Regression models from portable NIR spectra for predicting the carcass traits and meat quality of beef cattle

The aims of this study were to predict carcass and meat traits, as well as the chemical composition of the 9th to 11th rib sections of beef cattle from portable NIR spectra. The 9th to 11th rib section was obtained from 60 Nellore bulls and cull cows. NIR spectra were acquired at: P1 -center of Longissimus muscle; and P2 -subcutaneous fat cap. The models accurately estimated (P ≥ 0.083) all carcass and meat quality traits, except those for predicting red (a*) and yellow (b*) intensity from P1, and 12th-rib fat from P2. However, precision was highly variable among the models; those for the prediction of carcass pHu, 12th rib fat, toughness from P1, and those for 12th rib fat, a* and b* from P2 presented high precision (R2 ≥ 0.65 or CCC ≥ 0.63), whereas all other models evaluated presented moderate to low precision (R2 ≤ 0.39). Models built from P1 and P2 accurately estimated (P ≥ 0.066) the chemical composition of the meat plus fat, bones and, meat plus fat plus bones, except those for predicting the ether extract (EE) and crude protein (CP) of bones and the EE of Meat plus bones fraction from P2. However, precision was highly variable among the models (-0.08 ≤ R2 ≤ 0.86) of the 9th and 11th rib section. Those models for the prediction of dry matter (DM) and EE of the bones from P1; of EE from P1; and of EE, mineral matter (MM), CP from P2 of meat plus fat plus bones presented high precision (R2 ≥ 0.76 or CCC ≥ 0.62), whereas all other models evaluated presented moderate to low precision (R2 ≤ 0.45). Thus, models built from portable NIR spectra acquired at different points of the 9th to 11th rib section were recommended for predicting carcass and muscle quality traits as well as for predicting the chemical composition of this section of beef cattle. However, it is noteworthy, that the small sample size was one of the limitations of this study.

Espectroscopía visible y del infrarrojo cercano para el análisis de nutrientes en tejido vegetal de caña de azúcar para producción de panela

La espectroscopía de reflectancia en el infrarrojo cercano (NIRS) es una tecnología rápida, multiparamétrica, amigable con el ambiente, de bajo costo y gran exactitud, para el análisis de diversos componentes en alimentos, en suelo y en agricultura. El objetivo del presente estudio fue construir modelos de calibración NIRS, para la predicción de nutrientes en tejido vegetal de caña de azúcar, para producción de panela, cultivada en la región de la Hoya del río Suárez. Un total de 416 muestras de tejido fueron escaneadas en el segmento espectral Vis-NIR. El análisis quimiométrico, se realizó con el software WinISI V4.10, aplicando la regresión de mínimos cuadrados parciales modificados, junto a una validación cruzada. Se evaluaron cuatro modelos con diferentes tratamientos matemáticos y el rendimiento de las calibraciones, se hizo por medio de la validación externa, analizando las medidas de bondad de ajuste, como el coeficiente de determinación de la predicción, el error estándar de la predicción ajustado por el sesgo y la desviación predictiva residual. Los resultados muestran que el modelo de calibración para N presentó el mayor poder predictivo. Para macronutrientes, las calibraciones, con mayor poder predictivo, fueron P y K y para micronutrientes, el modelo para B, mientras que para Cu presentó el más bajo poder predictivo. Se encontraron modelos adecuados para la predicción de los contenidos de N, Ca y P; para los demás nutrientes, se recomienda ampliar el conjunto de calibración.

Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects

BACKGROUND

Optical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge.

METHODS

As training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0-10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)].

RESULTS

For both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2).

CONCLUSION

We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models.

Field Detection of Rhizoctonia Root Rot in Sugar Beet by Near Infrared Spectrometry

Rhizoctonia root and crown rot (RRCR) is an important disease in sugar beet production areas, whose assessment and control are still challenging. Therefore, breeding for resistance is the most practical way to manage it. Although the use of spectroscopy methods has proven to be a useful tool to detect soil-borne pathogens through leaves reflectance, no study has been carried out so far applying near-infrared spectroscopy (NIRS) directly in the beets. We aimed to use NIRS on sugar beet root pulp to detect and quantify RRCR in the field, in parallel to the harvest process. For the construction of the calibration model, mainly beets from the field with natural RRCR infestation were used. To enrich the model, artificially inoculated beets were added. The model was developed based on Partial Least Squares Regression. The optimized model reached a Pearson correlation coefficient (R) of 0.972 and a Ratio of Prediction to Deviation (RPD) of 4.131. The prediction of the independent validation set showed a high correlation coefficient (R = 0.963) and a root mean square error of prediction (RMSEP) of 0.494. These results indicate that NIRS could be a helpful tool in the assessment of Rhizoctonia disease in the field.

