Near Infrared Reflectance Spectroscopy Prediction 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.

Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm.

In the present era of climate change, underutilized crops such as rice beans and adzuki beans are gaining prominence to ensure food security due to their inherent potential to withstand extreme conditions and high nutritional value. These legumes are bestowed with higher nutritional attributes such as protein, fiber, vitamins, and minerals than other major legumes of the Vigna family. With the typical nutrient evaluation methods being expensive and time-consuming, non-invasive techniques such as near infrared reflectance spectroscopy (NIRS) combined with chemometrics have emerged as a better alternative. The present study aims to develop a combined NIRS prediction model for rice bean and adzuki bean flour samples to estimate total starch, protein, fat, sugars, phytate, dietary fiber, anthocyanin, minerals, and RGB value. We chose 20 morphometrically diverse accessions in each crop, of which fifteen were selected as the training set and five for validation of the NIRS prediction model. Each trait required a unique combination of derivatives, gaps, smoothening, and scatter correction techniques. The best-fit models were selected based on high RSQ and RPD values. High RSQ values of >0.9 were achieved for most of the studied parameters, indicating high-accuracy models except for minerals, fat, and phenol, which obtained RSQ <0.6 for the validation set. The generated models would facilitate the rapid nutritional exploitation of underutilized pulses such as adzuki and rice beans, showcasing their considerable potential to be functional foods for health promotion.

Fast screening of total nutrient contents in strawberry leaves and spent growing media using NIRS.

In closed-loop soilless cultivation, the main nutrient sinks are nutrients retained either by the crop or in spent growing media. Measurement of nutrients in spent growing media and in the aboveground vegetative plant biomass at crop termination can be a tool for assessing and optimizing nutrient efficiency. The first aim of this study was to test the potential of near-infrared reflectance spectroscopy (NIRS) to forecast the various nutrient contents in strawberry leaves, which would then allow for assessment of crop nutrient status and total nutrient uptake by strawberry plants. The second aim was to test NIRS as a high throughput technique for assessing the N, K, Ca, Mg and organic matter (OM) content and the pH, EC and C:N and C:P ratios for a dataset of composts, plant fibers and spent growing media. The NIRS prediction model for fast screening of the total nutrient contents in spent growing media was compared with a single extraction method. A database with 369 dried and ground strawberry leaf samples with known contents of N, P, K, Ca, and Mg were scanned using NIRS. The database covered a range of leaf contents of 6-35 g N/kg dry matter (DM), 0.7-6.3 g P/kg DM and 2-29 g K/kg DM. A dataset of 458 samples of different types of materials used in growing media was validated with a dataset of 109 samples. Validation for the strawberry leaves indicated potential for this application, with R2 values of 0.90 or higher for N, K and Ca, and R2 values higher than 0.85 for P and Mg. Validation for the dataset of composts, plant fibers and spent growing media also indicated the potential for this application, with R2 values of 0.90 or higher for organic matter, and with R2 values of 0.85 or higher for total Ca, pH and C:N. A first test indicated potential for the calibration based on fresh samples of compost, plant fiber as well as spent growing media or dried (not ground) samples. Use of NIRS on fresh samples would eliminate the need for drying and grinding the samples and would reduce screening time. The ammonium acetate extraction is a reliable alternative to NIRS for fast screening of the total P, K, Ca, and Mg contents in composts, plant fibers and spent growing media.

Development of NIRS calibration curves for sugars in baked sweetpotato.

