Near Infrared Spectroscopy Calibration Research Articles

Evaluating NIRS for predicting faecal fibre, protein fractions and digestibility in dairy sheep and goats fed alfalfa hay and concentrate

This study evaluated the effectiveness of near-infrared spectroscopy (NIRS) in predicting faecal fibre, protein fractions and total tract apparent digestibility in dairy sheep and goats fed varying ratios of alfalfa-hay and concentrate. A total of 180 faecal samples from dairy sheep and goats were collected and chemically analysed. Faecal samples underwent also NIRS spectral acquisition and calibration models were obtained through modified partial least square regression and tested for robustness. The NIRS provided excellent estimations for N and CP as % as is (R 2 ExV 0.91 and 0.91, RPD 3.16 and 3.33, respectively), good estimation for N, CP and ND soluble CP % DM (R 2 ExV 0.90, 0.90 and 0.85, RPD 3.14, 2.90 and 2.48) and ND soluble CP expressed as % as is (R 2 ExV 0.87, RPD 2.55), approximative quantitative prediction for acid detergent insoluble crude protein (ADICP) and ADIN % acid detergent fibre (ADF) (R 2 ExV 0.79 and 0.71, RPD 1.93 and 1.78, respectively) and ADICP as % as is (R 2 ExV 0.67, RPD 1.70) but resulted to be less effective when predicting all the other components. The prediction models for total-tract digestibility (ttD) of ADICP performed best among the developed equations (R 2 ExV 0.57, RPD 1.38), but the model was not sufficient for precise predictions, suggesting a good capacity of NIRS to predict faecal N excretion but also intrinsic limitations of faecal spectra due to the partial residual nutrients content, compared to the diet. Increasing the number of calibration samples and values, the forage species, and the F:C ratios in the diet offered to the animals could improve the NIRS performance.

377 Developing visible near-infrared spectroscopy calibration equations to predict the chemical composition of feces and nutrient digestibility based on pig fecal spectra

377 Developing visible near-infrared spectroscopy calibration equations to predict the chemical composition of feces and nutrient digestibility based on pig fecal spectra

A three-stage wavelength selection algorithm for near-infrared spectroscopy calibration

The near-infrared spectral data is highly high dimensional and contains redundant information, it is necessary to identify the most representative characteristic wavelengths before modeling to improve model accuracy and reliability. At present, there are many methods for selecting the characteristic wavelengths of NIR spectroscopy, but the collinearity among wavelengths is still a main issue that leads to poor model effects. Therefore, this study proposes a three-stage wavelength selection algorithm (Stage III) to reduce redundancy in NIR spectral data and collinearity between wavelength variables, resulting in a simpler and more accurate predictive model. The research uses a public NIR data set of corn samples as its subject. Initially, the wavelengths with the higher correlation coefficients are chosen after calculating the relationship coefficients between every wavelength vector and the concentration vector. On this basis, the correlation coefficients between the vectors of each wavelength point are calculated, and those wavelength points with smaller correlation coefficients with other wavelength points are selected. Ultimately, the stepwise regression analysis selects the wavelengths that provide substantial value to the model as the variables for modeling, leading to the development of a multiple linear regression model. The results show that the model using the three-stage wavelength selection algorithm outperforms those using the full spectrum, Stages I and Stage II, and the coefficient of determination of the test set of the Stage III-MLR model achieved an accuracy of 0.9360. Instead of the successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS), Stage III is better in the model prediction accuracy. Therefore, the three-stage wavelength selection algorithm is an effective wavelength selection algorithm that can effectively model NIR spectroscopy, reduce the collinearity between the wavelength variables, simplify the complexity of the model, and improve the prediction precision of the model.