Real-Time Quantification of Crude Protein and Neutral Detergent Fibre in Pastures under Montado Ecosystem Using the Portable NIR Spectrometer

The Montado is a Mediterranean agro–forestry–pastoral ecosystem. Knowledge of pastures’ nutritional value is critical for farm managers’ decision-making. Laboratory determinations are very expensive, destructive and costly, in terms of time and labour. The objective of this experimental work was to calibrate and validate a portable near-infrared spectrometer (micro-NIR) to predict the nutritive value (neutral detergent fibre, NDF and crude protein, CP) of pastures in the peak of spring 2021. Thus, a total of 87 pasture samples were collected at eight experimental fields located in the Alentejo, Southern region of Portugal. The results show good correlations between in-situ micro-NIR measurements and pasture NDF reference values (R2 of 0.73 and 0.69 for calibration and validation models, respectively), and a moderate correlation between micro-NIR measurements and pasture CP reference values (R2 of 0.51 and 0.36 for calibration and validation models, respectively). These results show the potential of this tool for the quick evaluation of pasture quality and constitute a starting point for future work, which should include the monitoring of temporal variability (throughout the entire vegetative cycle of the pasture) and spatial (with geo-referenced information) diversity of pastures characteristic of the Montado ecosystem in the Mediterranean region.

Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem

Pasture quality monitoring is a key element in the decision making process of a farm manager. Laboratory reference methods for assessing quality parameters such as crude protein (CP) or fibers (neutral detergent fiber: NDF) require collection and analytical procedures involving technicians, time, and reagents, making them laborious and expensive. The objective of this work was to evaluate two technological and expeditious approaches for estimating and monitoring the evolution of the quality parameters in biodiverse Mediterranean pastures: (i) near infrared spectroscopy (NIRS) combined with multivariate data analysis and (ii) remote sensing (RS) based on Sentinel-2 imagery to calculate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). Between February 2018 and March 2019, 21 sampling processes were carried out in nine fields, totaling 398 pasture samples, of which 315 were used during the calibration phase and 83 were used during the validation phase of the NIRS approach. The average reference values of pasture moisture content (PMC), CP, and NDF, obtained in 24 tests carried out between January and May 2019 in eight fields, were used to evaluate the RS accuracy. The results of this study showed significant correlation between NIRS calibration models or spectral indices obtained by remote sensing (NDVIRS and NDWIRS) and reference methods for quantifying pasture quality parameters, both of which open up good prospects for technological-based service providers to develop applications that enable the dynamic management of animal grazing.

Determination of Loline Alkaloids and Mycelial Biomass in Endophyte-Infected Schedonorus Pratensis by Near-Infrared Spectroscopy and Chemometrics

Near infrared spectroscopy (NIRS) is an accurate, fast and nondestructive technique whose use in predicting forage quality has become increasingly relevant in recent decades. Epichloë-infected grass varieties are commonly used in areas with high pest pressure due to their better performances compared to endophyte-free varieties. The insect resistance of Epichloë-infected grasses has been associated with four main groups of endophyte secondary metabolites: ergot alkaloids, indole-diterpenes, lolines and peramine. Concentrations of these alkaloids are usually measured with high performance liquid chromatography or gas chromatography analysis, which are accurate methods but relatively expensive and laborious. In this paper, we developed a rapid method based on NIRS to detect and quantify loline alkaloids in wild accessions of Schedonorus pratensis infected with the fungal endophyte Epichloë uncinata. The quantitative NIR equations obtained by modified partial least squares algorithm had coefficients of correlation of 0.90, 0.78, 0.85, 0.90 for N-acetylloline, N-acetylnorloline and N-formylloline and the sum of the three, respectively. The acquired NIR spectra were also used for developing an equation to predict in planta fungal biomass with a coefficient of correlation of 0.75. These results showed that the use of NIRS and chemometrics allows the quantification of loline alkaloids and mycelial biomass in a heterogeneous set of endophyte-infected meadow fescue samples.

