Compound Feedingstuffs Research Articles

Scientific Opinion on risk assessment of parasites in fishery products

Human fishery product-borne parasitic diseases are caused by cestodes, trematodes and nematodes and are caused by infection following ingestion of viable parasites, or as allergic (hypersensitivity) reactions against parasite antigens. For allergy, the only parasite in fishery products implicated is the nematode Anisakis simplex, and sensitisation occurs via infection by live larvae. Once sensitised, response to nematode allergens can be highly aggressive and generate severe disease. In a sensitised individual, infection can provoke a concurrent A. simplex allergic episode or can be elicited by exposure to allergen alone from killed parasite: the relative epidemiological impact for each is unknown. Allergy to A. simplex is relatively common in some regions in Spain but rarely reported in other parts of Europe. Prevention of sensitisation is most likely to be effective by control of A. simplex infection. There is more information on the resistance to physical and chemical treatments by A. simplex than for other fishery parasites, and the properties of other parasites are likely to be similar. Many traditional marinating and cold smoking methods are not sufficient to kill A. simplex and freezing or heat treatments remain the most effective processes guaranteeing killing. All wild caught seawater and freshwater fish are must be considered at risk of containing any viable parasites of human health concern if these products are to be eaten raw or almost raw. For wild-catch fish, no sea fishing grounds can be considered free of A. simplex. For farmed Atlantic salmon reared in floating cages or onshore tanks and fed on compound feedstuffs however, the current risk of infection with anisakids is negligible. Apart from farmed Atlantic salmon, sufficient monitoring data are not available for any other farmed fish therefore it is not possible to identify which fish species do not present a health hazard with respect to the presence of parasites.

Spectral library validation to identify ingredients of compound feedingstuffs by near infrared reflectance microscopy

To guarantee feed quality and safety the development and improvement of analytical methods for feed authentication and detection of contaminants is fundamental. Near infrared reflectance microscopy (NIRM) has been investigated as an alternative method to contribute to control systems for feed materials. The major task is the need to build NIRM reference spectral libraries that must represent the variability in feed ingredients. The aim of the present work was to evaluate the performance of a NIRM reference spectral library on animal feed, with external samples of animal feed ingredients and possible contaminants such as processed animal proteins, and in particular to assess its ability to identify ingredients in mixtures. Three external sample sets were used: (A) artificial mixtures, (B) synthetic mixtures and (C) synthetic binary mixtures. The prediction and repeatability results for set A, in which the spectra are from pure ingredients, were very good for both animal and vegetable ingredients and confirm that the spectral library is very good at identifying spectra from pure ingredients. For sets B and C, in which the spectra were measured on mixtures, the prediction results were very disappointing compared with the artificial samples. This means that a strategy that tries to match the spectra taken from a mixture with those of pure ingredients is unlikely to meet with much success. It is possible that an interpolation between pure ingredients for suitably chosen spectral ranges may provide a way to extend this system to mixtures, including mixtures of several ingredients.

Novel approach for interlaboratory transfer of real-time PCR methods: detecting bovine meat and bone meal in feed

The availability of robust methods for the species-specific detection of meat and bone meal (MBM) in compound feedingstuffs is an important prerequisite to enforce current and upcoming European legislation on the use of processed animal proteins in animal nutrition. Among possible methods, those based on DNA turned out to be a reliable tool for this aim, since DNA is a quite thermostable molecule able to resist severe heat treatments applied in the manufacturing of animal meals. The application of such methods by control laboratories implies that the method has been validated including an assessment of its robustness. Successful transferability between laboratories is considered an important robustness criterion of the method. However, corresponding guidelines regarding the design of such a study relevant to this field are missing. Here, we demonstrate the feasibility of an alternative concept that was applied to check for the transferability of a qualitative assay for the detection of banned MBM in feedingstuffs at trace level based on real-time PCR. The concept was based on an experimental nested design applying analysis of variance (ANOVA) that was conducted independently in two laboratories and which allows for establishing major factors influencing the result of analysis. Statistical assessment of the results confirmed the importance of the DNA extraction/purification step utilised, whereas the PCR step turned out to be a minor factor regarding the overall variability of the results. Furthermore, blind samples comprised of compound feed adulterated with MBM at 0.1% and blank compound feed were correctly classified as "positive" or "negative" samples, thus confirming fitness of purpose for the method. This approach can be of interest for other research groups working in the development of real-time PCR methods and in their use by control laboratories.