Assessing the Application of Near-Infrared Spectroscopy to Determine Saccharification Efficiency of Corn Biomass

Nowadays, in the bioethanol production process, improving the simplicity and yield of cell wall saccharification procedure represent the main technical hurdles to overcome. This work evaluated the application of a rapid and cost-effective technology such as near -infrared spectroscopy (NIRS) for easily predict saccharification efficiency from corn stover biomass. Calibration process focussing on the number of samples and the genetic background of the maize inbred lines were tested; while Modified Partial Least Squares Regression (MPLS) and Multiple Linear Regression (MLR) were assessed in predictions. The predictive capacity of the NIRS models was mainly determined by the coefficient of determination (r2ev) and the index of prediction to deviation (RPDev) in external validation. Overall, we could check a better efficiency of the NIRS calibration process for saccharification using larger number of observations (1500 sample set) and genetic backgrounds; while MPLS regression provided better prediction statistics (r2ev = 0.80; RPDev = 2.21) compared to MLR (r2ev = 0.68; RPDev = 1.75). These results indicate that NIRS could be successfully implemented as a large-phenotyping tool in order to test the saccharification potential of corn biomass.

Comparative near Infrared (NIR) spectroscopy calibrations performance of dried and undried forage on dry and wet matter bases

The application of Near Infrared (NIR) spectroscopy for analyzing wet feed directly on farms is increasingly recognized for its role in supporting harvest-time decisions and refining the precision of animal feeding practices. This study aims to evaluate the accuracy of NIR spectroscopy calibrations for both undried, unprocessed samples and dried, ground samples. Additionally, it investigates the influence of the bases of reference data (wet vs. dry basis) on the predictive capabilities of the NIR analysis. The study utilized 492 Corn Whole Plant (CWP) and 405 High Moisture Corn (HMC) samples, sourced from various farms across Italy. Spectral data were acquired from both undried, unground and dried, ground samples using laboratory bench NIR instruments, covering a spectral range of 1100 to 2498 nm. The reference chemical composition of these samples was analyzed and presented in two formats: on a wet matter basis and on a dry matter basis.The study revealed that calibrations based on undried samples generally exhibited lower predictive accuracy for most traits, with the exception of Dry Matter (DM). Notably, the decline in predictive performance was more pronounced in highly moist products like CWP, where the average error increased by 60–70%. Conversely, this reduction in accuracy was relatively contained (10–15%) in drier samples such as HMC. The Standard Error of Cross-Validation (SECV) values for DMres, Ash, CP, and EE were notably low, at 0.39, 0.30, 0.29, 0.21% for CWP and 0.49, 0.14, 0.25, 0.14% for HMC, respectively. These results align with previous studies, indicating the reliability of NIR spectroscopy in diverse moisture contexts.The study attributes this variance to the interference caused by water in 'as is' samples, where the spectral features predominantly reflect water content, thereby obscuring the spectral signatures of other nutrients. In terms of calibration development strategies, the study concludes that there is no significant difference in predictive performance between undried calibrations based on either 'dry matter' or 'as is' basis. This finding emphasizes the potential of NIR spectroscopy in diverse moisture contexts, although with varying degrees of accuracy contingent upon the moisture content of the analyzed samples. Overall, this research provides valuable insights into the calibration strategies of NIR spectroscopy and its practical applications in agricultural settings, particularly for on-farm forage analysis.

Development of near-infrared spectroscopy calibration model and monitoring software: For monitoring hexamethylenetetramine concentration in hexamethylenetetramine–acetic acid solution

