Dataset Information

Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS).

ABSTRACT: Background:In Brazil, over the last few years there has been an increase in the production and consumption of goat cheeses. In addition, there was also a demand to create options to use the whey extracted during the production of cheeses. Whey can be used as an ingredient in the development of many products. Therefore, knowing its composition is a matter of utmost importance, considering that the reference methods of food analysis require time, trained labor and expensive reagents for its execution. Methods:Goat whey samples produced in winter and summer were submitted to proximate composition analysis (moisture, total solids, ashes, proteins, fat and carbohydrates by difference) using reference methods and near infrared spectroscopy (NIRS). The spectral data was preprocessed by baseline correction and the Savitzky-Golay derivative. The models were built using Partial Least Square Regression (PLSR) with raw and preprocessed data for each dependent variable (proximate composition parameter). Results:The average whey composition values obtained using the referenced methods were in accordance with the consulted literature. The composition did not differ significantly (p > 0.05) between the summer and winter whey samples. The PLSR models were made available using the following figures of merit: coefficients of determination of the calibration and prediction models (R 2cal and R 2pred, respectively) and the Root Mean Squared Error Calibration and Prediction (RMSEC and RMSEP, respectively). The best models used raw data for fat and protein determinations and the values obtained by NIRS for both parameters were consistent with their referenced methods. Consequently, NIRS can be used to determine fat and protein in goat whey.

SUBMITTER: Galdino IKCPO

PROVIDER: S-EPMC7023836 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS).

Galdino Isadora Kaline Camelo Pires de Oliveira IKCPO Salles Hévila Oliveira HO Dos Santos Karina Maria Olbrich KMO Veras Germano G Alonso Buriti Flávia Carolina FC

PeerJ 20200212

<h4>Background</h4>In Brazil, over the last few years there has been an increase in the production and consumption of goat cheeses. In addition, there was also a demand to create options to use the whey extracted during the production of cheeses. Whey can be used as an ingredient in the development of many products. Therefore, knowing its composition is a matter of utmost importance, considering that the reference methods of food analysis require time, trained labor and expensive reagents for it ...[more]

PMID: 32095381

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Project description:IntroductionAneurysmal subarachnoid haemorrhage (aSAH) is a devastating disease associated with high mortality and morbidity. The main threat to patients is delayed cerebral ischaemia (DCI). Near-infrared spectroscopy (NIRS) is a recent technology allowing continuous, non-invasive cerebral oximetry that could permit timely detection of impending DCI and appropriate intervention to improve outcomes. However, the ability of regional oxygen saturation to detect DCI, its association to the outcome, or benefits of any interventions based on NIRS data, are lacking. Our aims are to evaluate NIRS technology both as a therapeutic tool to improve outcomes in aSAH patients and as a diagnostic tool for DCI.Methods and analysisMEDLINE, EMBASE, Web of Science, the Cochrane Central Register of Controlled Trials and the Cochrane Database of Systematic Reviews will be searched from their inception and without language restriction. Our search strategy will cover the themes of subarachnoid haemorrhage and cerebral oximetry, without limitations regarding studied outcomes. We will identify all observational and interventional human studies of adult patients hospitalised after aSAH that were monitored using NIRS. Functional outcome measures, including the modified Rankin Scale, the Glasgow Outcome Scale and the Barthel Index, will constitute the primary outcome. The Cochrane Risk of Bias tool will be used for randomised controlled trials, the ROBINS-I tool to assess non-randomised studies of interventions and the Newcastle-Ottawa Scale for cohort or case-control studies. The Quality Assessment of Diagnostic Accuracy Studies-2 will be applied to studies evaluating NIRS diagnostic accuracy for DCI. We will evaluate the quality of the evidence of the effect based on the Grading of Recommendations Assessment, Development and Evaluation methodology.Ethics and disseminationDissemination will proceed through submission for journal publication, trial registry completion and abstract presentation. Ethics approval is not required.Prospero registration numberCRD42020077522.

Project description:Background:Glucobrassicin (GBS) and its hydrolysis product indole-3-carbinol are important nutritional constituents implicated in cancer chemoprevention. Dietary consumption of vegetables sources of GBS, such as cabbage and Brussels sprouts, is linked to tumor suppression, carcinogen excretion, and cancer-risk reduction. High-performance liquid-chromatography (HPLC) is the current standard GBS identification method, and quantification is based on UV-light absorption in comparison to known standards or via mass spectrometry. These analytical techniques require expensive equipment, trained laboratory personnel, hazardous chemicals, and they are labor intensive. A rapid, nondestructive, inexpensive quantification method is needed to accelerate the adoption of GBS-enhancing production systems. Such an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties. Near-infrared reflectance spectroscopy (NIRS) paired with partial least squares regression (PLSR) could be a useful tool to develop such a method. Results:Here we demonstrate that GBS concentrations of freeze-dried tissue from a wide variety of cabbage and Brussels sprouts can be predicted using partial least squares regression from NIRS data generated from wavelengths between 950 and 1650 nm. Cross-validation models had R2?=?0.75 with RPD?=?2.3 for predicting µmol GBS·100 g-1 fresh weight and R2?=?0.80 with RPD?=?2.4 for predicting µmol GBS·g-1 dry weight. Inspections of equation loadings suggest the molecular associations used in modeling may be due to first overtones from O-H stretching and/or N-H stretching of amines. Conclusions:A calibration model suitable for screening GBS concentration of freeze-dried leaf tissue using NIRS-generated data paired with PLSR can be created for cabbage and Brussels sprouts. Optimal NIRS wavelength ranges for calibration remain an open question.

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Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS).

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Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS).

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