Linseed oil or sunflower oil as supplements in horse compound feedstuffs : effects on plasma insulin and on plasma triglycerides fatty acids

Sport horses tend to be offered diets high in concentrate during trainings and races. These concentrates are mainly made of cereals, which are high in starch. Owing to this high level of starch, associated disturbances, such as gastrointestinal disorders or laminitis can be observed. Fat has been suggested as an alternative compound to starch. Oil can increase the energy content of the diet and also supplies essential fatty acids. Linseed oil, incorporated into equine diets has been shown to reduce the extent of the plasma insulin profile and the arachidonic acid (C20:4 n-6) content in the plasma, while it increased the alpha linolenic acid (C18:3 n-3) content (Istasse et al, 2007). The aim of the present trial was to compare linseed oil high in C18:3 n-3 with sunflower oil high in linoleic acid (C18:2 n-6), when incorporated in a compound feedstuff.

Reducing NIR prediction errors with nonlinear methods and large populations of intact compound feedstuffs

According to the current demands of the authorities, the manufacturers and the consumers, controls and assessments of the feed compound manufacturing process have become a key concern. Among others, it must be assured that a given compound feed is well manufactured and labelled in terms of the ingredient composition. When near-infrared spectroscopy (NIRS) together with linear models were used for the prediction of the ingredient composition, the results were not always acceptable. Therefore, the performance of nonlinear methods has been investigated. Artificial neural networks and least squares support vector machines (LS-SVM) have been applied to a large (N = 20 320) and heterogeneous population of non-milled feed compounds for the NIR prediction of the inclusion percentage of wheat and sunflower meal, as representative of two different classes of ingredients. Compared to partial least squares regression, results showed considerable reductions of standard error of prediction values for both methods and ingredients: reductions of 45% with ANN and 49% with LS-SVM for wheat and reductions of 44% with ANN and 46% with LS-SVM for sunflower meal. These improvements together with the facility of NIRS technology to be implemented in the process make it ideal for meeting the requirements of the animal feed industry.

Spelt, an ancient cereal and first pressure linseed oil as ingredients of compound feedstuffs for modern horse feeding

Spelt is a covered cereal with large glumellas. In experiment 1, it has been compared in terms of chemical composition with barley, oat and maize. Spelt is characterized by rather low protein and ether extract (EE) contents. The neutral detergent fibre (NDF) content of spelt was slightly higher than that of oat but the acid detergent fibre (ADF) content was lower. Two compound feedstuffs were fed along with hay to six horses used in a cross-over design. Both diets were well appreciated by the horses and there were no significant differences in the apparent digestibility coefficients, except for EE which was significantly higher, when oat was included in the compound feedstuff. In experiment 2, first pressure linseed oil was incorporated at a rate of 8% in a control compound feedstuff, where the barley was partly substituted by the oil. The fat content was 9.6% dry matter (DM) in the linseed oil compound feedstuff and 5.4% in the hay-concentrate ration. The inclusion of linseed oil increased the digestibility of DM, EE and NDF of the diet. There were no effects on the post-prandial concentrations of glucose, triglycerides and non-esterified fatty acids (NEFA) but the insulin concentration was significantly reduced with the linseed oil inclusion. There were significant increases in the plasma concentrations of the total fatty acids, polyunsaturated fatty acids (PUFA), saturated fatty acids (SFA), C18:3 n-3 and C18:2 n-6 and significant reductions in the contents of monounsaturated fatty acids (MUFA), C18:1 n-7 + n-9 and C20:4 n-6. It is concluded that the inclusions of spelt and first pressure linseed oil in compound feed stuff for horse are of interest for modern horse feeding.

