Dataset Information

Near-infrared spectra dataset of milk composition in transmittance mode.

ABSTRACT: Monitoring of milk composition can support several dimensions of dairy management such as identification of the health status of individual dairy cows and the safeguarding of dairy quality. The quantification of milk composition has been traditionally executed employing destructive chemical or laboratory Fourier-transform infrared (FTIR) spectroscopy analyses which can incur high costs and prolonged waiting times for continuous monitoring. Therefore, modern technology for milk composition quantification relies on non-destructive near-infrared (NIR) spectroscopy which is not invasive and can be performed on-farm, in real-time. The current dataset contains NIR spectral measurements in transmittance mode in the wavelength range from 960 nm to 1690 nm of 1224 individual raw milk samples, collected on-farm over an eight-week span in 2017, at the experimental dairy farm of the province of Antwerp, 'Hooibeekhoeve' (Geel, Belgium). For these spectral measurements, laboratory reference values corresponding to the three main components of raw milk (fat, protein and lactose), urea and somatic cell count (SCC) are included. This data has been used to build multivariate calibration models to predict the three milk compounds, as well as develop strategies to monitor the prediction performance of the calibration models.

SUBMITTER: Diaz-Olivares JA

PROVIDER: S-EPMC10700509 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Near-infrared spectra dataset of milk composition in transmittance mode.

Diaz-Olivares Jose A JA van Nuenen Arnout A Gote Martin J MJ Díaz Valeria Fonseca VF Saeys Wouter W Adriaens Ines I Aernouts Ben B

Data in brief 20231104

Monitoring of milk composition can support several dimensions of dairy management such as identification of the health status of individual dairy cows and the safeguarding of dairy quality. The quantification of milk composition has been traditionally executed employing destructive chemical or laboratory Fourier-transform infrared (FTIR) spectroscopy analyses which can incur high costs and prolonged waiting times for continuous monitoring. Therefore, modern technology for milk composition quanti ...[more]

PMID: 38075623

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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.

Project description:As robots undertake increasingly complex tasks, such as real-time visible image sensing, environmental analysis, and weather monitoring under harsh conditions, design of an appropriate robot shell has become crucial to ensure the reliability of internal electronic components. Several key factors, such as the cooling efficiency, visible transparency, mechanical performance, and weathering resistance of the shell material, are proposed in this research to ensure future robot functionality. In this study, a polymeric double-layered shell for fabrication by stereolithography 3D printing was designed, featuring a porous outer layer and a spherical inner shell. The inner spherical shell provides approximately 90% transmission in the visible to near-infrared wavelength range (450-1050 nm) and ensures the proper functioning of the optical devices, such as cameras, lidar, and solar cells, inside the robot. In addition, the inner shell material displays high emittance in the mid-infrared range (5-20 μm) to facilitate effective radiative cooling and protect the robot control system from thermal damage. The 3D-printed inner shell is exposed to a real environment for three months, and its stable optical and mechanical performance confirms its weather resistance ability. Moreover, the 3D-printed outer robot shell promotes mechanical strength while the robot is moving. The optimal 50% porous outer shell is designed to protect the inner shell from continuous moving impact. Finite element simulations are also used to show that the 50% porosity of the outer shell significantly reduces the strain energy upon impact. Compared with a conventional single-layer design with a strain energy of 130 mJ, the double-layered shell with 50% porosity exhibits a reduced strain energy of 22.09 mJ. This double-layered design, which offers excellent weather resistance, high visible transparency, and effective radiative cooling, is promising for future applications in both land and water robot shells.

Dataset Information

Near-infrared spectra dataset of milk composition in transmittance mode.

Publications

Near-infrared spectra dataset of milk composition in transmittance mode.

Similar Datasets

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