Huang Xue, Xu Jiayi, Gao Feng, Zhang Hongyan, Guo Ling
College of Plant Science, Tarim University, Alar, 843300 China.
The National-Local Joint Engineering Laboratory of High Efficiency and Superior-Quality Cultivation and Fruit Deep Processing Technology on Characteristic Fruit Trees in South Xinjiang, Alar, 843300 China.
Food Sci Biotechnol. 2022 Jun 23;31(9):1123-1131. doi: 10.1007/s10068-022-01095-y. eCollection 2022 Aug.
Amygdalin content in apricot kernels is an essential factor in the rapid and nondestructive identification of sweet or bitter apricot kernels through spectroscopy. Now, amygdalin content has been determined by high-performance liquid chromatography and near-infrared spectral database to construct a model so that the sweet or bitter apricot kernels could be identified and classified. Principal component analysis-K-nearest neighbor classification algorithm combined with multivariate scattering correction pretreatment method could distinguish sweet and bitter apricot kernels in the wavelength range of 1650-1740 nm with 98.3% accuracy and apricot kernel species with 96.3% recognition rate in the full wavelength spectrum. Furthermore, prediction of amygdalin content in bitter and sweet apricot kernels by partial least squares model was superior to that by back-propagation neural network model. This study provides a theoretical basis for quality identification of apricot kernel quality, as well as a method for nondestructive and rapid detection of sweet and bitter apricot kernels.
The online version contains supplementary material available at 10.1007/s10068-022-01095-y.
杏仁中的苦杏仁苷含量是通过光谱法快速无损鉴别甜杏仁和苦杏仁的关键因素。目前，已通过高效液相色谱法和近红外光谱数据库测定苦杏仁苷含量以构建模型，从而对甜杏仁和苦杏仁进行鉴别和分类。主成分分析 - K近邻分类算法结合多元散射校正预处理方法，在1650 - 1740 nm波长范围内鉴别甜杏仁和苦杏仁的准确率可达98.3%，在全波长光谱中鉴别杏仁品种的识别率为96.3%。此外，偏最小二乘模型对苦杏仁和甜杏仁中苦杏仁苷含量的预测优于反向传播神经网络模型。本研究为杏仁品质鉴定提供了理论依据，也为甜杏仁和苦杏仁的无损快速检测提供了方法。
网络版包含可在10.1007/s10068 - 022 - 01095 - y获取的补充材料。