Advanced Nonlinear Approaches for Predicting the Ingredient Composition in Compound Feedingstuffs by Near-Infrared Reflection Spectroscopy

For quantitative applications, the most common usage of near-infrared reflection spectroscopy (NIRS) technology, calibration involves establishing a mathematical relationship between spectral data and data provided by the reference. This model may be fairly complex, since the near-infrared spectrum is highly variable and contains physical/chemical information for the sample that may be redundant, and multivariate calibration is usually required. When the relationship to be modeled is nonlinear, classical regression methods are inadequate, and more complex strategies and algorithms must be sought in order to model this nonlinearity. The development of NIRS calibrations to predict the ingredient composition, i.e., the inclusion percentage of each ingredient, in compound feeds is a complex task, due to the nature of the parameters to be predicted and to the heterogeneous nature of the matrices/formulas in which each ingredient participates. The present paper evaluates the use of least squares support vector machines (LSSVM) and two local calibration methods, CARNAC and locally biased regression, for developing NIRS models to predict two of the most representative ingredients in compound feed formulations, wheat and sunflower meal, using a large spectral library of 7523 commercial compound feed samples. For both ingredients, the best results were obtained using CARNAC, with standard errors of prediction (SEP) of 1.7% and 0.60% for wheat and sunflower meal, respectively, and even better results when the algorithm was allowed to refuse to predict 10% of the unknowns. Meanwhile, LSSVM performed less well on wheat (SEP 2.6%) but comparably on sunflower meal (SEP 0.60%), giving results very similar to those reported previously for artificial neural networks. Locally biased regression was the least successful of the three methods, with SEPs of 3.3% for wheat and 0.72% for sunflower meal. All the nonlinear methods improved on the standard approach using partial least squares (PLS), which gave SEPs of 5.3% for wheat and 0.81% for sunflower meal.

EXTERNAL VALIDATION AND TRANSFERABILITY OF NIRS MODELS DEVELOPED FOR DETECTING AND QUANTIFYING MBM IN INTACT COMPOUND FEEDING STUFFS

ABSTRACT The ban on the use of animal‐origin by‐products such as meat and bone meal (MBM) in compound feeds was one of the measures implemented in the European Union to stop the spread of bovine spongiform encephalopathy (BSE) and to prevent its reoccurrence. It is now clear that the blanket ban will only be lifted if reliable analytical methods are available that ensure the detection of animal by‐products. Near‐infrared spectroscopy (NIRS) is likely to be the most rapid method for testing feedingstuffs, enabling a substantial increase in the number of samples tested and providing instant detection of adulteration, especially when samples are analyzed in intact form. This study aimed to demonstrate the feasibility of NIRS predictive models for detecting and quantifying MBM in intact compound feedingstuffs, and to demonstrate the transferability of calibration models between two NIRS instruments. PRACTICAL APPLICATIONSThis study shows the potential of near infrared spectroscopy (NIRS) as a fast screening method for detecting the adulteration of compound feedingstuffs with animal‐origin meals, useful for the feed industry and the inspection bodies. NIRS could provide the first line of defense of the food chain, allowing a large‐scale analysis and making more costly methods to be used more productively on suspect specimens.

Near Infrared Analysis as a First-Line Screening Technique for Identifying Animal Species in Rendered Animal by-Product Meals

The only official method currently available for the identification of feed ingredients in mixtures and compound feedingstuffs is inspection by optical microscopy (OM). From October 2003, the EU adopted Regulation (EC) No. 1774/2002 governing animal by-products (ABPs), which seeks to address the possible risks inherent in recycling potential infectivity due to the absence of barriers within species and to exclude the cannibalism which may be induced by intra-species recycling. The main aim of the present work was to develop and validate near infrared (NIR) spectroscopy chemometric models for the identification of the animal species in ABPs. A total of 352 meat and bone meal (MBM) samples from different species ( n = 80 pure poultry MBM, 75 pure pork MBM and 197 mixtures of cattle MBM with other species) were scanned using a monochromator instrument equipped with a transport module. The total sample set was split into a training set ( n = 234) and a validation set ( n = 118). Various mathematical and scatter correction treatments were tested and used to develop two different PLS2 discriminant models: Model I, to discriminate between ruminant and non-ruminant meals; and Model II, to discriminate between poultry by-product meal, pork meal and a mixture of cattle meal with other species. All models yielded SECV values approaching 0.2 and over 95% correct classification in the training step; whereas in the validation step, approaching 90% (Model I and II) of samples were correctly classified. The methodology presented here, based on discriminant analysis of NIR spectra, for use as a “first line of defence” in identifying animal species in rendered ABP meals, provides a reliable, fast and affordable means of enforcing legislation concerning the ban on MBM.

Optimization of Discriminant Partial Least Squares Regression Models for the Detection of Animal By-Product Meals in Compound Feedingstuffs by Near-Infrared Spectroscopy

This paper evaluates two multivariate strategies for classifying near-infrared (NIR) spectroscopic data for the detection of animal by-product meals (henceforth generically termed AbP) as an ingredient in compound feedingstuffs. Classification models were developed to discriminate between the presence and absence of animal-origin meals in compound feeds using two forms of discriminant partial least squares (PLS) regression: the algorithms PLS1 and PLS2. The training set comprised 433 commercial feeds, of which 148 contained AbP and the other 285 were stated to be AbP-free. Since the initial set contained unequal numbers of each class, the effect of this imbalance was analyzed by applying the same algorithms to a training set containing equal numbers of AbP-free and AbP-containing samples. The best classification model (97.42% of samples correctly classified), obtained with PLS2, that showed less sensitivity to the use of class-unbalanced sets, was externally validated using a set of 18 samples (10 AbP-containing and 8 AbP-free); all samples were correctly classified, except for one AbP-free sample that was classified as containing AbP (false positive). The results suggest that the application of PLS discriminant analysis to NIR spectroscopic data enables detection of AbP, a feed ingredient banned since the bovine spongiform encephalopathy (BSE) crisis; this confirms the value of NIRS qualitative analysis for product authentication purposes.

Use of Artificial Neural Networks in Near-Infrared Reflectance Spectroscopy Calibrations for Predicting the Inclusion Percentages of Wheat and Sunflower Meal in Compound Feedingstuffs

The use of near-infrared reflectance spectroscopy (NIRS) calibrations to predict the ingredient composition in compound feeds (i.e., inclusion percentage of each ingredient) is a complex task, regarding both the nature of the parameters to be predicted, since they are not well-defined chemical entities, and the heterogeneousness of the matrices/formulas in which each ingredient participates. The present paper evaluates the use of nonlinear regression methods, such as artificial neural networks (ANN), for developing NIRS calibrations to predict these parameters. Two of the most representative ingredients in the Spanish compound feed formulations (wheat and sunflower meal) were selected for evaluating ANN possibilities, using a large spectral library comprising a total of 7523 commercial compound feed samples; 7423 were used as training set and 100 as validation set. Three general models of networks were studied: multilayer perceptron with back-propagation training (BP), multilayer perceptron with Levenberg-Maquartd training (LM), and radial basis function nets (RBF); moreover, in accordance with a factorial design, more complex architectures were evaluated gradually, changing the number of hidden layers and hidden neurons, for the determination of the optimal network topology. For both ingredients, the best results were obtained using ANN with BP training, showing prediction error values (SEP) of 2.72% and 0.66% for wheat and sunflower meal, respectively. These SEP values showed a significant improvement (19%-49% for sunflower meal and wheat, respectively) in comparison with those obtained using calibrations developed with linear methods.