In recent years, near-infrared spectroscopy has been increasingly used in chemical engineering processes. However, accurately and in real-time monitoring concentrations remains highly challenging. This study presents a method for accurately monitoring the hexamethylenetetramine concentration in hexamethylenetetramine-acetic acid solution by integrating near-infrared spectroscopy with software techniques. Compared to the methods of zero mean standardization (Z-score), standard normal variate (SNV), first derivative (D1), second derivative (D2), and vector normalization (VN), the Savitzky-Golay first derivative (SGFD) method effectively addresses issues related to baseline drift, noise, and other interferences. This results in clearer identification of characteristic absorption peaks of different components and a significant improvement in the accuracy of the analysis. The study combined the uninformative variable elimination (UVE), differential evolution (DE), and moth flame optimization (MFO) algorithms to optimize a feature wavenumber selection method, which reduced the number of variables by 78.74 %, and the values of root mean square error of calibration set (RMSEC) and root mean square error of prediction (RMSEP) by 18.86 % and 30.38 %, respectively. Additionally, the analytical method was integrated with real-time monitoring software, enabling the monitoring of hexamethylenetetramine concentration. This integration supports the improvement of the quality and safety of the manufacturing process. The results demonstrate the potential application of near-infrared spectroscopy in real-time monitoring and automated control of chemical processes in the chemical industry.

Application of near-infrared spectroscopy for hay evaluation at different degrees of sample preparation.

A study was conducted to quantify the performance differences of the nearinfrared spectroscopy (NIRS) calibration models developed with different degrees of hay sample preparations. A total of 227 imported alfalfa (Medicago sativa L.) and another 360 imported timothy (Phleum pratense L.) hay samples were used to develop calibration models for nutrient value parameters such as moisture, neutral detergent fiber, acid detergent fiber, crude protein, and in vitro dry matter digestibility. Spectral data of hay samples prepared by milling into 1-mm particle size or unground were separately regressed against the wet chemistry results of the abovementioned parameters. The performance of the developed NIRS calibration models was evaluated based on R2, standard error, and ratio percentage deviation (RPD). The models developed with ground hay were more robust and accurate than those with unground hay based on calibration model performance indexes such as R2 (coefficient of determination), standard error, and RPD. Although the R2 of calibration models was mainly greater than 0.90 across the feed value indexes, the R2 of cross-validations was much lower. The R2 of cross-validation varies depending on feed value indexes, which ranged from 0.61 to 0.81 in alfalfa, and from 0.62 to 0.95 in timothy. Estimation of feed values in imported hay can be achievable by the calibrated NIRS. However, the NIRS calibration models must be improved by including a broader range of imported hay samples in the modeling. Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparations compromising analysis accuracy by NIRS.

Near-Infrared Spectroscopy Procedure for Online Determination of Sodium and Potassium Content in Low-Salt Cured Hams.

This paper reports the development of a near-infrared spectroscopy (NIRS) calibration procedure for the determination of sodium and potassium content in cured ham samples. Sliced samples of hams treated with different salts in different percentages were included in the study. Calibration models developed using partial least squares regression were cross-validated and predictive models were tested using the samples of cured ham with low sodium content. The results showed that the developed NIRS procedure is capable of directly measuring the potassium content of packaged dry-cured ham slices with low sodium content with a fitting accuracy of 91.44%, and that it can indirectly determine the sodium content by applying a correction factor to the values obtained for potassium. The prediction error between the calculated and actual sodium values determined using inductively coupled plasma atomic emission spectrophotometry (ICP-AES) was 0.004%, and this confirms that the NIRS procedure is a viable option for the determination of sodium and potassium content in this type of sample.

