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Prediction of protein and amino acid composition and digestibility in individual feedstuffs and mixed diets for pigs using near-infrared spectroscopy

ABSTRACT: Knowledge of the amounts and digestibility of amino acids in pig feedstuffs is essential for calculating the appropriate inclusion level in a complete diet. Wet chemical analysis and in vivo digestibility trials are time-consuming and costly and cannot be used for routine assessment. Near-infrared spectroscopy (NIRS) offers a rapid, cost effective and environmentally friendly method for evaluating feedstuffs. Calibrations models were developed using NIRS to predict the content of crude protein and 18 amino acids from a wide range of feedstuffs used in pig production (n = 607). The samples ranged from single feed ingredients (containing amino acids from 0.3 to 129.8 g/kg of dry matter) to feed mixtures (containing amino acids from 1.2 to 53.2 g/kg of dry matter). The predictive ability of the calibrations was tested with an independent dataset (n = 150) and with cross-validation. Furthermore, we compare these calibrations with calibrations developed on more narrowly defined groups of samples and with predictions from regression analysis of crude protein. The models were able to predict the concentrations of crude protein and 18 amino acids with good levels of precision and high coefficients of determination for calibration (RSQ CAL) from 0.91 to 0.99 and validation (RSQVAL) from 0.87 to 0.97. Calibration models were able to predict all amino acids except tryptophan and valine with greater accuracy than those from protein regression. We also developed calibration models to predict the apparent ileal and total tract digestibility of protein and amino acids. With the exception of tryptophan, RSQ values (>0.7) and standard error of cross validation (SECV) values (<5%) were obtained for the digestibility of most of the amino acids. In conclusion, NIRS can be used to predict crude protein and amino acid concentrations from a wide range of single ingredients and feed mixtures used for pig diets without separate models for each feedstuff. The digestibility of protein and amino acids can be predicted with an acceptable accuracy to be useful in formulating pig diets.

SUBMITTER: Noel S

PROVIDER: S-EPMC8556607 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSugarcane is one of the most crucial energy crops that produces high yields of sugar and lignocellulose. The cellulose crystallinity index (CrI) and lignin are the two kinds of key cell wall features that account for lignocellulose saccharification. Therefore, high-throughput screening of sugarcane germplasm with excellent cell wall features is considered a promising strategy to enhance bagasse digestibility. Recently, there has been research to explore near-infrared spectroscopy (NIRS) assays for the characterization of the corresponding wall features. However, due to the technical barriers of the offline strategy, it is difficult to apply for high-throughput real-time analyses. This study was therefore initiated to develop a high-throughput online NIRS assay to rapidly detect cellulose crystallinity, lignin content, and their related proportions in sugarcane, aiming to provide an efficient and feasible method for sugarcane cell wall feature evaluation.ResultsA total of 838 different sugarcane genotypes were collected at different growth stages during 2018 and 2019. A continuous variation distribution of the near-infrared spectrum was observed among these collections. Due to the very large diversity of CrI and lignin contents detected in the collected sugarcane samples, seven high-quality calibration models were developed through online NIRS calibration. All of the generated equations displayed coefficient of determination (R2) values greater than 0.8 and high ratio performance deviation (RPD) values of over 2.0 in calibration, internal cross-validation, and external validation. Remarkably, the equations for CrI and total lignin content exhibited RPD values as high as 2.56 and 2.55, respectively, indicating their excellent prediction capacity. An offline NIRS assay was also performed. Comparable calibration was observed between the offline and online NIRS analyses, suggesting that both strategies would be applicable to estimate cell wall characteristics. Nevertheless, as online NIRS assays offer tremendous advantages for large-scale real-time screening applications, it could be implied that they are a better option for high-throughput cell wall feature prediction.ConclusionsThis study, as an initial attempt, explored an online NIRS assay for the high-throughput assessment of key cell wall features in terms of CrI, lignin content, and their proportion in sugarcane. Consistent and precise calibration results were obtained with NIRS modeling, insinuating this strategy as a reliable approach for the large-scale screening of promising sugarcane germplasm for cell wall structure improvement and beyond.

