Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum

Predicting Perennial Ryegrass Cultivars and the Presence of an Epichloë Endophyte in Seeds Using Near-Infrared Spectroscopy (NIRS)

Perennial ryegrass is an important temperate grass used for forage and turf worldwide. It forms symbiotic relationships with endophytic fungi (endophytes), conferring pasture persistence and resistance to herbivory. Endophyte performance can be influenced by the host genotype, as well as environmental factors such as seed storage conditions. It is therefore critical to confirm seed quality and purity before a seed is sown. DNA-based methods are often used for quality control purposes. Recently, near-infrared spectroscopy (NIRS) coupled with hyperspectral imaging was used to discriminate perennial ryegrass cultivars and endophyte presence in individual seeds. Here, a NIRS-based analysis of bulk seeds was used to develop models for discriminating perennial ryegrass cultivars (Alto, Maxsyn, Trojan and Bronsyn), each hosting a suite of eight to eleven different endophyte strains. Sub-sampling, six per bag of seed, was employed to minimize misclassification error. Using a nested PLS-DA approach, cultivars were classified with an overall accuracy of 94.1-98.6% of sub-samples, whilst endophyte presence or absence was discriminated with overall accuracies between 77.8% and 96.3% of sub-samples. Hierarchical classification models were developed to discriminate bulked seed samples quickly and easily with minimal misclassifications of cultivars (<8.9% of sub-samples) or endophyte status within each cultivar (<11.3% of sub-samples). In all cases, greater than four of the six sub-samples were correctly classified, indicating that innate variation within a bag of seeds can be overcome using this strategy. These models could benefit turf- and pasture-based industries by providing a tool that is easy, cost effective, and can quickly discriminate seed bulks based on cultivar and endophyte content.

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

OBJECTIVE

A study was conducted to quantify the performance differences of the nearinfrared spectroscopy (NIRS) calibration models developed with different degrees of hay sample preparations.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Mixed legume-grass seeding and nitrogen fertilizer input enhance forage yield and nutritional quality by improving the soil enzyme activities in Sichuan, China

Information regarding relationships between forage yield and soil enzymes of legume-grass mixtures under nitrogen (N) fertilization can guide the decision-making during sustainable forage production. The objective was to evaluate the responses of forage yield, nutritional quality, soil nutrients, and soil enzyme activities of different cropping systems under various N inputs. Alfalfa (Medicago sativa L.), white clover (Trifolium repens L.), orchardgrass (Dactylis glomerata L.), and tall fescue (Festuca arundinacea Schreb.) were grown in monocultures and mixtures (A1: alfalfa, orchardgrass, and tall fescue; A2: alfalfa, white clover, orchardgrass, and tall fescue) under three N inputs (N1: 150 kg ha^-1; N2, 300 kg ha^-1; and N3: 450 kg ha^-1) in a split plot arrangement. The results highlight that A1 mixture under N2 input had a greater forage yield of 13.88 t ha^-1 year^-1 than the other N inputs, whereas A2 mixture under N3 input had a greater forage of 14.39 t ha^-1 year^-1 than N1 input, but it was not substantially greater than N2 input (13.80 t ha^-1 year^-1). The crude protein (CP) content of grass monocultures and mixtures significantly (P < 0.05) increased with an increase in the rate of N input, and A1 and A2 mixtures under N3 input had a greater CP content of 18.91% and 18.94% dry matter, respectively, than those of grass monocultures under various N inputs. The A1 mixture under N2 and N3 inputs had a substantially greater (P < 0.05) ammonium N content of 16.01 and 16.75 mg kg^-1, respectively, whereas A2 mixture under N3 had a greater nitrate N content of 4.20 mg kg^-1 than the other cropping systems under various N inputs. The A1 and A2 mixtures under N2 input had a substantial higher (P < 0.05) urease enzyme activity of 0.39 and 0.39 mg g^-1 24 h^-1 and hydroxylamine oxidoreductase enzyme activity of 0.45 and 0.46 mg g^-1 5 h^-1, respectively, than the other cropping systems under various N inputs. Taken together, growing legume-grass mixtures under N2 input is cost-effective, sustainable, and eco-friendly, which provide greater forage yield and improved nutritional quality by the better utilization of resources.