127 How to Best Calculate Acid Binding Capacity at pH 4 in Feed Ingredients for Weanling Pigs

Abstract The value of acid binding capacity at pH 4 (ABC-4) of feed ingredients can be used to formulate weanling pig diets for improved feed efficiency and free of ZnO. In practice, it is expensive and time consuming to analyze all ingredients used in pig diet formulation. Therefore, the objective of this study was to determine the most efficient and cost-effective method to accurately predict ABC-4 in feed ingredients used in weanling pig diets. Variation of the ABC-4 value is low in most feed ingredients, and the average value of ABC-4 is accurate enough for use in feed formulation [Lawlor et al. (2005)]. However, limestone and soybean meal (SBM) were studied because of their contribution to dietary ABC-4, and their inherit variability in ABC-4. Results showed that when ABC-4 of limestone and SBM is analyzed, the error in ABC-4 of final feed decreases by 58%. Therefore, if SBM and limestone are analyzed for ABC-4, and fixed values are used for all other ingredients, the value of ABC-4 in the final diet will be +/-20 mEq/kg of the target ABC-4 (P < 0.05). When ingredients are not analyzed for ABC-4, the variability increases and value in final feed will vary between +/- 48 mEq ABC-4/kg (P < 0.05). Next, 39 samples of SBM were collected from the US, Brazil, and Argentina to develop a prediction equation of ABC-4 for SBM from its macro-nutrient composition. Values of ABC-4 of SBM ranged between 773 and 878 mEq/kg, with an average of 824 mEq/kg and STD of 28 mEq ABC-4/kg. No significant relationships between the analyzed macro-nutrients and ABC-4 of SBM were observed (R2 = 0.01, with cross-validation). Therefore, the accuracy and precision of predicting ABC-4 of SBM were not improved by developing a derivative formula to estimate ABC-4 in SBM from its macro-nutrients. Next, 24 samples of limestone were collected from Canada to develop methods to predict ABC-4 accurately and quickly. The value of ABC-4 ranged between 20,823 and 23,412 mEq/kg, with an average of 22,117 mEq/kg and STD of 431 mEq/kg. A good correlation was obtained between ABC-4 and near infrared spectroscopy calibration (NIR) lines (R2 = 0.79, unground, with cross-validation). We conclude that for accurate and rapid determination of ABC-4, and ease of application in nursery diet formulation, ABC-4 can be analyzed in limestone using NIR, in SBM using wet chemistry, and for all other ingredients fixed values can be used. Additionally, for accurate formulation of nursery diets, a safety margin of 20 mEq/kg can be applied to the target ABC-4 value in the final diet.

A high-throughput phenotyping method for sugarcane rind penetrometer resistance and breaking force characterization by near-infrared spectroscopy

BackgroundSugarcane (Saccharum spp.) is the core crop for sugar and bioethanol production over the world. A major problem in sugarcane production is stalk lodging due to weak mechanical strength. Rind penetrometer resistance (RPR) and breaking force are two kinds of regular parameters for mechanical strength characterization. However, due to the lack of efficient methods for determining RPR and breaking force in sugarcane, genetic approaches for improving these traits are generally limited. This study was designed to use near-infrared spectroscopy (NIRS) calibration assay to accurately assess mechanical strength on a high-throughput basis for the first time.ResultsBased on well-established laboratory measurements of sugarcane stalk internodes collected in the years 2019 and 2020, considerable variations in RPR and breaking force were observed in the stalk internodes. Following a standard NIRS calibration process, two online models were obtained with a high coefficient of determination (R2) and the ratio of prediction to deviation (RPD) values during calibration, internal cross-validation, and external validation. Remarkably, the equation for RPR exhibited R2 and RPD values as high as 0.997 and 17.70, as well as showing relatively low root mean square error values at 0.44 N mm−2 during global modeling, demonstrating excellent predictive performance.ConclusionsThis study delivered a successful attempt for rapid and precise prediction of rind penetrometer resistance and breaking force in sugarcane stalk by NIRS assay. These established models can be used to improve phenotyping jobs for sugarcane germplasm on a large scale.

