were performed both on raw and freeze-dried (FD) meat. Raw meat predictions for protein, fat and ash were performed, using reference values expressed as percentage on dry-matter (DM) or fresh-minced (FM) weights. In addition, FD meat predictions for the same chemical components were developed. FA predictive models were based on the relative composition of FA (weight percent of total FA). The predictability of calibrations based on FD meat was higher than that of models based on raw samples, except for n−3. Regarding the raw meat, the calibration models obtained from reference values expressed on DM basis were better than those obtained from data expressed on FM basis. In addition, because of the low coefficients of determination of prediction (R2p) and the low RPD obtained, NIRS models of FA families are not useful for a quantitative fatty acid analysis.

51 Feedstuff fatty acid content, variation, techniques and implications on practical animal nutrition

Abstract Fatty acid nutrition in ruminants, dairy cattle primarily, has increased as a point of emphasis with nutrition formulation in the past decade, as the diet fatty acid profile and metabolism has been found to impact milk fat concentration and animal health. Both the fatty acid supply and rumen degradation warrant further investigation in dairy diets for improved formulation strategies in the future. As supply and degradation are better understood, improved formulation approaches will be possible now that routine feed fatty acid measures have become more practical with the development of near-infrared reflectance spectroscopy (NIR) models at commercial feed analysis laboratories. NIR model development techniques vary; however, the general approach is to calibrate against a wide ranging database of feedstuff wet chemistry measures using a partial least squares approach. Models relate spectral observations (i.e. reflectance at a specific near-infrared light wavelength) to wet chemistry observations. NIR models should reflect both the mean and variation observed in the wet chemistry database. The NIR models developed by Rock River Laboratory, and resulting feed library database information presented in Table 1, were developed by calibrating against feedstuff chemistry performed at the Lock laboratory with Michigan State University. Wet chemistry fatty acid determination by analytical laboratories, using gas chromatography techniques against known fatty acid standards, deserves further discussion to agree upon peak identification schemes. Differences in peak identification from one laboratory to the another will result in different total fatty acid measures and NIR models. The fatty acid content and profile coefficients of variation, determined from the mean and standard deviations presented in Table 1, range from less than 10 to over 100% of the mean with an average CV of 27%. This suggests substantial variation is present in commercial feeds, and opportunities may exist to better account for variation and fatty acid supply in dairy diets.

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

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

Comparison and rapid prediction of lignocellulose and organic elements of a wide variety of rice straw based on near infrared spectroscopy

Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy. Rapid prediction of the lignocellulose (cellulose, hemicellulose, and lignin) and organic elements (carbon, hydrogen, nitrogen, and sulfur) of rice straw would help to decipher its growth mechanisms and thereby improve its sustainable usages. In this study, 364 rice straw samples featuring different rice subspecies (japonica and indica), growing seasons (early-, middle-, and late-season), and growing environments (irrigated and rainfed) were collected, the differences among which were examined by multivariate analysis of variance. Statistic results showed that the cellulose content exhibited significant differences among different growing seasons at a significant level (p < 0.01), and the contents of cellulose and nitrogen had significant differences between different growing environments (p < 0.01). Near infrared reflectance spectroscopy (NIRS) models for predicting the lignocellulosic and organic elements were developed based on two algorithms including partial least squares (PLS) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS). Modeling results showed that most CARS-PLS models are of higher accuracy than the PLS models, possibly because the CARS-PLS models selected optimal combinations of wavenumbers, which might have enhanced the signal of chemical bonds and thereby improved the predictive efficiency. As a major contributor to the applications of rice straw, the nitrogen content was predicted precisely by the CARS-PLS model. Generally, the CARS-PLS models efficiently quantified the lignocellulose and organic elements of a wide variety of rice straw. The acceptable accuracy of the models allowed their practical applications. Keywords: rice straw, near infrared reflectance spectroscopy models, rapid prediction, competitive adaptive reweighted sampling, partial least-squares, lignocellulose DOI: 10.25165/j.ijabe.20191202.4374 Citation: Diallo A A, Yang Z L, Shen G H, Ge J Y, Li Z C, Han L J. Comparison and rapid prediction of lignocellulose and organic elements of a wide variety of rice straw based on near infrared spectroscopy. Int J Agric & Biol Eng, 2019; 12(2): 166–172.

Log‐ratio transformation is the key to determining soil organic carbon fractions with near‐infrared spectroscopy

SummaryInformation about soil organic carbon fractions is important in understanding the vulnerability of soil carbon to climate change and land management. Soil organic carbon can be divided into fractions that are labile and others that are more stable. All existing methods to fractionate soil organic carbon are time consuming and complex. Near‐infrared reflectance spectroscopy (NIRS) is a rapid analytical technique. In this study we evaluated and optimized the use of NIRS to predict soil organic carbon fractions with the constraint that the carbon fractions (labile and stabilized) should add up to 100% (total organic carbon content). We used samples from two datasets from agricultural and forest sites in Germany (dataset A) and Europe (dataset B). Samples were fractionated by two different methods (density and physical‐chemical fractionation) as reference methods. Soil samples were scanned in the NIR range and calibration models were developed using partial least squares regression. The key to improving model performance was the log‐ratio transformation proposed by Aitchison for compositional data that enabled us to model the fractions as dependent variables. Traditional methods for the optimization of NIRS models, such as selection of wavelength range and pretreatment of spectra, showed no effective reduction in error. With the constraint that both fractions add to 100% (log‐ratio transformation), the ratio of performance to deviation (RPD) increased from 1.6 to 2.8 for the labile C fraction and from 1.5 to 3.2 for the stabilized C fraction. Root mean square error of cross‐validation of the labile fraction was 4.3 g C kg−1for dataset A and 2.7 g C kg−1for dataset B, which corresponds to anR2of 0.88–0.80 and RPD of 2.9–2.2, respectively. The models performed equally well for different soil textures and land‐use types. With log‐ratio transformation, more precise calibrations of NIRS for C fractions could be obtained.HighlightsNear‐infrared spectroscopy can predict soil carbon fractions at different spatial extentsLog‐ratio transformation satisfies the constraint that fractions should add up to 100%Log‐ratio transformation is the key to improving calibrations for soil C fractions.The NIR models were tested with two fractionation schemes comprising different land uses and soil texture

Models of near infrared spectroscopy of fatty acid contents in rapeseed

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

Using near-infrared reflectance spectroscopy to predict physical parameters of beef

ABSTRACTThe objective of this study was to develop near-infrared reflectance spectroscopy models for predicting the physical parameters of beef from commercial cuts. Four hundred and forty-two minced beef samples from various commercial cuts of two cattle breeds were used for the modeling process and randomly divided into two subsets: a calibration set and an independent prediction set (75 vs. 25%). Reflectance spectra (1000–1800 nm) were collected from both subsets of samples, and calibration models were built using partial least square regression on the calibration set of samples. Different mathematical pretreatments were tested and mean centering or multiplicative scatter correction combined with first-derivative preprocessing gave the best calibration models on all the beef physical traits. Based on the selected calibration equations, the coefficients of determination in calibration and prediction for Warner-Bratzler shear force, pH, color L*, and a* were higher than 0.60, which means models in the calibration were acceptable. However, the ratio performance deviation for all parameters was less than 2.0, indicating that the prediction abilities on independent validation set were not adequate for routine analysis. Further studies are required to establish more robust models for practical application of using near-infrared reflectance spectroscopy to predict physical traits of beef.