Variability in sugar content between raw and cooked sweetpotato storage roots impact nutritional and dietary importance with implications for consumer preference. High-throughput phenotyping is required to breed varieties that satisfy consumer preferences. Near-infrared reflectance spectroscopy (NIRS) calibration curves were developed for analysing sugars in baked storage roots using 147 genotypes from a population segregating for sugar content and other traits. The NIRS prediction curves had high coefficients of determination in calibration (R2 c) of 0.96 (glucose), 0.93 (fructose), 0.96 (sucrose), and 0.96 (maltose). The corresponding coefficients of determination for cross-validation (R2 cv) were 0.92 (glucose), 0.89 (fructose), 0.96 (sucrose) and 0.93 (maltose) and were similar to the R2 c for all sugars measured. The ratios of the standard deviation of the reference set to the standard error of cross-validation were greater than three for all sugars. These results confirm the applicability of the NIRS curves in efficiently determining sugar content in baked sweetpotato storage roots. External validation was performed on an additional 70 genotypes. Coefficients of determination (r2) were 0.88 (glucose), 0.88 (fructose), 0.86 (sucrose) and 0.49 (maltose). The results were comparable to those found for the calibration and cross-validation in fructose, glucose, and sucrose, but were moderate for maltose due to the low variability of maltose content in the population. NIRS can be used for screening sugar content in baked sweetpotato storage roots in breeding programs and can be used to assist with the development of improved sweetpotato varieties that better meet consumer preferences. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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 functional characteristics of gari (cassava flakes) using near-infrared reflectance spectrometry.

Gari is a creamy, granular flour obtained from roasting fermented cassava mash. Its preparation involves several unit operations, including fermentation, which is essential in gari production. Fermentation brings about specific biochemical changes in cassava starch due to the actions of lactic acid bacteria. Consequently, it gives rise to organic acids and a significant reduction in the pH. Consumer preferences for gari are influenced by these changes and impact specific functional characteristics, which are often linked to cassava genotypes. Measurement of these functional characteristics is time-consuming and expensive. Therefore, this study aimed to develop high-throughput and less expensive prediction models for water absorption capacity, swelling power, bulk density, and dispersibility using Near-Infrared Reflectance Spectroscopy (NIRS). Gari was produced from 63 cassava genotypes using the standard method developed in the RTB foods project. The prediction model was developed by dividing the gari samples into two sets of 48 samples for calibration and 15 samples as the validation set. The gari samples were transferred into a ring cell cup and scanned on the NIRS machine within the Vis-NIR range of 400-2,498nm wavelength, though only the NIR range of 800-2,400nm was used to build the model. Calibration models were developed using partial least regression algorithms after spectra pre-processing. Also, the gari samples were analysed in the laboratory for their functional properties to generate reference data. Results showed an excellent coefficient of determination in calibrations (R2 Cal) of 0.99, 0.97, 0.97, and 0.89 for bulk density, swelling power, dispersibility, and water absorption capacity, respectively. Also, the performances of the prediction models were tested using an independent set of 15 gari samples. A good prediction coefficient (R2 pred) and low standard error of prediction (SEP) was obtained as follows: Bulk density (0.98), Swelling power (0.93), WAC (0.68), Dispersibility (0.65), and solubility index (0.62), respectively. Therefore, NIRS prediction models in this study could provide a rapid screening tool for cassava breeding programs and food scientists to determine the food quality of cassava granular products (Gari).

Estimating the accuracy of the NIRS prediction model based on soil t

Near infrared reflectance spectroscopy (NIRS) proved to be a rapid, reliable and cost-effective method for soil analysis, but its prediction accuracy relies on the proper calibration model. In this paper, NIR spectroscopy in combination with partial least-squares regression (PLSR) was used to predict soil chemical properties on regional and local scales. The results obtained show a higher potential of NIRS to assess soil fertility on local scale, compared to a regional scale, with excellent prediction for organic C, carbonates and total N content (R2 ≥ 0.98 and RPD &gt; 6), and good prediction for soil pH (R2 =0.88 and RPD &gt; 3).

Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice.

Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide a rapid assessment tool but their applicability is limited by the sample diversity, used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions, and reference data were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with R2 = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant.