Screening of compound feeds using NIR hyperspectral data

Recent developments in spectroscopy have led to the use of spectroscopic imaging instruments for the control and monitoring of food and feed products. This kind of instruments offers the possibility of collecting thousands of spectra of particles being the result of the grinding of compound feedstuffs. The major advantages are that the recognition of feed ingredients is independent on the expertise of the analyst and that it is possible to automate all procedures and to analyse more samples per unit of time than classical microscopy or NIR microscopy. The objective of this study is the development of a new method for a rapid, precise and reliable screening of compound feeds. For that, a classification tree was built by sorting the particles in a dichotomist way where each node constitutes a discriminating step. These steps are completed by discriminant equations created from the hyperspectral databases obtained with a near infrared (NIR) camera for each class of raw materials. Discriminant equations were constructed using Support Vector Machines (SVM). For a new sample the aim is to determine its composition by using the classification tree. As general conclusion, hyperspectral data in combination with SVM as classification technique is a promised methodology for the determination of open formulations.

Effective PCR detection of animal species in highly processed animal byproducts and compound feeds

In this paper we present a polymerase chain reaction (PCR)-based method for detecting meat and bone meal (MBM) in compound feedingstuffs. By choosing adequate DNA targets from an appropriate localisation in the genome, the real-time PCR method developed here proved to be robust to severe heat treatment of the MBM, showing high sensitivity in the detection of MBM. The method developed here permits the specific detection of processed pig and cattle materials treated at 134 degrees C in various feed matrices down to a limit of detection of about 0.1%. This technique has also been successfully applied to well-characterised MBM samples heated to as high as 141 degrees C, as well as to various blind feed samples with very low MBM contents. Finally, the method also passed several official European ring trials.

Remote near Infrared Instrument Cloning and Transfer of Calibrations to Predict Ingredient Percentages in Intact Compound Feedstuffs

This study examined the possibility of transferring calibrations to predict ingredient inclusion percentages in intact compound feedstuffs. For standardisation purposes, the cloning algorithm proposed by Shenk and Westerhaus was used. Three different cloning sets were evaluated for the design of seven spectral correction files or cloning matrices. Six of these correction files, when applied to spectra obtained from the satellite instrument, yielded results similar to those obtained using the master instrument. Moreover, using a validation set ( N = 15) to compare predicted values from unstandardised and standardised satellite spectra with master spectra using the same calibrations, the standard error of differences ( SED) was greatly reduced with all six cloning matrices. This indicates the success of the tuning method for standardisation and transfer of ingredient composition prediction equations in compound feedstuffs analysed unground.

Evaluation of Pretreatment Strategies for Near-Infrared Spectroscopy Calibration Development of Unground and Ground Compound Feedingstuffs

Chemometric procedures are usually applied to near-infrared (NIR) spectra in order to obtain prediction models. These procedures include the application of different combinations of spectral mathematical pretreatments for the improvement of calibrations and the selection of the best model on the basis of validation results. In this work, we used an automatic routine to obtain calibrations for unground and ground compound feedingstuffs (N=354 samples), including 49 combinations of pretreatments (first and second derivatives, an auto scaling procedure, detrending and two versions of multiplicative scatter correction). Calibrations for crude fiber and crude protein were developed without elimination of outliers and with 2 or 9 maximum passes of elimination of outliers. Validation statistics were highly influenced by the pretreatments used, as a combined result of their ability to improve the detection of outliers and the model adjustment. The standard error of prediction (SEP) values ranged from 0.61 to 1.27 for crude protein (CP) and from 0.74 to 1.33 for crude fiber (CF). In spite of the fact that validation statistics did not show a clear distribution pattern, some combinations of pretreatments provided consistently better results.

Implementation of LOCAL Algorithm with Near-Infrared Spectroscopy for Compliance Assurance in Compound Feedingstuffs

Seven thousand four hundred and twenty-three compound feed samples were used to develop near-infrared (NIR) calibrations for predicting the percentage of each ingredient used in the manufacture of a given compound feedingstuff. Spectra were collected at 2 nm increments using a FOSS NIRSystems 5000 monochromator. The reference data used for each ingredient percentage were those declared in the formula for each feedingstuff. Two chemometric tools for developing NIRS prediction models were compared: the so-called GLOBAL MPLS (modified partial least squares), traditionally used in developing NIRS applications, and the more recently developed calibration strategy known as LOCAL. The LOCAL procedure is designed to select, from a large database, samples with spectra resembling the sample being analyzed. Selected samples are used as calibration sets to develop specific MPLS equations for predicting each unknown sample. For all predicted ingredients, LOCAL calibrations resulted in a significant improvement in both standard error of prediction (SEP) and bias values compared with GLOBAL calibrations. Determination coefficient values (r(2)) also improved using the LOCAL strategy, exceeding 0.90 for most ingredients. Use of the LOCAL algorithm for calibration thus proved valuable in minimizing the errors in NIRS calibration equations for predicting a parameter as complex as the percentage of each ingredient in compound feedingstuffs.