A near-infrared reflectance spectroscopic method for the direct analysis of several fodder-related chemical components in drumstick (Moringa oleifera Lam.) leaves

The drumstick tree has traditionally been used as foodstuff and fodder in several countries. Due to its high nutritional value and good biomass production, interest in this plant has increased in recent years. It has therefore become important to rapidly and accurately evaluate drumstick quality. In this study, we addressed the optimization of Near-infrared spectroscopy (NIRS) to analyze crude protein, crude fat, crude fiber, iron (Fe), and potassium (K) in a variety of drumstick accessions (N = 111) representing different populations, cultivation programs, and climates. Partial least-squares regression with internal cross-validation was used to evaluate the models and identify possible spectral outliers. The calibration statistics for these fodder-related chemical components suggest that NIRS can predict these parameters in a wide range of drumstick types with high accuracy. The NIRS calibration models developed in this study will be useful in predicting drumstick forage quality for these five quality parameters.

Use of near infrared reflectance spectroscopy to predict phytate phosphorus, total phosphorus, and crude protein of common poultry feed ingredients

The purpose of the study that is presented herein was to develop near-infrared reflectance spectroscopy (NIRS) calibrations to predict total phosphorus (P), phytate-P, and protein concentrations of feed ingredients commonly used in monogastric feed formulation. Samples representing 14 vegetable ingredients (cereals, cereal by-products, and oilseed meals) were collected worldwide throughout 2013. The samples were assayed by standard wet chemical techniques for total P, phytate-P, and protein content. There was substantial variability in protein, phytate-P, and total P within and between ingredients used in the calibration set. Protein content varied from 76 to 487 g/kg. Total P ranged from 2.09 and 22.5 g/kg and phytate-P ranged from 0.99 and 13.8 g/kg. Within these broad ranges, NIRS values were highly correlated for determination of protein, total P, and phytate-P with a standard error of prediction equal to 9.06 g/kg, 0.80 g/kg, and 0.66 g/kg, respectively. The wide diversity and heterogeneity of the mix of feed ingredients allowed the development of NIRS calibrations of sufficient accuracy to help nutritionists control the nutritional composition of their feed.

Prediction of crude protein and neutral detergent fibre concentration in residues of in situ ruminal degradation of pasture samples by near-infrared spectroscopy (NIRS)

An important constraint of in situ degradability studies is the need to analyse a high number of samples and often with insufficient amount of residue, especially after the longer incubations of high-quality forages, that impede the study of more than one nutritional component. Near-infrared spectroscopy (NIRS) has been established as a reliable method for predicting composition of many entities, including forages and other animal feedstuffs. The objective of this work was to evaluate the potential of NIRS for predicting the crude protein (CP) and neutral detergent fibre (NDF) concentration in rumen incubation residues of permanent and sown temperate pastures in a vegetative stage. In situ residues (n = 236) from four swards were scanned for their visible-NIR spectra and analysed for CP and NDF. Selected equations developed by partial least-squares multivariate regression presented high coefficients of determination (CP = 0.99, NDF = 0.95) and low standard errors (CP = 4.17 g/kg, NDF = 7.91 g/kg) in cross-validation. These errors compare favourably to the average concentrations of CP and NDF (146.5 and 711.2 g/kg, respectively) and represent a low fraction of their standard deviation (CP = 38.2 g/kg, NDF = 34.4 g/kg). An external validation was not as successful, with R2 of 0.83 and 0.82 and a standard error of prediction of 14.8 and 15.2 g/kg, for CP and NDF, respectively. It is concluded that NIRS has the potential to predict CP and NDF of in situ incubation residues of leafy pastures typical of humid temperate zones, but more robust calibrations should be developed.

