The Analysis of Wheat by Near‐Infrared Spectroscopy

Williams, Phil

doi:10.1002/0470027320.s8950

Cited by 7 publications

(9 citation statements)

References 19 publications

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“…However, NIRS was able to predict steroid concentrations in hair with an acceptable accuracy for screening, considering that the predictive performance decrease with materials that are more complex. 39,40 Our calibration and validation results are relatively similar to other studies that aimed to predict steroid concentrations (progestogens, estrogens, or glucocorticoids) from complex matrices like feces or urine by NIRS. 20,21,24,26,41,42 Increasing the number of samples in the calibration set may achieve more robust calibration, as predictive equations would be less affected by the number of samples withdrawn for the validation.…”

Section: Discussionsupporting

confidence: 76%

Prediction of Cortisol and Progesterone Concentrations in Cow Hair Using Near-Infrared Reflectance Spectroscopy (NIRS)

et al. 2017

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Concentrations of different steroid hormones have been used in cows as a measure of adrenal or gonadal activity and, thus, as indicators of stress or reproductive state. Detecting cortisol and progesterone in cow hair provides a long-term integrative value of retrospective adrenal or gonadal/placental activity, respectively. Current techniques for steroid detection require a hormone-extraction procedure that involves time, several types of equipment, management of reagents, and some assay procedures (which can also be time-consuming and can destroy the samples). In contrast, near-infrared reflectance spectroscopy (NIRS) is a multi-component predictor technique, characterized as rapid, nondestructive for the sample, and reagent-free. However, as a predictor technique, NIRS needs to be calibrated and validated for each matrix, hormone, and species. The main objective of this study was to evaluate the predictive value of the NIRS technique for hair cortisol and progesterone quantification in cows by using specific enzyme immunoassay as a reference method. Hair samples from 52 adult Friesian lactating cows from a commercial dairy farm were used. Reflectance spectra of hair samples were determined with a NIR reflectance spectrophotometer before and after trimming them. Although similar results were obtained, a slightly better relationship between the reference data and NIRS predicted values was found using trimmed samples. Near infrared reflectance spectroscopy demonstrated its ability to predict cortisol and progesterone concentrations with certain accuracy (R= 0.90 for cortisol and R= 0.87 for progesterone). Although NIRS is far from being a complete alternative to current methodologies, the proposed equations can offer screening capability. Considering the advantages of both fields, our results open the possibility for future work on the combination of hair steroid measurement and NIRS methodology.

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Section: Discussionsupporting

confidence: 76%

Prediction of Cortisol and Progesterone Concentrations in Cow Hair Using Near-Infrared Reflectance Spectroscopy (NIRS)

et al. 2017

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“…If the standard error of cross validation is large compared with the standard deviation, resulting in a small RPD, the PLS model is considered less robust. An RPD value between 2 and 5 indicates that the calibration may be useful for screening purposes (i.e., qualitative determination; Williams, 2010). The R 2 for the validation regression data set is consistent with the RPD for the PLS model.…”

supporting

confidence: 56%

The relationship of excessive energy deficit with milk somatic cell score and clinical mastitis