Near-infrared spectroscopy for rapid evaluation of winter cereals and Italian ryegrass forage mixtures

Near-infrared (NIR) spectroscopy was employed to determine the differences between forage mixtures of winter cereals and Italian ryegrass and to evaluate fermentation characteristics of mixed silages. Forages were harvested on five phases (Cuts 1-5), with 1 week interval (n = 100). The yield of the last harvest (Cut 5) was ensiled and analyzed on four different days (D0, D7, D14, and D90) (n = 80). Principal component analysis based on the NIR data revealed differences according to the days of harvest, differences between winter cereals and Italian ryegrass forages, and differences in the fermentation stages of silages. The partial least square regression models for crude protein (CP), crude fiber (CF), and ash gave excellent determination coefficient in cross-validation (R² _CV  > 0.9), while models for ether extract (EE) and total sugar content were weaker (R² _CV  = 0.87 and 0.74, respectively). The values of root mean square error of cross-validation were 0.59, 0.76, 0.22, 0.31, and 2.36 %DM, for CP, CF, EE, ash, and total sugar, respectively. NIR proved to be an efficient tool in evaluating type and growth differences of the winter cereals and Italian ryegrass forage mixtures and the quality changes that occur during ensiling.

Multi-Cutting Improves Forage Yield and Nutritional Value and Maintains the Soil Nutrient Balance in a Rainfed Agroecosystem

Increasing forage yield and nutritional quality under the premise of maintaining relatively stable land area and soil nutrient content is a necessary condition for the sustainable development of grassland animal husbandry. Different cutting models [simulated grazing (SG), hay harvesting (H)] of oat (Avena sativa), common vetch (Vicia sativa) and their mixture (Avena sativa + Vicia sativa) were studied on the Loess Plateau. The results show that (1) SG could increase forage yield, crude protein, and crude fat content and decrease crude ash content. In 2014, the yield of Avena sativa per hectare was 3,578.11 kg higher than that of H; (2) the model analysis for predicting nutritional components showed that the Crude protein (CP) and EE contents of forages in each variety (combination) showed a linear downward trend with increasing forage yield. Redundancy analysis showed that precipitation, especially in the growing season, was positively correlated with grass yield and CP content; and (3) there were significant differences in soil organic carbon, total nitrogen, NO₃ ^--N, and NH₄ ⁺-N contents for the different forage varieties (combinations) under different use modes; the values first decreased, then increased, and finally decreased. According to the comprehensive evaluation value calculated by Technique for Order Preference by Similarity to an Ideal Solution, mixed sowing was better than monoculture, and SG obtained better results than H. Overall, mixed sowing under SG can improve forage yield and nutritional quality. At the same time, precipitation regulation is the key factor affecting the production performance of rainfed cultivated grassland on the Loess Plateau.

Can Grassland Chemical Quality Be Quantified Using Transform Near-Infrared Spectroscopy?

Simple Summary

Near-infrared spectroscopy (NIRS) has been applied to analyse the quality of forage and animal feed. However, grasslands more than other raw materials are linked to many variability factors (e.g., site, year, occurring species, etc.) that can represent strong points as well as weak points in NIRS estimation. This research is aimed at testing NIRS application for the determination of chemical characteristics of fresh, undried and unground samples of meadows and grasslands located in north-central Apennine. The interest lies in the possibility of monitoring grassland resources, supporting the decision in terms of the need of supplementation and identifying the critical periods for cutting grassland intended for animal feeding. The results indicated that FT-NIRS models could be used in the real-time quantification of crude protein, fibrous fraction and dry matter, while for lignin only a screening test could be considered. Minor components of grassland such as ash and lipids need improvement. As a practical point, a key factor of FT-NIRS in grassland chemical quality estimation is the absence of samples preparation and the importance of the parameters that have obtained the best results in animal diet formulation.

