Zhang Yiyi, Huang Lunfang, Deng Guojian, Wang Yujie
State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
Foods. 2023 Jan 7;12(2):282. doi: 10.3390/foods12020282.
The reduction in freshness during green tea storage leads to a reduction in its commercial value and consumer acceptance, which is thought to be related to the oxidation of fatty acids. Here, we developed a novel and rapid method for the assessment of green tea freshness during storage. Hyperspectral images of green tea during storage were acquired, and fatty acid profiles were detected by GC-MS. Partial least squares (PLS) analysis was used to model the association of spectral data with fatty acid content. In addition, competitive adaptive reweighted sampling (CARS) was employed to select the characteristic wavelengths and thus simplify the model. The results show that the constructed CARS-PLS can achieve accurate prediction of saturated and unsaturated fatty acid content, with residual prediction deviation (RPD) values over 2. Ultimately, chemical imaging was used to visualize the distribution of fatty acids during storage, thus providing a fast and nondestructive method for green tea freshness evaluation.
绿茶储存过程中新鲜度的降低会导致其商业价值和消费者接受度下降，这被认为与脂肪酸氧化有关。在此，我们开发了一种新颖且快速的方法来评估绿茶储存期间的新鲜度。获取了绿茶储存期间的高光谱图像，并通过气相色谱 - 质谱联用仪（GC - MS）检测脂肪酸谱。使用偏最小二乘法（PLS）分析对光谱数据与脂肪酸含量之间的关联进行建模。此外，采用竞争性自适应重加权采样（CARS）来选择特征波长，从而简化模型。结果表明，构建的CARS - PLS能够实现对饱和脂肪酸和不饱和脂肪酸含量的准确预测，残差预测偏差（RPD）值超过2。最终，利用化学成像可视化储存期间脂肪酸的分布，从而为绿茶新鲜度评估提供了一种快速且无损的方法。
