• Assessment of physico-chemical traits related to eating quality of young dairy bull beef at different ageing times using Raman spectroscopy and chemometrics

      Nian, Yingqun; Zhao, Ming; O'Donnell, Colm P.; Downey, Gerard; Kerry, Joseph P.; Allen, Paul; Teagasc Walsh Fellowship Programme (Elsevier, 2017-06-27)
      Raman 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.
    • Detection of adulteration in fresh and frozen beefburger products by beef offal using mid-infrared ATR spectroscopy and multivariate data analysis

      Zhao, Ming; Downey, Gerard; O'Donnell, Colm P.; Teagasc Walsh Fellowship Programme; Food Safety Authority of Ireland (Elsevier, 17/10/2013)
      A series of authentic and offal-adulterated beefburger samples was produced. Authentic product (36 samples) comprised either only lean meat and fat (higher quality beefburgers) or lean meat, fat, rusk and water (lower quality product). Offal adulterants comprised heart, liver, kidney and lung. Adulterated formulations (46 samples) were produced using a D-optimal experimental design. Fresh and frozen-then-thawed samples were modelled, separately and in combination, by a classification (partial least squares discriminant analysis) and class-modelling (soft independent modelling of class analogy) approach. With the former, 100% correct classification accuracies were obtained separately for fresh and frozen-then-thawed material. Separate class-models for fresh and frozen-then-thawed samples exhibited high sensitivities (0.94 to 1.0) but lower specificities (0.33 – 0.80 for fresh samples and 0.41 – 0.87 for frozen-then-thawed samples). When fresh and frozen-then-thawed samples were modelled together, sensitivity remained 1.0 but specificity ranged from 0.29 to 0.91. Results indicate a role for this technique in monitoring beefburger compliance to label.
    • Detection of offal adulteration in beefburgers using near infrared reflectance spectroscopy and multivariate modelling

      Zhao, Ming; O'Donnell, Colm P.; Downey, Gerard; Food Safety Authority of Ireland; Teagasc Walsh Fellowship Programme (IM Publications, 2013)
      The main aim of this study was to develop a rapid and reliable tool using near infrared (NIR) reflectance spectroscopy to confirm beefburger authenticity in the context of offal (kidney, liver, heart and lung) adulteration. An experimental design was used to develop beefburger formulations to simultaneously maximise the variable space describing offal-adulterated samples and minimise the number of experiments required. Authentic (n = 36) and adulterated (n = 46) beefburger samples were produced using these formulations. Classification models (partial least squares discriminant analysis, PLS1-DA) and class-modelling tools (soft independent modelling of class analogy, SIMCA) were developed using raw and pre-treated NIR reflectance spectra (850-1098 nm wavelength range) to detect authentic and adulterated beefburgers in (1) fresh, (2) frozen-then-thawed and (3) fresh or frozen-then-thawed states. In the case of authentic samples, the best PLS1-DA models achieved 100% correct classification for fresh, frozen-then-thawed and fresh or frozen-then-thawed samples. SIMCA models correctly identified all the fresh samples but not all the frozen-then-thawed and fresh or frozen-then-thawed samples. For the adulterated samples, PLS1-DA models correctly classified 95.5% of fresh, 91.3% of frozen-then-thawed and 88.9% of fresh or frozen-then-thawed beefburgers. SIMCA models exhibited specificity values of 1 for both fresh and frozen-then-thawed samples, 0.99 for fresh or frozen-then-thawed samples; sensitivity values of 1, 0.88 and 0.97 were obtained for fresh, frozen-then-thawed and fresh or frozen-then-thawed products respectively. Quantitative models (PLS1 regression) using both 850-1098 nm and 1100-2498 nm wavelength ranges were developed to quantify (1) offal adulteration and (2) added fat in adulterated beefburgers, both fresh and frozen-then-thawed. Models predicted added fat in fresh samples with acceptable accuracy (RMSECV = 2.0; RPD = 5.9); usefully-accurate predictions of added fat in frozen-then-thawed samples were not obtained nor was prediction of total offal possible in either sample form.
    • Exploration of microwave dielectric and near infrared spectroscopy with multivariate data analysis for fat content determination in ground beef