Project description:A study evaluated the effects of adding multi-enzyme mixture to diets deficient in net energy (NE), standardized ileal digestible (SID) amino acids (AA), standardized total tract digestible (STTD) P, and Ca on growth performance, bone mineralization, nutrient digestibility, and fecal microbial composition of grow-finish pigs. A total of 300 pigs (initial body weight [BW] = 29.2 kg) were housed by sex and BW in 45 pens of 7 or 6 pigs and fed 5 diets in a randomized complete block design. Diets were positive control (PC), and negative control 1 (NC1) or negative control 2 (NC2) without or with multi-enzyme mixture. The multi-enzyme mixture supplied at least 1,800, 1,244, 6,600, and 1,000 units of xylanase, β -glucanase, arabinofuranosidase, and phytase per kilogram of diet, respectively. The PC was adequate in all nutrients. The NC1 diet had lower content NE, SID AA, STTD P, and Ca than PC diet by about 7%, 7%, 32%, and 13%, respectively. The NC2 diet had lower NE, SID AA, STTD P, and Ca than PC diet by 7%, 7%, 50%, and 22%, respectively. The diets were fed in four phases based on BW: Phase 1: 29-45 kg, Phase 2: 45-70 kg, Phase 3: 70-90 kg, and Phase 4: 90-120 kg. Nutrient digestibility, bone mineralization, and fecal microbial composition were determined at the end of Phase 1. Pigs fed PC diet had greater (P < 0.05) overall G:F than those fed NC1 diet or NC2 diet. Multi-enzyme mixture increased (P < 0.05) overall G:F, but the G:F of the multi-enzyme mixture-supplemented diets did not reach (P < 0.05) that of PC diet. Multi-enzyme mixture tended to increase (P = 0.08) femur breaking strength. Multi-enzyme mixture increased (P < 0.05) the ATTD of GE for the NC2 diet, but unaffected the ATTD of GE for the NC1 diet. Multi-enzyme mixture decreased (P < 0.05) the relative abundance of the Cyanobacteria and increased (P < 0.05) relative abundance of Butyricicoccus in feces. Thus, the NE, SID AA, STTD P, and Ca could be lowered by about 7%, 7%, 49%, and 22%, respectively, in multi-enzyme mixture-supplemented diets without negative effects on bone mineralization of grow-finish pigs. However, multi-enzyme mixture supplementation may not fully restore G:F of the grow-finish pigs fed diets that have lower NE and SID AA contents than recommended by 7%. Since an increase in content of Butyricicoccus in intestine is associated with improved gut health, addition of the multi-enzyme mixture in diets for pigs can additionally improve their gut health.

Project description:Significance: Designing optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain-computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Aim: Available resources often dictate the approach researchers use for optode-layout design. We investigated whether guiding optode layout design using different amounts of subject-specific magnetic resonance imaging (MRI) data affects the fNIRS signal quality and sensitivity to brain activation when healthy participants perform mental-imagery tasks typically used in fNIRS-BCI experiments. Approach: We compared four approaches that incrementally incorporated subject-specific MRI information while participants performed mental-calculation, mental-rotation, and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB) employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional, and vascular information of the same subject (fVASC). Results: The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI, and fVASC approaches resulted in a similar outcome. Conclusions: We conclude that additional individual MRI data lead to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.

Project description:Organic waste products (OWPs) from livestock have a high fertilizer value (N, P, K), but can also lead to environmental problems when applied in excessive quantities. Because their composition varies greatly, it is important to develop fast, reliable and inexpensive methods for determining their chemical contents. Near-infrared spectroscopy (NIRS) offers the possibility of rapid analysis of samples and requires little sample preparation, and previous studies have demonstrated that NIRS could be able to determine the most important compositional parameters of solid animal manure. The recent development of low-cost miniaturized spectrometers even enables manure-spreading equipment to be equipped with sensors to measure the composition in real time, and some applications are already being commercialized for the spreading of liquid OWPs. In-situ analysis of these very heterogeneous products (roughness, humidity) is a challenge for such applications, because spectral acquisition must be performed on raw samples with no preparation. To evaluate the accuracy with which NIRS estimates dry matter content, organic matter, total and ammonium nitrogen, phosphorus, potassium, calcium and magnesium contents, we created a large calibration database representative of raw solid animal manures encountered in Brittany. A total of 490 samples of solid OWPs from livestock farms were collected in the early spring from 270 farms in Brittany (western France), in 2 campaigns conducted in 2018 and 2019. The sampling was designed to capture the large diversity of animal species (mainly cattle, pigs and poultry), type of farming and storage modes. Compositional parameters were analyzed according to analysis methods certified by the French standards organization (AFNOR). Samples were scanned using a Q-interline AgriQuant B8 equipped with a patented spiral sampler, which aggregates the heterogeneity of the sample. NIRS measurements were made in triplicate. Because the dataset covers a wide range of variability in the composition of solid animal manure, these data are of great interest to chemometrics experts and agronomists in search of references on the fertilizing value of products.

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Prediction of protein and amino acid composition and digestibility in individual feedstuffs and mixed diets for pigs using near-infrared spectroscopy

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