Predicting Nutritional Quality of Dual-Purpose Cowpea Using NIRS and the Impacts of Crop Management

Cowpea fodder has been one of the favored livestock forages for centuries in sub-Saharan Africa, particularly in Senegal. However, little research has been conducted on quantifying the nutritional quality of cowpea fodder because of the costly wet chemistry analysis. The main objective of this study was to develop predictive equations for a sustainable quantification of the nutritional quality of dual-purpose cowpea fodder using near infrared spectroscopy (NIRS) and to investigate the influence of cropping system, fertilizer, genotype, and their interaction on biomass yield and cowpea forage nutritional value. In this study, 120 samples from a dual-purpose cowpea variety trial were used to develop NIRS equations to estimate forage quality parameters including concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), calcium (Ca), phosphorus (P), potassium (K), and iron (Fe). Partial least squares (PLS) regression generated prediction equations using NIRS wavelength measurements, and reference wet chemistry analysis from calibration samples were developed. The PLS prediction equations for the different forage quality parameters had an R2 of calibration 0.94, 0.93, 0.88, 0.63, 0.69, 0.87, and 0.94 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. Using these prediction equations, correlation of the predicted values of the calibration subset and the prediction test subset resulted in significant positive relationships, with R2 of 0.83, 0.74, 0.70, 0.63, 0.59, 0.75, and 0.83 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The corresponding RMSE of these relationships was 0.91, 2.68, 3.45, 0.23, 0.06, 0.11, and 100 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The range and mean concentrations of the calibration subset overlapped with that of the prediction subset for all parameters evaluated. Cross-validation procedures indicated good correlations between wet chemistry analysis and NIRS forage quality estimates. Results of the second experiment showed that the cropping system had no significant effect on cowpea forage yield and nutritive value. However, cowpea variety and fertilizer, both individually and their interaction, had a significant effect on fodder yield and cowpea forage quality. We conclude that the NIRS calibration equations developed can be used to accurately predict the cowpea forage quality parameters evaluated in this study.

New strategies to analyze argentatins A and B in guayule (Parthenium argentatum, A. Gray)

There is considerable interest in the exploitation of compounds belonging to the triterpenoid family from guayule (Parthenium argentatum, A. Gray), as they offer several beneficial effects to human health. The most abundant triterpenoids in guayule resin are the argentatins, which are currently analyzed by labor-intensive and time-consuming techniques. The purpose of the present study was to estimate argentatins and isoargentatins A and B in guayule using near-infrared spectroscopy (NIRS) and flow injection analysis (FIA). Results revealed that the best partial least squares regression model exhibited excellent correlation with the values estimated by NIRS calibration (r2c = 0.99–1.00) and cross-validation (r2cv = 0.94–0.99), and the residual predictive deviation was >3 in all cases. After optimization of the liquid chromatography–mass spectrometry and FIA parameters, the FIA mode could reliably collect data for argentatin A and B after applying a calculated coverage factor. In sum, NIRS and FIA appear to be a robust option for the estimation and routine analysis of argentatins in guayule stems and resin, respectively.

Assessing the Repeatability and Reliability of NIRS to Predict Nutritional Values and to Evaluate Two Lignin Methods in Urochloa spp. Grasses

Reliable forage analysis is crucial for proper ration formulation of ruminant herds. Through its fast, inexpensive, and non-destructive procedures, near-infrared spectroscopy (NIRS) has become a valuable method for forage evaluating. Notwithstanding, NIRS needs calibration before routine analysis. In addition, to evaluate the best method for lignin quantification in Urochloa spp. grasses is crucial under a digestibility perspective in grass-fed ruminant production. The aims of this study were to use 149 samples from different Urochloa species to develop NIRS calibration curves (partial least squares regressions) for acid detergent lignin (ADL), acetyl bromide lignin (ABL), as well as for ash, cell wall (CW), neutral detergent fiber (NDF), acid detergent fiber (ADF), in vitro DM digestibility (IVDMD), and in vitro NDF digestibility (IVNDFD). Moreover, the aim of this study was to correlate the in vitro digestibility with lignin quantification methods: ADL and ABL. Near-infrared spectroscopy showed potential for the quantification of Urochloa spp. properties, such as lignin contents (ADL and ABL) and ash, CW, NDF, ADF, IVDMD, and IVNDFD. However, calibrations performed using NIRS to measure ADF, ADL, IVDMD, and IVNDFD need to be thought about with caution before their utilization as a routine analysis for determining the potential for nutrient measurement and digestibility of Urochloa spp. grasses. In addition, the ABL method used for lignin quantification was better correlated with IVDMD and IVNDFD than the ADL method.

