Infrared spectral data of dried and roasted cocoa (Theobroma cacao L.) for calibrating classification models of cocoa varieties and predicting theobromine and caffeine content

The dataset compiles the mid-infrared spectra of dried and roasted cocoa beans (Theobroma cacao L.), as well as their theobromine and caffeine content. The Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy technique was employed to spectrally characterize the dried and roasted cocoa samples, while high-performance liquid chromatography (HPLC) was used to quantify the theobromine and caffeine content. Based on the theobromine/caffeine ratio, the cocoa variety was determined.
This dataset provides valuable data for researchers, food scientists, and industry professionals to develop and calibrate robust multivariate statistical models, as well as machine learning and deep learning techniques, to classify different varieties of dried and roasted cocoa beans in real-time. Additionally, the infrared spectra could be used to develop precise models for simultaneously predicting both theobromine and caffeine content based on the spectral data. These calibrated models could significantly support decision-making processes within the cocoa industry, enabling real-time quality control and optimizing bean selection, blending, and product formulation processes. The dataset is organized in Excel sheets and structured according to experimental conditions and replicates, allowing for detailed exploration and analysis.

本数据集汇编了干制和烘焙可可豆（可可树科可可属可可树 L.）的中红外光谱，以及其中可可碱和咖啡因的含量。采用衰减全反射-傅里叶变换红外光谱法（ATR-FTIR）对干制和烘焙可可样本进行光谱表征，而高效液相色谱法（HPLC）则用于定量分析可可碱和咖啡因的含量。根据可可碱/咖啡因比例，可确定可可品种。该数据集为研究人员、食品科学家和行业专业人士提供了宝贵的数据资源，以开发并校准稳健的多变量统计模型、机器学习和深度学习技术，以实时分类不同品种的干制和烘焙可可豆。此外，红外光谱还可用于开发精确模型，根据光谱数据同时预测可可碱和咖啡因含量。这些校准模型能够显著支持可可行业内的决策过程，实现实时质量控制，并优化豆子选择、混合和产品配方过程。数据集以 Excel 表格形式组织，并按照实验条件和重复次数进行结构化，便于详细探索和分析。