Abstract

Near-infrared spectroscopy (NIRS) and closed spectroscopy methods have been applied to analyse the quality of forage and animal feed. However, grasslands are linked to variability factors (e.g., site, year, occurring species, etc.) which restrict the prediction capacity of the NIRS. The aim of this study is to test the Fourier transform NIRS application in order to determine the chemical characteristics of fresh, undried and unground samples of grassland located in north-central Apennine. The results indicated the success of FT-NIRS models for dry matter (DM), crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF) and acid detergent lignin (ADL) on fresh grassland samples (R2 > 0.90, in validation). The model can be used to quantitatively determine CP and ADF (residual prediction deviation-RPD > 3 and range error ratio- RER > 10), followed by DM and NDF that maintain a RER > 10, and are sufficient for screening for the lignin fraction (RPD = 2.4 and RER = 8.8). On the contrary, models for both lipid and ash seem not to be usable at a practical level. The success of FT-NIRS quantification for the main chemical parameters is promising from the practical point of view considering both the absence of samples preparation and the importance of these parameters for diet formulation.

Application of Optical Quality Control Technologies in the Dairy Industry: An Overview

Sustainable development of the agricultural industry, in particular, the production of milk and feed for farm animals, requires accurate, fast, and non-invasive diagnostic tools. Currently, there is a rapid development of a number of analytical methods and approaches that meet these requirements. Infrared spectrometry in the near and mid-IR range is especially widespread. Progress has been made not only in the physical methods of carrying out measurements, but significant advances have also been achieved in the development of mathematical processing of the received signals. This review is devoted to the comparison of modern methods and devices used to control the quality of milk and feed for farm animals.

Additive genetic variation in Pinus radiata bark chemistry and the chemical traits associated with variation in mammalian bark stripping

Secondary metabolites are suggested as a major mechanism explaining genetic variation in herbivory levels in Pinus radiata. The potential to incorporate these chemical traits into breeding/deployment programmes partly depends on the presence of additive genetic variation for the relevant chemical traits. In this study, near-infrared spectroscopy was used to quantify the constitutive and induced levels of 54 compounds in the bark of trees from 74 P. radiata full-sib families. The trees sampled for chemistry were protected from browsing and induced levels were obtained by subjecting half of the trees to artificial bark stripping. The treatment effect on bark chemistry was assessed along with narrow-sense heritability, the significance of non-additive genetic effects and the additive genetic correlations of compounds with bark stripping by mammalian herbivores that was observed in unprotected replicates of the field trial. The results indicated: (i) significant additive genetic variation, with low-moderate narrow-sense heritability estimates for most compounds; (ii) while significant induced effects were detected for some chemicals, no significant genetic variation in inducibility was detected; and (iii) sugars, fatty acids and a diterpenoid positively genetically correlated while a sesquiterpenoid negatively genetically correlated with bark stripping by the mammalian herbivore, the Bennett's wallaby (Macropus rufogriseus). At the onset of browsing, a trade-off with height was detected for selecting higher amounts of this sesquiterpenoid. However, overall, results showed potential to incorporate chemical traits into breeding/deployment programmes. The quantitative genetic analyses of the near infrared predicted chemical traits produced associations with mammalian bark stripping that mostly conform with those obtained using standard wet chemistry.

Rapid detection of cellulose and hemicellulose contents of corn stover based on near-infrared spectroscopy combined with chemometrics

The feasibility of near-infrared spectroscopy (NIRS) combined with chemometrics for the rapid detection of the cellulose and hemicellulose contents in corn stover is discussed. Competitive adaptive reweighted sampling (CARS) and genetic simulated annealing algorithm (GSA) were combined (CARS-GSA) to select the characteristic wavelengths of cellulose and hemicellulose and to reduce the dimensionality and multicollinearity of the NIRS data. The whole spectra contained 1845 wavelength variables. After CARS-GSA optimization, the number of characteristic wavelengths of cellulose (hemicellulose) was reduced to 152 (260), accounting for 8.24% (14.09%) of all wavelengths. The coefficients of determination of the regression models for predicting the cellulose and hemicellulose contents were 0.968 and 0.996, the root mean square errors of prediction (RMSEPs) were 0.683 and 0.648, and the residual predictive deviations (RPDs) were 5.213 and 16.499, respectively. The RMSEP of the cellulose and hemicellulose regression models was 0.152 and 0.190 lower for CARS-GSA than for the full-spectrum, and the RPD was increased by 0.949 and 3.47, respectively. The results showed that the CARS-GSA model substantially reduced the number of characteristic wavelengths and significantly improved the predictive ability of the regression model.

Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis

This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique's principle and focused on said technologies' applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample.

Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques

Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this research was to assess the performance of NIRS in predicting the forage quality parameters of five warm-season legumes—guar (Cyamopsis tetragonoloba), tepary bean (Phaseolus acutifolius), pigeon pea (Cajanus cajan), soybean (Glycine max), and mothbean (Vigna aconitifolia)—using three machine learning techniques: partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP). Additionally, the efficacy of global models in predicting forage quality was investigated. A set of 70 forage samples was used to develop species-based models for concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) of guar and tepary bean forages, and CP and IVTD in pigeon pea and soybean. All species-based models were tested through 10-fold cross-validations, followed by external validations using 20 samples of each species. The global models for CP and IVTD of warm-season legumes were developed using a set of 150 random samples, including 30 samples for each of the five species. The global models were tested through 10-fold cross-validation, and external validation using five individual sets of 20 samples each for different legume species. Among techniques, PLS consistently performed best at calibrating (R²_c = 0.94–0.98) all forage quality parameters in both species-based and global models. The SVM provided the most accurate predictions for guar and soybean crops, and global models, and both SVM and PLS performed better for tepary bean and pigeon pea forages. The global modeling approach that developed a single model for all five crops yielded sufficient accuracy (R²_cv/R²_v = 0.92–0.99) in predicting CP of the different legumes. However, the accuracy of predictions of in vitro true digestibility (IVTD) for the different legumes was variable (R²_cv/R²_v = 0.42–0.98). Machine learning algorithms like SVM could help develop robust NIRS-based models for predicting forage quality with a relatively small number of samples, and thus needs further attention in different NIRS based applications.

Quantification of Water, Protein and Soluble Sugar in Mulberry Leaves Using a Handheld Near-Infrared Spectrometer and Multivariate Analysis

Mulberry (Morus alba L.) leaves are not only used as the main feed for silkworms (Bombyx mori) but also as an added feed for livestock and poultry. In order to rapidly select high-quality mulberry leaves, a hand-held near-infrared (NIR) spectrometer combined with partial least squares (PLS) regression and wavelength optimization methods were used to establish a predictive model for the quantitative determination of water content in fresh mulberry leaves, as well as crude protein and soluble sugar in dried mulberry leaves. For the water content in fresh mulberry leaves, the R-square of the calibration set (R2 C), R-square of the cross-validation set (R2 CV) and R-square of the prediction set (R2 P) are 0.93, 0.90 and 0.91, respectively, the corresponding root mean square error of calibration set (RMSEC), root mean square error of cross-validation set (RMSECV) and root mean square error of prediction set (RMSEP) are 0.96%, 1.13%, and 1.18%, respectively. The R2 C, R2 CV and R2 P of the crude protein prediction model are 0.91, 0.83 and 0.92, respectively, and the corresponding RMSEC, RMSECV and RMSEP are 0.71%, 0.97% and 0.61%, respectively. The soluble sugar prediction model has R2 C, R2 CV, and R2 P of 0.64, 0.51, and 0.71, respectively, and the corresponding RMSEC, RMSECV, and RMSEP are 2.33%, 2.73%, and 2.36%, respectively. Therefore, the use of handheld NIR spectrometers combined with wavelength optimization can fastly detect the water content in fresh mulberry leaves and crude protein in dried mulberry leaves. However, it is a slightly lower predictive performance for soluble sugar in mulberry leaves.