Novel Incremental Regularized Extreme Learning Machine Method Based on Improved Particle Swarm Optimization for Nonlinear Multivariate Calibration of Visible and Near-Infrared Spectroscopy

Novel incremental regularized extreme learning machine (IR-ELM) based upon improved particle swarm optimization (IPSO-IR-ELM) is reported for the nonlinear multivariate calibration of visible/near-infrared (Vis/NIR) spectroscopy. IR-ELM is employed to construct a nonlinear calibration model for samples. Combined with IPSO, three parts of the IR-ELM algorithm are intelligently optimized. First, the regularization coefficient of the initial network in IR-ELM is optimized by IPSO. Second, IPSO is used again to select the optimal input weights and hidden biases while adding new hidden nodes in IR-ELM. Third, the 2-norm of the output matrix in IR-ELM is introduced as the conditional constraint in IPSO for updating the particle position. The performance of the reported method was tested with two Vis/NIR spectra datasets: blood hemoglobin and water pH. Key spectral variables were selected by successive projections algorithm and employed to establish the calibration models. Compared with partial least squares, ELM, error minimized extreme learning machine, and IR-ELM, IPSO-IR-ELM achieved the highest accuracy and best generalization. For the blood hemoglobin dataset, the RMSEP (root mean square error of prediction) was 0.210 g·dL−1, and the R p 2 (coefficient of determination of prediction) was 0.973. For the water pH dataset, the RMSEP was 0.825, and the R p 2 was 0.899. The results demonstrate that IPSO-IR-ELM is an alternative nonlinear multivariate calibration approach for Vis/NIR spectroscopy.

Rapid determination of essential oils functional groups using compositional methods and VisNIR spectroscopy

Essential oils (EOs) are natural products formed by plant volatile compounds. EOs are frequently used in the cosmetic and food industries as well as for domestic purposes, because of their physiochemical, biological and sensory properties. The functional groups (FG), corresponding to various chemical structures present in EOs, are responsible for their biological activities. Therefore, simple, rapid, and economical techniques suitable to characterize the EOs features by measuring their contents, are of great interest. Near-infrared spectroscopy (NIRS) highlights because of its rapidity, and being no-contaminant, as a potential solution. Multivariate correlation methods are commonly used to build NIRS calibrations. These methods were designed for the real space, that is for values comprised between - ∞ and + ∞. However, EOs components are co-dependent data restricted to a simplex space. These are the so-called compositional data (CoDa), needing specific methods to be correlated with a set of spectral explicative variables. In this study, compositional visible and near-infrared (VISNIRS) models have been assessed to quantify the FG of the analyzed EOs. For this purpose, the FG were organized according to their greater frequency in 1) alcohol; 2) ether; 3) ester; 4) aldehydes; 5) ketones, and the hydrocarbon fraction representing the remainder EOs mass, to characterize them. The approach of this study, based on compositional models from VISNIRS spectra, has provided a satisfactory predictive performance for the quantitative estimation of the main FG of the EOs. The proposed approach can be an alternative to traditional chemical methods to characterize EOs.

Comparison of feed tables, empirical models and near-infrared spectroscopy to predict chemical composition and net energy of pelleted pig feeds