Bach

Barbano

McArt

2021

Journal of Dairy Science

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Periparturient cows go through a period of immune suppression often marked by immune cell dysfunction. Further exacerbation of this dysfunction through earlylactation excessive energy deficit (EED) has been associated with increased susceptibility to infectious conditions such as mastitis. Our objective was to explore the association of milk somatic cell score (SCS) and clinical mastitis (CM) diagnosis in cows identified with EED, diagnosed using each of the following: blood and milk β-hydroxybutyrate (BHB), milk predicted blood nonesterified fatty acid (mpbNEFA) concentrations, or milk de novo fatty acid (FA) relative percentages (rel %). We analyzed data collected from 396 multiparous Holstein cows from 2 New York farms in a prospective cohort study. Coccygeal vessel blood samples and composite milk samples were collected twice weekly from 3 to 18 days in milk (DIM) for a total of 4 time points per cow (T1, T2, T3, T4). Blood was analyzed using a hand-held meter, and milk was analyzed using Fourier-transform mid-infrared spectrometry for milk BHB and mpbNEFA concentrations, milk de novo FA rel %, and somatic cell count. Excessive energy deficit was diagnosed as blood BHB ≥ 1.2 mmol/L, milk BHB ≥ 0.14 mmol/L, mpbNEFA ≥ 0.55 mmol/L, or de novo FA ≤ 22.7 rel %, depending on the model. Clinical mastitis cultures were collected from 4 to 60 DIM by on-farm personnel. Incidence of hyperketonemia as determined by blood BHB was 13.4%, and incidence of CM was 23.9%. Separate repeated-measures ANOVA models were developed for each EED diagnostic analyte for parity groups 2, 3, and ≥4 to assess differences in SCS; t-test analyses were similarly used to assess the association of each diagnostic analyte with CM at each time point. For all diagnostic analytes, apart from milk BHB, cows diagnosed with EED tended to have lower SCS than their non-EED counterparts. This was especially apparent at T1 for all parity groups, and at T2, T3, and T4 for blood BHB and mpbNEFA. For EED diagnosis via mpbNEFA, mean SCS were lower in parity ≥4, with a difference in mean SCS between EED and non-EED animals of 0.7 SCS units, equating to a somatic cell count in EED animals approaching half that of non-EED (EED = 67,000 cells/mL, non-EED = 107,000 cell/mL). No important relationships were observed between CM diagnosis and blood BHB, milk BHB, or mpbNEFA. For de novo FA rel %, reductions in this analyte were noted before CM diagnosis at all time points. Although the relationship between EED and CM is still unclear, our findings suggest that cows in EED, diagnosed using blood BHB or mpbNEFA during the first 18 DIM, have a tendency toward lower SCS compared with their non-EED counterparts.

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“…43 Additional bands associated with fatty acids and fat composition were evident around 1200 nm (CH second overtone), 1726 and 1760 nm (CH first overtone), and 2308 and 2335 nm (CH combination and deformation in CH 2 group), as described by Williams. 44…”

Section: Resultsmentioning

confidence: 99%

Enhancing quality control in emulsion-type sausage production: Predicting chemical composition of intact samples with near infrared spectroscopy

Ritthiruangdej,

Vangnai,

Kasemsumran

et al. 2024

Journal of Near Infrared Spectroscopy

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This research actively explores the potential of near infrared spectroscopy (NIR) for analyzing the chemical composition of emulsion-type sausages, focusing on critical factors like residual nitrite, moisture, protein, and fat content. To establish robust and generalizable models, we utilized a dataset of 100 experimentally prepared sausages encompassing a wide range of pork back fat replacement levels (5%, 15%, 30%, 45%, and 60%) and added sodium nitrite amounts (0, 80, 125, 250, and 375 ppm). An external validation set of 20 commercially sourced sausages further assessed the model’s real-world applicability. Partial least squares (PLS) regression calibration models with multiplicative scatter correction (MSC) pre-treatment demonstrated impressive accuracy for moisture (RMSECV = 0.57%, RPD = 9.8), fat (RMSECV = 1.17%, RPD = 9.5), and protein (RMSECV = 0.30%, RPD = 7.6). While residual nitrite prediction presented challenges due to its inherent complexity, the external validation yielded a competitive root mean square error of prediction (RMSEP) of 12.02 ppm, surpassing the average performance reported in similar studies (RMSEP ∼15 ppm) by 3 ppm. Importantly, sample homogenization did not significantly affect parameter prediction, highlighting the robustness of the NIR-based approach. These findings suggest that NIR spectroscopy, with its non-destructive, rapid, and cost-effective nature, could provide valuable tools for quality control and monitoring in the emulsion-type sausage industry. More importantly, improved nitrite prediction could pave the way for enhanced precision and control in sausage production, ultimately contributing to improved food safety and sustainability.

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The Analysis of Wheat by Near‐Infrared Spectroscopy

Cited by 7 publications

References 19 publications

Prediction of Cortisol and Progesterone Concentrations in Cow Hair Using Near-Infrared Reflectance Spectroscopy (NIRS)

Prediction of Cortisol and Progesterone Concentrations in Cow Hair Using Near-Infrared Reflectance Spectroscopy (NIRS)

The relationship of excessive energy deficit with milk somatic cell score and clinical mastitis

Enhancing quality control in emulsion-type sausage production: Predicting chemical composition of intact samples with near infrared spectroscopy

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