Abstract

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 beef burger 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 1100–2498 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.

© 2013 IM Publications LLP

Nondestructive detection model of soluble solids content of an apple using visible/near-infrared spectroscopy combined with CARS and MPGA

Yong Chen, Chaoyuan Cui, and Yun Wu
Appl. Opt. 60(27) 8400-8407 (2021)

Utilization of laser-induced breakdown spectroscopy, with principal component analysis and artificial neural networks in revealing adulteration of similarly looking fish fillets

Omnia Hamdy, Zienab Abdel-Salam, and Mohamed Abdel-Harith
Appl. Opt. 61(34) 10260-10266 (2022)

Identification and quantification of vegetable oil adulteration with waste frying oil by laser-induced fluorescence spectroscopy

Shiguo Hao, Lian Zhu, Ronglong Sui, Mengling Zuo, Ningning Luo, Jiulin Shi, Weiwei Zhang, Xingdao He, and Zhongping Chen
OSA Continuum 2(4) 1148-1154 (2019)

Rapid determination of the main components of corn based on near-infrared spectroscopy and a BiPLS-PCA-ELM model

Lili Xu, Jinming Liu, Chunqi Wang, Zhijiang Li, and Dongjie Zhang
Appl. Opt. 62(11) 2756-2765 (2023)

Rapid detection of carbon-nitrogen ratio for anaerobic fermentation feedstocks using near-infrared spectroscopy combined with BiPLS and GSA

Jinming Liu, Nan Li, Feng Zhen, Yonghua Xu, Wenzhe Li, and Yong Sun
Appl. Opt. 58(18) 5090-5097 (2019)