Prediction of diet quality for sheep from faecal characteristics: comparison of near-infrared spectroscopy and conventional chemistry predictive models

This study evaluated the ability of equations developed from the analysis of faecal material by conventional chemistry (F.CHEM), and by near-infrared spectroscopy (F.NIRS), to predict intake and digestibility of forages fed with or without supplements. In vivo datasets were obtained using 30 sheep and 25 diets to provide 124 diet–faecal pairs, with each sheep fed four or five of the diets. The diets were five forages fed alone or with urea, molasses, cottonseed meal or sorghum grain supplements. Ninety-nine diet–faecal pairs were selected at random, but ensuring that all diets were represented and both the F.CHEM and F.NIRS prediction equations were developed from this dataset. The remaining 25 diet–faecal pairs were used as a validation dataset. Regressions for F.CHEM were developed by stepwise regression, and F.NIRS prediction equations were developed by partial least-squares regression. Prediction equations based solely on faecal analyte concentrations (F.CHEMc) had poor predictive ability, and models incorporating faecal constituent excretion rates (F.CHEMe) were the best at predicting feed constituent intakes. These models had slightly lower standard errors of prediction (SEP) for organic matter (OM) intake and digestible OM intake compared with the F.NIRS models that did not include faecal excretion rates. However, F.NIRS models had lower SEP for protein intake and OM digestibility. Good agreement between the F.CHEMe and F.NIRS methods was evident (according to the 95% limits-of-agreement test), and both predicted the reference values precisely and with small bias. Equations derived from a dataset that included representatives of all diets used in the experiment gave much better prediction of diet characteristics than those developed from a dataset constructed entirely at random. Equations for F.NIRS developed in this way successfully predicted the characteristics of diets that included forages fed alone and with the type of supplements used in tropical Australia.

A novel approach for a functional group to predict protein in undigested residue and protein digestibility by mid-infrared spectroscopy

To evaluate nutrient digestibility, we propose the novel approach of functional group digestibility (FGD). The FGD was based on the absorbance of specific Fourier transform infrared (FT-IR) peaks and the ratio of an inorganic indigestible marker in diet and digesta, without calibration. For application, samples of diet and digesta of wheat with predetermined crude protein (CP) digestibility were scanned on an FT-IR spectrometer equipped with a single-reflection attenuated total reflection (ATR) attachment. The FGD in the amide I region (1689-1631 cm (-1)) of digesta spectra was strongly related (R(2) = 0.99) with CP digestibility. The measured diet CP digestibility ranged from 60.4 to 87.8% with a standard error of prediction of 1.09%. In conclusion, instead of predictions based on calibrations, FGD can be calculated directly from spectra, provided the ratio of marker in diet and undigested residue is known, and then accurately predicts nutrient digestibility.

A review of the application of near-infrared spectroscopy for the analysis of potatoes

Potato (Solanum tuberosum L.) is one of the most important crops in the world being considered as a staple food in many developing countries. The potato industry like other vegetable and fruit industries is subject to the current demand of quality products. In order to meet this challenge, the food industry is relying on the adoption of nondestructive and environmentally friendly techniques to determine quality of products. Near-infrared spectroscopy (NIRS) is currently one of the most advanced nondestructive technologies regarding instrumentation and application, and it also complies with the environment requirements as it does not generate emissions or waste. This paper reviews research progress on the analysis of potatoes by NIRS both in terms of determination of constituents and classification according to the different constituents of the tubers. A brief description of the fundamentals of NIRS technology and its advantages over other quality assessment techniques is included. Finally, future prospects of the development of NIRS technology at the industrial level are explored.

Near infrared spectroscopy for the analysis of macro and micro nutrients in sugarcane leaves

About ten years ago, NIR technology was implemented at the Colombian Sugarcane Research Center Cenicaña for the analysis of sugarcane quality, followed by the development of methodologies for the quantification of some elements in soils and sugarcane leaves. Methodologies were based on the presence of high content materials, such nitrogen, potassium, calcium, and magnesium; however, determination of micronutrients which are found in soils and leaves in the range of parts per million has been challenging. Development of a NIR methodology for quantification of macro (N, P, K, Ca, Mg) and micronutrients (Cu, Zn, Mn, Fe) in a single experiment is here reported. Calibration curves were constructed using approximately 500 samples that were previously analyzed by methods of reference such atomic absorption. Statistical analysis of the data showed that there are not significant differences between the methods of reference and NIR, suggesting that NIR is a very fast, economical and convenient methodology for the daily analysis of sugarcane leaves.