The determination of the net energy (NE) of a pig feed requires expensive in vivo experiments. Alternatives are available whether the feed ingredient composition is known or unknown. We compared three estimation methods: feed tables, empirical models based on chemical parameters and in vitro digestibility and near-infrared spectroscopy (NIRS) calibrations based on feed and feces spectra. All methods were evaluated for their accuracy in predicting chemical composition with a focus on NE, the applicability in practice, and practical limitations. From a total dataset of 80 feeds and 480 feces samples originating from 3 in vivo digestibility trials, 28 feeds were used to examine the use of feed tables, 62 feeds for empirical models and 62 feeds and 310 feces samples for NIRS calibrations. Feed tables accurately predicted most chemical components (R² > 0.90), except for crude ash, sugar, acid detergent lignin and moisture. The dry matter (DM) content was underestimated on average by 16 g/kg and 17 g/kg from Dutch (CVB) and French (INRA) tables, respectively. The direct use of tabular NE values overestimated feeds containing beet pulp. The prediction of NE, calculated according to the CVB system, from the tabular chemical composition and digestibility coefficients of the feed ingredients resulted in a standard error of estimate (SEE) of 0.29 and 0.43 MJ/kg for CVB and INRA tables, respectively. Expressing these values on DM basis decreased SEE to 0.22 and 0.30 MJ/kg DM for CVB and INRA, respectively. The best empirical model to predict NE based on chemical composition resulted in a standard error of cross-validation (SECV) of 0.21 MJ/kg. Incorporating the in vitro digestibility of organic matter allowed to decrease SECV to 0.18 MJ/kg. A NIRS calibration based on feed spectra alone resulted in a SECV of 0.33 MJ/kg, whereas the combination of feed and feces spectra decreased SECV to 0.26 MJ/kg. All three estimation methods have advantages and disadvantages. Feed tables allow a quick estimation of the composition and average NE content, but they can only be used for feeds with known and common ingredient composition. Empirical models allow the most accurate estimate of the NE of feeds of unknown composition, but they require expensive and time-consuming analyses. Finally, a NIRS calibration based on feed and feces spectra enables a fast estimation of NE, but is less precise than empirical models and requires sample preparation and a large dataset.

A quick and precise online near-infrared spectroscopy assay for high-throughput screening biomass digestibility in large scale sugarcane germplasm

The near-infrared spectroscopy (NIRS) has been used for efficient characterization and rapid assay of biomass saccharification in other energy plants, but its application in sugarcane is not reported yet. The current study collected a total of 541 sugarcane accessions to take an online NIRS assay. Among these sugarcane collections, we observed large variations in biomass digestibility, particularly for fermentable hexose and total sugar yield from fresh sugarcane stalks, which were detected ranging from 69.88 to 239.86 kg t -1 and 66.56–228.55 kg t -1 respectively. Using the modified partial least squares method, six reliable NIRS models were obtained with a high coefficient of determination ( R 2 ) and the ratio of prediction to deviation (RPD) values during calibration, internal-cross validation and external validation. Notably, the equation for fermentable hexose exhibited the most consistently high R 2 (0.98) and RPD (6.62) values, as well as retaining relatively low root mean square error during calibration (3.74 kg t -1 ) and validation (4.19 kg t -1 ), indicating excellent predictive capacity. All models demonstrated accurate and stable prediction performance in the two-year large-scale germplasm resources evaluation, and the optima accessions with high or low biomass digestibility can be screened out consistently. Therefore, this study provides a precise and consistent NIRS assay for high throughput scanning of biomass digestibility in sugarcane. • Sugarcane is one of the significant bioenergy crops. • Diverse sugarcane germplasm collections allowing for reliable NIRS calibration. • Six excellent equations were generated for biomass digestibility characterization. • Fermentable hexoses and total sugar displayed the best prediction performance. • Model-based NIRS application screened out optima genotypes consistently from large-scale sugarcane germplasm.

Use of near-infrared spectroscopy to estimate physical, anatomical and hydraulic properties of Eucalyptus wood.

