Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil.
Department of Forest Science, Federal University of Lavras, Lavras, Brazil.
J Sci Food Agric. 2021 Jun;101(8):3500-3507. doi: 10.1002/jsfa.10981. Epub 2020 Dec 20.
The chemical compounds in coffee are important indicators of quality. Its composition varies according to several factors related to the planting and processing of coffee. Thus, this study proposed to use near-infrared spectroscopy (NIR) associated with partial least squares (PLS) regression to estimate quickly some chemical properties (moisture content, soluble solids, and total and reducing sugars) in intact green coffee samples. For this, 250 samples produced in Brazil were analyzed in the laboratory by the standard method and also had their spectra recorded.
The calibration models were developed using PLS regression with cross-validation and tested in a validation set. The models were elaborated using original spectra and preprocessed by five different mathematical methods. These models were compared in relation to the coefficient of determination, root mean square error of cross-validation (RMSECV), root mean square error of test set validation (RMSEP), and ratio of performance to deviation (RPD) and demonstrated different predictive capabilities for the chemical properties of coffee. The best model was obtained to predict grain moisture and the worst performance was observed for the soluble solids model. The highest determination coefficients obtained for the samples in the validation set were equal to 0.810, 0.516, 0.694 and 0.781 for moisture, soluble solids, total sugar, and reducing sugars, respectively.
The statistics associated with these models indicate that NIR technology has the potential to be applied routinely to predict the chemical properties of green coffee, and in particular, for moisture analysis. However, the soluble solid and total sugar content did not show high correlations with the spectroscopic data and need to be improved. © 2020 Society of Chemical Industry.
咖啡中的化学物质是质量的重要指标。其组成因与咖啡种植和加工相关的几个因素而异。因此，本研究提出使用近红外光谱（NIR）结合偏最小二乘（PLS）回归来快速估计完整绿咖啡豆样品中的一些化学性质（水分含量、可溶性固形物、总糖和还原糖）。为此，对 250 个在巴西生产的样本进行了实验室分析，采用标准方法，并记录了其光谱。
采用交叉验证的 PLS 回归建立了校准模型，并在验证集进行了测试。模型使用原始光谱并通过五种不同的数学方法进行预处理后进行了编制。这些模型与决定系数、交叉验证均方根误差（RMSECV）、测试集验证均方根误差（RMSEP）和性能偏差比（RPD）进行了比较，结果表明不同的模型对咖啡的化学性质具有不同的预测能力。最佳模型用于预测谷物水分，而可溶性固形物模型的性能最差。在验证集样本中获得的最高决定系数分别为 0.810、0.516、0.694 和 0.781，对应于水分、可溶性固形物、总糖和还原糖。
这些模型的统计数据表明，NIR 技术有可能常规应用于预测绿咖啡的化学性质，特别是用于水分分析。然而，可溶性固形物和总糖含量与光谱数据相关性不高，需要改进。© 2020 英国化学学会。