      Zhao, Ming; Downey, Gerard; O'Donnell, Colm P.; Department of Agriculture, Food and the Marine (Elsevier, 19/03/2016)
      This study investigated using microwave dielectric and near infrared (NIR) spectroscopy for the determination of fat content in ground beef samples (n=69) in a designed experiment. Multivariate data analysis (principal component analysis (PCA) and partial least squares (PLS) regression modelling) was used to explore the potential of these spectroscopic techniques over selected multiple frequency or wavelength ranges. Chemical reference data for fat and water content in ground beef were obtained using a nuclear magnetic resonance-based SMART Trac analyser. Best performace of PLS prediction models for fat content revealed a coefficient of determination in prediction (R²P) of 0.87 and a root mean square error of prediction (RMSEP) of 2.71% w/w for microwave spectroscopy; in a similar manner, R²P of 0.99 and RMSEP of 0.71% w/w were obtained for NIR spectroscopy. The influence of water content on fat content prediction by microwave spectroscopy was investigated by comparing the prediction performance of PLS regression models developed using a single Y-variable (PLS1; fat or water content) and using both Y-variables (PLS2; fat and water contents).
    • Investigation of Raman Spectroscopy (with Fiber Optic Probe) and Chemometric Data Analysis for the Determination of Mineral Content in Aqueous Infant Formula

      Zhao, Ming; Shaikh, Saif; Kang, Renxi; Markiewicz-Keszycka, Maria (MDPI AG, 2020-07-22)
      This 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.
    • Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes

      Zhao, Ming; Nian, Yingqun; Allen, Paul; Downey, Gerard; Kerry, Joseph P.; O’Donnell, Colm P.; Teagasc Walsh Fellowship Programme (Elsevier BV, 2018-04-23)
      The data presented in this article are related to the research article entitled “Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef” [1]. Partial least squares regression (PLSR) models were developed on Raman spectral data pre-treated using Savitzky Golay (S.G.) derivation (with 2nd or 5th order polynomial baseline correction) and results of sensory analysis on bull beef samples (n = 72). Models developed using selected Raman shift ranges (i.e. 250–3380 cm−1, 900–1800 cm−1 and 1300–2800 cm−1) were explored. The best model performance for each sensory attributes prediction was obtained using models developed on Raman spectral data of 1300–2800 cm−1.
    • Prediction of naturally-occurring, industrially-induced and total trans fatty acids in butter, dairy spreads and Cheddar cheese using vibrational spectroscopy and multivariate data analysis

      Zhao, Ming; Beattie, Renwick J.; Fearon, A.M.; O'Donnell, Colm P.; Downey, Gerard; Department of Agriculture, Food and the Marine; Teagasc Walsh Fellowship Programme (Elsevier, 08/08/2015)
      This study investigated the use of vibrational spectroscopy [near infrared (NIR), Fourier-transform mid-infrared (FT-MIR), Raman] and multivariate data analysis for (1) quantifying total trans fatty acids (TT), and (2) separately predicting naturally-occurring (NT; i.e., C16:1 t9; C18:1 trans-n, n = 6 … 9, 10, 11; C18:2 trans) and industrially-induced trans fatty acids (IT = TT – NT) in Irish dairy products, i.e., butter (n = 60), Cheddar cheese (n = 44), and dairy spreads (n = 54). Partial least squares regression models for predicting NT, IT and TT in each type of dairy product were developed using FT-MIR, NIR and Raman spectral data. Models based on NIR, FT-MIR and Raman spectra were used for the prediction of NT and TT content in butter; best prediction performance achieved a coefficient of determination in validation (R2V) ∼ 0.91–0.95, root mean square error of prediction (RMSEP) ∼ 0.07–0.30 for NT; R2V ∼ 0.92–0.95, RMSEP ∼ 0.23–0.29 for TT.