Castillejos-Mijangos Lucero Azusena, Meza-Márquez Ofelia Gabriela, Osorio-Revilla Guillermo, Jiménez-Martínez Cristian, Gallardo-Velázquez Tzayhri
Departamento de Ingeniería Bioquímica, Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas-Zacatenco, Av. Wilfrido Massieu s/n, Esq. Cda. Miguel Stampa, Col. Unidad Profesional Adolfo López Mateos, Zacatenco, Alcaldía Gustavo A. Madero, Ciudad de México C.P. 07738, Mexico.
Departamento de Biofísica, Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas-Santo Tomás, Prolongación de Carpio y Plan de Ayala s/n, Col. Santo Tomás, Alcaldía Miguel Hidalgo, Ciudad de México C.P. 11340, Mexico.
Foods. 2023 Nov 16;12(22):4144. doi: 10.3390/foods12224144.
Cocoa is rich in polyphenols and alkaloids that act as antioxidants, anticarcinogens, and anti-inflammatories. Analytical methods commonly used to determine the proximal chemical composition of cocoa, total phenols, and antioxidant capacity are laborious, costly, and destructive. It is important to develop fast, simple, and inexpensive methods to facilitate their evaluation. Chemometric models were developed to identify the variety and predict the chemical composition (moisture, protein, fat, ash, pH, acidity, and phenolic compounds) and antioxidant capacity (ABTS and DPPH) of three cocoa varieties. SIMCA model showed 99% reliability. Quantitative models were developed using the PLS algorithm and favorable statistical results were obtained for all models: 0.93 < Rc < 0.98 (Rc: calibration determination coefficient); 0.03 < SEC < 4.34 (SEC: standard error of calibration). Independent validation of the quantitative models confirmed their good predictive ability: 0.93 < Rv < 0.97 (Rv: validation determination coefficient); 0.04 < SEP < 3.59 (SEP: standard error of prediction); 0.08 < % error < 10.35). SIMCA model and quantitative models were applied to five external cocoa samples, obtaining their chemical composition using only 100 mg of sample in less than 15 min. FT-MIR spectroscopy coupled with chemometrics is a viable alternative to conventional methods for quality control of cocoa beans without using reagents, and with the minimum sample preparation and quantity.
可可富含多酚和生物碱，这些物质具有抗氧化、抗癌和抗炎作用。常用于测定可可的近似化学成分、总酚含量和抗氧化能力的分析方法既费力、成本高又具有破坏性。开发快速、简单且廉价的方法以促进对它们的评估很重要。建立了化学计量学模型来识别三个可可品种，并预测其化学成分（水分、蛋白质、脂肪、灰分、pH值、酸度和酚类化合物）以及抗氧化能力（ABTS和DPPH）。SIMCA模型显示出99%的可靠性。使用PLS算法建立了定量模型，所有模型均获得了良好的统计结果：0.93 < Rc < 0.98（Rc：校准决定系数）；0.03 < SEC < 4.34（SEC：校准标准误差）。对定量模型的独立验证证实了它们良好的预测能力：0.93 < Rv < 0.97（Rv：验证决定系数）；0.04 < SEP < 3.59（SEP：预测标准误差）；0.08 < %误差 < 10.35）。将SIMCA模型和定量模型应用于五个外部可可样品，仅使用100毫克样品在不到15分钟内就获得了它们的化学成分。傅里叶变换红外光谱结合化学计量学是一种可行的替代传统方法的手段，可用于可可豆的质量控制，无需使用试剂，且样品制备最少、用量最少。