Near-infrared reflectance spectroscopy (NIRS) for the mandatory labelling of compound feedingstuffs: chemical composition and open-declaration

Near-infrared (NIR) calibrations were developed for the instantaneous and simultaneous prediction of the chemical and ingredient composition of compound feedingstuffs for different types of animals. Different sample presentation modes were also compared (ground versus unground) in order to demonstrate that, although the ground presentation is common in NIR analysis, the analysis of unground samples can also be viable, obviating the need for tedious milling. Calibration equations for the prediction of chemical composition in compound feedingstuffs showed similar accuracy for the different modes of analysis assayed. The equations obtained for unground compound feedingstuffs ( N = 433) are reliable enough for quality control at the feed mill. The coefficient of determination ( R 2) and the standard error of cross-validation (SECV) values ranged from good to very good, depending on the parameter: moisture (0.87 and 4.9 g kg −1), crude protein (0.97 and 5.5 g kg −1), crude fat (0.94 and 6.1 g kg −1), crude fibre (0.98 and 5.2 g kg −1) and ash (0.85 and 6.9 g kg −1). Equations for predicting ingredient composition (g kg −1) also showed similar accuracy for the analysis of ground and unground compound feedingstuffs. The equations showed an excellent ability ( R 2 ≥ 0.9; RPD ≥ 3) to predict the amount of sunflower meal, gluten feed, lucerne, beet pulp, palm meal, meat and bone meal (MBM), poultry meal, total meat meal (MBM + poultry meal), animal fat and mineral–vitamin supplement. For other ingredients, including corn, barley, lupin, manioc, soybean meal, wheat bran, molasses, calcium carbonate, dicalcium phosphate and methionine, the precision and accuracy of the equations obtained could be considered good ( R 2 ≥ 0.7). Calibrations for wheat, fish meal, lysine and salt displayed limited predictive ability (0.5 ≤ R 2 ≤ 0.7). Moreover, a partial least squares (PLS) discriminant model was developed, which classified correctly all samples as free or as containing MBM.

Penicillium crustosum . [Descriptions of Fungi and Bacteria

Abstract A description is provided for Penicillium crustosum . Information is included on the disease caused by the organism, its transmission, geographical distribution, and hosts. HOSTS: Although primarily considered to be a weak pathogen of pomaceous fruits, the species is an ubiquitous spoilage organism, particularly in cereals and compound feedstuffs, and probably also on decaying vegetation in temperate climates (PITT & HOCKING, 1997). Penicillium crustosum has also been isolated from cheese, blueing powder for cheese, fruit juice, thyme, cassava, citrus, copper electroplating solution, PVA emulsion, poultry litter, soil and tobacco (CABI BIOSCIENCE, 1999). DISEASES: It is considered to be a weak pathogen of pomaceous fruits, particularly apples (SANDERSON & SPOTTS, 1995). GEOGRAPHIC DISTRIBUTION: World-wide, but more commonly isolated in temperate climates.

Ad Hoc Labelling of Compound Feedingstuffs: The Feed Industry Bets by near Infrared Spectroscopy

<i>Ad Hoc</i> Labelling of Compound Feedingstuffs: The Feed Industry Bets by near Infrared Spectroscopy

Transfer of chiortetracycline from contaminated feedingstuff to cows' milk

Three groups of four Friesian cows in mid-lactation were fed a compound feedingstuff contaminated with 2, 10 or 300 mg chlortetracycline/kg for 21 days, and were then fed an uncontaminated...