Prediction of Mineral Composition in Commercial Extruded Dry Dog Food by Near-Infrared Reflectance Spectroscopy

Effectiveness of near-infrared spectroscopy to predict the chemical composition of feces and total-tract apparent nutrients digestibility estimated with uNDF or AIA in lactating buffaloes' feces

Following a comparison of nutrient total-tract digestibility estimates in lactating buffaloes using single-point undigestible NDF (uNDF) or acid-insoluble ash (AIA) as internal markers, the potential of fecal near-infrared spectroscopy (NIRS) to provide calibration equations for the assessment of the chemical composition of feces and nutrient total-tract digestibility estimated with internal markers was explored. Chemical analyses were performed on 147 fecal samples from lactating buffaloes reared in 5 farms in central Italy (Naples). Each farm fed a silage-based total mixed ration (TMR) to the buffaloes, which was sampled in the 2 d before the fecal collection. The TMR and individual fecal samples were collected and analyzed for dry matter (DM), organic matter (OM), ash, AIA, ether extract (EE), starch, fiber fractions (aNDFom, aNDF, ADFom, ADF, hemicellulose, cellulose, ADL, uNDF), N, CP and CP bound to aNDF (NDICP) and to ADF (ADICP). The uNDF content was determined through a 240-h in vitro fermentation and employed, together with AIA as markers to estimate the total-tract apparent digestibility (ttaDe) and total-tract digestibility (ttDe) of DM, OM, ash, N, CP, EE, aNDFom, aNDF, NDIP, ADFom and ADF, ADIN, ADL, hemicellulose, cellulose, starch, non-fibrous carbohydrates (NFC) and fraction B3 of N. No correlation was found between DM and OM digestibility estimated with AIA and uNDF as internal markers. Weak correlations were detected for all the other nutients digestibilities while strong correlations were observed for EE, ADFom, HC, NDIN, ADIN, NB3, NFC and starch.Inizio modulo The sample set (n = 147) was divided in a calibration set (n = 111) and a validation set (n = 36) to "train" and "validate" the fecal NIRS curve through an external validation process. An estimation usable for preliminary or initial evaluation was obtained for N, CP and aNDF fecal content. An excellent prediction was obtained for ttADINDe (R2 = 0.90) when estimated with uNDF as internal marker. The NIRS technology was not able to accurately predict all the other traits and the estimated nutrient digestibility of lactating buffalo diets from fecal spectra.

Detection and analysis of hyaluronic acid raw materials from different sources by NIR and aquaphotomics

Hyaluronic acid (HA), a polysaccharide, is widely used for its essential physiological functions. Although the structures of low molecular weight HA produced by both acid and enzyme degradation methods are extremely similar, there are still differences due to the different degradation principles. There is currently no clear way to distinguish between HA prepared by acidolysis and enzymatic hydrolysis. Based on near-infrared (NIR) spectroscopy and aquaphotomics technology, a method for distinguishing HA raw materials and their mixtures from different sources was proposed, and HA with different mixed ratios was accurately quantified. First, NIR spectra of the HA samples were collected. The spectra were then preprocessed to improve the spectral resolution. Spectral information was extracted based on wavelet transform and principal component analysis, resulting in a final selection of 12 characteristic wavelengths containing classification information. The discriminative and quantitative models were then constructed using the 12 wavelengths. The discriminative model achieved a 100% identification rate for HA from different sources. The correlation coefficient of calibration (Rc), validation (Rp), external test (Rt), root mean square error of cross validation (RMSECV), calibration (RMSEC), validation (RMSEP), and external test (RMSET) of the mixed proportion quantitative model were 0.9876, 0.9876, 0.9898, 0.0546, 0.0433, 0.0440, and 0.0347, respectively. In this study, the problem of structural similarity and non-identifiability of HA produced by acidolysis and enzymatic hydrolysis was addressed, and quality monitoring of HA feedstock in HA circulating links was achieved. This is the first time to achieve accurate quantification of solid mixtures using the aquaphotomics method.

In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle

Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize nutrition and productivity and to detect health issues at an early stage. The possibility of predicting blood metabolites from milk samples obtained during routine milking by means of infrared spectroscopy has become increasingly attractive. We developed, for the first time, prediction equations for a set of blood metabolites using diverse machine learning methods and milk near-infrared spectra collected by the AfiLab instrument. Our dataset was obtained from 385 Holstein Friesian dairy cows. Stacking ensemble and multi-layer feedforward artificial neural network outperformed the other machine learning methods tested, with a reduction in the root mean square error of between 3 and 6% in most blood parameters. We obtained moderate correlations (r) between the observed and predicted phenotypes for γ-glutamyl transferase (r = 0.58), alkaline phosphatase (0.54), haptoglobin (0.66), globulins (0.61), total reactive oxygen metabolites (0.60) and thiol groups (0.57). The AfiLab instrument has strong potential but may not yet be ready to predict the metabolic stress of dairy cows in practice. Further research is needed to find out methods that allow an improvement in accuracy of prediction equations.