Prediction of the nutritional composition of the crop contents of free-living scarlet macaw chicks by near-infrared reflectance spectroscopy

Context It is difficult to determine with accuracy the nutrition of bird diets through observation and analysis of dietary items. Collection of the ingested material from the birds provides an alternative but it is often limited by the small sizes of samples that can be obtained. Aims We tested the efficacy of near-infrared reflectance spectroscopy (NIRS) to assess the nutritional composition of very small samples of growing-parrot crop content. Methods We used 30 samples of the crop content of free-living scarlet macaw (Ara macao) chicks. Samples were scanned with a near-infrared reflectance analyser, and later analysed by traditional wet laboratory methods for crude protein/N, fat, ash, neutral detergent fibre, P, K, Ca, Mg, Cu, Zn and S. A calibration model was developed using principal components analysis. Key results Coefficients of determination in the calibration (R2) and standard errors of cross-validation (SECV) for most of the nutrients showed a good performance (mean R2 of 0.91 ± 0.11 s.d., n = 10) when excluding Zn (R2 of 0.15, SECV = 25.37). Conclusions The present results established NIRS as a valid technique for the non-destructive, low-cost prediction of a variety of nutritional attributes of avian crop contents as small as 0.5-g dry weight. Implications The use of NIRS expands the possibilities of wild-animal nutrition research.

Robustness of analyses of imaging data

Successful classifications of reflectance and vibrational data are to a large extent dependent upon robustness of input data. In this study, a well-known geostatistical approach, variogram analysis, was described and its robustness was assessed through comprehensive evaluation of 3,200 variogram settings. High-resolution hyperspectral imaging data were acquired from greenhouse maize plants, and the robustness (radiometric repeatability) of three variogram parameters (nugget, sill, and range) was examined when generated from imaging data collected from two different sets of plants and with imaging data collected on seven different days in two years. Robustness of variogram parameters was compared with average reflectance values in six spectral bands, three standard vegetation indices (NDVI, SI, and PRI), and PCA scores from principal component analysis.

Rapid assessment of mineral concentration in meadow grasses by near infrared reflectance spectroscopy

A near infrared reflectance spectroscopy (NIRS) method for rapid determination of nitrogen, phosphorous and potassium in diverse meadow grasses was developed with a view towards utilizing this material for biogas production and organic fertilizer. NIRS spectra between 12,000 cm⁻¹ and 4,000 cm⁻¹ were used. When validated on samples from different years to those used for the calibration set, the NIRS prediction of nitrogen was considered moderately useful with R² = 0.77, ratio of standard error of prediction to reference data range (RER) of 9.32 and ratio of standard error of prediction to standard deviation of reference data (RPD) of 2.33. Prediction of potassium was less accurate, with R² = 0.77, RER of 6.56 and RPD of 1.45, whilst prediction of phosphorous was not considered accurate enough to be of any practical use. This work is of interest from the point of view of both the removal of excess nutrients from formerly intensively farmed areas and also for assessing the plant biomass suitability for conversion into carbon neutral energy through biogas production.

Variogram analysis of hyperspectral data to characterize the impact of biotic and abiotic stress of maize plants and to estimate biofuel potential

A considerable challenge in applied agricultural use of reflection-based spectroscopy is that most analytical approaches are quite sensitive to radiometric noise and/or low radiometric repeatability. In this study, hyperspectral imaging data were acquired from individual maize leaves and the main objective was to evaluate a classification system for detection of drought stress levels and spider mite infestation levels across maize hybrids and vertical position of maize leaves. A second objective was to estimate biomass and biofuel potential (heating value) of growing maize plants. Stepwise discriminant analysis was used to identify the five spectral bands (440, 462, 652, 706, and 784 nm) that contributed most to the classification of three levels of drought stress (moderate, subtle, and none) across hybrids, leaf position, and spider mite infestation. Regarding the five selected spectral bands, average reflectance values and standard variogram parameters ("nugget", "sill", and "range" derived from variogram analysis) were examined as indicators of spider mite and/or drought stress. There was consistent significant effect of drought stress on average reflectance values, while only one spectral band responded significantly to spider mite infestations. Different variogram parameters provided reliable indications of spider mite infestation and drought stress. Based on independent validation, variogram parameters could be used to accurately predict spider mite density but were less effective as indicators of drought stress. In addition, variogram parameters were used as explanatory variables to predict biomass and biofuel potential of individual maize plants. The potential of using variogram analysis as part of hyperspectral imaging analysis is discussed.

