Dataset Information

Using Near-infrared reflectance spectroscopy (NIRS) to predict glucobrassicin concentrations in cabbage and brussels sprout leaf tissue.

ABSTRACT: 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.

SUBMITTER: Renner IE

PROVIDER: S-EPMC7552462 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Using Near-infrared reflectance spectroscopy (NIRS) to predict glucobrassicin concentrations in cabbage and brussels sprout leaf tissue.

Renner Ilse E IE Fritz Vincent A VA

Plant methods 20201012

<h4>Background</h4>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 know ...[more]

PMID: 33062037

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Project description:Adequate biogeochemical characterization and monitoring of aquatic ecosystems, both for scientific purposes and for water management, pose high demands on spatial and temporal replication of chemical analyses. Near-infrared reflectance spectroscopy (NIRS) may offer a rapid, low-cost and reproducible alternative to standard analytical sample processing (digestion or extraction) and measuring techniques used for the chemical characterization of aquatic sediments. We analyzed a total of 191 sediment samples for total and NaCl-extractable concentrations of Al, Ca, Fe, K, Mg, Mn, N, Na, P, S, Si, and Zn as well as oxalate- extractable concentrations of Al, Fe, Mn and P. Based on the NIR spectral data and the reference values, calibration models for the prediction of element concentrations in unknown samples were developed and tested with an external validation procedure. Except Mn, all prediction models of total element concentrations were found to be acceptable to excellent (ratio of performance deviation: RPD 1.8-3.1). For extractable element fractions, viable model precision could be achieved for NaCl-extractable Ca, K, Mg, NH4 (+)-N, S and Si (RPD 1.7-2.2) and oxalate-extractable Al, Fe and P (RPD 1.9-2.3). For those elements that showed maximum total values below 3 g kg(-1) prediction models were found to become increasingly critical (RPD <2.0). Low concentrations also limited the performance of NIRS calibrations for extracted elements, with critical concentration thresholds <0.1 g kg(-1) and 3.3 g kg(-1) for NaCl and oxalate extractions, respectively. Thus, reliable NIRS measurements of trace metals are restricted to sediments with high metal content. Nevertheless, we demonstrated the suitability of NIRS measurements to determine a large array of chemical properties of aquatic sediments. The results indicate great potential of this fast technique as an analytical tool to better understand the large spatial and temporal variation of sediment characteristics in an economically viable way.

Project description:Optimal management of free-ranging herbivores requires the accurate assessment of an animal's nutritional status. For this purpose 'near-infrared reflectance spectroscopy' (NIRS) is very useful, especially when nutritional assessment is done through faecal indicators such as faecal nitrogen (FN). In order to perform an NIRS calibration, the default protocol recommends starting by generating an initial equation based on at least 50-75 samples from the given species. Although this protocol optimises prediction accuracy, it limits the use of NIRS with rare or endangered species where sample sizes are often small. To overcome this limitation we tested a single NIRS equation (i.e., multispecies calibration) to predict FN in herbivores. Firstly, we used five herbivore species with highly contrasting digestive physiologies to build monospecies and multispecies calibrations, namely horse, sheep, Pyrenean chamois, red deer and European rabbit. Secondly, the equation accuracy was evaluated by two procedures using: (1) an external validation with samples from the same species, which were not used in the calibration process; and (2) samples from different ungulate species, specifically Alpine ibex, domestic goat, European mouflon, roe deer and cattle. The multispecies equation was highly accurate in terms of the coefficient of determination for calibration R2 = 0.98, standard error of validation SECV = 0.10, standard error of external validation SEP = 0.12, ratio of performance to deviation RPD = 5.3, and range error of prediction RER = 28.4. The accuracy of the multispecies equation to predict other herbivore species was also satisfactory (R2 > 0.86, SEP < 0.27, RPD > 2.6, and RER > 8.1). Lastly, the agreement between multi- and monospecies calibrations was also confirmed by the Bland-Altman method. In conclusion, our single multispecies equation can be used as a reliable, cost-effective, easy and powerful analytical method to assess FN in a wide range of herbivore species.

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.

Dataset Information

Using Near-infrared reflectance spectroscopy (NIRS) to predict glucobrassicin concentrations in cabbage and brussels sprout leaf tissue.

Publications

Using Near-infrared reflectance spectroscopy (NIRS) to predict glucobrassicin concentrations in cabbage and brussels sprout leaf tissue.

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