Queensland Alliance for Agriculture and Food Innovation, Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia.
School of Agriculture and Food Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia.
Sensors (Basel). 2022 Jul 1;22(13):4988. doi: 10.3390/s22134988.
Issues related to food authenticity, traceability, and fraud have increased in recent decades as a consequence of the deliberate and intentional substitution, addition, tampering, or misrepresentation of food ingredients, where false or misleading statements are made about a product for economic gains. This study aimed to evaluate the ability of a portable NIR instrument to classify egg samples sourced from different provenances or production systems (e.g., cage and free-range) in Australia. Whole egg samples (n: 100) were purchased from local supermarkets where the label in each of the packages was used as identification of the layers' feeding system as per the Australian legislation and standards. The spectra of the albumin and yolk were collected using a portable NIR spectrophotometer (950-1600 nm). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data. The results obtained in this study showed how the combination of chemometrics and NIR spectroscopy allowed for the classification of egg albumin and yolk samples according to the system of production (cage and free range). The proposed method is simple, fast, environmentally friendly and avoids laborious sample pre-treatment, and is expected to become an alternative to commonly used techniques for egg quality assessment.
近几十年来,由于食品成分的故意和有意替代、添加、篡改或伪造,以及对产品做出虚假或误导性陈述以获取经济利益,与食品真实性、可追溯性和欺诈相关的问题有所增加。本研究旨在评估便携式近红外(NIR)仪器对源自不同产地或生产系统(例如笼养和散养)的澳大利亚鸡蛋样品进行分类的能力。从当地超市购买全蛋样品(n=100),每个包装上的标签根据澳大利亚法规和标准被用作蛋鸡饲养系统的识别。使用便携式 NIR 分光光度计(950-1600nm)采集白蛋白和蛋黄的光谱。采用主成分分析(PCA)和线性判别分析(LDA)对 NIR 数据进行分析。本研究结果表明,化学计量学和近红外光谱的结合如何允许根据生产系统(笼养和散养)对蛋白蛋白和蛋黄样品进行分类。该方法简单、快速、环保,避免了繁琐的样品预处理,有望成为常用鸡蛋质量评估技术的替代方法。