We propose a method for predicting the concentration of a functional ingredient, procyanidin, in apple using Raman spectroscopy in combination with multivariate calibration analysis. A regression model was constructed by partial least-squares (PLS) regression using the collected Raman spectra and the procyanidin concentrations measured by high-performance liquid chromatography (HPLC). Four different preprocessing algorithms—baseline correction, noise removal, averaging, and multiplicative scatter correction—were applied to the acquired Raman spectra. HPLC was used to determine the procyanidin concentrations in the edible part of apple specimens. The PLS regression model predicted the procyanidin concentration in apple with a coefficient of determination of 0.74, a root-mean-square error of calibration of 7.09 µg/g, and a root-mean-square error of prediction of 14.89 µg/g. In addition, the spectra of the carotenoid pigments were observed from the factors extracted from the PLS analysis. Consequently, we found that the procyanidin concentration in apple can be predicted using Raman spectroscopy measurements of carotenoid pigments of apple peel. Compared with conventional destructive measurements, Raman spectroscopy with the aid of multivariate analysis shows strong potential for the rapid and nondestructive quantitative analysis of procyanidin in apples.

我们提出了一种使用拉曼光谱结合多变量校准分析来预测苹果中功能成分原花青素浓度的方法。使用收集的拉曼光谱和通过高效液相色谱法 (HPLC) 测量的原花青素浓度，通过偏最小二乘法 (PLS) 回归构建回归模型。四种不同的预处理算法——基线校正、噪声去除、平均和乘法散射校正——应用于采集的拉曼光谱。使用高效液相色谱法测定苹果标本可食部分中的原花青素浓度。PLS 回归模型预测苹果中原花青素浓度，确定系数为 0.74，校准的均方根误差为 7.09 µg/g，预测的均方根误差为 14.89 µg/g。此外，从 PLS 分析中提取的因素观察了类胡萝卜素色素的光谱。因此，我们发现可以使用苹果皮类胡萝卜素色素的拉曼光谱测量来预测苹果中的原花青素浓度。与传统的破坏性测量相比，拉曼光谱在多变量分析的帮助下显示出对苹果中原花青素进行快速和无损定量分析的巨大潜力。