Faecal near-infrared reflectance spectroscopy profiling for the prediction of dietary nutritional characteristics for equines

Faecal near infrared reflectance spectroscopy (NIRS) profiling involves the use of NIRS for the prediction of nutrient components of animal diets from their faecal samples. This study evaluated the potential use of faecal NIRS profiling for the prediction of minerals, fibre fractions, and organic matter (OM) in horses’ diet. Two feeding trials were conducted consisting of 29 horses housed in individual stables and fed unique diets (D) to obtain diet reference data and faecal (F) NIR spectra (D:F) pairs. The D:F pairs were used for the development of faecal NIRS predictive equations. The trials ran for a total of five weeks (7 days adaptation and 4 weeks of measurement). Diets were adjusted weekly by changing the forage and concentrate supplied to include variation in the diet. Faecal and diet samples were pooled per week per horse resulting in 116 D:F pairs (29 horses x 4 weeks). Samples from weeks 1–3 (n = 87) were used to develop calibration and week 4 samples were used as an independent validation set (n = 29). Modified partial least square regression was used for calibration development. Results obtained showed that faecal NIRS prediction of dietary OM was excellent [coefficient of determination of validation (R2val) = 0.92 and ratio performance deviation (RPD) = 3.54], while that of neutral detergent fibre (aNDF) was very good (R2val and RPD: 0.83 and 2.12). The validation statistics (R2val: 0.77 and RPD: 1.56) showed the prediction model for acid detergent fibre (ADF) was acceptable but not as precise as those of OM and aNDF. Prediction of acid detergent lignin (ADL) was the poorest (R2val = 0.41and RPD = 1.05). For the mineral components, prediction of copper (Cu) was very good (R2val = 0.81, RPD= 2.11) and better than those of Phosphorus (P), Sulphur (S) and Manganese (Mn) which all had R2val of 0.60–0.67 and RPD of 1.60–1.64. Overall, this study showed that faecal NIRS profiling can be used to successfully predict OM, aNDF and Cu concentration in horses’ diet. Although, the predictions of other fibre fractions were less satisfactory, faecal NIRS can still be used for monitoring purposes and improved for future application to independent samples.

Near infrared reflectance spectroscopy as a tool to predict non-starch polysaccharide composition and starch digestibility profiles in common monogastric cereal feed ingredients

Carbohydrates are the main components of cereal grain ingredients and are comprised predominantly of starch which varies in the rate and extent of digestibility, and non-starch polysaccharides (NSP) from plant cell walls. These components are traditionally measured by chemical methods but there is interest in developing more rapid, cost effective predictions by use of near infrared reflectance spectroscopy (NIRS) technology. In this study, around 400 samples of wheat, corn, barley, wheat bran, oats, rye, corn germ, and sorghum were collected over several years to develop global NIR calibrations. Chemical analysis was performed for NSP, including details on solubility and individual constituent sugars, as well as separate determinations of cellulose and lignin. Total starch content and starch digestibility were characterized by the method of Englyst as rapidly (RDS) and slowly (SDS) digestible starch, and resistant starch (RS) determined as total starch content minus RDS and SDS. The same samples were scanned (400 to 2500 nm, every 0.5 nm) by two NIRS machines: a Foss model DS2500 and a Bruker Tango FT-NIR (Fourier transform near-infrared spectroscopy). The NIRS calibrations were developed by using the combined set of different cereal grains. The modified Partial Least Squares Regression method was employed to establish correlations between the reference chemical analysis and the collected NIRS spectra. The correlation between the NIRS and reference data for total and insoluble NSP and the main component sugars (arabinose, xylose, and glucose) were robust (R2 = >0.97) with a 1-VR > 0.96 and RPD values > 5.3. Similarly, the NIRS predicted lignin (R2 = 0.94; 1-VR = 0.93) and to a lesser extent cellulose (R2 = 0.70; 1-VR = 0.98) but still highly correlated with the reference data. The predicted total starch content was excellent (R2 = 0.99; 1-VR = 0.99). The NIRS prediction for RDS and SDS were lower in linearity (R2 = 0.81 and R2 = 0.97, respectively) but more than sufficient to provide an estimate of the starch digestion profile for many of the raw materials evaluated. In addition, the robust NIRS predictions for total starch content, RDS and SDS could then provide information to calculate the RS fraction of the ingredients. In conclusion, compared with the reference chemical data for this large set of cereal grains the global NIR calibrations developed provide a good prediction of NSP, monosaccharide sugars, lignin, cellulose and total starch content in cereal grains, and for the first time an NIR relationship with starch digestibility fractions in the grains has been shown.

