Pappoe Justice Allotey, Mongson Olivia, Amuah Charles Lloyd Yeboah, Opoku-Ansah Jerry, Adueming Peter Osei-Wusu, Boateng Rabbi, Eghan Moses Jojo, Sackey Samuel Sonko, Anyidoho Elliot Kwaku, Huzortey Andrew Atiogbe, Anderson Benjamin, Vowotor Michael Kwame, Teye Ernest
Laser and Fibre Optics Centre, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana.
Department of Space Environment, Institute of Basic and Applied Sciences, Egypt-Japan University of Science and Technology, Alexandria, Egypt.
J Fluoresc. 2025 Jan;35(1):9-19. doi: 10.1007/s10895-023-03499-3. Epub 2023 Nov 16.
The craving for organic cocoa beans has resulted in fraudulent practices such as mislabeling, adulteration, all known as food fraud, prompting the international cocoa market to call for the authenticity of organic cocoa beans before export. In this study, we proposed robust models using laser-induced fluorescence (LIF) and chemometric techniques for rapid classification of cocoa beans as either organic or conventional. The LIF measurements were conducted on cocoa beans harvested from organic and conventional farms. From the results, conventional cocoa beans exhibited a higher fluorescence intensity compared to organic ones. In addition, a general peak wavelength shift was observed when the cocoa beans were excited using a 445 nm laser source. These results highlight distinct characteristics that can be used to differentiate between organic and conventional cocoa beans. Identical compounds were found in the fluorescence spectra of both the organic and conventional ones. With preprocessed fluorescence spectra data and utilizing principal component analysis, classification models such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Network (NN) and Random Forest (RF) models were employed. LDA and NN models yielded 100.0% classification accuracy for both training and validation sets, while 99.0% classification accuracy was achieved in the training and validation sets using SVM and RF models. The results demonstrate that employing a combination of LIF and either LDA or NN can be a reliable and efficient technique to classify authentic cocoa beans as either organic or conventional. This technique can play a vital role in maintaining integrity and preventing fraudulent practices in the cocoa bean supply chain.
对有机可可豆的渴望导致了诸如错误标签、掺假等欺诈行为，这些都被称为食品欺诈，促使国际可可市场在出口前要求保证有机可可豆的真实性。在本研究中，我们提出了使用激光诱导荧光（LIF）和化学计量技术的稳健模型，用于快速将可可豆分类为有机或传统可可豆。对从有机农场和传统农场收获的可可豆进行了LIF测量。结果显示，与有机可可豆相比，传统可可豆表现出更高的荧光强度。此外，当使用445nm激光源激发可可豆时，观察到了一般的峰值波长偏移。这些结果突出了可用于区分有机和传统可可豆的明显特征。在有机和传统可可豆的荧光光谱中发现了相同的化合物。利用预处理后的荧光光谱数据并运用主成分分析，采用了线性判别分析（LDA）、支持向量机（SVM）、神经网络（NN）和随机森林（RF）等分类模型。LDA和NN模型在训练集和验证集上的分类准确率均达到100.0%，而使用SVM和RF模型在训练集和验证集上的分类准确率为99.0%。结果表明，结合使用LIF和LDA或NN可以成为一种可靠且有效的技术，将正宗可可豆分类为有机或传统可可豆。该技术在维护可可豆供应链的完整性和防止欺诈行为方面可以发挥至关重要的作用。