Exploring near and midinfrared spectroscopy to predict trace iron and zinc contents in powdered milk

Near infrared (NIR) and mid-infrared (MIR) spectroscopy were investigated to predict iron and zinc contents in powdered milk. A hybrid variable selection method, namely, uninformative variable elimination (UVE) combined with successive projections algorithm (SPA), was applied to select the most effective wavenumber variables from full 2756 NIR and 3727 MIR variables, respectively. Finally, 18 NIR and 18 MIR variables were selected for iron content prediction, and 17 NIR and 12 MIR variables for zinc content prediction. The obtained effective wavenumber variables were input into partial least-squares (PLS) and least-squares-support vector machines (LS-SVM), respectively. The selected MIR variables obtained much better results than NIR to predict both iron and zinc contents in both the PLS and LS-SVM models. The iron content prediction results based on LS-SVM with 18 MIR spectra were as follows: coefficient of determination (r(2)) was 0.920, residual predictive deviation (RPD) was 3.321, and root-mean-square error of prediction (RMSEP) was 1.444. The zinc content prediction results based on LS-SVM with 12 selected MIR spectra were as follows:r(2) was 0.946, RPD was 4.361, and RMSEP was 0.321. The good performance shows that UVE-SPA is a powerful variable selection tool. The overall results indicate that MIR spectroscopy incorporated to UVE-SPA-LS-SVM could be applied as an alternative fast and accurate method to determine trace mineral content in powdered milk, such as iron and zinc.

Verification of silage type using near-infrared spectroscopy combined with multivariate analysis

The ability to authenticate the feed given to animals has become a major challenge in animal production, where the diet fed to the animal is one of the most important production factors affecting the composition of milk and meat from cattle, sheep, and goats. Hence, there is currently an increased consumer demand for information on herbivore production factors and particularly the animal diet. The aim of this study was to evaluate the reliability and accuracy of near-infrared (NIR) reflectance spectroscopy as a tool to verify and authenticate the type of silage used as fed for ruminants. Grain silage (GrS, n = 94), grass and legume silage (GLegS, n = 121), and sunflower silage (SunS, n = 50) samples were collected from commercial farms and analyzed in the visible and NIR regions (400-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS1-DA), and linear discriminant analysis (LDA) models were used as methods to verify the different silage types. The classification models based on the NIR data correctly classified more than 90% of the silage samples according to their type. The results from this study showed that NIR spectra combined with multivariate analysis could be used as a tool to objectively authenticate silage samples used as a feed for ruminants.

Feed conversion efficiency as a key determinant of dairy herd performance: a review

This paper focuses on dairy herd performance in the United Kingdom and southern Australia, where feed costs have been estimated to comprise between 40 and 67% of the total costs of production. The efficiency of conversion of grazed pasture, home grown forages and purchased feeds into milk has a major bearing on farm profit. Feed conversion efficiency (FCE), defined as ‘kg milk of standardised composition with respect to protein and fat concentrations produced per kg feed dry matter consumed’, is a useful measure of the performance of a farm feeding system, but is seldom used by dairy farmers. It could also be defined as ‘g protein plus fat produced per kg feed dry matter consumed’, given that farmers are often paid for these components. The value of estimating FCE on an annual or shorter-term basis is discussed in relation to accepted principles of feed utilisation and dairy cow energy requirements. The implications of feed intake, conversion of ingested nutrients into absorbed nutrients and the subsequent utilisation of these nutrients for milk production or other purposes, as well as the effects of stage of lactation on FCE, are reviewed. Measuring FCE and identifying opportunities for improvement is relatively straightforward in housed feeding systems, but is more problematic under grazing. Hence, approaches and the key assumptions in estimating FCE in grazing situations, as well as possible limitations of these estimates, are discussed. Finally, a case study examining the potential impact of improved nutritional strategies on FCE and on margin over feed costs is presented. It is concluded that, to remain profitable, dairy farmers need to have a sound knowledge of cow nutrition, along with appropriate measures of FCE to monitor the performance of their milk production system. Such indicators of the biological performance of the farming system are most useful when used in conjunction with appropriate measures of economic performance.