PSV-15 Using near infrared reflectance spectroscopy to predict lab scoured yield in Rambouillet sheep

Abstract Lab scoured yield (LSY) is a major indicator of wool quality. LSY is used for the valuation of wool in commercial settings and can be used by growers as selection criteria for breeding stock. Current laboratory methods for LSY are costly and labor intensive. Evaluation of fleece core samples using Near-Infrared Reflectance Spectroscopy (NIR) may present an efficient, cost-effective alternative to predict LSY. Lamb and yearling fleece core samples from flocks originating from Texas were scanned on a FOSS 6500 spectrometer. Constituent data were obtained from the Bill Sims Wool and Mohair Laboratory using ASTM methodology. LSY ranged from 48–68%. Spectral data were pretreated with a 14 nm moving average and Savitsky-Golay 2nd derivative. Eight outlier spectra were removed. Samples were parsed from the center of the distribution to minimize the Dunn effect creating calibration (n = 108) and test (n = 41) sets. Calibrations were executed using a partial least squares regression on spectra from 1100 to 2492 nm. Test set calibration statistics for LSY were: r2=0.64, RMSE=3.39, and slope=0.91. Independent validation statistics for LSY using spectra for different years were: r2=0.33, RMSE=3.69, and slope=0.29. RMSE for independent validation and lab methods on side samples are similar. Between flock independent validations were less promising. Accuracy of laboratory methods for estimating yield is 2 percentage units. NIRS calibrations can be improved by developing calibration sets with a uniform distribution, which can be difficult within flocks because of the small number of fleeces in the tails of the distribution. These data demonstrate that when calibration and test sets are developed such that test samples are drawn from the calibration population, NIR is a reliable predictor of LSY. However, when test samples are drawn from populations dissimilar to the calibration set, reliability of NIR predictions are greatly reduced.

Comparison of two in situ reference methods to estimate indigestible NDF by near infrared reflectance spectroscopy in alfalfa

Undigested forage neutral detergent fiber (uNDF) from long-term ruminal in situ incubations are used to estimate indigestible neutral detergent fiber (iNDF). Measurement of iNDF is important in forage evaluation because it defines the potentially digestible pool of neutral detergent fiber (NDF). Near-infrared reflectance spectroscopy (NIRS) can be calibrated to in situ reference sets to rapidly predict uNDF. Our objective was to compare uNDF estimates after 240 h of incubation when two types of bags were used in the in situ reference method. The bags compared were 4 cm × 5 cm Ankom F57 bags (25 micron pore size), and 5 cm × 10 cm Ankom in situ bags (50 micron pore size). Alfalfa samples from Pennsylvania and Wisconsin (n = 144) of different varieties and harvest intervals were used. One-half or two gram samples, respectively, were weighed into the small and large bags in triplicate. Mass to surface area was 0.05 and 0.02 g/cm2 for the small and large bags, respectively. The iNDF content after 240 h incubation was evaluated by two types bags in three rumen-cannulated Holstein cows. Each dried and ground forage was also scanned to determine the visible–near-infrared-reflectance spectra with a FOSS 6500 spectrophotometer. Prediction equations were developed for each bag type using modified partial least square regressions. The estimated iNDF fraction from small and large bags were 13.75% and 9.97%, respectively (SED = 0.39, P < 0.001). The coefficient of determination for calibration (R2), cross-validation (1 - VR), calibration standard deviation (SEC), and interactive authentication standard deviation (SECV) was 0.94, 0.92, 0.85 and 0.98 for values determined with the small bag and 0.88, 0.85, 1.12 and 1.27 for iNDF for values determined with the large bag, respectively. Results indicate that iNDF varies among alfalfa cultivars and NIRS can be used to quickly and quantitatively estimate iNDF content in alfalfa. Bag type influences 240h NDF residues. NIRS predictions of iNDF from the small bag calibration set had higher R2 and lower SEC and SECV than the large bag calibrations.

