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Detection of offal adulteration in beefburgers using near infrared reflectance spectroscopy and multivariate modelling

机译:使用近红外反射光谱和多元建模检测牛肉汉堡内脏掺假

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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 (850-1098nm 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 850-1098nm and 1100-2498nm 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.
机译:这项研究的主要目的是开发一种使用近红外(NIR)反射光谱法的快速可靠的工具,以在杂碎(肾脏,肝脏,心脏和肺脏)掺假的情况下确认牛肉汉堡的真实性。实验设计被用于开发牛肉汉堡配方,以同时最大化描述内脏掺假样品的可变空间并最小化所需的实验数量。使用这些配方可以制作出正宗的(n = 36)和掺假的(n = 46)牛肉汉堡样品。使用原始和预处理的近红外反射光谱(850-1098nm波长范围)开发了分类模型(偏最小二乘判别分析,PLS1-DA)和分类模型工具(分类模拟的软独立建模,SIMCA)来检测真实(1)新鲜,(2)冷冻然后解冻和(3)新鲜或冷冻然后解冻的州和掺假牛肉汉堡。对于真实的样品,最好的PLS1-DA模型可以对新鲜,冷冻然后解冻的和新鲜或冷冻然后解冻的样品实现100%正确的分类。 SIMCA模型可以正确识别所有新鲜样品,但不能正确识别所有冷冻然后解冻的样品和新鲜或冷冻然后解冻的样品。对于掺假样品,PLS1-DA模型正确分类了新鲜汉堡的95.5%,冷冻后解冻的91.3%和新鲜或冷冻后解冻的88.9%。 SIMCA模型对新鲜和冷冻后解冻的样品的特异性值为1,对于新鲜或冷冻后解冻的样品的特异性值为0.99;对于新鲜,冷冻然后解冻的产品以及新鲜或冷冻然后解冻的产品,灵敏度值分别为1、0.88和0.97。开发了使用850-1098nm和1100-2498nm波长范围的定量模型(PLS1回归),以量化(1)内脏掺假和(2)掺假的牛肉汉堡中的脂肪,包括新鲜的和冷冻后解冻的。模型预测了新鲜样品中添加的脂肪具有可接受的准确度(RMSECV = 2.0; RPD = 5.9);既没有获得有用的,准确的冰冻融化样品中添加脂肪的准确预测,也没有可能以两种样品形式预测总内脏。

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