BACKGROUND 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 by near infrared spectroscopy (FT-NIR) to determine the pH values and acidity in coffee beans in a practical and non-destructive way. PLS 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 models was also accessed. RESULTS The results obtained showed that the best methods of pre-processing were the first derivative for the pH variable and the standard normal variate (SNV) for the acidity, which produced models with correlations of 0.78 and 0.92, ratio of prediction to deviation (RPD) of 2.061 and 2.966 and bias of -0.00011 and -0.152 to test set validation, respectively. The average percentage of errors between predicted and experimental values were lower than 7%. CONCLUSION 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 in line evaluation of samples facilitating industrial and commercial processing. This article is protected by copyright. All rights reserved.

中文翻译：

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近红外光谱法和多元回归法测定生咖啡中的 pH 值和酸度

背景咖啡是全球关注的原材料。由于其相关性，这项工作评估了由近红外光谱 (FT-NIR) 获得的光谱数据构建的校准模型的性能，以实用且无损的方式确定咖啡豆的 pH 值和酸度。在校准和交叉验证期间使用 PLS 回归来优化潜在变量的数量。还访问了光谱预处理方法模型的预测能力。结果 获得的结果表明，最好的预处理方法是 pH 变量的一阶导数和酸度的标准正态变量 (SNV)，其产生的模型具有 0.78 和 0.92 的相关性、预测偏差比 (RPD) ) 为 2.061 和 2.966，偏差为 -0.00011 和 -0。152 分别用于测试集验证。预测值和实验值之间的平均误差百分比低于 7%。结论 NIR 已成功应用于预测与咖啡质量相关的特性。该方法被证明是一种快速且无损的工具，可以对样品进行快速在线评估，促进工业和商业加工。本文受版权保护。版权所有。本文受版权保护。版权所有。本文受版权保护。版权所有。