Energy evaluation of extruded compound foods for dogs by near-infrared spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition, apparent digestibility and digestible nutrients and energy content of commercial extruded compound foods for dogs. Fifty-six foods of known chemical composition and in vivo apparent digestibility were analysed overall and 51 foods were used to predict gross energy digestibility and digestible energy content. Modified partial least square calibration models were developed for organic matter (OM), crude protein (CP), ether extract (EE), crude fibre (CF), nitrogen free extracts (NFE) and gross energy (GE) content, the apparent digestibility (OMD, CPD, EED, NFED and GED) and the digestible nutrient and energy content (DOM, DCP, DEE, DNFE and DE) of foods. The calibration equations obtained were evaluated by the standard error and the determination coefficient of cross-validation. The cross-validation coefficients of determination (R) were 0.61, 0.99, 0.91, 0.96, 0.94 and 0.92 for OM, CP, EE, CF, NFE and GE, the corresponding standard error of cross-validation (SECV) being 5.80, 3.51, 13.35, 3.64 and 16.95 g/kg dry matter (DM) and 0.29 MJ/kg DM respectively. The prediction of apparent digestibility was slightly less accurate, but NIRS prediction of digestible nutrient (g/kg DM) and DE (MJ/kg DM) gave satisfactory results, with high R (0.93, 0.97, 0.93, 0.83 and 0.93 for DOM, DCP, DEE, DNFE and DE respectively) and relatively low SECV (11.55, 6.85, 12.14 and 22.98 g/kg DM and 0.47 MJ/kg DM). It is concluded that the precision of NIRS in predicting the energy value of compound extruded foods for dogs is similar or better than by proximate analysis, as well as being faster and more accurate.

Use of near-infrared reflectance spectroscopy in predicting nitrogen, phosphorus and calcium contents in heterogeneous woody plant species

Near-infrared reflectance spectroscopy was applied to determine nitrogen (N), phosphorus (P) and calcium (Ca) content in leaf samples of 18 woody species. A total of 183 samples from mountain, riparian and dry areas from the Central-Western Iberian Peninsula were collected for this purpose. The wide intervals of variation observed in nutrient concentrations (6.6-45.0 g kg(-1) for N, 0.24-2.97 g kg(-1) for P, and 1.00-20.06 g kg(-1) for Ca) were due to the great heterogeneity of the samples. To develop calibration equations, multiple linear regression, and partial least-squares regression (PLSR) were used. In both cases, three mathematical transformations of the data were applied: log1/R and first and second derivatives. The best calibration statistics were obtained using PLSR and derivative transformations (second derivative for N and first derivative for P and Ca). The following coefficients of multiple determination (R2) and standard errors of cross validation were obtained: 0.99 and 0.93 for N, 0.94 and 0.15 for P, and 0.95 and 0.88 for Ca. In the external validation the standard errors of prediction obtained were 0.76 (N), 0.11 (P) and 0.60 (Ca).

Rapid prediction of gross energy and utilizable energy in cereal food products using near-infrared reflectance spectroscopy

Near-infrared (NIR) spectroscopy has been used in foods for the rapid assessment of several macronutrients; however, little is known about its potential for the evaluation of the utilizable energy of foods. Using NIR reflectance spectra (1104-2494 nm) of ground cereal products (n = 127) and values for energy measured by bomb calorimetry, chemometric models were developed for the prediction of gross energy and available energy of diverse cereal food products. Standard errors of cross-validation for NIR prediction of gross energy (range = 4.05-5.49 kcal/g), energy of samples after adjustment for unutilized protein (range = 3.99-5.38 kcal/g), and energy of samples after adjustment for unutilized protein and insoluble dietary fiber (range = 2.42-5.35 kcal/g) were 0.053, 0.053, and 0.088 kcal/g, respectively, with multiple coefficients of determination of 0.96. Use of the models on independent validation samples (n = 58) gave energy values within the accuracy required for U.S. nutrition labeling legislation. NIR spectroscopy, thus, provides a rapid and accurate method for predicting the energy of diverse cereal foods.