Optimizing near Infrared Reflectance Spectroscopy to Predict Nutritional Quality of Chickpea Straw for Livestock Feeding

Simple Summary

The potential of near infrared reflectance spectroscopy (NIRS) to predict the nutritive value of chickpea straw was identified. Spectral data of 480 samples of chickpea straw (40 genotypes) were scanned with a spectral range of 1108 to 2492 nm. The samples were reduced to 190 representative samples based on the spectral data then divided into a calibration set (160 samples) and a cross-validation set (30 samples). All 190 samples were analysed for dry matter, ash, crude protein, neutral detergent fibre, acid detergent fibre, acid detergent lignin, Zn, Mn, Ca, Mg, Fe, P, and in vitro gas production metabolizable energy using conventional methods. The prediction equations were generated by multiple regression analysis. The NIRS prediction equations in the study accurately predicted the nutritive value of chickpea straw (R² of cross validation > 0.68; standard error of prediction < 1%). Chickpea straw nutritive value could be predicted using NIRS.

Abstract

Multidimensional improvement programs of chickpea require screening of a large number of genotypes for straw nutritive value. The ability of near infrared reflectance spectroscopy (NIRS) to determine the nutritive value of chickpea straw was identified in the current study. A total of 480 samples of chickpea straw representing a nation-wide range of environments and genotypic diversity (40 genotypes) were scanned at a spectral range of 1108 to 2492 nm. The samples were reduced to 190 representative samples based on the spectral data then divided into a calibration set (160 samples) and a cross-validation set (30 samples). All 190 samples were analysed for dry matter, ash, crude protein, neutral detergent fibre, acid detergent fibre, acid detergent lignin, Zn, Mn, Ca, Mg, Fe, P, and in vitro gas production metabolizable energy using conventional methods. Multiple regression analysis was used to build the prediction equations. The prediction equation generated by the study accurately predicted the nutritive value of chickpea straw (R² of cross validation > 0.68; standard error of prediction < 1%). Breeding programs targeting improving food-feed traits of chickpea could use NIRS as a fast, cheap, and reliable tool to screen genotypes for straw nutritional quality.

Handheld near-infrared spectrometer allows on-line prediction of beef quality traits

The aim of this study was to evaluate the ability of a miniaturized near-infrared spectrometer to predict chemical parameters, technological and quality traits, fatty acids and minerals in intact Longissimus thoracis and Trapezius obtained from the ribs of 40 Charolais cattle. Modified partial least squares regression analysis to correlate spectra information to reference values, and several scatter correction and mathematical treatments have been tested. Leave-one-out cross-validation results showed that the handheld instrument could be used to obtain a good prediction of moisture and an approximate quantitative prediction of fat or protein contents, a*, b*, shear force and purge loss with coefficients of determination above 0.66. Moreover, prediction models were satisfactory for proportions of MUFA, PUFA, oleic and palmitic acids, for Fe and Cu contents. Overall, results exhibited the usefulness of the on-line miniaturized tool to predict some beef quality traits and the possibility to use it with commercial cuts without sampling, carcass deterioration nor grinding and consequent meat products' loss.

Clinical Findings in Healthy Dogs Fed With Diets Characterized by Different Carbohydrates Sources

In recent years, pet owners have become more interested in the ingredients, and quality of pet-food, and several studies have demonstrated that feed management could affect healthy status. Recently, some authors indicated that commercial diets formulated without cereals, or using unconventional protein, and starch sources, can cause a reduction in taurine levels in both whole blood, and plasma. Nevertheless, the specific mechanism by means of which nutritional factors determine this reduction is not completely clear. Thirty neutered half-breed dogs were recruited at a kennel in the province of Naples (Italy) to investigate the influence of carbohydrates sources, and dietary density of nutrients on healthy status of dogs in terms of blood count, and biochemical parameters. The dogs were housed in the kennel and divided into three distinct groups. Three iso-energy, and iso-nitrogen commercial kibble diets (named GF1, GF2, and CB) with different protein, and carbohydrates contents, and carbohydrates sources were chosen for the trial. The chemical composition and amino acid profile of each of the three tested diets were analyzed. Moreover, blood samples of each dog were collected to evaluate the hematological and biochemical profiles. The taurine level was determined both on plasma and whole blood. The effect of the diets was analyzed statistically, and all tested diets were compared to the control one. There were significant differences between the three tested diets as regards their chemical composition. The concentrations of all amino acids seem to reflect protein content diets. The hematological profile resulted within the ranges considered physiological for the canine species for all subjects. Compared to the control diet, the three tested diets showed significant differences in blood count for MCHC and platelets. The biochemical profile showed significant differences between the diets, particularly their AST, fructosamine, lipase, and triglycerides values. The diets did not affect the blood and plasma taurine levels. They resulted in higher than optimal reserve levels. Preliminary results showed that the sources of carbohydrates and use of balanced diets affected only some biochemical parameters and did not alter the levels of taurine in healthy adult dogs.

