Prediction of Purine Derivatives, Creatinine and Total Nitrogen Concentrations in Urine by FT-Near-Infrared Reflectance Spectroscopy (FT-NIR)

Susmel, P.; Piani, Barbara; Toso, Barbara; Stefanon, Bruno

doi:10.1007/978-1-4020-2844-1_19

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…Additionally, the cost related to the technique could be unwarranted, considering all problems that might occur with the use of markers, such as difference across markers and marker recoveries (Oliveira and da Silva, 2007). Evidence in the literature shows accurate predictions of allantoin, uric acid, creatinine, and nitrogen in urine samples of cows through near-infrared spectroscopy (Susmel et al, 2004). This method of analysis could be an alternative to have faster predictions of DMI in practical conditions.…”

Section: Model Validation (External Data Set)-dairy Cattlementioning

confidence: 99%

Urinary purine derivatives as a tool to estimate dry matter intake in cattle: A meta-analysis

Dórea

Danes

Zanton

et al. 2017

Journal of Dairy Science

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The objectives of this study were to investigate the relationship between dry matter intake (DMI) and urinary purine derivative (PD) excretion, to develop equations to predict DMI and to determine the endogenous excretion of PD for beef and dairy cattle using a meta-analytical approach. To develop the models, 62 published studies for both dairy (45 studies) and beef cattle (17 studies) were compiled. Twenty models were tested using DMI (kg/d) and digestible DMI (dDMI, kg/d) as response variables and PD:creatinine (linear term: PD:C, and quadratic term: PD:C), allantoin:creatinine (linear term: ALLA:C, and quadratic term: ALLA:C), metabolic body weight (BW, kg), milk yield (MY, kg/d), and their combination as explanatory variables for dairy and beef (except for MY) cattle. The models developed to predict DMI for dairy cattle were validated using an independent data set from 2 research trials carried out at the University of Wisconsin (trial 1: n = 45; trial 2: n = 50). A second set of models was developed to estimate the endogenous PD excretion. In all evaluated models, the effect of PD (either as PD:C or ALLA:C) was significant, supporting our hypothesis that PD are in fact correlated with DMI. Despite the BW-independent relationship between PD and DMI, the inclusion of BW in the models with PD:C and ALLA:C as predictors slightly decreased the values of root mean square error (RMSE) and Akaike information criterion for the models of DMI. Our models suggest that both DMI and dDMI can be equally well predicted by PD-related variables; however, predicting DMI seems more useful from a practical and experimental standpoint. The inclusion of MY into the dairy models substantially decreased RMSE and Akaike information criterion values, and further increased the precision of the equations. The model including PD:C, BW, and MY presented greater concordance correlation coefficient (0.93 and 0.63 for trials 1 and 2, respectively) and lower RMSE of prediction (1.90 and 3.35 kg/d for trials 1 and 2, respectively) when tested in the validation data set, emerging as a potentially useful estimator of nutrient intake in dairy cows. Endogenous PD excretion was estimated by the intercept of the linear regression between DMI (g/kg of BW) and PD excretion (mmol/kg of BW) for beef (0.404 mmol/kg of BW) and dairy cattle (0.651 mmol/kg of BW). Based on the very close agreement between our results for beef cattle and the literature, the linear regression appears to be an adequate method to estimate endogenous PD excretion.

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Section: Model Validation (External Data Set)-dairy Cattlementioning

confidence: 99%

Urinary purine derivatives as a tool to estimate dry matter intake in cattle: A meta-analysis

Dórea

Danes

Zanton

et al. 2017

Journal of Dairy Science

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“…length selection re ects the molecular structure. However, negative absorbance was observed for the wavelengths of 1400 nm and 1800 nm, and these wavelengths were not used for measurement in previous studies [10][11][12][13][14]. Several papers reported the reason of negative absorbance region.…”

Section: Discussionmentioning

confidence: 99%

“…However, these studies did not consider aspects related to practical use, such as reducing the number of wavelengths. Predicted results of purine derivatives, creatinine, and total nitrogen concentrations in urine samples from cattle, buffaloes, and rabbits using wavelengths in the 7000-4200 cm −1 (1429-2500 nm) range but excluding the 5400-5000 cm −1 (1852-2000 nm) range were reported by Susmel et al [14]. However, they did not consider wavelength reduction and their urine samples were not of human origin.…”

Section: Introductionmentioning

confidence: 93%

“…However, the methods have some disadvantages because they measure only a single component, use a qualitative complicated procedure, and are unsanitary. To solve the above-mentioned problems, many studies related to the measurement of urine components using near-infrared (NIR) spectroscopy have been conducted [10][11][12][13][14]. Pezzaniti et al [10] investigated the feasibility of NIR spectroscopy for quantitating the concentrations of urea, creatinine, glucose, ketone, and protein in urine.…”

Section: Introductionmentioning

confidence: 99%

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Reagentless Estimation of Urea and Creatinine Concentrations Using Near-Infrared Spectroscopy for Spot Urine Test of Urea-to-Creatinine Ratio

Suzuki

Ogawa

Seino

et al. 2018

ABE

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In this work, we developed a measurement system that uses LEDs to estimate multiple components such as urea and creatinine in spot urine samples using near-infrared spectroscopy, considering future transition to LED light sources. In this study, we chose LEDs with 10 standard wavelengths (1400-2300 nm, in 100 nm increments). A multiple regression analysis using all combinations of 10 wavelengths was performed. We prepared glucose-added urine samples (GAU, urine samples from 10 healthy adults, each mixed with glucose). Wavelength selection was performed by comparing the minimum standard error of prediction (SEP, calculated from actual concentration and predicted concentration) for each wavelength combination. We obtained high accuracy for estimating urinary urea and creatinine levels (SEP: 42.4 mg/dl and 7.34 mg/dl, respectively) using four wavelengths for urea including two wavelengths showing negative absorbance, and ve wavelengths for creatinine. Furthermore, an extremely high correlation coef cient (γ > 0.99) was obtained for both components. We calculated urea concentration, creatinine concentration, and urea-to-creatinine ratio using this optical, reagentless method. The low SEP and high γ show that our method is suitable for practical determination of urea-to-creatinine ratio. Thus, this method of analyzing urine samples using NIR spectroscopy can be used to assess protein intake in CKD patients.

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Estimation of Microbial Protein Supply in Ruminants Using Urinary Purine Derivatives

Makkar¹,

Chen²

2004

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Prediction of Purine Derivatives, Creatinine and Total Nitrogen Concentrations in Urine by FT-Near-Infrared Reflectance Spectroscopy (FT-NIR)

Cited by 4 publications

References 10 publications

Urinary purine derivatives as a tool to estimate dry matter intake in cattle: A meta-analysis

Urinary purine derivatives as a tool to estimate dry matter intake in cattle: A meta-analysis

Reagentless Estimation of Urea and Creatinine Concentrations Using Near-Infrared Spectroscopy for Spot Urine Test of Urea-to-Creatinine Ratio

Estimation of Microbial Protein Supply in Ruminants Using Urinary Purine Derivatives

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