Postgraduate Program in Food Science and Technology, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil.
Department of Agronomy, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil.
J Sci Food Agric. 2020 Apr;100(6):2488-2493. doi: 10.1002/jsfa.10270. Epub 2020 Feb 12.
Coffee is a raw material of global interest. Due to its relevance, this work evaluated the performance of calibration models constructed from spectral data obtained using near-infrared spectroscopy (FT-NIR) to determine the pH values and acidity in coffee beans in a practical and non-destructive way. Partial least squares regression was used during the calibration and the cross-validation to optimize the number of latent variables. The predictive capacity of the spectral pre-processing methods was also accessed.
The results obtained showed that the best methods of pre-processing were the first derivative for the pH variable and the standard normal variate for the acidity, which produced models with correlations of 0.78 and 0.92, ratios of prediction to deviation of 2.061 and 2.966 and biases of -0.00011 and -0.152 to test set validation, respectively. The average errors between predicted and experimental values were lower than 7%.
FT-NIR was successfully applied to predict properties related to the quality of coffee. The method was demonstrated to be a fast and non-destructive tool which allows the rapid inline evaluation of samples facilitating industrial and commercial processing. © 2020 Society of Chemical Industry.
咖啡是一种具有全球意义的原材料。鉴于其重要性，本工作评估了使用近红外光谱（FT-NIR）获得的光谱数据构建校准模型来实际且非破坏性地测定咖啡豆 pH 值和酸度的性能。偏最小二乘回归用于校准和交叉验证以优化潜在变量的数量。还评估了光谱预处理方法的预测能力。
获得的结果表明，pH 值变量的最佳预处理方法是一阶导数，酸度的最佳预处理方法是标准正态变量，这两种方法生成的模型相关系数分别为 0.78 和 0.92，预测偏差比分别为 2.061 和 2.966，测试集验证的偏差分别为-0.00011 和-0.152。预测值与实验值之间的平均误差低于 7%。
FT-NIR 成功地应用于预测与咖啡质量相关的性质。该方法被证明是一种快速且无损的工具，可允许对样品进行快速在线评估，从而促进工业和商业加工。© 2020 英国化学学会。