Zinc in Dog Nutrition, Health and Disease: A Review

Simple Summary

This work compiles the current state of knowledge regarding zinc requirements of healthy dogs and biomarkers of zinc status. To ensure an adequate zinc status, it is important to know the zinc content of foods and their bioavailability to assess the need and the ideal supplementation strategy regarding levels and sources of additives in complete dog foods. As zinc is required for enzymatic, structural, and regulatory functions in the animal body, its nutritional status has been associated with several pathologies that may be due to, or exacerbated by, a deficit of dietary zinc supply.

Abstract

Zinc is an essential trace element, required for enzymatic, structural, and regulatory functions. As body reserves are scarce, an adequate zinc status relies on proper dietary supply and efficient homeostasis. Several biomarkers have been proposed that enable the detection of poor zinc status, but more sensitive and specific ones are needed to detect marginal deficiencies. The zinc content of commercial dry dog foods has great variability, with a more frequent non-compliance with the maximum authorized limit than with the nutritional requirement. The bioavailability of dietary zinc also plays a crucial role in ensuring an adequate zinc status. Despite controversial results, organic zinc sources have been considered more bioavailable than inorganic sources, albeit the zinc source effect is more evident after a restriction period of dietary zinc. Many disorders have been associated with inadequate zinc status, not being clear whether the occurrence of the disease is the consequence or the cause. This review presents data on zinc requirements and biomarkers for zinc status, that can be applied for the development of supplementation strategies of zinc in complete pet foods. Moreover, it provides an understanding of the role zinc plays in the health of dogs, and how altered zinc status affects diseases in dogs.

Mineral Composition of Cereal and Cereal-Free Dry Dog Foods versus Nutritional Guidelines

The aims of the present work are to estimate the nutritional value and to evaluate and compare the levels of macroelements (Ca, P, K, Na, Mg), microelements (Fe, Zn, Mn, Cu), heavy metals (Co, Cd, Pb, Mo, Cr, Ni), and their ratios in extruded complete foods for adult dogs, their compatibility with nutritional guidelines, as well as food profile similarity. Basic composition was determined according to Association of Official Analytical Chemists (AOAC). Analyses for elements were performed using an atomic absorption spectrometer. All the evaluated dry dog foods met the minimum recommended levels for protein and fat. Eighteen tested dog foods (60%) did not meet at least one recommendation of nutritional guidelines. Four dog foods exceeded the legal limit of Fe and five foods exceeded the legal limit of Zn; in one of them, Zn level was almost twice higher. Dog foods with insect protein exceeded the legal limit for Mn content. Eight dog foods had an inappropriate Ca:P ratio. Heavy metals were below detection limit in all analyzed dog foods. The results seem to show the need for regular feed analyses of the elemental composition in raw materials before introducing supplementation and for the monitoring of the mineral composition of finished pet food.

Application of a Handheld Near-Infrared Spectrometer to Predict Gelatinized Starch, Fiber Fractions, and Mineral Content of Ground and Intact Extruded Dry Dog Food

Simple Summary

The pet food industry is interested in performing fast analyses to control the nutritional quality of their products. Despite having some limitations related to the need to modify the production process or to have a laboratory to prepare the samples for analysis through desktop instruments, near-infrared spectroscopy is one of the most used technologies for inexpensive analysis of foodstuffs. Thus, the miniaturization of infrared devices allows a wider industrial applicability of this technique. Information on the use of miniaturized infrared tools in the pet food sector is currently very limited, and the present research is the first attempt to predict the total and gelatinized starch, insoluble fibrous fractions, and mineral content of ground and intact dry pet food using the handheld NIR scanner SCiO™. Results from the current study revealed no significant differences in the predictive ability of the instrument using both ground and intact samples. The instrument offers a potential for screening purposes of both total and gelatinized starch, revealing the potential to monitor their content and ratio in commercial dog food on a large scale. Improvements such as widening the wavelength range is expected to increase prediction models’ accuracy.

