Browsing Food Programme by Subject "Raman spectroscopy"
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Assessment of physico-chemical traits related to eating quality of young dairy bull beef at different ageing times using Raman spectroscopy and chemometricsRaman spectroscopy and chemometrics were investigated for the prediction of eating quality related physico-chemical traits of Holstein-Friesian bull beef. Raman spectra were collected on the 3rd, 7th and 14th days post-mortem. A frequency range of 1300–2800 cm− 1 was used for partial least squares (PLS) modelling. PLS regression (PLSR) models for the prediction of WBSF and cook loss achieved an R2CV of 0.75 with RMSECV of 6.82 N and an R2CV of 0.77 with RMSECV of 0.97%w/w respectively. For the prediction of intramuscular fat, moisture and crude protein content, R2CV values were 0.85, 0.91 and 0.70 with RMSECV of 0.52%w/w, 0.39%w/w and 0.38%w/w respectively. An R2CV of 0.79 was achieved for the prediction of both total collagen and hydroxyproline content, while for collagen solubility the R2CV was 0.88. All samples (100%) from 15- and 19-month old bulls were correctly classified using PLS discriminant analysis (PLS-DA), while 86.7% of samples from different muscles (longissimus thoracis, semitendinosus and gluteus medius) were correctly classified. In general, PLSR models using Raman spectra on the 3rd day post-mortem had better prediction performance than those on the 7th and 14th days. Raman spectroscopy and chemometrics have potential to assess several beef physical and chemical quality traits.
Investigation of Raman Spectroscopy (with Fiber Optic Probe) and Chemometric Data Analysis for the Determination of Mineral Content in Aqueous Infant FormulaThis study investigated the use of Raman spectroscopy (RS) and chemometrics for the determination of eight mineral elements (i.e., Ca, Mg, K, Na, Cu, Mn, Fe, and Zn) in aqueous infant formula (INF). The samples were prepared using infant formula powder reconstituted to concentrations of 3%–13% w/w (powder: water) (n = 83). Raman spectral data acquisition was carried out using a non-contact fiber optic probe on the surface of aqueous samples in 50–3398 cm−1. ICP-AES was used as a reference method for the determination of the mineral contents in aqueous INF samples. Results showed that the best performing partial least squares regression (PLSR) models developed for the prediction of minerals using all samples for calibration achieved R2CV values of 0.51–0.95 with RMSECVs of 0.13–2.96 ppm. The PLSR models developed and validated using separate calibration (n = 42) and validation (n = 41) samples achieved R2CVs of 0.93, 0.94, 0.91, 0.90, 0.97, and 0.94, R2Ps of 0.75, 0.77, 0.31, 0.60, 0.84, and 0.80 with RMSEPs of 3.17, 0.29, 3.45, 1.51, 0.30, and 0.25 ppm for the prediction of Ca, Mg, K, Na, Fe, and Zn respectively. This study demonstrated that RS equipped with a non-contact fiber optic probe and combined with chemometrics has the potential for timely quantification of the mineral content of aqueous INF during manufacturing.