Development of NIR spectroscopy based prediction models for nutritional profiling of pearl millet (Pennisetum glaucum (L.)) R.Br: A chemometrics approach

Pearl millet can be viably used for food diversification due to its balanced nutritional composition. Nutritional parameters are conventionally assessed using labour and time-intensive strenuous conventional methods for germplasm screening. Near-infrared reflectance spectroscopy (NIRS) uses near-infrared sections of the electromagnetic spectrum for precise and speedy determination of biochemical parameters for large germplasm. MPLS (Modified Partial Least Squares) regression based NIRS prediction models were developed to assess starch, resistant starch, amylose, protein, oil, total dietary fibre, phenolics, total soluble sugars, phytic acid for high throughput screening of pearl millet germplasm. Mathematical treatments executed by permutation and combinations for calibrating the model, where 2nd, 3rd, and 4th derivatives produced the best results. Treatments “4,5,4,1” was finalized for protein, oil, resistant starch, total dietary fibre, “3,4,4,1” for phenolics, “2,8,4,1” for amylose, “2,4,4,1” for phytic acid, “4,7,4,1” for total soluble sugars and “2,8,4,1” for starch. Treatments with the highest 1-Variance ratio, RSQinternal (coefficient of determination) values, lowest SEC(V) (standard error of cross-validation), SEP(C) (standard error of performance) were identified for subsequent validation. External validation determined the prediction accuracy based on RSQexternal, RPD (residual prediction deviation), SD (standard deviation), p-value ≥ 0.05 and low SEP(C).

Influence of forage particle size and residual moisture on near infrared reflectance spectroscopy (NIRS) calibration accuracy for macro-mineral determination

Near infrared reflectance spectroscopy (NIRS) is routinely used for the determination of nutrient components of forages. However, little is known about the impact of sample preparation on the accuracy of the calibration to predict minerals. Three types of forages, hay (n = 117), grass (n = 109) and haylage (n = 119) were used to determine the impact of different sample preparation procedures: particle size (1.0 mm and 0.5 mm) and presence or absence of residual moisture (dried and re-dried) on resultant NIRS prediction statistics. All forages were scanned using a total of four combinations of sample pre-treatments (1 mm dried, 1 mm re-dried, 0.5 mm dried and 0.5 mm re-dried). Each sample preparation combination was subjected to spectra pre-processing methods such as standard normal variate (SNV), detrending (DT), combination of SNV and DT (SNV&DT) and None (log1/R) together with mathematical treatments (1,4,4,1; 2,4,4,1; 2,6,4,1; 3,5,5,1 and 2,4,4,2). Reduction of particle size from 1 mm to 0.5 mm slightly improved calibration statistics for the prediction of macro-minerals in hay and haylage samples. However, for the grass samples, improved calibration statistics was observed at a particle size of 1 mm for most of the minerals studied. Furthermore, the removal of residual moisture through additional oven drying improved calibration statistics for all minerals examined in the hay, haylage and grass samples. These results highlight the importance of the reduction in particle sizes for the improvement of calibration statistics for the determination of macro-minerals. In addition, re-drying of samples will improve calibration statistics for macro-minerals at particle sizes of 1 mm and/or 0.5 mm.