Abstract

The aim of the present study was to investigate the ability of a handheld near-infrared spectrometer to predict total and gelatinized starch, insoluble fibrous fractions, and mineral content in extruded dry dog food. Intact and ground samples were compared to determine if the homogenization could improve the prediction performance of the instrument. Reference analyses were performed on 81 samples for starch and 99 for neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and minerals, and reflectance infrared spectra (740 to 1070 nm) were recorded with a SCiO™ near-infrared (NIR) spectrometer. Prediction models were developed using modified partial least squares regression and both internal (leave-one-out cross-validation) and external validation. The best prediction models in cross-validation using ground samples were obtained for gelatinized starch (residual predictive deviation, RPD = 2.54) and total starch (RPD = 2.33), and S (RPD = 1.92), while the best using intact samples were obtained for gelatinized starch (RPD = 2.45), total starch (RPD = 2.08), and K (RPD = 1.98). Through external validation, the best statistics were obtained for gelatinized starch, with an RPD of 2.55 and 2.03 in ground and intact samples, respectively. Overall, there was no difference in prediction models accuracy using ground or intact samples. In conclusion, the miniaturized NIR instrument offers the potential for screening purposes only for total and gelatinized starch, S, and K, whereas the results do not support its applicability for the other traits.

Miniaturized Fluorimetric Method for Quantification of Zinc in Dry Dog Food

Zinc is an essential trace element for animals in several biological processes, particularly in energy production, and it is acquired from food ingestion. In this context, a microplate-based fluorimetric assay was developed for simple, fast, and low-cost determination of zinc in pet food using 2,2'-((4-(2,7-difluoro-3,6-dihydroxy-4aH-xanthen-9-yl)-3-methoxyphenyl)azanediyl)diacetic acid (FluoZin-1) as fluorescent probe. Several aspects were studied, namely, the stability of the fluorescent product over time, the FluoZin-1 concentration, and the pH of reaction media. The developed methodology provided a limit of detection of 1 μg L-1 in sample acid digests, with a working range of 10 to 200 μg L-1, corresponding to 100-2000 mg of Zn per kg of dry dog food samples. Intraday repeatability and interday repeatability were assessed, with relative standard deviation values < 3.4% (100 μg L-1) and <11.7% (10 μg L-1). Sample analysis indicated that the proposed fluorimetric assay provided results consistent with ICP-MS analysis. These results demonstrated that the developed assay can be used for rapid determination of zinc in dry dog food.

At-line Prediction of Gelatinized Starch and Fiber Fractions in Extruded Dry Dog Food Using Different Near-Infrared Spectroscopy Technologies

Simple Summary

Starch is a non-fibrous carbohydrate that represents an important percentage of pet food composition. The degree of its gelatinization, due to the cooking process, can be a useful indicator of starch digestibility in the diet. Moreover, fiber fractions are important for animals’ health and nutritional status, so pet food industry is interested in the development of an easy and cost-effective method to measure these parameters. Results of this study revealed the applicability of visible/near-infrared spectroscopy to predict total and gelatinized starch, neutral detergent fiber, acid detergent fiber, and acid detergent lignin in pet food. On the other hand, near-infrared transmittance technology showed a scarce accuracy. The developed prediction models for total and gelatinized starch and fiber fractions using visible/near-infrared spectroscopy could be applied during the manufacturing process to perform quality controls.

Abstract

This study aimed to assess the feasibility of visible/near-infrared reflectance (Vis-NIR) and near-infrared transmittance (NIT) spectroscopy to predict total and gelatinized starch and fiber fractions in extruded dry dog food. Reference laboratory analyses were performed on 81 samples, and the spectrum of each ground sample was obtained through Vis-NIR and NIT spectrometers. Prediction equations for each instrument were developed by modified partial least squares regressions and validated by cross- (CrV) and external validation (ExV) procedures. All studied traits were better predicted by Vis-NIR than NIT spectroscopy. With Vis-NIR, excellent prediction models were obtained for total starch (residual predictive deviation; RPD_CrV = 6.33; RPD_ExV = 4.43), gelatinized starch (RPD_CrV = 4.62; RPD_ExV = 4.36), neutral detergent fiber (NDF; RPD_CrV = 3.93; RPD_ExV = 4.31), and acid detergent fiber (ADF; RPD_CrV = 5.80; RPD_ExV = 5.67). With NIT, RPD_CrV ranged from 1.75 (ADF) to 2.61 (acid detergent lignin, ADL) and RPD_ExV from 1.71 (ADL) to 2.16 (total starch). In conclusion, results of the present study demonstrated the feasibility of at-line Vis-NIR spectroscopy in predicting total and gelatinized starch, NDF, and ADF, with lower accuracy for ADL, whereas results do not support the applicability of NIT spectroscopy to predict those traits.