Tree breeding programs and wood industries require simple, time- and cost-effective techniques to process large volumes of samples. In recent decades, near-infrared spectroscopy (NIRS) has been acknowledged as one of the most powerful techniques for wood analysis, making it the most used tool for high-throughput phenotyping. Previous studies have shown that a significant number of anatomical, physical, chemical and mechanical wood properties can be estimated through NIRS, both for angiosperm and gymnosperm species. However, the ability of this technique to predict functional traits related to drought resistance has been poorly explored, especially in angiosperm species. This is particularly relevant since determining xylem hydraulic properties by conventional techniques is complex and time-consuming, clearly limiting its use in studies and applications that demand large amounts of samples. In this study, we measured several wood anatomical and hydraulic traits and collected NIR spectra in branches of two Eucalyptus L'Hér species. We developed NIRS calibration models and discussed their ability to accurately predict the studied traits. The models generated allowed us to adequately calibrate the reference traits, with high R2 (≥0.75) for traits such as P12, P88, the slope of the vulnerability curves to xylem embolism or the fiber wall fraction, and with lower R2 (0.39-0.52) for P50, maximum hydraulic conductivity or frequency of ray parenchyma. We found that certain wavenumbers improve models' calibration, with those in the range of 4000-5500cm-1 predicting the highest number of both anatomical and functional traits. We concluded that the use of NIRS allows calibrating models with potential predictive value not only for wood structural and chemical variables but also for anatomical and functional traits related to drought resistance in wood types with complex structure as eucalypts. These results are promising in light of the required knowledge about species and genotypes adaptability to global climatic change.

Optimisation of the predictive ability of NIR models to estimate nutritional parameters in elephant grass through LOCAL algorithms

Elephant grass is a tropical forage widely used for livestock feed. The analytical techniques traditionally used for its nutritional evaluation are costly and time consuming. Alternatively, Near Infrared Spectroscopy (NIRS) technology has been used as a rapid analysis technique. However, in crops with high variability due to genetic improvement, predictive models quickly lose accuracy and must be recalibrated. The use of non-linear models such as LOCAL calibrations could mitigate these issues, although a number of parameters need to be optimized to obtain accurate results. The objective of this work was to compare the predictive results obtained with global NIRS calibrations and with LOCAL calibrations, paying special attention to the configuration parameters of the models.The results obtained showed that the prediction errors with the LOCAL models were between 1.6 and 17.5 % lower. The best results were obtained in most cases with a low number of selected samples (n = 100–250) and a high number of PLS terms (n = 20). This configuration allows a reduced computation time with high accuracy, becoming a valuable alternative for analytical determinations that require ruminal fluid, which would improve the welfare of the animals by avoiding the need to surgically prepare animals to estimate the nutritional value of the feeds.

Adaptive Artificial Neural Network in near infrared spectroscopy for standard-free calibration transfer

Near infrared spectroscopy (NIRS) is a secondary analytical method that could use machine learning to predict components of interest for various applications. Calibration transfer (CT) algorithms have been used to transfer the existing NIRS calibration model to another NIRS instruments to reduce the involvement of expensive and tedious conventional chemical analytical methods. However, most of CT algorithms involve both primary and secondary instruments to acquire NIRS signals from standard samples for spectral standardization. On the other hand, artificial neural network (ANN) that can adapt to new environmental conditions may be an alternative standard-free CT algorithm in transferring a trained ANN to secondary instruments without the involvement of the primary instrument. Although ANN has been widely implemented in NIRS research as a calibration model, its feasibility as an alternative CT method has not been evaluated. Thus, this study evaluates the feasibility of an adaptive ANN (AANN) in transferring ANN model from primary to secondary instruments using two traceable NIRS datasets. First, ANN and ANN with principal components (PCs-ANN) were developed and optimized using Bayesian learning algorithm. After that, these ANNs were adapted to secondary instruments using respective transfer data, in which, only the weights and biases of the ANNs were updated. Findings show that the lowest averaged RMSEP was achieved by the proposed PCs-AANN and AANN in the corn and wheat datasets, respectively. Particularly, the computation cost of having a calibration model in secondary instruments has been substantially reduced by means of the proposed AANN algorithm.