Dataset Information

Dataset on equine cartilage near infrared spectra, composition, and functional properties.

ABSTRACT: Near infrared (NIR) spectroscopy is a well-established technique that is widely employed in agriculture, chemometrics, and pharmaceutical engineering. Recently, the technique has shown potential in clinical orthopaedic applications, for example, assisting in the diagnosis of various knee-related diseases (e.g., osteoarthritis) and their pathologies. NIR spectroscopy (NIRS) could be especially useful for determining the integrity and condition of articular cartilage, as the current arthroscopic diagnostics is subjective and unreliable. In this work, we present an extensive dataset of NIRS measurements for evaluating the condition, mechanical properties, structure, and composition of equine articular cartilage. The dataset contains NIRS measurements from 869 different locations across the articular surfaces of five equine fetlock joints. A comprehensive library of reference values for each measurement location is also provided, including results from a mechanical indentation testing, digital densitometry imaging, polarized light microscopy, and Fourier transform infrared spectroscopy. The published data can either be used as a model of human cartilage or to advance equine veterinary research.

SUBMITTER: Sarin JK

PROVIDER: S-EPMC6717194 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:If a non-destructive and rapid technique to determine the textural properties of cooked germinated brown rice (GBR) was developed, it would hold immense potential for the enhancement of the quality control process in large-scale commercial rice production. We combined the Fourier transform near-infrared (NIR) spectral data of uncooked whole grain GBR with partial least squares (PLS) regression and an artificial neural network (ANN) for an evaluation of the textural properties of cooked germinated brown rice (GBR); in addition, data separation and spectral pretreatment methods were investigated. The ANN was outperformed in the evaluation of hardness by a back extrusion test of cooked GBR using the smoothing combined with the standard normal variate pretreated NIR spectra of 188 whole grain samples in the range of 4000-12,500 cm-1. The calibration sample set was separated from the prediction set by the Kennard-Stone method. The best ANN model for hardness, toughness, and adhesiveness provided R2, r2, RMSEC, RMSEP, Bias, and RPD values of 1.00, 0.94, 0.10 N, 0.77 N, 0.02 N, and 4.3; 1.00, 0.92, 1.40 Nmm, 9.98 Nmm, 1.6 Nmm, and 3.5; and 0.97, 0.91, 1.35 Nmm, 2.63 Nmm, -0.08 Nmm, and 3.4, respectively. The PLS regression of the 64-sample KDML GBR group and the 64-sample GBR group of various varieties provided the optimized models for the hardness of the former and the toughness of the latter. The hardness model was developed by using 5446.3-7506 and 4242.9-4605.4 cm-1, which included the amylose vibration band at 6834.0 cm-1, while the toughness model was from 6094.3 to 9403.8 cm-1 and included the 6834.0 and 8316.0 cm-1 vibration bands of amylose, which influenced the texture of the cooked rice. The PLS regression models for hardness and toughness had the r2 values of 0.85 and 0.82 and the RPDs of 2.9 and 2.4, respectively. The ANN model for the hardness, toughness, and adhesiveness of cooked GBR could be implemented for practical use in GBR production factories for product formulation and quality assurance and for further updating using more samples and several brands to obtain the robust models.

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.

Dataset Information

Dataset on equine cartilage near infrared spectra, composition, and functional properties.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets