Effect of Fat on Protein Estimation in Milk and Its Correlation with Lactose in Different Milk Types: A Small-Scale Study

Background: Estimation of macronutrients like protein and lactose is important to assess the quality of milk. To estimate these two macronutrients, ten raw milk samples obtained from each group of different animals (cow, goat, buffalo), ten pasteurized cow milk and ten human milk samples were analysed. Methods: Bicinchoninic acid (BCA) method was used to estimate protein from different milk samples. Four different sample preparation protocols were compared to check the effect of fat on BCA based protein estimation: dilution (D), fat removal-protein precipitation (FR and PP), fat removal-dilution (FR and D) and dilution-fat removal (D and FR). For lactose quantification, ultrahigh-performance liquid chromatography-mass spectrometry-selected reaction monitoring (UHPLC-MS/SRM) method was developed and validated using 13C6 lactose as internal standard (ISTD).Result: Among these four different protocols, D and FR method showed consistent data for total protein content in animal milk (cow-3.16%, goat-3.21%, buffalo-3.81%, pasteurized-2.98%) and FR and PP showed consistent data in human milk samples (1.2%). Though BCA method is simple to use, proper sample preparation protocol has to be applied prior to protein estimation to avoid the interference due to fat or lactose. In case of lactose, inter-day validation showed the accuracy ranging from 97.13 to 100.54%, coefficient of variation varying between 0.1 to 1.53%, correlation R2=0.999. Lactose is in the range of 4.1 to 4.8% in animal milk and 6.6% in human milk samples. The internal ratio of lactose/protein (1.28 to 1.55 in animal milk and 5.33 in human milk) will be useful to differentiate human milk from animal milk type and to assess the milk quality.

Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level

Crude protein estimation is an important parameter for perennial ryegrass (Lolium perenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top-of-canopy reflectance. The study contrasts different spectral ranges while selecting a minimal number of bands and analyzing achievable accuracies for crude protein expressed as a dry matter fraction or on a weight-per-area basis. In addition, the model’s prediction performance in known and new locations is compared. This data collection comprised 266 full-range (350–2500 nm) proximal spectral measurements and corresponding ground truth observations in Australia and the Netherlands from May to November 2018. An exhaustive-search (based on a genetic algorithm) successfully selected band subsets within different regions and across the full spectral range, minimizing both the number of bands and an error metric. For field conditions, our results indicate that the best approach for crude protein estimation relies on the use of the visible to near-infrared range (400–1100 nm). Within this range, eleven sparse broad bands (of 10 nm bandwidth) provide performance better than or equivalent to those of previous studies that used a higher number of bands and narrower bandwidths. Additionally, when using top-of-canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein on its weight-per-area basis (RMSEP 80 kg.ha−1). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha−1). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5–3.0 percent dry matter in optimal models (requiring ten bands). However, these models display a low explanatory ability for the observed variability (R2 > 0.5), rendering them only suitable for qualitative grading.

Bradford assay as a high-throughput bioanalytical screening method for conforming pathophysiological state of the animal

Proteins are the essential components of the tissues that play a key role in the body. Its expression in the cell or tissue under a specified set of conditions and at a particular time regulates the different body conditions either as a normal body function or as a disease state. Protein is an important building block of muscles, skin, cartilage, bones and blood. Bradford assay is a reliable advanced and cost-effective protein estimation test for determining the exact concentration of protein in different tissues of the animal. In this study, we have taken a rat suffering from protein deficiency disorder and total protein concentration in the heart, brain, liver, blood and kidney was determined. It was found that the total protein concentration in different tissues of rat i.e., heart, brain, liver, plasma and kidney was found to be 8.39 ± 0.75, 10.46 ± 0.76, 6.74 ± 0.39, 8.12 ± 0.32 mg/g of tissue and 61.27 ± 0.95 mg/mL of plasma respectively (mean ± SEM). As compared to earlier published reports the total protein concentration in different tissues like hear, brain, liver and kidney found much lower to standard value as reported by Beyer, the reason behind obtaining this kind of results may be due to the presence of insufficient amount of the protein content in different tissue of animal as suffering from protein degeneration disorder. The rat was unable to digest and store the protein or catabolism was much faster than anabolism. Keywords: Anabolism, Bradford assay, Catabolism, Protein estimation.

Perbandingan Model Pendugaan Total Digestible Nutrients (TDN) dan Protein Tercerna pada Domba Garut Jantan yang Diberi Ransum Berbasis Bahan Pakan Lokal

ABSTRAK. Perhitungan total digestible nutrients (TDN) dan Protein tercerna secara biologis sering­kali mengalami kesulitan sehingga dilakukan perhitungan dengan menggunakan model pendugaan. Penelitian bertujuan untuk mem­bandingkan model pendugaan TDN dan protein tercerna pada domba Garut jantan yang diberi ran­sum berbahan baku pakan lokal. Dua puluh empat ekor domba Garut diberi ransum berbasis bahan pakan lokal dengan kandungan TDN dan protein berbeda, lalu diukur nilai TDN dan protein tercerna. Nilai keakuratan model pendugaan TDN dan protein tercerna diukur dengan perhitungan ratio prediction to deviation (RPD), Hubungan TDN dan protein tercerna in vivo dengan berbagai model pendugaan dilakukan dengan menggunakan analisis regresi. Model pendugaan yang digunakan untuk mengukur TDN adalah model Sutardi, Wardeh dan Harris et al., sedangkan model pendugaan protein tercerna menggunakan model Beenson dan Knight dan Haris. Hasil menunjukkan bahwa model pendugaan TDN Wardeh lebih akurat dibandingan dengan model Sutardi maupun Beenson dengan nilai ratio prediction to deviation (RDP) = 2,45, R2 = 08629 dan r = 0,9289. Model pendugaan protein tercerna Beenson dan Knight dan Haris tidak dapat digunakan karena memiliki nilai RDP yang sangat rendah. Kesimpulannya model pendugaan Wardeh lebih akurat dalam mengukur TDN pada domba Garut jantan. (Comparison of the total digestible nutrients (TDN) and digestible proteins models in male Garut sheep fed local feed-based rations) ABSTRACT. Calculation of total digestible nutrients (TDN) and digested proteins biologically are often difficult, so calculations are made using the estimation model. The study aimed to compare the estimation model of TDN and digestible proteins in male Garut sheep fed local feed-based rations. Twenty-four of male Garut sheep were given various types of rations based on local feed ingredients with different TDN and protein content, then measured the value of TDN and digested protein. Then the accuracy of the TDN and digested protein estimation model was measured by calcu­lating the ratio of prediction to deviation (RPD), while measuring the relationship of TDN and digested proteins In Vivo with various estimation models was carried out using regression analysis. The estimation model used to measure TDN was the Sutardi, Wardeh and Harris et al. models, while the digested protein estima­tion model is using Beenson and Knight and Haris models. The results show that the Wardeh TDN estimation model is more accu­rate compared to the Sutardi and Beenson models with the RDP = 2.45, R2 = 0.8629 and r = 0.9289. Beenson and Knight and Haris digestible protein estimation model cannot be used because it has a very low RDP value. The con­clusion is Wardeh estimation model is more accurate in measuring TDN in male Garut sheep.