Prediction Accuracy Improvement of Indonesian Dairy Cattle Fiber Feed Compositions Using Near-Infrared Reflectance Spectroscopy Local Database

The accuracy of near infrared reflectance spectroscopy (NIRS) depends on the database generated from the conventional wet chemistry (CWC). Currently, the local database of fiber-source feeds for tropical dairy cattle are still limited. The study aimed to compare CWC and NIRS initial database (NIRSID) results, to predict CWC from NIRSID, and to improve the accuracy of NIRS prediction using local database (NIRSLD). Five feeds as sources of fiber (Napier grass, natural grass, corn leaves, corn husk, and rice straw) from 4 areas of dairy cattle farming were used (4 farms from each area). For external calibration, 20 independent Napier grass samples were tested. Samples were analyzed using NIRS and CWC to measure dry matter (DM), ash, crude protein (CP), ether extract (EE), crude fiber (CF), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and silica (Si) to calculate hemicellulose, cellulose, and lignin contents. The results obtained by NIRSID were compared to those obtained by CWC using T-test. Predictions of CWC from the results obtained by NIRSID were attempted using regressions. The NIRSLD was developed by inputting the CWC value to NIRS spectrums. Internal calibration and validation as well as external calibration, were run. The results showed that NIRSID has low capacity in determining CWC (R2 2 except for DM, EE, CF, ADL, and lignin). External validation showed that CWC and NIRSLD were similar in all parameters (p 2 for CP, CF, and ADF. It is concluded that the local database of NIRS of fiber-source feeds is necessary to improve the prediction accuracy of local dairy fiber-source feeds values using NIRS.

Gravimetric and Spectrophotometric Determination of Surface Wax Content in Maize Kernels

There are several methods available to determine wax content in maize kernel. These methods are based on gravimetric, spectrophotometric, and chromatographic analyses. This study aims to determine the surface wax content on maize kernel with different reference methods and develop NIR (Near Infrared Reflectance Spectroscopy) calibration models based on those reference methods. In this study, samples from 250 different maize genotypes were used. Surface wax content in the samples was determined by gravimetric and UV-Spectrophotometric methods. The results of each reference method were used to develop NIR prediction models for surface wax content in kernel samples. Our results suggest that there is a medium-level correlation (r=0.708) between the data obtained from spectrophotometric and gravimetric methods. Also, significant differences were observed between the wax content obtained from reference methods. It has been found that the NIR calibration model (RMSE =4.381, R2 =0.143, r =0,378, RPD =1.08) based on the gravimetric method had lower estimation power than the NIR calibration model (RMSEC =7.257, R2 =0.221, r =0.471, RPD =1.34) based on the UV spectroscopic method. Keywords: Near Infrared Spectroscopy, Biochemical test, Zea mays, Regression DOI: 10.7176/JSTR/5-9-11

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

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

Fecal Near-Infrared Reflectance Spectroscopy Prediction of the Feed Value of Temperate Forages for Ruminants and Some Parameters of the Chemical Composition of Feces: Efficiency of Four Calibration Strategies.

The forage feed value determined by organic matter digestibility (OMD) and voluntary intake (VI) is hard and expensive. Thus, several indirect methods such as near infrared reflectance (NIR) spectroscopy have been developed for predicting the feed value of forages. In this study, NIR spectra of 1040 samples of feces from sheep fed fresh temperate forages were used to develop calibration models for the prediction of fecal crude ash (CA), fecal crude protein (CP), fresh forage OMD, and VI. Another 136 samples of feces were used to assess these models. Four calibration strategies were compared: (1) species-specific calibration; (2) family-specific calibration; (3) a global procedure; and (4) a LOCAL approach. The first three strategies were based on classical regression models developed on different sample populations, whereas the LOCAL approach is based on the development models from selected samples spectrally similar to the sample to be predicted. The first two strategies use feces-samples grouping based on the species or the family of the forage ingested. Forage calibration data sets gave value ranges of 79-327 g/kg dry matter (DM) for CA, 65-243 g/kg DM for CP, 0.52-0.85 g/g for OMD, and 34.7-100.5 g DM/kg metabolic body weight (BW0.75) for VI. The prediction of CA and CP content in feces by species-specific fecal NIR (FNIR) spectroscopy models showed lower standard error of prediction (SEP) (CA 15.03 and CP 7.48 g/kg DM) than family-specific (CA 21.93 and CP 7.69 g/kg DM), global (CA 19.83 and CP 7.98 g/kg DM), or LOCAL (CA 30.85 and CP 8.10 g/kg DM) models. For OMD, the LOCAL procedure led to a lower SEP (0.018 g/g) than the other approaches (0.023, 0.024, and 0.023 g/g for species-specific, family-specific, and global models, respectively). For VI, the LOCAL approach again led to a lower SEP (6.15 g/kg BW0.75) than the other approaches (7.35, 8.00, and 8.13 g/kg BW0.75 for the species-specific, family-specific, and global models, respectively). LOCAL approach performed on FNIR spectroscopy samples gives more precise models for predicting OMD and VI than species-specific, family-specific, or global approaches.