Real-time monitoring of particle size distribution in a continuous granulation and drying process by near infrared spectroscopy

In continuous granulation, it can be important to control granules particle size distribution (PSD), as it may affect final product quality. Near infrared spectroscopy (NIRS) is already a routine analytical procedure within pharmaceutical continuous manufacturing for the in-line analysis of chemical material-characteristics. Consequently, the extraction of additional information related to granules' physical properties like particle size distribution is tempting, as it would enhance process knowledge without the need for new capital investments. Three in-line NIRS methods were developed via partial least squares regression, to predict dried granules PSD-fractions X10, X50, and X90 within a GMP-qualified continuous twin-screw wet granulation and fluid-bed drying process. Methods were developed for the size range of 20-234 µm (X10), 98-1017 µm (X50), and 748-2297 µm (X90) and assessed with one internal and three external validation datasets in agreement with current guidelines on NIRS. Internal validation indicated root mean square error of predictions (RMSEPs) of 17 µm, 97 µm, and 174 µm, for PSD X10, X50, and X90 respectively, with acceptable linearity, slope, and bias. Furthermore, the ratio of prediction to deviation (RPD), the ratio of prediction error to laboratory error (PRL), and the range error ratio (RER) were evaluated, with all values within the acceptance range for adequate to good NIR methods (1.75 > RPD < 3, PRL ≤ 2, RER ≥ 10). Methods applicability to in-line processes and their robustness towards water content and active pharmaceutical ingredient content was further demonstrated with three independent in-line datasets in real-time, showing good agreement between predicted and reference values. In summary, methods demonstrated to be sufficient for their intended purpose to monitor trends and sudden changes in dried granules PSD during continuous granulation and drying. Because of their fast response time, they are unique tools to characterize the dynamic behavior and navigate the agglomeration state of the material in static and transient process conditions during continuous granulation and drying.

Spatial and Temporal Monitoring of Pasture Ecological Quality: Sentinel-2-Based Estimation of Crude Protein and Neutral Detergent Fiber Contents

Frequent, region-wide monitoring of changes in pasture quality due to human disturbances or climatic conditions is impossible by field measurements or traditional ecological surveying methods. Remote sensing imagery offers distinctive advantages for monitoring spatial and temporal patterns. The chemical parameters that are widely used as indicators of ecological quality are crude protein (CP) content and neutral detergent fiber (NDF) content. In this study, we investigated the relationship between CP, NDF, and reflectance in the visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) spectral range, using field, laboratory measurements, and satellite imagery (Sentinel-2). Statistical models were developed using different calibration and validation data sample sets: (1) a mix of laboratory and field measurements (e.g., fresh and dry vegetation) and (2) random selection. In addition, we used three vegetation indices (Normalized Difference Vegetative Index (NDVI), Soil-adjusted Vegetation Index (SAVI) and Wide Dynamic Range Vegetation Index (WDRVI)) as proxies to CP and NDF estimation. The best models found for predicting CP and NDF contents were based on reflectance measurements (R2 = 0.71, RMSEP = 2.1% for CP; and R2 = 0.78, RMSEP = 5.5% for NDF). These models contained fresh and dry vegetation samples in calibration and validation data sets. Random sample selection in a model generated similar accuracy estimations. Our results also indicate that vegetation indices provide poor accuracy. Eight Sentinel-2 images (December 2015–April 2017) were examined in order to better understand the variability of vegetation quality over spatial and temporal scales. The spatial and temporal patterns of CP and NDF contents exhibit strong seasonal dependence, influenced by climatological (precipitation) and topographical (northern vs. southern hillslopes) conditions. The total CP/NDF content increases/decrease (respectively) from December to March, when the concentrations reach their maximum/minimum values, followed by a decline/incline that begins in April, reaching minimum values in July.

Breeding to adapt agriculture to climate change: affordable phenotyping solutions

Breeding is one of the central pillars of adaptation of crops to climate change. However, phenotyping is a key bottleneck that is limiting breeding efficiency. The awareness of phenotyping as a breeding limitation is not only sustained by the lack of adequate approaches, but also by the perception that phenotyping is an expensive activity. Phenotyping is not just dependent on the choice of appropriate traits and tools (e.g. sensors) but relies on how these tools are deployed on their carrying platforms, the speed and volume of data extraction and analysis (throughput), the handling of spatial variability and characterization of environmental conditions, and finally how all the information is integrated and processed. Affordable high throughput phenotyping aims to achieve reasonably priced solutions for all the components comprising the phenotyping pipeline. This mini-review will cover current and imminent solutions for all these components, from the increasing use of conventional digital RGB cameras, within the category of sensors, to open-access cloud-structured data processing and the use of smartphones. Emphasis will be placed on field phenotyping, which is really the main application for day-to-day phenotyping.