Browsing Food Quality & Sensory Science by Funder "Food Safety Authority of Ireland"
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Detection of adulteration in fresh and frozen beefburger products by beef offal using mid-infrared ATR spectroscopy and multivariate data analysisA 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 modellingThe 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.