Application of fecal near-infrared reflectance spectroscopy profiling for the prediction of diet nutritional characteristics and voluntary intake in beef cattle.

The objective of this study was to evaluate the use of fecal near-infrared reflectance spectroscopy (NIRS) profiling to predict diet nutritional characteristics and voluntary DMI in beef cattle. Fecal samples were collected for growing cattle across 11 experiments in which individual animal performance and DMI was measured. Dried and ground fecal composite samples collected from each animal were subjected to fecal NIRS analysis by a Foss NIRS 6500 scanning monochromator (Foss, Eden Prairie, MN) at the Grazingland Animal Nutrition Laboratory (Temple, TX). Fecal spectra were then used to develop equations to predict diet composition (trials 1 to 11; = 408), digestibility (trials 1 to 5; = 155), and DMI (trials 1 to 11; = 408). Coefficients of determination for calibration () and cross-validation () for prediction of diet nutritional characteristics were lower for NDF ( = 0.85; = 0.82) than for CP ( = 0.90; = 0.88). For the prediction of DMI, and ranged from 0.69 and 0.67 for the prediction of trial-average DMI to 0.76 and 0.73 for the prediction of fecal-collection-period DMI. While the and obtained for the prediction of DMI were lower than those obtained for the prediction of diet composition or digestibility, fecal NIRS prediction equations for DMI were successful in predicting the mean DMI of groups, as no differences were found for the prediction of fecal-collection-period DMI (Diff. = 1.10; = 0.72) or trial DMI (Diff. = -0.47; = 0.86).

Near-infrared reflectance spectroscopy for the prediction of chemical composition in walnut kernel

ABSTRACTIn the present work, 116 samples were collected and near-infrared reflectance spectroscopy prediction model for determination of moisture, protein, and fat contents of walnut meal were obtained and evaluated. All the samples were analyzed based on the chemical methods. Meanwhile, they were scanned to obtain their near-infrared reflectance spectrum in the wavelength range of 570–1840 nm. Several preprocess treatments including scattering pretreatments, mathematical pretreatments, and aggression methods were optimized to increase the accuracy of the calibration models according to the coefficient of determination for calibration (Rc2) and the cross-validation (one minus the variance ratio, 1-VR), and the standard error of calibration and cross-validation. The results showed modified partial least square loading was the better aggression method to predict the moisture, proteins, and fats in walnut kernel. The calibration equations obtained indicated that near-infrared reflectance spectroscopy had excellent predictive capacity for the three components with Rc2 = 0.965, standard error of calibration = 0.052 for moisture, and Rc2 = 0.967, standard error of calibration = 0.191 for proteins, and Rc2 = 0.979, standard error of calibration = 0.171 for fats, respectively. The external validation further confirmed the robustness and reliability of the near-infrared reflectance spectroscopy prediction models with the correlation coefficient of actual and predicted values (R2) = 0.952, ratio of performance deviation = 4.14, the standard error of prediction =0.058 for moisture, and R2 = 0.977, ratio of performance deviation = 5.55, standard error of prediction = 0.182 for proteins, and R2 = 0.990, ratio of performance deviation = 8.64, standard error of prediction = 0.191 for fats, respectively. Near-infrared reflectance spectroscopy is a reliable technology to predict these constituents in walnuts.