Near infrared reflectance spectroscopy (NIRS) analyses of nutrient composition and condensed tannin concentrations in carolina willow (Salix caroliniana)

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.

Measurements of Chemical Compositions in Corn Stover and Wheat Straw by Near-Infrared Reflectance Spectroscopy

Simple Summary

Rapid and non-destructive methods play an important role in assessing forage quality. This study is aimed at establishing a calibration model that predicts the moisture, CP, NDF, ADF, and hemicellulose of corn stover and wheat straw by NIRS. In addition, we also intended to compared the predictive accuracy of combined calibration models to the individual models of chemical compositions for corn stover and wheat straw by NIRS. We show that accurately combining calibrated models would be useful for a broad range of end users. Furthermore, the accuracy of the calibration models was improved by increasing the sample numbers (the range of variability) of different straw species.

Abstract

Rapid, non-destructive methods for determining the biochemical composition of straw are crucial in ruminant diets. In this work, ground samples of corn stover (n = 156) and wheat straw (n = 135) were scanned using near-infrared spectroscopy (instrument NIRS DS2500). Samples were divided into two sets, with one set used for calibration (corn stover, n = 126; wheat straw, n = 108) and the remaining set used for validation (corn stover, n = 30; wheat straw, n = 27). Calibration models were developed utilizing modified partial least squares (MPLS) regression with internal cross validation. Concentrations of moisture, crude protein (CP), and neutral detergent fiber (NDF) were successfully predicted in corn stover, and CP and moisture were in wheat straw, but other nutritional components were not predicted accurately when using single-crop samples. All samples were then combined to form new calibration (n = 233) and validation (n = 58) sets comprised of both corn stover and wheat straw. For these combined samples, the CP, NDF, and ADF were predicted successfully; the coefficients of determination for calibration (RSQ_C) were 0.9625, 0.8349, and 0.8745, with ratios of prediction to deviation (RPD) of 6.872, 2.210, and 2.751, respectively. The acid detergent lignin (ADL) and moisture were classified as moderately useful, with RSQ_C values of 0.7939 (RPD = 2.259) and 0.8342 (RPD = 1.868), respectively. Although the prediction of hemicellulose was only useful for screening purposes (RSQ_C = 0.4388, RPD = 1.085), it was concluded that NIRS is a suitable technique to rapidly evaluate the nutritional value of forage crops.

Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

BACKGROUND

The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT's full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000-550 cm^-1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization-mDP, procyanidins:prodelphidins ratio-PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods.

RESULTS

The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R²P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R²P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R²P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R²P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R²P = 0.66; RPD = 1.86; RER = 7.16).

CONCLUSIONS

MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

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

Italian ryegrass (Lolium multiflorum) is an important cool-season, annual forage crop for the grassland rotation system in Southern China. The primary aim of breeding programs is always to seek to improve forage quality in the animal productivity system; however, it is time- and labor-consuming when analyzed excessive large number of samples. The main objectives of this study were to construct near-infrared reflectance spectroscopy (NIRS) models to predict the forage chemistry quality of Italian ryegrass including the concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and water soluble carbohydrate (WSC). The results showed that a broader range of CP, NDF, ADF and WSC contents (%DM) were obtained (4.45–30.60, 21.29–60.47, 11.66–36.17 and 3.95–51.52, respectively) from the samples selected for developing NIRS models. In addition, the critical wavelengths identified in this study to construct optimal NIRS models were located in 4,247–6,102 and 4,247–5,450 cm^-1 for CP and NDF content, and both wavelengths 5,446–6,102 and 4,247–4,602 cm^-1 could for ADF and WSC. Finally, the optimal models were developed based on the laboratory data and the spectral information by partial least squares (PLS) regression, with relatively high coefficients of determination (R²_CV, CP = 0.99, NDF = 0.94, ADF = 0.92, WSC = 0.88), ratio of prediction to devitation (RPD, CP = 8.58, NDF = 4.25, ADF = 3.64, WSC = 3.10). The further statistics of prediction errors relative to laboratory (PRL) and the range error ratio (RER) give excellent assessments of the models with the PRL ratios lower than 2 and the RER values greater than 10. The NIRS models were validated using a completely independent set of samples and have coefficients of determination (R²_V, CP = 0.99, NDF = 0.91, ADF = 0.95, WSC = 0.91) and ratio of prediction to deviation (RPD, CP = 9.37, NDF = 3.44, ADF = 4.40, WSC = 3.39). The result suggested that routine screening for forage quality parameters with large numbers of samples is available with the NIRS model in Italian ryegrass breeding programs, as well as facilitating graziers to monitor the forage development stage for improving grazing efficiency.